Note: Descriptions are shown in the official language in which they were submitted.
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MULTI-SENSOR SYNERGY
RELATED APPLICATIONS
[0001] This application is a Continuation-in-Part of International
Patent Application No.
PCT/US2021/015378, filed January 28, 2021, titled, "SENSOR CALIBRATION AND
OPERATION," and further claims the benefit of U.S. Provisional Patent
Application No.
63/263,806, filed November 9,2021, entitled "MULTI-SENSOR SYNERGY," both of
which are
assigned to the assignee hereof and incorporated by reference herein in their
entirety.
BACKGROUND
[0002] A sensor may be configured (e.g., designed) to measure one or more
environmental
characteristics, for example, temperature, humidity, ambient noise, carbon
dioxide, and/or other
aspects of an ambient environment. Under typical operational conditions, the
one or more
environmental characteristics may have a natural state or operating range.
Various activities
(e.g., automated and/or human activities) can disrupt this natural state or
operating range, and
environmental characteristics reflective of these activities may be measured
by a sensor.
Because different sensor types may be capable of measuring different
environmental
characteristics at different respective timescales, using sensor data from a
single sensor type
alone may have one or more shortcomings when determining an attribute (e.g.,
activity) of the
environment based on the environmental characteristics, including being time-
consuming and/or
inaccurate.
SUMMARY
[0003] Various aspects disclosed herein alleviate at least part of the one or
more
shortcomings related to the use of sensors to determine an attribute of an
environment.
[0004] Various aspects disclosed herein may relate to a plurality of sensors
of a facility from
which a first sensor measures a first attribute at its first sampling rate.
Due to a correlation of the
first sensor and a second sensor that measures a second attribute at a second
sampling rate
slower than the first sampling rate, the second attribute can be determined
and/or predicted at
least in part by using measurements of the first attribute by the first
sensor.
[0005] In another aspect, a method of determining an attribute
includes: using a first sensor to
measure a first attribute at a first sampling rate, the first sampling rate
being faster than a second
sampling rate of a second sensor configured to sense a second attribute; and
determining and/or
predicting the second attribute at least in part by using measurements of the
first attribute by the
first sensor rather than measurements of the second sensor, the first sensor
and the second
sensor being of a facility. In some embodiments, the second attribute
comprises an activity. In
some embodiments, the activity comprises (i) cleaning of an enclosure, (ii)
movement of one or
more personnel in the enclosure, (iii) a change in an environmental condition,
(iv) one or more
personnel entering into the enclosure, (v) one or more personnel exiting the
enclosure, (vi) activity
in the enclosure, (vii) exceeding of a maximum occupancy of the enclosure, or
(viii) an arrival of
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a vehicle at a transportation hub. In some embodiments, the enclosure
comprises a building,
room, or any other confined space. In some embodiments, the environmental
condition comprises
a weather condition, a sound level, an electromagnetic radiation level, an air
quality level, a gas
level, a particulate matter level, or a volatile organic compound level. In
some embodiments, the
transportation hub comprises an airport, a train station, a bus station, a
tram stop, a ferry slip, a
pilot station, a sailing station, or any other transit station; and optionally
the other transit station
comprises a rapid transition station. In some embodiments, the vehicle
comprises an airplane, a
train, a bus, a car, a subway car, a light rail car, a tram, a ferry, a boat,
a ship, a helicopter, or a
rocket; and optionally the car comprises a taxi car. In some embodiments, the
second attribute
comprises occupancy status of an enclosure, number of occupants in the
enclosure, sound,
electromagnetic radiation, an indicator of a level of comfort, an indicator of
energy efficiency, air
quality, temperature, gas, particulate matter, or volatile organic compounds.
In some
embodiments, the electromagnetic radiation comprises visible, infrared,
ultrasound, or radio
frequency radiation. In some embodiments, the second attribute that comprises
gas comprises
one or more of: gas type, velocity, and pressure. In some embodiments, the
second attribute that
comprises gas comprises one or more of: humidity, carbon dioxide, carbon
monoxide, hydrogen
sulfide, radon, nitrogen oxides, halogen, organic halogens, and formaldehyde.
In some
embodiments, the first sampling rate is at least about one order of magnitude
faster than the
second sampling rate. In some embodiments, the second sensor and the first
sensor are disposed
in the facility. In some embodiments, the first sensor and the second sensor
are disposed in an
enclosure of the facility. In some embodiments, the second attribute comprises
temperature,
sound, pressure, humidity, gas, particulate matter, volatile organic compound,
or electromagnetic
radiation. In some embodiments, the gas comprises carbon dioxide, carbon
monoxide, radon, or
hydrogen sulfide. In some embodiments, the second attribute is associated with
an activity. In
some embodiments, the activity comprises a human activity or a mechanical
activity. In some
embodiments, the first sensor is disposed in a housing enclosing another
sensor, a transceiver,
or an emitter. In some embodiments, the housing is disposed in a fixture of
the facility, or is
attached to a fixture of the facility. In some embodiments, the first sensor
is utilized to control an
environment of the facility. In some embodiments, the method includes using a
third sensor to
measure a third attribute at a third sampling rate, the third sampling rate
being faster than the
second sampling rate of the second sensor; and determining and/or predicting
the second
attribute at least in part by using measurements of (i) the first attribute by
the first sensor and (ii)
the third attribute by the third sensor, the third sensor being of a facility.
In some embodiments,
the first sensor and the third sensor are disposed in a housing. In some
embodiments, the housing
encloses another sensor, a transceiver, or an emitter. In some embodiments,
the housing
encloses at least two or seven different sensors. In some embodiments, the
housing is disposed
in a fixture of the facility, or is attached to a fixture of the facility. In
some embodiments,
synergistically and/or symbiotically evaluating measurements of the first
sensor and
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measurements of the third sensor to determine and/or predict the second
attribute. In some
embodiments, the method includes using artificial intelligence to determine
and/or predict the
second attribute. In some embodiments, the artificial intelligence comprises
machine learning. In
some embodiments, the method includes using the first sensor to collect first
sensed data during
a period of time at the first sampling rate; using the third sensor to collect
third sensed data during
the period of time at the third sampling rate, wherein the third sensor
comprises a different sensor
type than the first sensor; obtaining data indicative of an occurrence of an
activity during the period
of time; responsive to obtaining the data indicative of the occurrence of the
activity: (i) evaluating
the first sensed data to determine a first correlation between the activity
and a first range of values
of the first sensed data, and (ii) evaluating the third sensed data to
determine a second correlation
between the activity and a third range of values of the third sensed data;
determining a relationship
between the activity, the first range of values, and the third range of
values, wherein determining
the relationship is based at least in part on the first correlation and the
second correlation; and
storing data indicative of the determined relationship. In some embodiments,
(i) the first sensed
data, (ii) the third sensed data, or both the first sensed data and the third
sensed data, occur prior
to and/or during the occurrence of the activity. In another aspect, a non-
transitory computer
readable program instructions for determining an attribute comprises the non-
transitory computer
readable program instructions, when read by one or more processors operatively
coupled to a
first sensor, causes the one or more processors to execute, or direct
execution of, the method of
any of the previously-described embodiments.
[0006] In another aspect, a non-transitory computer readable
program instructions for
determining an attribute includes: using, or directing use of, the first
sensor to measure a first
attribute at a first sampling rate, the first sampling rate being faster than
a second sampling rate
of a second sensor configured to sense a second attribute; and (i) determining
or directing
determination of, and/or (ii) prediction or directing prediction of: the
second attribute at least in
part by using measurements of the first attribute by the first sensor rather
than measurements of
the second sensor, the first sensor and the second sensor being of a facility.
In some
embodiments, the one or more processors comprises a hierarchical system of
processors having
at least three levels of hierarchy. In some embodiments, the one or more
processors comprises
a processor disposed in a device ensemble having a housing enclosing at least
one sensor. In
some embodiments, the device ensemble comprises another sensor, an emitter, or
a transceiver.
In some embodiments, the processor comprises a graphic processing unit. In
some embodiments,
the operations comprise utilizing, or directing utilization of, an artificial
intelligence computational
scheme for prediction of the second attribute. In some embodiments, the one or
more processors
comprises a processor disposed in, or attached to, a fixture of the facility.
In some embodiments,
the one or more processors comprises a processor disposed externally to the
facility. In some
embodiments, externally to the facility comprises a cloud server. In some
embodiments,
operations comprise remotely updating, or directing remote update, from a
source external to the
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facility. In another aspect, an apparatus for determining an attribute
comprises at least one
controller, which at least one controller is configured to (a) operatively
couple to the first sensor,
and (b) execute, or direct execution of, the method of any of the previously-
described
embodiments.
[0007] In another aspect, an apparatus for determining an
attribute includes at least one
controller, which at least one controller is configured to: operatively couple
to a first sensor; use,
or direct use of, the first sensor to measure a first attribute at a first
sampling rate, the first sampling
rate being faster than a second sampling rate of a second sensor configured to
sense a second
attribute; and (i) determine or direct determination of, and/or (ii) predict
or direct prediction of: the
second attribute at least in part by using measurements of the first attribute
by the first sensor
rather than measurements of the second sensor, the first sensor and the second
sensor being of
a facility. In some embodiments, the at least one controller comprises a
hierarchical control
system having at least three levels of hierarchy. In some embodiments, the at
least one controller
comprises a controller disposed in a device ensemble having a housing
enclosing at least one
sensor. In some embodiments, the device ensemble comprises another sensor, an
emitter, or a
transceiver. In some embodiments, the at least one controller comprises a
microcontroller. In
some embodiments, the at least one controller is configured to utilize, or
direct utilization of,
artificial intelligence for predictive control. In some embodiments, the at
least one controller
comprises a controller disposed in, or attached to, a fixture of the facility.
In another aspect, a
system for determining an attribute comprises a network configured to
operatively coupled to the
first sensor, and transmit one or more signals facilitating the method of any
of the previously-
described embodiments.
[0008] In another aspect, a system for determining an attribute
includes: a network configured
to: operatively couple to a first sensor; transmit measurement data of a first
attribute measured
using the first sensor at a first sampling rate, the first sampling rate being
faster than a second
sampling rate of a second sensor configured to sense a second attribute; and
transmit a
determination and/or prediction of the second attribute, wherein the
determination and/or
prediction of the second attribute uses measurements of the first attribute by
the first sensor rather
than measurements of the second sensor, the first sensor and the second sensor
being of a
facility. In some embodiments, the network is configured to transmit
communication and power
on a single cable. In some embodiments, the network is configured to transmit
communication
protocols, at least two of the communication protocols are different. In some
embodiments, the
communication protocols comprise at least a fourth generation, or a fifth
generation cellular
communication protocol. In some embodiments, the communication protocols
facilitate cellular,
media, control, security, and/or other data communication. In some
embodiments, the
communication protocols comprise a control protocol that comprises building
automation control
protocol. In some embodiments, the network is configured to operatively
coupled to one or more
antennas, and optionally the one or more antennas comprise a distributed
antenna system. In
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some embodiments, the network is configured to facilitate remote software
update from a source
external to the facility. In another aspect, an apparatus for determining an
attribute comprises: a
device ensemble disposed in a housing, the device ensemble comprising the
first sensor, the
device ensemble configured to execute, or facilitate execution of, the method
of any of the
previously-described embodiments.
[0009] In another aspect, an apparatus for determining an
attribute includes: a device
ensemble disposed in a housing, the device ensemble comprising at least a
first sensor, the
device ensemble configured to (i) measure a first attribute by the first
sensor at a first sampling
rate, the first sampling rate being faster than a second sampling rate of a
second sensor
configured to sense a second attribute, and (ii) facilitate determination
and/or prediction of the
second attribute, wherein the determination and/or prediction of the second
attribute uses
measurements of the first attribute by the first sensor rather than
measurements of the second
sensor, the first sensor and the second sensor being of a facility. In some
embodiments, the
device ensemble is configured to facilitate the determination and/or
prediction at least in part by
being configured to operatively couple to a power and/or communication
network. In some
embodiments, the device ensemble comprises communication and/or power port. In
some
embodiments, the device ensemble comprises at least one processor and/or at
least one
memory. In some embodiments, the device ensemble comprises at least one
printed circuit
boards having devices operatively coupled at both sides of the at least one
printed circuit board.
In some embodiments, the housing comprises at least one hole that facilitates
measuring the
first attribute. In some embodiments, the device ensemble comprises a radio
transceiver or an
accelerometer. In some embodiments, the radio transceiver is configured to
measure ultrawide
bandwidth radiation. In some embodiments, the device ensemble comprises two
printed circuit
boards that are separate from each other. In some embodiments, the non-
transitory computer
program product comprises at least one medium (e.g., non-transitory computer
readable
medium).
[0010] In another aspect, the present disclosure provides systems,
apparatuses (e.g.,
controllers), and/or non-transitory computer-readable medium (e.g., software)
that implement
any of the methods disclosed herein.
[0011] In another aspect, the present disclosure provides methods
that use any of the
systems, computer readable media, and/or apparatuses disclosed herein, e.g.,
for their intended
purpose.
[0012] In another aspect, an apparatus comprises at least one controller that
is programmed
to direct a mechanism used to implement (e.g., effectuate) any of the method
disclosed herein,
which at least one controller is configured to operatively couple to the
mechanism. In some
embodiments, at least two operations (e.g., of the method) are
directed/executed by the same
controller. In some embodiments, at less at two operations are
directed/executed by different
controllers.
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[0013] In another aspect, an apparatus comprises at least one
controller that is configured
(e.g., programmed) to implement (e.g., effectuate) any of the methods
disclosed herein. The at
least one controller may implement any of the methods disclosed herein. In
some embodiments,
at least two operations (e.g., of the method) are directed/executed by the
same controller. In
some embodiments, at less at two operations are directed/executed by different
controllers.
[0014] In another aspect, a system comprises at least one controller that is
programmed to
direct operation of at least one another apparatus (or component thereof), and
the apparatus (or
component thereof), wherein the at least one controller is operatively coupled
to the apparatus
(or to the component thereof). The apparatus (or component thereof) may
include any
apparatus (or component thereof) disclosed herein. The at least one controller
may be
configured to direct any apparatus (or component thereof) disclosed herein.
The at least one
controller may be configured to operatively couple to any apparatus (or
component thereof)
disclosed herein. In some embodiments, at least two operations (e.g., of the
apparatus) are
directed by the same controller. In some embodiments, at less at two
operations are directed by
different controllers.
[0015] In another aspect, a computer software product, comprising
a non-transitory
computer-readable medium in which program instructions are stored, which
instructions, when
read by at least one processor (e.g., computer), cause the at least one
processor to direct a
mechanism disclosed herein to implement (e.g., effectuate) any of the method
disclosed herein,
wherein the at least one processor is configured to operatively couple to the
mechanism. The
mechanism can comprise any apparatus (or any component thereof) disclosed
herein. In some
embodiments, at least two operations (e.g., of the apparatus) are
directed/executed by the
same processor. In some embodiments, at less at two operations are
directed/executed by
different processors.
[0016] In another aspect, the present disclosure provides a non-
transitory computer-readable
medium comprising machine-executable code that, upon execution by one or more
processors,
implements any of the methods disclosed herein. In some embodiments, at least
two operations
(e.g., of the method) are directed/executed by the same processor. In some
embodiments, at
less at two operations are directed/executed by different processors.
[0017] In another aspect, the present disclosure provides a non-
transitory computer-readable
medium comprising machine-executable code that, upon execution by one or more
processors,
effectuates directions of the controller(s) (e.g., as disclosed herein). In
some embodiments, at
least two operations (e.g., of the controller) are directed/executed by the
same processor. In
some embodiments, at less at two operations are directed/executed by different
processors.
[0018] In another aspect, the present disclosure provides a computer system
comprising one
or more computer processors and a non-transitory computer-readable medium
coupled thereto.
The non-transitory computer-readable medium comprises machine-executable code
that, upon
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execution by the one or more processors, implements any of the methods
disclosed herein
and/or effectuates directions of the controller(s) disclosed herein.
[0019] The content of this summary section is provided as a simplified
introduction to the
disclosure and is not intended to be used to limit the scope of any invention
disclosed herein or
the scope of the appended claims.
[0020] Additional aspects and advantages of the present disclosure will become
readily
apparent to those skilled in this art from the following detailed description,
wherein only
illustrative embodiments of the present disclosure are shown and described. As
will be realized,
the present disclosure is capable of other and different embodiments, and its
several details are
capable of modifications in various obvious respects, all without departing
from the disclosure.
Accordingly, the drawings and description are to be regarded as illustrative
in nature, and not as
restrictive.
[0021] These and other features and embodiments will be described in more
detail with
reference to the drawings.
INCORPORATION BY REFERENCE
[0022] All publications, patents, and patent applications
mentioned in this specification are
herein incorporated by reference to the same extent as if each individual
publication, patent, or
patent application was specifically and individually indicated to be
incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The novel features of the invention are set forth with particularity in
the appended
claims. A better understanding of the features and advantages of the present
invention will be
obtained by reference to the following detailed description that sets forth
illustrative
embodiments, in which the principles of the invention are utilized, and the
accompanying
drawings or figures (also "Fig." and "Figs." herein), of which:
[0024] Fig. 1 shows a depiction of an example system that may perform multi-
sensor synergy
as described herein;
[0025] Fig. 2 shows a control system and its various components;
[0026] Fig. 3 schematically shows an electrochromic device;
[0027] Fig. 4 schematically shows a cross section of an Integrated
Glass Unit (IGU);
[0028] Fig. 5 shows an apparatus and its components and connectivity options;
[0029] Fig. 6 schematically depicts a controller;
[0030] Fig. 7 shows a schematic flow chart;
[0031] Fig. 8 shows a schematic flow chart;
[0032] Fig. 9 shows a schematic example of sensor arrangement and sensor data;
[0033] Fig. 10 shows a schematic example of sensor arrangement and sensor
data;
[0034] Fig. 11 shows a schematic example of sensor arrangement;
[0035] Fig. 12 shows a schematic example of sensor arrangement and sensor
data;
[0036] Figs. 13A-13E show time dependent graphs;
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[0037] Fig. 14 depicts a time dependent graph of carbon dioxide
concentrations;
[0038] Fig. 15 shows a topographical map of measured property values;
[0039] Fig. 16 shows a table providing example attributes that may be detected
using sensor
synergy;
[0040] Figs. 17A and 17B show graphs that illustrate a
relationship between relative humidity
and air temperature in an example environment;
[0041] Fig. 18 shows an example graph of relative humidity and temperature;
[0042] Fig. 19 shows a table of example timescales for different sensors;
[0043] Fig. 20 shows an example graph plotting sensor data obtained from three
sensor
types;
[0044] Fig. 21 shows another example graph plotting sensor data obtained from
three sensor
types;
[0045] Fig. 22 shows timing diagrams illustrating sound and lux
data values of the graph of
Fig. 21 overtime;
[0046] Fig. 23 shows additional timing diagrams illustrating sound
and lux data values of the
graph of Fig. 21 over time;
[0047] Fig. 24 shows a flowchart of a method for determining an attribute,
according to an
embodiment;
[0048] Fig. 25 shows a flowchart of a method for establishing a relationship
between an
attribute values of sensor data from first and second sensors, according to an
embodiment; and
[0049] Fig. 26 schematically depicts a processing system.
[0050] The figures and components therein may not be drawn to scale. Various
components
of the figures described herein may not be drawn to scale.
DETAILED DESCRIPTION
[0051] While various embodiments of the invention have been shown, and
described herein,
it will be obvious to those skilled in the art that such embodiments are
provided by way of
example only. Numerous variations, changes, and substitutions may occur to
those skilled in the
art without departing from the invention. It should be understood that various
alternatives to the
embodiments of the invention described herein might be employed.
[0052] Terms such as "a," "an," and "the" are not intended to
refer to only a singular entity but
include the general class of which a specific example may be used for
illustration. The
terminology herein is used to describe specific embodiments of the
invention(s), but their usage
does not delimit the invention(s).
[0053] When ranges are mentioned, the ranges are meant to be inclusive, unless
otherwise
specified. For example, a range between value 1 and value 2 is meant to be
inclusive and
include value 1 and value 2. The inclusive range will span any value from
about value 1 to about
value 2. The term "adjacent" or "adjacent to," as used herein, includes "next
to," "adjoining," "in
contact with," and "in proximity to."
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[0054] The term "operatively coupled" or "operatively connected" refers to a
first element
(e.g., mechanism) that is coupled (e.g., connected) to a second element, to
allow the intended
operation of the second and/or first element. The coupling may comprise
physical or non-
physical coupling. The non-physical coupling may comprise signal-induced
coupling (e.g.,
wireless coupling). Coupled can include physical coupling (e.g., physically
connected), or non-
physical coupling (e.g., via wireless communication).
[0055] An element (e.g., mechanism) that is "configured to" perform a function
includes a
structural feature that causes the element to perform this function. A
structural feature may
include an electrical feature, such as a circuitry or a circuit element. A
structural feature may
include an actuator. A structural feature may include a circuitry (e.g.,
comprising electrical or
optical circuitry). Electrical circuitry may comprise one or more wires.
Optical circuitry may
comprise at least one optical element (e.g., beam splitter, mirror, lens
and/or optical fiber). A
structural feature may include a mechanical feature. A mechanical feature may
comprise a
latch, a spring, a closure, a hinge, a chassis, a support, a fastener, or a
cantilever, and so forth.
Performing the function may comprise utilizing a logical feature. A logical
feature may include
programming instructions. Programming instructions may be executable by at
least one
processor. Programming instructions may be stored or encoded on a medium
accessible by one
or more processors. Additionally, in the following description, the phrases
"operable to,"
"adapted to," "configured to," "designed to," "programmed to," or "capable of"
may be used
interchangeably where appropriate.
[0056] In some embodiments, an enclosure comprises an area defined by at least
one
structure. The at least one structure may comprise at least one wall. An
enclosure may
comprise and/or enclose one or more sub-enclosure. The at least one wall may
comprise metal
(e.g., steel), clay, stone, plastic, glass, plaster (e.g., gypsum), polymer
(e.g., polyurethane,
styrene, or vinyl), asbestos, fiber-glass, concrete (e.g., reinforced
concrete), wood, paper, or a
ceramic. The at least one wall may comprise wire, bricks, blocks (e.g., cinder
blocks), tile,
drywall, or frame (e.g., steel frame).
[0057] In some embodiments, the enclosure comprises one or more openings. The
one or
more openings may be reversibly closable. The one or more openings may be
permanently
open. A fundamental length scale of the one or more openings may be smaller
relative to the
fundamental length scale of the wall(s) that define the enclosure. A
fundamental length scale
may comprise a diameter of a bounding circle, a length, a width, or a height.
A surface of the
one or more openings may be smaller relative to the surface the wall(s) that
define the
enclosure. The opening surface may be a percentage of the total surface of the
wall(s). For
example, the opening surface can measure about 30%, 20%, 10%, 5%, or 1% of the
walls(s).
The wall(s) may comprise a floor, a ceiling or a side wall. The closable
opening may be closed
by at least one window or door. The enclosure may be at least a portion of a
facility. The
enclosure may comprise at least a portion of a building. The building may be a
private building
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and/or a commercial building. The building may comprise one or more floors.
The building (e.g.,
floor thereof) may include at least one of: a room, hall, foyer, attic,
basement, balcony (e.g.,
inner or outer balcony), stairwell, corridor, elevator shaft, façade,
mezzanine, penthouse,
garage, porch (e.g., enclosed porch), terrace (e.g., enclosed terrace),
cafeteria, and/or Duct. In
some embodiments, an enclosure may be stationary and/or movable (e.g., a
train, a plane, a
ship, a vehicle, or a rocket).
[0058] In some embodiments, a plurality of devices may be
operatively (e.g.,
communicatively) coupled to the control system. The devices may include a
sensor, emitter,
transceiver, antenna, radar, media display construct, processor, and/or
controller. The display
(e.g., display matrix) may comprise a light emitting diode (LED). The LED may
comprise an
organic material (e.g., organic light emitting diode abbreviated herein as
"OLED"). The OLED
may comprise a transparent organic light emitting diode display (abbreviated
herein as
"TOLED"), which TOLED is at least partially transparent. The plurality of
devices may be
disposed in a facility (e.g., including a building and/or room). The control
system may comprise
the hierarchy of controllers. The devices may comprise an emitter, a sensor,
or a window (e.g.,
IGU). The device may be any device as disclosed herein. At least two of the
plurality of devices
may be of the same type. For example, two or more IGUs may be coupled to the
control
system. At least two of the plurality of devices may be of different types.
For example, a sensor
and an emitter may be coupled to the control system. At times the plurality of
devices may
comprise at least 20, 50, 100, 500, 1000, 2500, 5000, 7500, 10000, 50000,
100000, or 500000
devices. The plurality of devices may be of any number between the
aforementioned numbers
(e.g., from 20 devices to 500000 devices, from 20 devices to 50 devices, from
50 devices to 500
devices, from 500 devices to 2500 devices, from 1000 devices to 5000 devices,
from 5000
devices to 10000 devices, from 10000 devices to 100000 devices, or from 100000
devices to
500000 devices). For example, the number of windows in a floor may be at least
5, 10, 15, 20,
25, 30, 40, or 50. The number of windows in a floor can be any number between
the
aforementioned numbers (e.g., from 5 to 50, from 5 to 25, or from 25 to 50).
At times the
devices may be in a multi-story building. At least a portion of the floors of
the multi-story building
may have devices controlled by the control system (e.g., at least a portion of
the floors of the
multi-story building may be controlled by the control system). For example,
the multi-story
building may have at least 2, 8, 10, 25, 50, 80, 100, 120, 140, or 160 floors
that are controlled
by the control system. The number of floors (e.g., devices therein) controlled
by the control
system may be any number between the aforementioned numbers (e.g., from 2 to
50, from 25
to 100, or from 80t0 160). The floor may be of an area of at least about 150
m2, 250 m2, 500m2,
1000 m2, 1500 m2, or 2000 square meters (m2). The floor may have an area
between any of the
aforementioned floor area values (e.g., from about 150 m2t0 about 2000 m2,
from about 150 m2
to about 500 m2 from about 250 m2 to about 1000 m2, or from about 1000 m2 to
about 2000 m2).
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The facility may comprise a commercial or a residential building. The
residential facility may
comprise a multi or a single family building.
[0059] In some embodiments, the device may comprise a display construct (e.g.,
an TOLED
display construct). The display may have at its fundamental length scale 2000,
3000, 4000,
5000, 6000, 7000, or 8000 pixels. The display may have at its fundamental
length scale any
number of pixels between the aforementioned number of pixels (e.g., from about
2000 pixels to
about 4000 pixels, from about 4000 pixels to about 8000 pixels, or from about
2000 pixels to
about 8000 pixels). A fundamental length scale may comprise a diameter of a
bounding circle, a
length, a width, or a height. The fundamental length scale may be abbreviated
herein as "FLS."
The display construct may comprise a high resolution display. For example, the
display
construct may have a resolution of at least about 550, 576, 680, 720, 768,
1024, 1080, 1920,
1280, 2160, 3840, 4096, 4320, or 7680 pixels, by at least about 550, 576, 680,
720, 768, 1024,
1080, 1280, 1920, 2160, 3840, 4096, 4320, or 7680 pixels (at 30Hz or at 60Hz).
The first
number of pixels may designate the height of the display and the second pixels
may designates
the length of the display. For example, the display may be a high resolution
display having a
resolution of 1920 x 1080, 3840 x 2160, 4096 x2160, or 7680 x 4320. The
display may be a
standard definition display, enhanced definition display, high definition
display, or an ultra-high
definition display. The display may be rectangular. The image projected by the
display matrix
may be refreshed at a frequency (e.g., at a refresh rate) of at least about 20
Hz, 30 Hz, 60 Hz,
70 Hz, 75 Hz, 80 Hz, 100 Hz, or 120 Hertz (Hz). The FLS of the display
construct may be at
least 20", 25", 30", 35", 40", 45", 50", 55", 60", 65", 80", or 90 inches (").
The FLS of the display
construct can be of any value between the aforementioned values (e.g., from
about 20" to about
55", from about 55" to about 100", or from about 20" to about 100"). The
display construct may
be operatively (e.g., physically) coupled to a tintable window. The display
construct may operate
in tandem with the display construct. Examples of display constructs, tintable
windows, their
operation, control, and any related software may be found in U.S. Provisional
Patent Application
Serial No. 63/085,254 filed September 30, 2020, titled "TANDEM VISION WINDOW
AND
MEDIA DISPLAY," which is incorporated herein by reference in its entirety.
[0060] In some embodiments, the enclosure encloses an atmosphere. The
atmosphere may
comprise one or more gases. The gases may include inert gases (e.g., argon or
nitrogen)
and/or non-inert gases (e.g., oxygen or carbon dioxide). The enclosure
atmosphere may
resemble an atmosphere external to the enclosure (e.g., ambient atmosphere) in
at least one
external atmosphere characteristic that includes: temperature, relative gas
content, gas type
(e.g., humidity, and/or oxygen level), debris (e.g., dust and/or pollen),
and/or gas velocity. The
enclosure atmosphere may be different from the atmosphere external to the
enclosure in at
least one external atmosphere characteristic that includes: temperature,
relative gas content,
gas type (e.g., humidity, and/or oxygen level), debris (e.g., dust and/or
pollen), and/or gas
velocity. For example, the enclosure atmosphere may be less humid (e.g.,
drier) than the
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external (e.g., ambient) atmosphere. For example, the enclosure atmosphere may
contain the
same (e.g., or a substantially similar) oxygen-to-nitrogen ratio as the
atmosphere external to the
enclosure. The velocity of the gas in the enclosure may be (e.g.,
substantially) similar
throughout the enclosure. The velocity of the gas in the enclosure may be
different in different
portions of the enclosure (e.g., by flowing gas through to a vent that is
coupled with the
enclosure).
[0061] Certain disclosed embodiments provide a network
infrastructure in the enclosure (e.g.,
a facility such as a building). The network infrastructure is available for
various purposes such
as for providing communication and/or power services. The communication
services may
comprise high bandwidth (e.g., wireless and/or wired) communications services.
The
communication services can be to occupants of a facility and/or users outside
the facility (e.g.,
building). The network infrastructure may work in concert with, or as a
partial replacement of,
the infrastructure of one or more cellular carriers. The network
infrastructure can be provided in
a facility that includes electrically switchable windows. Examples of
components of the network
infrastructure include a high speed backhaul. The network infrastructure may
include at least
one cable, switch, physical antenna, transceivers, sensor, transmitter,
receiver, radio, processor
and/or controller (that may comprise a processor). The network infrastructure
may be
operatively coupled to, and/or include, a wireless network. The network
infrastructure may
comprise wiring. One or more sensors can be deployed (e.g., installed) in an
environment as
part of installing the network and/or after installing the network. The
network may be configured
for transmission of a plurality of communication types and power on the same
cable. The
communication types may comprise data. The communication types may comprise
cellular
communication (e.g., conforming to at least a third (3G), fourth (4G), or
fifth (5G) generation
cellular communication). The communication type may comprise BACnet (building
automation
and control networks) protocol communication. The communication type may
comprise media
streaming. The media streaming may support HDMI, Digital Visual Interface
(DVI), DisplayPort
(DP) and/or Serial digital interface (SDI). The streaming may be of compressed
or
uncompressed (e.g., Moving Picture Experts Group (MPEG) or Advanced Video
Coding (AVC,
a.k.a., H.264)) digital media streams.
[0062] Fig. 1 shows a depiction of an example system 100 for controlling and
driving a
plurality of electrochromic windows 102. It may also be employed to control
the operation of one
or more window antennas as described elsewhere herein. The system 100 can be
adapted for
use with a building 104 such as a commercial office building or a residential
building. In some
implementations, the system 100 is designed to function in conjunction with
modern heating,
ventilation, and air conditioning (HVAC) systems 106, interior lighting
systems 107, security
systems 108 and power systems 109 as a single holistic and efficient energy
control system for
the entire building 104, or a campus of buildings 104. Some implementations of
the system 100
are particularly well-suited for integration with a building management system
(BMS) 110. The
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BMS 110 is a computer-based control system that can be installed in a building
to monitor and
control the building's mechanical and electrical equipment such as HVAC
systems, lighting
systems, power systems, elevators, fire systems, and security systems. The BMS
110 can
include hardware and associated firmware or software for maintaining
conditions in the building
104 according to preferences set by the occupants or by a building manager or
other
administrator. The software can be based on, for example, internet protocols
or open
standards.
[0063] A BMS can typically be used in large buildings where it functions to
control the
environment within the building. For example, the BMS 110 can control
lighting, temperature,
carbon dioxide levels, and humidity within the building 104. There can be
numerous mechanical
or electrical devices that are controlled by the BMS 110 including, for
example, furnaces or
other heaters, air conditioners, blowers, and vents. To control the building
environment, the
BMS 110 can turn on and off these various devices according to rules or in
response to
conditions. Such rules and conditions can be selected or specified by a
building manager or
administrator, for example. One primary function of the BMS 110 is to maintain
a comfortable
environment for the occupants of the building 104 while minimizing heating and
cooling energy
losses and costs. In some implementations, the BMS 110 can be configured not
only to monitor
and control, but also to optimize the synergy between various systems, for
example, to
conserve energy and lower building operation costs.
[0064] Some implementations are alternatively or additionally
designed to function
responsively or reactively based on feedback sensed through, for example,
thermal, optical, or
other sensors or through input from, for example, an HVAC or interior lighting
system, or an
input from a user control. Further information may be found in US Patent No.
8,705,162, issued
April 22, 2014, which is incorporated herein by reference in its entirety.
Some implementations
also can be utilized in existing structures, including both commercial and
residential structures,
having traditional or conventional HVAC or interior lighting systems. Some
implementations
also can be retrofitted for use in older residential homes.
[0065] The system 100 includes a network controller 112 configured to control
a plurality of
window controllers 114. For example, the network controller 112 can control
tens, hundreds, or
even thousands of window controllers 114. Each window controller 114, in turn,
can control and
drive one or more electrochromic windows 102. In some implementations, the
network
controller 112 issues high level instructions such as the final tint state of
an electrochromic
window and the window controllers receive these commands and directly control
their windows
by applying electrical stimuli to appropriately drive tint state transitions
and/or maintain tint
states. The number and size of the electrochromic windows 102 that each window
controller
114 can drive is generally limited by the voltage and current characteristics
of the load on the
window controller 114 controlling the respective electrochromic windows 102.
In some
implementations, the maximum window size that each window controller 114 can
drive is limited
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by the voltage, current, or power requirements to cause the desired optical
transitions in the
electrochromic window 102 within a desired time-frame. Such requirements are,
in turn, a
function of the surface area of the window. In some implementations, this
relationship is
nonlinear. For example, the voltage, current, or power requirements can
increase nonlinearly
with the surface area of the electrochromic window 102. For example, in some
cases the
relationship is nonlinear at least in part because the sheet resistance of the
first and second
conductive layers (see, for example, Fig. 3) may increase nonlinearly with
distance across the
length and width of the first or second conductive layers. In some
implementations, the
relationship between the voltage, current, or power requirements required to
drive multiple
electrochromic windows 102 of equal size and shape is, however, directly
proportional to the
number of the electrochromic windows 102 being driven.
[0066] In some embodiments, the sensor(s) are operatively coupled
to at least one controller
and/or processor. Sensor readings may be obtained by one or more processors
and/or
controllers. A controller may comprise a processing unit (e.g., CPU or GPU). A
controller may
receive an input (e.g., from at least one sensor). The controller may comprise
circuitry, electrical
wiring, optical wiring, socket, and/or outlet. A controller may deliver an
output. A controller may
comprise multiple (e.g., sub-) controllers. The controller may be a part of a
control system. A
control system may comprise a master controller, floor (e.g., comprising
network controller)
controller, a local controller. The local controller may be a window
controller (e.g., controlling an
optically switchable window), enclosure controller, or component controller.
For example, a
controller may be a part of a hierarchal control system (e.g., comprising a
main controller that
directs one or more controllers, e.g., floor controllers, local controllers
(e.g., window controllers),
enclosure controllers, and/or component controllers). A physical location of
the controller type in
the hierarchal control system may be changing. For example: At a first time: a
first processor
may assume a role of a main controller, a second processor may assume a role
of a floor
controller, and a third processor may assume the role of a local controller.
At a second time: the
second processor may assume a role of a main controller, the first processor
may assume a
role of a floor controller, and the third processor may remain with the role
of a local controller. At
a third time: the third processor may assume a role of a main controller, the
second processor
may assume a role of a floor controller, and the first processor may assume
the role of a local
controller. A controller may control one or more devices (e.g., be directly
coupled to the
devices). A controller may be disposed proximal to the one or more devices it
is controlling. For
example, a controller may control an optically switchable device (e.g., IGU),
an antenna, a
sensor, and/or an output device (e.g., a light source, sounds source, smell
source, gas source,
HVAC outlet, or heater). In one embodiment, a floor controller may direct one
or more window
controllers, one or more enclosure controllers, one or more component
controllers, or any
combination thereof. The floor controller may comprise a floor controller. For
example, the floor
(e.g., comprising network) controller may control a plurality of local (e.g.,
comprising window)
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controllers. A plurality of local controllers may be disposed in a portion of
a facility (e.g., in a
portion of a building). The portion of the facility may be a floor of a
facility. For example, a floor
controller may be assigned to a floor. In some embodiments, a floor may
comprise a plurality of
floor controllers, e.g., depending on the floor size and/or the number of
local controllers coupled
to the floor controller. For example, a floor controller may be assigned to a
portion of a floor. For
example, a floor controller may be assigned to a portion of the local
controllers disposed in the
facility. For example, a floor controller may be assigned to a portion of the
floors of a facility. A
master controller may be coupled to one or more floor controllers. The floor
controller may be
disposed in the facility. The master controller may be disposed in the
facility, or external to the
facility. The master controller may be disposed in the cloud. A controller may
be a part of, or be
operatively coupled to, a building management system. A controller may receive
one or more
inputs. A controller may generate one or more outputs. The controller may be a
single input
single output controller (SISO) or a multiple input multiple output controller
(MIM0). A controller
may interpret an input signal received. A controller may acquire data from the
one or more
components (e.g., sensors). Acquire may comprise receive or extract. The data
may comprise
measurement, estimation, determination, generation, or any combination
thereof. A controller
may comprise feedback control. A controller may comprise feed-forward control.
Control may
comprise on-off control, proportional control, proportional-integral (PI)
control, or proportional-
integral-derivative (PID) control. Control may comprise open loop control, or
closed loop control.
A controller may comprise closed loop control. A controller may comprise open
loop control. A
controller may comprise a user interface. A user interface may comprise (or
operatively coupled
to) a keyboard, keypad, mouse, touch screen, microphone, speech recognition
package,
camera, imaging system, or any combination thereof. Outputs may include a
display (e.g.,
screen), speaker, or printer.
[0067] Fig. 2 shows an example of a control system architecture 200 comprising
a master
controller 208 that controls floor controllers 206, that in turn control local
controllers 204. In
some embodiments, a local controller controls one or more IGUs, one or more
sensors, one or
more output devices (e.g., one or more emitters), or any combination thereof.
Fig. 2 shows an
example of a configuration in which the master controller is operatively
coupled (e.g., wirelessly
and/or wired) to a building management system (BMS) 224 and to a database 220.
Arrows in
FIG. 2 represents communication pathways. A controller may be operatively
coupled (e.g.,
directly/indirectly and/or wired and/wirelessly) to an external source 210.
The external source
may comprise a network. The external source may comprise one or more sensor or
output
device. The external source may comprise a cloud-based application and/or
database. The
communication may be wired and/or wireless. The external source may be
disposed external to
the facility. For example, the external source may comprise one or more
sensors and/or
antennas disposed, e.g., on a wall or on a ceiling of the facility. The
communication may be
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monodirectional or bidirectional. In the example shown in Fig. 2, the
communication all
communication arrows are meant to be bidirectional.
[0068] In various embodiments, a network infrastructure supports a control
system for one or
more windows such as tintable (e.g., electrochromic) windows. The control
system may comprise
one or more controllers operatively coupled (e.g., directly or indirectly) to
one or more windows.
While the disclosed embodiments describe tintable windows (also referred to
herein as "optically
switchable windows," or "smart windows") such as electrochromic windows, the
concepts
disclosed herein may apply to other types of switchable optical devices
comprising a liquid crystal
device, an electrochromic device, suspended particle device (SPD),
NanoChromics display
(NOD), Organic electroluminescent display (OELD), suspended particle device
(SPD),
NanoChromics display (NCD), or an Organic electroluminescent display (OELD).
The display
element may be attached to a part of a transparent body (such as the windows).
The tintable
window may be disposed in a (non-transitory) facility such as a building,
and/or in a transitory
facility (e.g., vehicle) such as a car, RV, bus, train, airplane, helicopter,
ship, or boat.
[0069] In some embodiments, a tintable window exhibits a (e.g.,
controllable and/or
reversible) change in at least one optical property of the window, e.g., when
a stimulus is
applied. The change may be a continuous change. A change may be to discrete
tint levels (e.g.,
to at least about 2, 4, 8, 16, or 32 tint levels). The optical property may
comprise hue, or
transmissivity. The hue may comprise color. The transmissivity may be of one
or more
wavelengths. The wavelengths may comprise ultraviolet, visible, or infrared
wavelengths. The
stimulus can include an optical, electrical and/or magnetic stimulus. For
example, the stimulus
can include an applied voltage and/or current. One or more tintable windows
can be used to
control lighting and/or glare conditions, e.g., by regulating the transmission
of solar energy
propagating through them. One or more tintable windows can be used to control
a temperature
within a building, e.g., by regulating the transmission of solar energy
propagating through the
window. Control of the solar energy may control heat load imposed on the
interior of the facility
(e.g., building). The control may be manual and/or automatic. The control may
be used for
maintaining one or more requested (e.g., environmental) conditions, e.g.,
occupant comfort. The
control may include reducing energy consumption of a heating, ventilation, air
conditioning
and/or lighting systems. At least two of heating, ventilation, and air
conditioning may be induced
by separate systems. At least two of heating, ventilation, and air
conditioning may be induced by
one system. The heating, ventilation, and air conditioning may be induced by a
single system
(abbreviated herein as "HVAC). In some cases, tintable windows may be
responsive to (e.g.,
and communicatively coupled to) one or more environmental sensors and/or user
control.
Tintable windows may comprise (e.g., may be) electrochromic windows. The
windows may be
located in the range from the interior to the exterior of a structure (e.g.,
facility, e.g., building).
However, this need not be the case. Tintable windows may operate using liquid
crystal devices,
suspended particle devices, microelectromechanical systems (MEMS) devices
(such as
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microshutters), or any technology known now, or later developed, that is
configured to control
light transmission through a window. Windows (e.g., with MEMS devices for
tinting) are
described in U.S. Patent Application Serial No. 14/443,353, filed May 15,
2015, now U.S. Patent
No. 10,359,681, issued July 23, 2019, titled, "MULTI-PANE WINDOWS INCLUDING
ELECTROCHROMIC DEVICES AND ELECTROMECHANICAL SYSTEMS DEVICES," which is
incorporated herein by reference in its entirety. In some cases, one or more
tintable windows
can be located within the interior of a building, e.g., between a conference
room and a hallway.
In some cases, one or more tintable windows can be used in automobiles,
trains, aircraft, and
other vehicles, e.g., in lieu of a passive and/or non-tinting window.
[0070] In some embodiments, the tintable window comprises an electrochromic
device
(referred to herein as an "EC device" (abbreviated herein as ECD), or "EC").
An EC device may
comprise at least one coating that includes at least one layer. The at least
one layer can
comprise an electrochromic material. In some embodiments, the electrochromic
material
exhibits a change from one optical state to another, e.g., when an electric
potential is applied
across the EC device. The transition of the electrochromic layer from one
optical state to
another optical state can be caused, e.g., by reversible, semi-reversible, or
irreversible ion
insertion into the electrochromic material (e.g., by way of intercalation) and
a corresponding
injection of charge-balancing electrons. For example, the transition of the
electrochromic layer
from one optical state to another optical state can be caused, e.g., by a
reversible ion insertion
into the electrochromic material (e.g., by way of intercalation) and a
corresponding injection of
charge-balancing electrons. Reversible may be for the expected lifetime of the
ECD. Semi-
reversible refers to a measurable (e.g. noticeable) degradation in the
reversibility of the tint of
the window over one or more tinting cycles. In some instances, a fraction of
the ions responsible
for the optical transition is irreversibly bound up in the electrochromic
material (e.g., and thus
the induced (altered) tint state of the window is not reversible to its
original tinting state). In
various EC devices, at least some (e.g., all) of the irreversibly bound ions
can be used to
compensate for "blind charge" in the material (e.g., ECD).
[0071] In some implementations, suitable ions include cations. The
cations may include
lithium ions (Li+) and/or hydrogen ions (H+) (i.e., protons). In some
implementations, other ions
can be suitable. Intercalation of the cations may be into an (e.g., metal)
oxide. A change in the
intercalation state of the ions (e.g. cations) into the oxide may induce a
visible change in a tint
(e.g., color) of the oxide. For example, the oxide may transition from a
colorless to a colored
state. For example, intercalation of lithium ions into tungsten oxide (W03-y
(0 <y < ¨0.3)) may
cause the tungsten oxide to change from a transparent state to a colored
(e.g., blue) state. EC
device coatings as described herein are located within the viewable portion of
the tintable
window such that the tinting of the EC device coating can be used to control
the optical state of
the tintable window.
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[0072] Fig. 3 shows an example of a schematic cross-section of an
electrochromic device
AA00 in accordance with some embodiments is shown in Fig. 3. The EC device
coating is
attached to a substrate 302, a transparent conductive layer (TCL) 304, an
electrochromic layer
(EC) 306 (sometimes also referred to as a cathodically coloring layer or a
cathodically tinting
layer), an ion conducting layer or region (IC) 308, a counter electrode layer
(CE) 310
(sometimes also referred to as an anodically coloring layer or anodically
tinting layer), and a
second TCL 314.
[0073] Elements 304, 306, 308, 310, and 314 are collectively referred to as an
electrochromic
stack 320. A voltage source 316 operable to apply an electric potential across
the
electrochromic stack 320 effects the transition of the electrochromic coating
from, e.g., a clear
state to a tinted state. In other embodiments, the order of layers is reversed
with respect to the
substrate. That is, the layers are in the following order: substrate, TCL,
counter electrode layer,
ion conducting layer, electrochromic material layer, TCL.
[0074] In various embodiments, the ion conductor region (e.g., 308) may form
from a portion
of the EC layer (e.g., 306) and/or from a portion of the CE layer (e.g., 310).
In such
embodiments, the electrochromic stack (e.g., 320) may be deposited to include
cathodically
coloring electrochromic material (the EC layer) in direct physical contact
with an anodically
coloring counter electrode material (the CE layer). The ion conductor region
(sometimes
referred to as an interfacial region, or as an ion conducting substantially
electronically insulating
layer or region) may form where the EC layer and the CE layer meet, for
example through
heating and/or other processing steps. Examples of electrochromic devices
(e.g., including
those fabricated without depositing a distinct ion conductor material) can be
found in U.S.
Patent Application Serial No. 13/462,725, filed May 2, 2012, titled
"ELECTROCHROMIC
DEVICES," that is incorporated herein by reference in its entirety. In some
embodiments, an EC
device coating may include one or more additional layers such as one or more
passive layers.
Passive layers can be used to improve certain optical properties, to provide
moisture, and/or to
provide scratch resistance. These and/or other passive layers can serve to
hermetically seal the
EC stack 320. Various layers, including transparent conducting layers (such as
304 and 314),
can be treated with anti-reflective and/or protective layers (e.g., oxide
and/or nitride layers).
[0075] In certain embodiments, the electrochromic device is
configured to (e.g., substantially)
reversibly cycle between a clear state and a tinted state. Reversible may be
within an expected
lifetime of the ECD. The expected lifetime can be at least about 5, 10, 15,
25, 50, 75, or 100
years. The expected lifetime can be any value between the aforementioned
values (e.g., from
about 5 years to about 100 years, from about 5 years to about 50 years, or
from about 50 years
to about 100 years). A potential can be applied to the electrochromic stack
(e.g., 320) such that
available ions in the stack that can cause the electrochromic material (e.g.,
306) to be in the
tinted state reside primarily in the counter electrode (e.g., 310) when the
window is in a first tint
state (e.g., clear). When the potential applied to the electrochromic stack is
reversed, the ions
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can be transported across the ion conducting layer (e.g., 308) to the
electrochromic material
and cause the material to enter the second tint state (e.g., tinted state).
[0076] It should be understood that the reference to a transition
between a clear state and
tinted state is non-limiting and suggests only one example, among many, of an
electrochromic
transition that may be implemented. Unless otherwise specified herein,
whenever reference is
made to a clear-tinted transition, the corresponding device or process
encompasses other
optical state transitions such as non-reflective-reflective, and/or
transparent-opaque. In some
embodiments, the terms "clear" and "bleached" refer to an optically neutral
state, e.g., untinted,
transparent and/or translucent. In some embodiments, the "color" or "tint" of
an electrochromic
transition is not limited to any wavelength or range of wavelengths. The
choice of appropriate
electrochromic material and counter electrode materials may govern the
relevant optical
transition (e.g., from tinted to untinted state).
[0077] In certain embodiments, at least a portion (e.g., all of)
the materials making up
electrochromic stack are inorganic, solid (i.e., in the solid state), or both
inorganic and solid.
Because various organic materials tend to degrade over time, particularly when
exposed to heat
and UV light as tinted building windows are, inorganic materials offer an
advantage of a reliable
electrochromic stack that can function for extended periods of time. In some
embodiments,
materials in the solid state can offer the advantage of being minimally
contaminated and
minimizing leakage issues, as materials in the liquid state sometimes do. One
or more of the
layers in the stack may contain some amount of organic material (e.g., that is
measurable). The
ECD or any portion thereof (e.g., one or more of the layers) may contain
little or no measurable
organic matter. The ECD or any portion thereof (e.g., one or more of the
layers) may contain
one or more liquids that may be present in little amounts. Little may be of at
most about
100ppm, 1Oppm, or 1ppm of the ECD. Solid state material may be deposited (or
otherwise
formed) using one or more processes employing liquid components, such as
certain processes
employing sol-gels, physical vapor deposition, and/or chemical vapor
deposition.
[0078] Fig. 4 shows an example of a cross-sectional view of a tintable window
embodied in
an insulated glass unit ("IGU") 400, in accordance with some implementations.
The terms "IGU,"
"tintable window," and "optically switchable window" can be used
interchangeably herein. It can
be desirable to have IGUs serve as the fundamental constructs for holding
electrochromic
panes (also referred to herein as "lites") when provided for installation in a
building. An IGU lite
may be a single substrate or a multi-substrate construct. The lite may
comprise a laminate, e.g.,
of two substrates. IGUs (e.g., having double- or triple-pane configurations)
can provide a
number of advantages over single pane configurations. For example, multi-pane
configurations
can provide enhanced thermal insulation, noise insulation, environmental
protection and/or
durability, when compared with single-pane configurations. A multi-pane
configuration can
provide increased protection for an ECD. For example, the electrochromic films
(e.g., as well as
associated layers and conductive interconnects) can be formed on an interior
surface of the
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multi-pane IGU and be protected by an inert gas fill in the interior volume
(e.g., 408) of the IGU.
The inert gas fill may provide at least some (heat) insulating function for an
IGU. Electrochromic
IGUs may have heat blocking capability, e.g., by virtue of a tintable coating
that absorbs (and/or
reflects) heat and light.
[0079] In some embodiments, an "IGU" includes two (or more) substantially
transparent
substrates. For example, the IGU may include two panes of glass. At least one
substrate of the
IGU can include an electrochromic device disposed thereon. The one or more
panes of the IGU
may have a separator disposed between them. An IGU can be a hermetically
sealed construct,
e.g., having an interior region that is isolated from the ambient environment.
A "window
assembly" may include an IGU. A "window assembly" may include a (e.g., stand-
alone)
laminate. A "window assembly" may include one or more electrical leads, e.g.,
for connecting
the IGUs and/or laminates. The electrical leads may operatively couple (e.g.
connect) one or
more electrochromic devices to a voltage source, switches and the like, and
may include a
frame that supports the IGU or laminate. A window assembly may include a
window controller,
and/or components of a window controller (e.g., a dock).
[0080] Fig. 4 shows an example implementation of an IGU 400 that includes a
first pane 404
having a first surface Si and a second surface S2. In some implementations,
the first surface
S1 of the first pane 404 faces an exterior environment, such as an outdoors or
outside
environment. The IGU 400 also includes a second pane 406 having a first
surface S3 and a
second surface S4. In some implementations, the second surface (e.g., S4) of
the second pane
(e.g., 406) faces an interior environment, such as an inside environment of a
home, building,
vehicle, or compartment thereof (e.g., an enclosure therein such as a room).
[0081] In some implementations, the first and the second panes
(e.g., 404 and 406) are
transparent or translucent, e.g., at least to light in the visible spectrum.
For example, each of the
panes (e.g., 404 and 406) can be formed of a glass material. The glass
material may include
architectural glass, and/or shatter-resistant glass. The glass may comprise a
silicon oxide (SO.).
The glass may comprise a soda-lime glass or float glass. The glass may
comprise at least about
75% silica (SiO2). The glass may comprise oxides such as Na2O, or CaO. The
glass may
comprise alkali or alkali-earth oxides. The glass may comprise one or more
additives. The first
and/or the second panes can include any material having suitable optical,
electrical, thermal,
and/or mechanical properties. Other materials (e.g., substrates) that can be
included in the first
and/or the second panes are plastic, semi-plastic and/or thermoplastic
materials, for example,
poly(methyl methacrylate), polystyrene, polycarbonate, allyl diglycol
carbonate, SAN (styrene
acrylonitrile copolymer), poly(4-methyl-1-pentene), polyester, and/or
polyamide. The first and/or
second pane may include mirror material (e.g., silver). In some
implementations, the first and/or
the second panes can be strengthened. The strengthening may include tempering,
heating,
and/or chemically strengthening.
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[0082] Sensors of a sensor ensemble may be organized into a sensor module. A
sensor
ensemble may comprise a circuit board, such as a printed circuit board, in
which a number of
sensors are adhered or affixed to the circuit board. Sensors can be removed
from a sensor
module. For example, a sensor may be plugged and/or unplugged from the circuit
board.
Sensors may be individually activated and/or deactivated (e.g., using a
switch). The circuit
board may comprise a polymer. The circuit board may be transparent or non-
transparent. The
circuit board may comprise metal (e.g., elemental metal and/or metal alloy).
The circuit board
may comprise a conductor. The circuit board may comprise an insulator. The
circuit board may
comprise any geometric shape (e.g., rectangle or ellipse). The circuit board
may be configured
(e.g., may be of a shape) to allow the ensemble to be disposed in a mullion
(e.g., of a window).
The circuit board may be configured (e.g., may be of a shape) to allow the
ensemble to be
disposed in a frame (e.g., door frame and/or window frame). The mullion and/or
frame may
comprise one or more holes to allow the sensor(s) to obtain (e.g., accurate)
readings. The
circuit board may include an electrical connectivity port (e.g., socket). The
circuit board may be
connected to a power source (e.g., to electricity). The power source may
comprise renewable or
non-renewable power source.
[0083] Fig. 5 shows an example of a diagram 500 of an ensemble of sensors
organized into a
sensor module. Sensors 510A, 510B, 510C, and 510D are shown as included in
sensor
ensemble 505. An ensemble of sensors organized into a sensor module may
include at least 1,
2, 4, 5, 8, 10, 20, 50, or 500 sensors. The sensor module may include a number
of sensors in a
range between any of the aforementioned values (e.g., from about 1 to about
1000, from about
1 to about 500, or from about 500 to about 1000). Sensors of a sensor module
may comprise
sensors configured or designed for sensing a parameter comprising,
temperature, humidity,
carbon dioxide, particulate matter (e.g., between 2.5 pm and 10 pm), total
volatile organic
compounds (e.g., via a change in a voltage potential brought about by surface
adsorption of
volatile organic compound), ambient light, audio noise level, pressure (e.g.
gas, and/or liquid),
acceleration, time, radar, lidar, radio signals (e.g., ultra-wideband radio
signals), passive
infrared, glass breakage, or movement detectors. The sensor ensemble (e.g.,
505) may
comprise non-sensor devices (e.g., emitters), such as buzzers and light
emitting diodes.
Examples of sensor ensembles and their uses can be found in U.S. Patent
Application Serial
No. 16/447169 ,filed June 20, 2019, titled, "SENSING AND COMMUNICATIONS UNIT
FOR
OPTICALLY SWITCHABLE WINDOW SYSTEMS," that is incorporated herein by reference
in
its entirety.
[0084] In some embodiments, the one or more devices comprise a sensor (e.g.,
as part of a
transceiver). In some embodiments, a transceiver may be configured transmit
and receive one
or more signals using a personal area network (PAN) standard, for example such
as IEEE
802.15.4. In some embodiments, signals may comprise Bluetooth, Wi-Fi, or
EnOcean signals
(e.g., wide bandwidth). The one or more signals may comprise ultra-wide
bandwidth (UWB)
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signals (e.g., having a frequency in the range from about 2.4 to about 10.6
Giga Hertz (GHz), or
from about 7.5 GHz to about 10.6GHz). An Ultra-wideband signal can be one
having a fractional
bandwidth greater than about 20%. An ultra-wideband (UWB) radio frequency
signal can have a
bandwidth of at least about 500 Mega Hertz (MHz). The one or more signals may
use a very low
energy level for short-range. Signals (e.g., having radio frequency) may
employ a spectrum
capable of penetrating solid structures (e.g., wall, door, and/or window). Low
power may be of at
most about 25 milli Watts (mW), 50 mW, 75 mW, or 100 mW. Low power may be any
value
between the aforementioned values (e.g., from 25mW to 100mW, from 25mW to
50mW, or from
75mW to 100mW). The sensor and/or transceiver may be configured to support
wireless
technology standard used for exchanging data between fixed and mobile devices,
e.g., over
short distances. The signal may comprise Ultra High Frequency (UHF) radio
waves, e.g., from
about 2.402 gigahertz (GHz) to about 2.480 GHz. The signal may be configured
for building
personal area networks (PANs).
[0085] In some embodiments, the device is configured to enable geo-
location technology (e.g.,
global positioning system (GPS), Bluetooth (BLE), ultrawide band (UWB) and/or
dead-reckoning).
The geo-location technology may facilitate determination of a position of
signal source (e.g.,
location of the tag) to an accuracy of at least 100 centimeters (cm), 75cm,
50cm, 25cm, 20cm,
10cm, or 5cm. In some embodiments, the electromagnetic radiation of the signal
comprises ultra-
wideband (UWB) radio waves, ultra-high frequency (UHF) radio waves, or radio
waves utilized in
global positioning system (GPS). In some embodiments, the electromagnetic
radiation comprises
electromagnetic waves of a frequency of at least about 300MHz, 500MHz, or
1200MHz. In some
embodiments, the signal comprises location and/or time data. In some
embodiments, the geo-
location technology comprises Bluetooth, UWB, UHF, and/or global positioning
system (GPS)
technology. In some embodiments, the signal has a spatial capacity of at least
about 1013 bits
per second per meter squared (bit/s/m2).
[0086] In some embodiments, pulse-based ultra-wideband (UWB) technology (e.g.,
ECMA-
368, or ECMA-369) is a wireless technology for transmitting large amounts of
data at low power
(e.g., less than about 1 millivolt (mW), 0.75mW, 0.5mW, or 0.25mW) over short
distances (e.g.,
of at most about 300 feet 0, 250', 230', 200', or 150'). A UWB signal can
occupy at least about
750MHz, 500 MHz, or 250MHz of bandwidth spectrum, and/or at least about 30%,
20%, or 10%
of its center frequency. The UWB signal can be transmitted by one or more
pulses. A component
broadcasts digital signal pulses may be timed (e.g., precisely) on a carrier
signal across a number
of frequency channels at the same time. Information may be transmitted, e.g.,
by modulating the
timing and/or positioning of the signal (e.g., the pulses). Signal information
may be transmitted by
encoding the polarity of the signal (e.g., pulse), its amplitude and/or by
using orthogonal signals
(e.g., pulses). The UWB signal may be a low power information transfer
protocol. The UWB
technology may be utilized for (e.g., indoor) location applications. The broad
range of the UWB
spectrum comprises low frequencies having long wavelengths, which allows UWB
signals to
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penetrate a variety of materials, including various building fixtures (e.g.,
walls). The wide range of
frequencies, e.g., including the low penetrating frequencies, may decrease the
chance of
multipath propagation errors (without wishing to be bound to theory, as some
wavelengths may
have a line-of-sight trajectory). UWB communication signals (e.g., pulses) may
be short (e.g., of
at most about 70cnn, 60 cm, or 50cm for a pulse that is about 600MHz, 500 MHz,
or 400MHz
wide; or of at most about 20cm, 23 cm, 25cm, or 30cm for a pulse that is has a
bandwidth of about
1GHz, 1.2GHz, 1.3 GHz, or 1.5GHz). The short communication signals (e.g.,
pulses) may reduce
the chance that reflecting signals (e.g., pulses) will overlap with the
original signal (e.g., pulse).
[0087] In some embodiments, an increase in the number and/or types of sensors
may be
used to increase a probability that one or more measured property is accurate
and/or that a
particular event measured by one or more sensor has occurred. In some
embodiments, sensors
of sensor ensemble may cooperate with one another. In an example, a radar
sensor of sensor
ensemble may determine presence of a number of individuals in an enclosure. A
processor
(e.g., processor 915) may determine that detection of presence of a number of
individuals in an
enclosure is positively correlated with an increase in carbon dioxide
concentration. In an
example, the processor-accessible memory may determine that an increase in
detected infrared
energy is positively correlated with an increase in temperature as detected by
a temperature
sensor. In some embodiments, network interface (e.g., 550) may communicate
with other
sensor ensembles similar to sensor ensemble. The network interface may
additionally
communicate with a controller.
[0088]
Individual sensors (e.g., sensor 510A, sensor 510D, etc.) of a sensor
ensemble may
comprise and/or utilize at least one dedicated processor. A sensor ensemble
may utilize a
remote processor (e.g., 554) utilizing a wireless and/or wired communications
link. A sensor
ensemble may utilize at least one processor (e.g., processor 552), which may
represent a
cloud-based processor coupled to a sensor ensemble via the cloud (e.g., 550).
Processors
(e.g., 552 and/or 554) may be located in the same building, in a different
building, in a building
owned by the same or different entity, a facility owned by the manufacturer of
the
window/controller/sensor ensemble, or at any other location. In various
embodiments, as
indicated by the dotted lines of Fig. 5, sensor ensemble 505 is not required
to comprise a
separate processor and network interface. These entities may be separate
entities and may be
operatively coupled to ensemble 505. The dotted lines in Fig. 5 designate
optional features. In
some embodiments, onboard processing and/or memory of one or more ensemble of
sensors
may be used to support other functions (e.g., via allocation of ensembles(s)
memory and/or
processing power to the network infrastructure of a building).
[0089] Fig. 6 shows an example of a controller 605 for controlling one or more
sensors.
Controller 605 comprises sensor correlator 610, model generator 615, event
detector 620,
processor and memory 625, and the network interface 650. Sensor correlator 610
operates to
detect correlations between or among various sensor types. For example, an
infrared radiation
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sensor measuring an increase in infrared energy may be positively correlated
with an increase
in measure temperature. A sensor correlator may establish correlation
coefficients, such as
coefficients for negatively-correlated sensor readings (e.g., correlation
coefficients between -1
and 0). For example, the sensor correlator may establish coefficients for
positively-correlated
sensor readings (e.g., correlation coefficients between 0 and +1).
[0090] In some embodiments, the sensor data may be time dependent. In some
embodiments, the sensor data may be space dependent. The model may utilize
time and/or
space dependency of the sensed parameter. A model generator may permit fitting
of sensor
readings as a function of one or more dimensions of an enclosure. In an
example, a model
provides sensor profile curves for carbon dioxide may utilize various gaseous
diffusion models,
which may allow prediction of a level of carbon dioxide at points in between
sensor locations.
Processor and memory (e.g., 625) may facilitate processing of models.
[0091] In some embodiments, the sensor and/or sensor ensemble may act as an
event
detector. The event detector may operate to direct activity of sensors in an
enclosure. In an
example, in response to event detector determining that very few individuals
remain in an
enclosure, event detector may direct carbon dioxide sensors to reduce a
sampling rate.
Reduction of a sampling rate may extend the life of a sensor (e.g., a carbon
dioxide sensor). In
another example, in response to event detector determining that a large number
of individuals
are present in a room, event detector may increase the sampling rate of a
carbon dioxide
sensor. In an example, in response to event detector receiving a signal from a
glass breakage
sensor, event detector may activate one or more movement detectors of an
enclosure, one or
more radar units of a detector. A network interface (e.g., 650) may be
configured or designed to
communicate with one or more sensors via wireless communications links, wired
communications links, or any combination thereof.
[0092] The controller may monitor and/or direct (e.g., physical)
alteration of the operating
conditions of the apparatuses, software, and/or methods described herein.
Control may
comprise regulate, manipulate, restrict, direct, monitor, adjust, modulate,
vary, alter, restrain,
check, guide, or manage. Controlled (e.g., by a controller) may include
attenuated, modulated,
varied, managed, curbed, disciplined, regulated, restrained, supervised,
manipulated, and/or
guided. The control may comprise controlling a control variable (e.g.
temperature, power,
voltage, and/or profile). The control can comprise real time or off-line
control. A calculation
utilized by the controller can be done in real time, and/or offline. The
controller may be a manual
or a non-manual controller. The controller may be an automatic controller. The
controller may
operate upon request. The controller may be a programmable controller. The
controller may be
programed. The controller may comprise a processing unit (e.g., CPU or GPU).
The controller
may receive an input (e.g., from at least one sensor). The controller may
deliver an output. The
controller may comprise multiple (e.g., sub-) controllers. The controller may
be a part of a
control system. The control system may comprise a master controller, floor
controller, local
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controller (e.g., enclosure controller, or window controller). The controller
may receive one or
more inputs. The controller may generate one or more outputs. The controller
may be a single
input single output controller (SISO) or a multiple input multiple output
controller (MIM0). The
controller may interpret the input signal received. The controller may acquire
data from the one
or more sensors. Acquire may comprise receive or extract. The data may
comprise
measurement, estimation, determination, generation, or any combination
thereof. The controller
may comprise feedback control. The controller may comprise feed-forward
control. The control
may comprise on-off control, proportional control, proportional-integral (PI)
control, or
proportional-integral-derivative (PID) control. The control may comprise open
loop control, or
closed loop control. The controller may comprise closed loop control. The
controller may
comprise open loop control. The controller may comprise a user interface. The
user interface
may comprise (or operatively coupled to) a keyboard, keypad, mouse, touch
screen,
microphone, speech recognition package, camera, imaging system, or any
combination thereof.
The outputs may include a display (e.g., screen), speaker, or printer.
[0093] The methods, systems and/or the apparatus described herein may comprise
a control
system. The control system can be in communication with any of the apparatuses
(e.g.,
sensors) described herein. The sensors may be of the same type or of different
types, e.g., as
described herein. For example, the control system may be in communication with
the first
sensor and/or with the second sensor. The control system may control the one
or more sensors.
The control system may control one or more components of a building management
system
(e.g., lightening, security, and/or air conditioning system). The controller
may regulate at least
one (e.g., environmental) characteristic of the enclosure. The control system
may regulate the
enclosure environment using any component of the building management system.
For example,
the control system may regulate the energy supplied by a heating element
and/or by a cooling
element. For example, the control system may regulate velocity of an air
flowing through a vent
to and/or from the enclosure. The control system may comprise a processor. The
processor
may be a processing unit. The controller may comprise a processing unit. The
processing unit
may be central. The processing unit may comprise a central processing unit
(abbreviated herein
as "CPU"). The processing unit may be a graphic processing unit (abbreviated
herein as
"GPU"). The controller(s) or control mechanisms (e.g., comprising a computer
system) may be
programmed to implement one or more methods of the disclosure. The processor
may be
programmed to implement methods of the disclosure. The controller may control
at least one
component of the forming systems and/or apparatuses disclosed herein.
[0094] In some embodiments, an enclosure includes one or more sensors. The
sensor may
facilitate controlling the environment of the enclosure such that inhabitants
of the enclosure may
have an environment that is more comfortable, delightful, beautiful, healthy,
productive (e.g., in
terms of inhabitant performance), easer to live (e.g., work) in, or any
combination thereof. The
sensor(s) may be configured as low or high resolution sensors. Sensor may
provide on/off
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indications of the occurrence and/or presence of a particular environmental
event (e.g., one
pixel sensors). In some embodiments, the accuracy and/or resolution of a
sensor may be
improved via artificial intelligence analysis of its measurements. Examples of
artificial
intelligence techniques that may be used include: reactive, limited memory,
theory of mind,
and/or self-aware techniques know to those skilled in the art). Sensors may be
configured to
process, measure, analyze, detect and/or react to one or more of: data,
temperature, humidity,
sound, force, pressure, electromagnetic waves, position, distance, movement,
flow,
acceleration, speed, vibration, dust, light, glare, color, gas(es), and/or
other aspects (e.g.,
characteristics) of an environment (e.g., of an enclosure). The gases may
include volatile
organic compounds (VOCs). The gases may include carbon monoxide, carbon
dioxide, water
vapor (e.g., humidity), oxygen, radon, and/or hydrogen sulfide. The one or
more sensors may
be calibrated in a factory setting. A sensor may be optimized to be capable of
performing
accurate measurements of one or more environmental characteristics present in
the factory
setting. In some instances, a factory calibrated sensor may be less optimized
for operation in a
target environment. For example, a factory setting may comprise a different
environment than a
target environment. The target environment can be an environment in which the
sensor is
deployed. The target environment can be an environment in which the sensor is
expected
and/or destined to operate. The target environment may differ from a factory
environment. A
factory environment corresponds to a location at which the sensor was
assembled and/or built.
The target environment may comprise a factory in which the sensor was not
assembled and/or
built. In some instances, the factory setting may differ from the target
environment to the extent
that sensor readings captured in the target environment are erroneous (e.g.,
to a measurable
extent). In this context, "erroneous" may refer to sensor readings that
deviate from a specified
accuracy (e.g., specified by a manufacture of the sensor). In some situations,
a factory-
calibrated sensor may provide readings that do not meet accuracy
specifications (e.g., by a
manufacturer) when operated in the target environments.
[0095] In certain embodiments, one or more shortcomings in sensor operation
may be at
least partially corrected and/or alleviated by allowing a sensor to be self-
calibrated in its target
environment (e.g., where the sensor is installed). In some instances, a sensor
may be calibrated
and/or recalibrated after installation in the target environment. In some
instances, a sensor may
be calibrated and/or recalibrated after a certain period of operation in the
target environment.
The target environment may be the location at which the sensor is installed in
an enclosure. In
comparison to a sensor that is calibrated prior to installation, in a sensor
calibrated and/or
recalibrated after installation in the target environment may provide
measurements having
increased accuracy (e.g., that is measurable). In certain embodiments, one or
more previously-
installed sensors in an enclosure provide readings that are used to calibrated
and/or recalibrate
a newly-installed sensor in the enclosure.
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[0096] In some embodiments, a target environment corresponding to a first
enclosure differs
from a target environment corresponding to a second enclosure. For example, a
target
environment of an enclosure that corresponds to a cafeteria or to an
auditorium may present
sensor readings different than a target enclosure that corresponds to a
conference room. A
sensor may consider the target environment (e.g., one or more characteristics
thereof) when
performing sensor readings and/or outputting sensor data. For example, during
lunchtime a
carbon dioxide sensor installed in an occupied cafeteria may provide higher
readings than a
sensor installed in an empty conference room. In another example, ambient
noise sensor
located in an occupied cafeteria during lunch may provide higher readings than
an ambient
noise sensor located in a library.
[0097] In some embodiments, a sensor (e.g., occasionally) provides
an output signal
indicating an erroneous measurement. The sensor may be operatively coupled to
at least one
controller. The controller(s) may obtain erroneous sensor reading from the
sensor. The
controller(s) may obtain readings of the same type, at a similar time (e.g.,
or simultaneously),
from one or more other (e.g., nearby) sensors. The one or more other sensors
may be disposed
at the same environment as the one sensor. The controller(s) may evaluate the
erroneous
sensor reading in conjunction with one or more readings of the same type made
by one or more
other sensors of the same type to identify the erroneous sensor reading as an
outlier. For
example, the controller may evaluate an erroneous temperature sensor reading
and one or
more readings of temperature made by one or more other temperature sensors.
The
controller(s) may determine that the sensor reading is erroneous in response
to consideration
(e.g., including evaluating and/or comparing with) the sensor reading with one
or more readings
from other sensors in the same environment (e.g., in the same enclosure).
Controller(s) may
direct the one sensor providing the erroneous reading to undergo recalibration
(e.g., by
undergoing a recalibration procedure). For example, the controller(s) may
transmit one or more
values and/or parameters to the sensor(s) providing the erroneous reading. The
sensor(s)
providing the erroneous reading may utilize the transmitted value and/or
parameter to adjust its
subsequent sensor reading(s). For example, the sensor(s) providing the
erroneous reading may
utilize the transmitted value and/or parameter to adjust its baseline for
subsequent sensor
reading(s). The baseline can be a value, a set of values, or a function.
[0098] A sensor may obtain a first reading of a first parameter from a first
sensor and a
second reading of the first parameter from a second sensor. The first sensor
may be disposed
at a first location in an enclosure and the second sensor may be disposed at a
second location
in the enclosure. A projected value of the first parameter measured at the
first location may be
estimated based, at least in part, on the second reading. A difference may be
determined
between the estimated projected value of the first parameter and the first
reading of the first
parameter. The difference between the estimated projected value of the first
parameter and the
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first reading of the first parameter may be considered and/or utilized in
modifying the first
reading of the first parameter.
[0099]
In some embodiments, self-calibration measurements performed in the field
(e.g., in
the target setting such as a deployment site) may be used to monitor a
measurable
characteristic (e.g., noise, objects, carbon dioxide (CO2) level, and/or
temperature) over a time-
window (e.g., of at least an hour, a day, or a week). A value may be monitored
over a time
window to obtain a best-known value. A best-known value may comprise values
that remain
within an error range over a time-window (also referred to herein as the
"minimal sampling
period"). An optimal value may be interpolated, anticipated, and/or
calculated. A minimal
sampling period may be a function of the number and/or frequency of sampling
needed to
establish a reliable baseline. A best-known value may be the most stable value
sensed (e.g.,
having smallest fluctuation range and/or lowest value) during at least the
sampling period). In
some cases, best-known values may be obtained during periods of low
disturbance in an
environment when fluctuations of the environmental characteristic (e.g.,
environmental property)
are at a minimum. For example, best-known values may be obtained during an
evening or
weekend, e.g., during periods of low occupancy in an environment (e.g.,
building) when noise
fluctuations and/or concentrations of gases such as CO2 are at a minimum. A
time-window
during which a field baseline is measured (e.g., during a sampling period),
can be pre-assigned,
or can be assigned using a (e.g., repetitive) occurrence of the minimal
sampling period. The
minimal sampling period can be a period sufficient to allow differentiation of
the measured signal
from noise. Any pre-assigned time window can be adjusted using the (e.g.,
repetitive)
occurrence of the minimal sampling period. Positional and/or stationary
characteristics (e.g.,
placement of walls and/or windows) of the enclosure may be utilized in
measuring the
characteristics of a give environment. The positional and/or stationary
characteristics of the
enclosure may be derived independently (e.g., from 3rd party data and/or from
non-sensor data).
The positional and/or stationary characteristics of the enclosure may be
derived using data from
the one or more sensors disposed in the environment. When the environment is
minimally
disturbed with respect to the measured environmental characteristic (e.g.,
when no one is
present in an environment, and/or when the environment is quiet), some sensor
data may be
used to sense position of (e.g., stationary and/or non-stationary) objects to
determine the
environment. Determining the position of objects comprises determining an
(e.g., human)
occupancy in the environment. Distance and/or location related measurements
may utilize
sensor(s) such as radar and/o ultrasonic sensors. Distance and location
related measurements
may derive from sensors that to not traditionally correlated to location
and/or distance. Objects
disposed in, or that are part of, an enclosure may have distinct sensor
signatures For example,
location of people in the enclosure may correlate to distinct temperature,
humidity and/or CO2
signatures. For example, location of a wall may correlate to an abrupt change
in the distribution
of temperature, humidity and/or CO2 in the enclosure. For example, location of
a window or door
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(whether open or closed) may correlate to a change in the distribution of
temperature, humidity
and/or CO2 next to the window or door. The one or more sensors in the
enclosure may monitor
any environmental changes and/or correlates such changes to changes in
subsequently
monitored values. In some cases, lack of fluctuations in monitored values may
be used as an
indication that a sensor is damaged, and that the sensor may need to be remove
or replaced.
[0100] In some embodiments, a best-known value is designated. The best-known
value may
be designated as a field-baseline, e.g., which may be compared to a factory
base-line. If a field
baseline is within an error range of the factory baseline, then the field
baseline may be equated
(e.g., and/or substituted with) a factory baseline. Otherwise new baseline may
be assigned to
the field baseline (e.g., baseline for the sensor deployed in the target
location). In some cases,
best-known values may be compared to, and/or derived from, historical values
and/or third-party
values. An accuracy of the field-baselines may be monitored overtime. If a
drift in a field-
baseline is detected, which field-baseline (i) is above a threshold (e.g.,
about 5% of the field-
baseline value drops), or (ii) is outside a field-baseline error range, then
the field-baseline may
be reset to the new (e.g., drifted) field-baseline value. The threshold may be
of at least 2%, 4%,
5%, 10%, 15%, 20%, or 30% value drop relative to a previously determined
baseline.
[0101] In some embodiments, a device (e.g., sensor) can be designated as a
golden device
that can be used as a reference (e.g., as the golden standard) for calibration
of the other
sensors (e.g., of the same type in this or in another facility). The golden
device may be a device
that is the most calibrated in the facility or in a portion thereof (e.g., in
the building, in the floor,
and/or in the room). A calibrated and/or localized device may be utilized as a
standard for
calibrating and/or localizing other devices (e.g., of the same type). Such
devices may be
referred to as the "golden device." The golden device be utilized as a
reference device. The
golden device may be the one most calibrated and/or accurately localized in
the facility (e.g.,
among devices of the same type).
[0102]
In some embodiments, self-calibration is performed based at least in part
on one or
more learning techniques (e.g., machine learning, artificial intelligence
(Al), heuristics, and/or
collaboration/correlation among differing sensor types). Self-calibration may
be performed on an
individual sensor and/or on a remote processor operatively coupled to the
sensor (e.g., on a
central processor and/or in the cloud). Self-calibration may periodically
determine any need for
new calibration of a sensor (e.g., by monitoring drift). Self-calibration may
consider a plurality of
sensors (e.g., a community of sensors). A community of sensors can be of the
same type, in the
same environment, in the same enclosure (e.g., space), and/or in a vicinity
(e.g., proximity) of
the sensor. For example, a community of sensors can be in the same enclosure,
same space,
same building, in the same floor, in the same room, in the same room portion,
within at most a
predetermined distance from each other, or any combination thereof. A
community of sensors
can include a dormant sensor, shut sensor, and/or actively functioning sensor.
Baseline(s) from
one or more actively functioning sensor may be compared against other
sensor(s) to find any
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baseline outliers. Baseline(s) from one or more functioning sensor may be
compared to (e.g.,
dormant) sensors that were previously calibrated. Non-functioning (e.g.,
dormant) sensor(s)
may serve as "memory sensors," e.g., for the purpose of baseline comparison.
For example, a
dormant state of a sensor may preserve its calibration value. Malfunctioning
sensors can be
functionally replaced by activating inactive sensors that were previously
installed in the
environment (e.g., instead of physically replacing them by installing a new
sensor introduced to
the environment). The environment may be an enclosure. When a sensor is added
to the
community of sensors, it may adopt a baseline value that considers the
baseline values of
adjacent sensor(s) of the community. For example, a new sensor may adopt
baseline values
(e.g., average, mean, median, mode, or midrange) of its (e.g., directly)
adjacent sensors.
Directly adjacent sensors 1 and 2 are two sensors that are adjacent to one
another, without any
other sensor (e.g., of the same type disposed in the distance between sensor 1
and sensor 2.
For example, a new sensor may adopt baseline values (e.g., average, mean,
median, mode, or
midrange) of a plurality of sensors (e.g., all sensors) in the environment.
[0103] In some embodiments, Self-calibration considers a ground
truth sensing value. A
ground truth sensing value can be monitored by an alternate (e.g., and more
sensitive) method.
For example, by physically monitoring (e.g., manually and/or automatically) an
individual sensor
against a known and/or different measurement methodology. In some cases,
ground truth may
be determined by a traveler (e.g., robot, or Field Service Engineer), or
external data (e.g., from
a 3rd party). The robot may comprise a drone, or a vehicle.
[0104] In some embodiments, a sensor transmits (e.g., beacons)
data to a receiver, e.g., a
sensor or suite of sensors. The suite of sensors can also be referred to as an
"ensemble of
sensors." The sensors in the suite of sensors can be analogous to those
deployed in a space of
an enclosure. At least one sensor of the suite of sensors may be out of
calibration, or not
calibrated (e.g., upon or after deployment). A sensor may be calibrated using
ground truth
measurement (e.g., performed by a traveler). The traveler may carry a similar
sensor to the one
to be calibrated/recalibrated. The sensor may be sensed by the traveler as
being non-calibrated
or out of calibration. The traveler may be a field service engineer. The
traveler may be a robot.
The robot may be mobile. The robot may comprise a wheel (e.g., wheels). The
robot may
comprise a vehicle. The robot may be air borne. The robot may comprise, or be
integrated into a
drone, helicopter, and/or airplane. The mobile enclosure (e.g., car or drone)
may be devoid of a
human operator. A receiver may be carried by the traveler, e.g., into the
space. The traveler
(e.g., using the receiver) may take one or more readings to determine ground
truth value(s). The
readings corresponding to the ground truth value(s) may be sent, directly or
indirectly (e.g. via
the cloud) to proximate uncalibrated and/or mis-calibrated sensor(s). A sensor
that is
reprogrammed with a ground truth value(s), may thus become calibrated. A
sensor (or suite of
sensors) of the traveler may be programmed to transmit (e.g., beacon) to non-
calibrated or mis-
calibrated sensors, its newly calibrated values. The transmission of the newly
calibrated values
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may be sent to sensors within a certain radius, e.g., depending on the
property measured by the
sensor and its location (e.g., geographical) susceptibility. In one example,
when a field service
engineer (abbreviated herein as "FSE") is within a radius of a sensor, and
ground truth readings
have been successfully programmed into the sensor that is now calibrated using
the ground
truth readings. In some embodiments, a signal indicates successful calibration
of the sensor.
Calibration of the sensor may include transferring data and/or reprograming
the sensor. The
signal may comprise a sound (e.g., chime), a light, or another signal type
that is detectable
(e.g., by an instrument and/or by a user). The FSE may move to the next
sensor(s) for
calibration assessment, calibration, and/or recalibration. Such procedure may
allow a traveler
(e.g., FSE) to enter a space of an enclosure, and travel (e.g., walk around)
in the space. The
traveler may enter one or more characteristics of the sensor. The one or more
characteristics of
the sensor may comprise property measured, range (e.g., radii), sensor type,
sensor fidelity,
sampling frequency, operating temperature (e.g., or range thereof), or
operating pressure. The
traveler may wait for a signal of the sensor (e.g., indicating completion of
calibration), and move
on to recalibrate sensor(s) in the space. The assessment of the calibration,
calibration, and/or
recalibration of the sensor may require physical coupling (e.g., via a wire)
to the sensor. The
assessment of the calibration, calibration, and/or recalibration of the sensor
may be devoid of
physical coupling to the sensor (e.g., be wireless). The wireless calibration
may be automated
(e.g., using a robot as a traveler). The wireless calibration utilizing the
traveler may require
physical travel within the environment in which the sensor(s) are deployed. To
ensure accuracy,
transmitted data can be compared (e.g., in real time, or at a later time) to a
standard and/or
alternate measurement methodology. Transmitted and/or compared sensor data may
be stored
and/or used for calibration of a sensor.
[0105] In some cases, a location of a sensor may be calibrated. For example,
there may be a
discrepancy between a registered location of a sensor, and a measured location
of the sensor
by the traveler. This may occur when the sensor is or is not calibrated as to
the property (e.g.,
humidity or pressure) it is designed to measure. The traveler may transmit the
discrepancy to
allow correction of any previously measured data by the (location mis-
calibrated or uncalibrated)
sensor. The transmission may be to a controller and/or to a processor that is
operatively
coupled with a controller, which controller is operatively coupled to the
sensor. The traveler may
initiate a location correction operation of the sensor, e.g., to calibrate/re-
calibrate its location.
[0106] In some embodiments, the location of a sensor carried by the traveler
differs from a
location of the sensor to be calibrated. For example, the sensor of the
traveler may be in the
middle of the room, and sensor(s) to be calibrated may be affixed to a wall.
The discrepancy of
these locations may contribute to a calibration error (e.g., of the property
measured by the
sensor). The traveler may transmit (e.g., along with the calibration data or
separate thereto) the
location at which the calibration data is measured (e.g., the location of the
sensor of the
traveler), e.g., to allow for any location discrepancy compensation. The
variability in the sensed
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quality(ies) (e.g., property(ies)) may be calculated, anticipated, and/or
applied to the sensed
data used for calibration, e.g., to compensate for any variability between the
sensor of the
traveler and the sensor being recalibrated/calibrated. The calculation may
comprise a
simulation, e.g., a real-time simulation. A simulation may consider the
enclosure (e.g., room),
fixtures in and/or defining the enclosure, directions of any enclosure
boundaries (e.g., wall, floor,
ceiling, and/or window), and/or any anticipated variability in the environment
of the enclosure
(e.g., at least one characteristic comprising location, volume, air flow or
temperature, of a vent
of the enclosure). A simulation may anticipate fixture(s) (e.g., desk, chair,
and/or lamp) and/or
bodies in the enclosure. The bodies may include (i) inhabitants disposed in
the enclosure during
specific time periods and/or (ii) inhabitant traffic patterns. The
anticipatory simulation may
resemble anticipating an existence, position, mass and/or other
characteristics of a black hole
from the behavior of its surroundings (e.g., as opposed to by direct
measurement on the black
hole). The simulation may comprise an indirect method of calibration. The
simulation may
comprise a recursive fitting methodology. A simulation may comprise auto
positioning of (i) a
structural grid of the environment (e.g., building walls) and/or (ii) a grid
to which the sensors are
affixed to. The calibration/recalibration may be adjusted in situ and/or in
real-time. The
calibration/recalibration of a sensor may utilize relative location
information. Relative may be to
at least one fixed structural element (e.g., relative to at least one fixed
sensor).
[0107] In some embodiments, a plurality of sensors is assembled
into a sensor suite (e.g.,
sensor ensemble). At least two sensors of the plurality of sensors may be of a
different type
(e.g., are configured to measure different properties). Various sensor types
can be assembled
together (e.g., bundled up) and form a sensor suite. The plurality of sensors
may be coupled to
one electronic board. The electrical connection of at least two of the
plurality of sensors in the
sensor suit may be controlled (e.g., manually and/or automatically). For
example, the sensor
suite may be operatively coupled to, or comprise, a controller (e.g., a
microcontroller). The
controller may control and on/off connectivity of the sensor to electrical
power. The controller
can thus control the time (e.g., period) at which the sensor will be
operative.
[0108] In some embodiments, baseline of one or more sensors of the sensor
ensemble may
drift. A recalibration may include one or more (e.g., but not all) sensors of
a sensor suite. For
example, a collective baseline drift can occur in at least two sensor types in
a given sensor
suite. A baseline drift in one sensor of the sensor suite may indicate
malfunction of the sensor.
Baseline drifts measured in a plurality of sensors in the sensor suite, may
indicate a change in
the environment sensed by the sensors in the sensor suite (e.g., rather than
malfunction of
these baseline drifted sensors). Such sensor data baseline drifts may be
utilized to detect
environmental changes. For example (i) that a building was erected/destroyed
next to the
sensor suite, (ii) that a ventilation channel was altered (e.g., damaged) next
to the sensor suite,
(iii) that a refrigerator is installed/dismantled next to the sensor suite,
(iv) that a working location
of a person is altered relative (e.g., and adjacent) to the sensor suite, (v)
that an electronic
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change (e.g., malfunction) is experienced by the sensor suite, (vi) that a
structure (e.g., interior
wall) has been changed, or (vii) any combination thereof. In this manner, the
data can be used
e.g. to update a three-dimensional (3D_ model of the enclosure.
[0109] In some embodiments, one or more sensors are added or
removed from a community
of sensors, e.g., disposed in the enclosure and/or in the sensor suite. Newly
added sensors may
inform (e.g., beacon) other members of a community of sensor of its presence
and relative
location within a topology of the community. Examples of sensor community(ies)
can be found,
for example, in U.S. Provisional Patent Application Serial No. 62/958,653,
filed January 8, 2020,
titled, "SENSOR AUTOLOCATION," that is incorporated by reference herein in its
entirety.
[0110] Fig. 7 shows a flowchart for a method 700 for detecting an
outlier based, at least in
part, on sensor readings. The method of Fig. 7 may be performed by an
individual sensor of a
sensor ensemble. The method of Fig. 7 may be performed by a first sensor
coupled to (e.g., in
communication with) a second sensor. The method of Fig. 7 may be directed by a
controller
coupled to (e.g., in communication with) the first and/or second sensors. The
method of Fig. 7
begins at 710, in which sensor readings are obtained from one or more sensors
of a sensor
ensemble. At 720, readings are processed (e.g., by considering the enclosure,
historical
reading, benchmarks, and/or modeling) to generate a result. At 730, the result
is utilized to
detect outlier data, to detect an outlier sensor, to detect an environmental
change (e.g., at a
particular time and/or location), and/or to predict future readings of the one
or more sensors.
[0111] In particular embodiments, sensor readings from a
particular sensor may be correlated
with sensor readings from a sensor of the same type or of a different type.
Receipt of a sensor
reading may give rise to a sensor accessing correlation data from other
sensors disposed within
the same enclosure. Based, at least in part, on the access correlation data,
the reliability of a
sensor may be determined or estimated. Responsive to determination or
estimation of sensor
reliability, a sensor output reading may be adjusted (e.g.,
increased/decreased). A reliability
value may be assigned to a sensor based on adjusted sensor readings.
[0112] Fig. 8 shows a flowchart for a method 850 for detecting and adjusting
an outlier based,
at least in part, on sensor readings. The method of Fig. 8 may be performed by
an individual
sensor of a sensor ensemble. The method of Fig. 8 may be performed by a first
sensor coupled
to (e.g., and in communication with) a second sensor. The method of Fig. 8 may
be directed by
at least one controller (e.g., processor) coupled to (e.g., in communication
with) first and/or
second sensors. The method of Fig. 8 begins at 855, in which sensor readings
are obtained
from one or more sensors of a sensor ensemble disposed in an enclosure. A
sensor reading
may be any type of reading, such as detection of movement of individuals
within an enclosure,
temperature, humidity, or any other property detected by the sensor At 860,
correlation data
may be accessed from other sensors disposed in the enclosure. Correlation data
may relate to
output readings from a sensor of the same type or a sensor of a different type
operating within
the enclosure. In an example, a noise sensor may access data from a movement
sensor to
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determine if one or more individuals have entered an enclosure. One or more
individuals
moving within an enclosure may emit a level of noise. In an example, output
signals from a
noise sensor may be corroborated by a second noise sensor and/or by a movement
detector. At
865, based, at least in part, on the accessed correlation data, reliability of
an obtained sensor
reading may be determined. In an example, responsive to output signals from a
faulty (e.g.,
uncalibrated, mis-calibrated, or otherwise malfunctioning) noise sensor
without movement
detection by movement detector, output signals from the noise sensor may be
determined to be
of decreased reliability. In an example, responsive to a calibrated noise
sensor reporting an
increase in detected noise and simultaneous movement detection, sensor
readings from the
calibrated noise sensor may be determined to be of increased reliability. At
870, based, at least
in part, on the determined reliability of obtained sensor readings, sensor
readings may be
adjusted (e.g., and re-calibrated). In an example, a faulty (e.g.,
uncalibrated, mis-calibrated, or
otherwise malfunctioning) noise sensor sensing a large increase in noise while
a movement
sensor detects very little movement may bring about adjustment (e.g.,
decreasing) of noise
sensor output readings. In an example, a faulty noise sensor sensing only a
small increase in
noise while a movement detector detects a large number of individuals entering
an enclosure
may bring about adjustment (e.g., increasing) of noise sensor output readings.
At 875, assigning
or updating a reliability value for one or more sensors based, at least in
part, on adjusted sensor
readings may be performed. In an example, a newly-installed sensor, which
repeatedly (e.g.,
two or more times) provides output readings inconsistent with other sensors of
the same type or
of a different type may be (i) assigned a lower value of reliability, (ii)
calibrated or re-calibrated,
and/or (iii) examined for any other reliability issues. In an example, a
calibrated sensor, which
repeatedly provides output readings consistent with other sensors of the same
type or of a
different type may be assigned a higher value of reliability.
[0113] In some embodiments, a plurality of sensors of the same type may be
distributed in an
enclosure. At least one of the plurality of sensors of the same type, may be
part of an ensemble.
For example, at least two of the plurality of sensors of the same type, may be
part of at least two
ensembles. The sensor ensembles may be distributed in an enclosure. An
enclosure may
comprise a conference room. For example, a plurality of sensors of the same
type may measure
an environmental parameter in the conference room. Responsive to measurement
of the
environmental parameter of an enclosure, a parameter topology of the enclosure
may be
generated. A parameter topology may be generated utilizing output signals from
any type of
sensor of sensor ensemble, e.g., as disclosed herein. Parameter topologies may
be generated
for any enclosure of a facility such as conference rooms, hallways, bathrooms,
cafeterias,
garages, auditoriums, utility rooms, storage facilities, equipment rooms,
and/or elevators.
[0114] Fig. 9 shows an example of a diagram 900 of an arrangement of sensor
ensembles
distributed within an enclosure. In the example shown in Fig. 9, a group 910
of individuals are
seated in conference room 902. The conference room includes an "X" dimension
to indicate
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length, a "Y" dimension to indicate height, and a "Z" dimension to indicate
depth. XYZ being
directions a Cartesian coordination system. Sensor ensembles 905A, 905B, and
905C comprise
sensors can operate similar to sensors described in reference to sensor
ensemble 505 of Fig. 5.
At least two sensor ensembles (e.g., 905A, 905B, and 905C) may be integrated
into a single
sensor module. Sensor ensembles 905A, 905B, and 905C can include a carbon
dioxide (CO2)
sensor, a carbon monoxide (CO) sensor, an ambient noise sensor, or any other
sensor
disclosed herein. In the example shown in Fig. 9, a first sensor ensemble 905A
is disposed
(e.g., installed) near point 915A, which may correspond to a location in a
ceiling, wall, or other
location to a side of a table at which the group 910 of individuals are
seated. In the example
shown in Fig. 9, a second sensor ensemble 905B is disposed (e.g., installed)
near point 915B,
which may correspond to a location in a ceiling, wall, or other location above
(e.g., directly
above) a table at which the group 910 of individuals are seated. In the
example shown in Fig. 9,
a third sensor ensemble 905C may be disposed (e.g., installed) at or near
point 915C, which
may correspond to a location in a ceiling, wall, or other location to a side
of the table at which
the relatively small group 910 of individuals are seated. Any number of
additional sensors
and/or sensor modules may be positioned at other locations of conference room
902. The
sensor ensembles may be disposed anywhere in the enclosure. The location of an
ensemble of
sensors in an enclosure may have coordinates (e.g., in a Cartesian coordinate
system). At least
one coordinate (e.g., of x, y, and z) may differ between two or more sensor
ensembles, e.g.,
that are disposed in the enclosure. At least two coordinates (e.g., of x, y,
and z) may differ
between two or more sensor ensembles, e.g., that are disposed in the
enclosure. All the
coordinates (e.g., of x, y, and z) may differ between two or more sensor
ensembles, e.g., that
are disposed in the enclosure. For example, two sensor ensembles may have the
same x
coordinate, and different y and z coordinates. For example, two sensor
ensembles may have
the same x and y coordinates, and a different z coordinate. For example, two
sensor ensembles
may have different x, y, and z coordinates.
[0115] In particular embodiments, one or more sensors of the sensor ensemble
provide
readings. In some embodiments, the sensor is configured to sense a parameter.
The parameter
may comprise temperature, particulate matter, volatile organic compounds,
electromagnetic
energy, pressure, acceleration, time, radar, lidar, glass breakage, movement,
or gas. The gas
may comprise a Nobel gas. The gas may be a gas harmful to an average human.
The gas may
be a gas present in the ambient atmosphere (e.g., oxygen, carbon dioxide,
ozone, chlorinated
carbon compounds, or nitrogen compound(s) such as Nitric oxide (NO) and/or
nitrogen dioxide
(NO2)). The gas(es) may comprise oxygen, nitrogen, carbon dioxide, carbon
monoxide,
hydrogen sulfide, nitrogen dioxide, inert gas, Nobel gas (e.g., radon),
cholorophore, ozone,
formaldehyde, methane, or ethane. The gas may comprise radon, carbon monoxide,
hydrogen
sulfide, hydrogen, oxygen, water (e.g., humidity). The electromagnetic sensor
may comprise an
infrared, visible light, ultraviolet sensor. The infrared radiation may be
passive infrared radiation
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(e.g., black body radiation). The electromagnetic sensor may sense radio
waves. The radio
waves may comprise wide band, or ultra-wideband radio signals. The radio waves
may
comprise pulse radio waves. The radio waves may comprise radio waves utilized
in
communication. The gas sensor may sense a gas type, flow (e.g., velocity
and/or acceleration),
pressure, and/or concentration. The readings may have an amplitude range. The
readings may
have a parameter range. For example, the parameter may be electromagnetic
wavelength, and
the range may be a range of detected wavelengths.
[0116] In some embodiments, the sensor data is responsive to the environment
in the
enclosure and/or to any inducer(s) of a change (e.g., any environmental
disruptor) in this
environment. The sensors data may be responsive to emitters operatively
coupled to (e.g., in)
the enclosure (e.g., an occupant, appliances (e.g., heater, cooler,
ventilation, and/or vacuum),
opening). For example, the sensor data may be responsive to an air
conditioning duct, or to an
open window. The sensor data may be responsive to an activity taking place in
the room. The
activity may include human activity, and/or non-human activity. The activity
may include
electronic activity, gaseous activity, and/or chemical activity. The activity
may include a sensual
activity (e.g., visual, tactile, olfactory, auditory, and/or gustatory). The
activity may include an
electronic and/or magnetic activity. The activity may be sensed by a person.
The activity may
not be sensed by a person. The sensors data may be responsive to the occupants
in the
enclosure, substance (e.g., gas) flow, substance (e.g., gas) pressure, and/or
temperature.
[0117] In one example, sensor ensembles 905A, 905B, and 905C include CO2
sensor, and
an ambient noise sensor. A carbon dioxide sensor of sensor ensemble 905A may
provide a
reading as depicted in sensor output reading profile 925A. A noise sensor of
sensor ensemble
905A may provide a reading also depicted in sensor output reading profile
925A. A carbon
dioxide sensor of sensor ensemble 905B may provide a reading as depicted in
sensor output
reading profile 925B. A noise sensor of sensor ensemble 905B may provide a
reading also as
depicted in sensor output reading profile 925B. Sensor output reading profile
925B may indicate
higher levels of carbon dioxide and noise relative to sensor output reading
profile 925A. Sensor
output reading profile 925C may indicate lower levels of carbon dioxide and
noise relative to
sensor output reading profile 925B. Sensor output reading profile 925C may
indicate carbon
dioxide and noise levels similar to those of sensor output reading profile
925A. Sensor output
reading profiles 925A, 925B, and 925C may comprise indications representing
other sensor
readings, such as temperature, humidity, particulate matter, volatile organic
compounds,
ambient light, pressure, acceleration, time, radar, lidar, ultra-wideband
radio signals, passive
infrared, and/or glass breakage, movement detectors.
[0118]
In some embodiments, data from a sensor in a sensor in the enclosure
(e.g., and in
the sensor ensemble) is collected and/or processed (e.g., analyzed). The data
processing can
be performed by a processor of the sensor, by a processor of the sensor
ensemble, by another
sensor, by another ensemble, in the cloud, by a processor of the controller,
by a processor in
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the enclosure, by a processor outside of the enclosure, by a remote processor
(e.g., in a
different facility), by a manufacturer (e.g., of the sensor, of the window,
and/or of the building
network). The data of the sensor may have a time indicator (e.g., may be time
stamped). The
data of the sensor may have a sensor location identification (e.g., be
location stamped). The
sensor may be identifiably coupled with one or more controllers.
[0119] In particular embodiments, sensor output reading profiles 925A, 925B,
and 9250 may
be processed. For example, as part of the processing (e.g., analysis), the
sensor output reading
profiles may be plotted on a graph depicting a sensor reading as a function of
a dimension (e.g.,
the "X" dimension) of an enclosure (e.g., conference room 902). In an example,
a carbon
dioxide level indicated in sensor output reading profile 925A may be indicated
as point 935A of
CO2 graph 930 of Fig. 9. In an example, a carbon dioxide level of sensor
output reading profile
925B may be indicated as point 935B of CO2 graph 930. In an example, a carbon
dioxide level
indicated in sensor output reading profile 925C may be indicated as point 935C
of CO2 graph
930. In an example, an ambient noise level indicated in sensor output reading
profile 925A may
be indicated as point 945A of noise graph 940. In an example, an ambient noise
level indicated
in sensor output reading profile 925B may be indicated as point 945B of noise
graph 940. In an
example, an ambient noise level indicated in sensor output reading profile
925C may be
indicated as point 945C of noise graph 940.
[0120] In some embodiments, processing data derived from the sensor comprises
applying
one or more models. The models may comprise mathematical models. The
processing may
comprise fitting of models (e.g., curve fitting). The model may be multi-
dimensional (e.g., two or
three dimensional). The model may be represented as a graph (e.g., 2 or 3
dimensional graph).
For example, the model may be represented as a contour map (e.g., as depicted
in Fig. 15).
The modeling may comprise one or more matrices. The model may comprise a
topological
model. The model may relate to a topology of the sensed parameter in the
enclosure. The
model may relate to a time variation of the topology of the sensed parameter
in the enclosure.
The model may be environmental and/or enclosure specific. The model may
consider one or
more properties of the enclosure (e.g., dimensionalities, openings, and/or
environmental
disrupters (e.g., emitters)). Processing of the sensor data may utilize
historical sensor data,
and/or current (e.g., real time) sensor data. The data processing (e.g.,
utilizing the model) may
be used to project an environmental change in the enclosure, and/or recommend
actions to
alleviate, adjust, or otherwise react to the change.
[0121] In particular embodiments, sensor ensembles 905A, 905B, and/or 905C,
may be
capable of accessing a model to permit curve fitting of sensor readings as a
function of one or
more dimensions of an enclosure. In an example, a model may be accessed to
generate sensor
profile curves 950A, 950B, 950C, 950D, and 950E, utilizing points 935A, 935B,
and 935C of
CO2 graph 930. In an example, a model may be accessed to generate sensor
profile curves
951A, 951B, 951C, 951B, and 951E utilizing points 945A, 945B, and 945C of
noise graph 940.
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Additional models may utilize additional readings from sensor ensembles (e.g.,
905A, 905B,
and/or 905C) to provide curves in addition to sensor profile curves 950 and
951 of Fig. 9.
Sensor profile curves generated in response to use of a model may sensor
output reading
profiles indicate a value of a particular environmental parameter as a
function of a dimension of
an enclosure (e.g., an "X" dimension, a "Y" dimension, and/or a "Z"
dimension).
[0122] In certain embodiments, one or more models utilized to form curves 950A-
950E and
951A-951E) may provide a parameter topology of an enclosure. In an example, a
parameter
topology (as represented by curves 950A-950E and 951A-951E) may be synthesized
or
generated from sensor output reading profiles. The parameter topology may be a
topology of
any sensed parameter disclosed herein. In an example, a parameter topology for
a conference
room (e.g., conference room 902) may comprise a carbon dioxide profile having
relatively low
values at locations away from a conference room table and relatively high
values at locations
above (e.g., directly above) a conference room table. In an example, a
parameter topology for a
conference room may comprise a multi-dimensional noise profile having
relatively low values at
locations away from a conference table and slightly higher values above (e.g.,
directly above) a
conference room table.
[0123] Fig. 10 shows an example of a diagram 1000 of an arrangement of sensor
ensembles
distributed within an enclosure. In the example shown in Fig. 10, a relatively
large group 1010 of
individuals (e.g., larger relative to conference room group 910) are assembled
in auditorium
1002. The auditorium includes an "X" dimension to indicate length, a "Y"
dimension to indicate
height, and a "Z" dimension to indicate depth. Sensor ensembles 1005A, 1005B,
and 1005C
may comprise sensors that operate similar to sensors described in reference to
sensor
ensemble 905 of Fig. 9. At least two sensor ensembles (e.g., 1005A, 1005B, and
1005C) may
be integrated into a single sensor module. Sensor ensembles 1005A, 1005B, and
1005C can
include a CO2 sensor, an ambient noise sensor, or any other sensor disclosed
herein. In the
example shown in Fig. 10, a first sensor ensemble 1005A is disposed (e.g.,
installed) near point
1015A, which may correspond to a location in a ceiling, wall, or other
location to a side of
seating area at which the relatively large group 1010 of individuals are
seated. In the example
shown in Fig. 10, a second sensor ensemble 1005B may be disposed (e.g.,
installed) at or near
point 1015B, which may correspond to a location in a ceiling, wall, or other
location above (e.g.,
directly above) an area at which the relatively large group 1010 of
individuals are congregated.
A third sensor ensemble 1005C may be disposed (e.g., installed) at or near
point 1015C, which
may correspond to a location in a ceiling, wall, or other location to a side
of the table at which
the relatively large group 1010 of individuals are positioned. Any number of
additional sensors
and/or sensor modules may be positioned at other locations of auditorium 1002.
The sensor
ensembles may be disposed anywhere in the enclosure.
[0124] In one example, sensor ensembles 1005A, 1005B, and 1005C, includes a
carbon
dioxide sensor of sensor ensemble 1005A may provide a reading as depicted in
sensor output
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reading profile 1025A. A noise sensor of sensor ensemble 1005A may provide a
reading also
depicted in sensor output reading profile 1025A. A carbon dioxide sensor of
sensor ensemble
1005B may provide a reading as depicted in sensor output reading profile
1025B. A noise
sensor of sensor ensemble 1005B may provide a reading also as depicted in
sensor output
reading profile 1025B. Sensor output reading profile 1025B may indicate higher
levels of carbon
dioxide and noise relative to sensor output reading profile 1025A. Sensor
output reading profile
1025C may indicate lower levels of carbon dioxide and noise relative to sensor
output reading
profile 1025B. Sensor output reading profile 1025C may indicate carbon dioxide
and noise
levels similar to those of sensor output reading profile 1025A. Sensor output
reading profiles
1025A, 1025B, and 10250 may comprise indications representing other sensor
readings of any
sensed parameter disclosed herein.
[0125]
In particular embodiments, sensor output reading profiles 1025A, 1025B,
and 10250
may be plotted on a graph depicting a sensor reading as a function of a
dimension (e.g., the "X"
dimension) of an enclosure (e.g., auditorium 1002). In an example, a carbon
dioxide level
indicated in sensor output reading profile 1025A (shown in Fig. 10) may be
indicated as point
1035A (shown in Fig. 10) of CO2 graph 1030. In an example, a carbon dioxide
level of sensor
output reading profile 1025B (shown in Fig. 10) may be indicated as point
1035B (shown in Fig.
10) of CO2 graph 1030. In an example, a carbon dioxide level indicated in
sensor output reading
profile 10250 may be indicated as point 1035C of CO2 graph 1030. In an
example, an ambient
noise level indicated in sensor output reading profile 1025A may be indicated
as point 1045A of
noise graph 1040. In an example, an ambient noise level indicated in sensor
output reading
profile 1025B may be indicated as point 1045B of noise graph 1040. In an
example, an ambient
noise level indicated in sensor output reading profile 10250 may be indicated
as point 10450 of
noise graph 1040.
[0126] In particular embodiments, sensor ensembles 1005A, 1005B, and/or 1005C,
may be
capable of utilizing and/or accessing (e.g., configured to utilize and/or
access) a model to permit
curve fitting of sensor readings as a function of one or more dimensions of an
enclosure. In an
example shown in Fig. 10, a model may be accessed to provide sensor profiles,
utilizing points
1035A, 1035B, and 10350 of CO2 graph 1030. In an example shown as an example
in Fig. 10,
a model may be accessed to provide sensor profile 1051 utilizing points 1045A,
1045B, and
10450 of noise graph 1040. Additional models may utilize additional readings
from sensor
ensembles (e.g., 1005A, 1005B, 10050) to provide sensor profile curves (e.g.
sensor profile
curve 1050A, 1050B, 10500, 1050D, and 1050E) of Fig. 10. Models may be
utilized to provide
sensor profile curves corresponding to ambient noise levels (e.g., sensor
profile curves 1050A,
1050B, 1050C, 1050D, and 1051E). Sensor profile curves generated in response
to use of a
model may indicate a value of a particular environmental parameter as a
function of a
dimension of an enclosure (e.g., an "X" dimension, a "Y" dimension, and/or a
"Z" dimension). In
certain embodiments, one or more models utilized to form sensor profile curves
1050 and 1051)
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may provide a parameter topology of an enclosure. A parameter topology may be
indicative of a
particular type of enclosure. In an example, a parameter topology may be
synthesized or
generated from sensor profile curves 1050 and 1051, which may correspond to a
parameter
topology for an auditorium. In an example, a parameter topology for an
auditorium may
comprise a carbon dioxide profile having at least moderately high values at
all locations and
very high values at locations near the center of the auditorium. In an
example, a parameter
topology for an auditorium may comprise a noise profile having relatively high
values at all
locations of an auditorium and higher values near the center of the
auditorium. In particular
embodiments, sensor readings from one or more sensors of a sensor ensemble may
be
obtained. Sensor readings may be obtained by the sensor itself. Sensor
readings may be
obtained by a cooperating sensor, which may be of the same type or a different
type of sensor.
Sensor readings may be obtained by one or more processors and/or controllers
Sensor reading
may be processed by considering one or more other readings from other sensors
disposed
(e.g., installed) within an enclosure, historical readings, benchmarks, and/or
modeling, to
generate a result (e.g., a prediction or an estimation of a sensor reading.) A
generated result
may be utilized to detect an outlier of a sensor reading and/or an outlier
sensor. A generated
result may be utilized to detect an environmental change at a time and/or
location. A generated
result may be utilized to predict future readings of the one or more sensors
in the enclosure.
[0127]
In some embodiments, a sensor has an operational lifespan. An operational
lifespan
of a sensor may be related to one or more readings taken by the sensor. Sensor
readings from
certain sensors may be more valuable and/or varied during certain time periods
and may be
less valuable and/or varied during other time periods. For example, movement
sensor readings
may be more varied during the day than during the night. The operational
lifespan of the sensor
may be extended. Extension of the operational lifespan may be accomplished by
permitting the
sensor to reduce sampling of environmental parameters at certain time periods
(e.g., having the
lower beneficial value). Certain sensors may modify (e.g., increase or
decrease) a frequency at
which sensor readings are sampled. Timing and/or frequency of the sensor
operation may
depend on the sensor type, location in the (e.g., target) environment, and/or
time of day. A
sensor type may require constant and/or more frequent operation during the day
(e.g., CO2,
VOCs, occupancy, and/or lighting sensor). Volatile organic compounds may be
animal and/or
human derived. VOCs may comprise a compound related to human produced odor. A
sensor
may require infrequent operation during at least a portion of the night. A
sensor type may
require infrequent operation during at least a portion of the day (e.g.,
temperature and/or
pressure sensor). A sensor may be assigned a timing and/or frequency of
operation. The
assignment may be controlled (e.g., altered) manually and/or automatically
(e.g., using at least
one controller operatively coupled to the sensor). Operatively coupled may
include
communicatively coupled, electrically coupled, optically coupled, or any
combination thereof.
Modification of the timing and/or frequency at which sensor readings are taken
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responsive to detection of an event by a sensor of the same type or of a
sensor of a different
type. Modification of the timing and/or frequency at which sensor readings may
utilize sensor
data analysis. The sensor data analysis may utilize artificial intelligence
(abbreviated herein as
"Al"). The control may be fully automatic or partially automatic. The
partially automatic control
may allow a user to (i) override a direction of the controller, and/or (ii)
indicate any preference
(e.g., of the user).
[0128] In some embodiments, processing sensor data comprises performing sensor
data
analysis. The sensor data analysis may comprise at least one rational decision
making process,
and/or learning. The sensor data analysis may be utilized to adjust and
environment, e.g., by
adjusting one or more components that affect the environment of the enclosure.
The data
analysis may be performed by a machine based system (e.g., a circuitry). The
circuitry may be
of a processor. The sensor data analysis may utilize artificial intelligence.
The sensor data
analysis may rely on one or more models (e.g., mathematical models). In some
embodiments,
the sensor data analysis comprises linear regression, least squares fit,
Gaussian process
regression, kernel regression, nonparametric multiplicative regression (NPMR),
regression
trees, local regression, semiparametric regression, isotonic regression,
multivariate adaptive
regression splines (MARS), logistic regression, robust regression, polynomial
regression,
stepwise regression, ridge regression, lasso regression, elasticnet
regression, principal
component analysis (PCA), singular value decomposition, fuzzy measure theory,
Bore!
measure, Han measure, risk-neutral measure, Lebesgue measure, group method of
data
handling (GMDH), Naive Bayes classifiers, k-nearest neighbors algorithm (k-
NN), support vector
machines (SVMs), neural networks, support vector machines, classification and
regression
trees (CART), random forest, gradient boosting, or generalized linear model
(GLM) technique.
Fig. 11 shows an example of a diagram 1100 of an arrangement of sensors
distributed among
enclosures. In the example shown in Fig. 11, controller 1105 is
communicatively linked 1108
with sensors located in enclosure A (sensors 1110A, 1110B, 1110C, ... 1110Z),
enclosure B
(sensors 1115A, 1115B, 1115C, 1115Z), enclosure C (sensors 1120A, 1120B,
1120C,...
1120Z), and enclosure Z (sensors 1185A, 1185B, 1185C,... 1185Z).
Communicatively linked
comprises wired and/or wireless communication. In some embodiments, a sensor
ensemble
includes at least two sensors of a differing types. In some embodiments, a
sensor ensemble
includes at least two sensors of the same type. In the example shown in Fig.
11, sensors
1110A, 1110B, 1110C, ... 1110Z of enclosure A represent an ensemble. An
ensemble of
sensors can refer to a collection of diverse sensors. In some embodiments, at
least two of the
sensors in the ensemble cooperate to determine environmental parameters, e.g.,
of an
enclosure in which they are disposed. For example, a sensor ensemble may
include a carbon
dioxide sensor, a carbon monoxide sensor, a volatile organic chemical sensor,
an ambient noise
sensor, a visible light sensor, a temperature sensor, and/or a humidity
sensor. A sensor
ensemble may comprise other types of sensors, and claimed subject matter is
not limited in this
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respect. The enclosure may comprise one or more sensors that are not part of
an ensemble of
sensors. The enclosure may comprise a plurality of ensembles. At least two of
the plurality of
ensembles may differ in at least one of their sensors. At least two of the
plurality of ensembles
may have at least one of their sensors that is similar (e.g., of the same
type). For example, an
ensemble can have two motion sensors and one temperature sensor. For example,
an
ensemble can have a carbon dioxide sensor and an IR sensor. The ensemble may
include one
or more devices that are not sensors. The one or more other devices that are
not sensors may
include sound emitter (e.g., buzzer), and/or electromagnetic radiation
emitters (e.g., light
emitting diode). In some embodiments, a single sensor (e.g., not in an
ensemble) may be
disposed adjacent (e.g., immediately adjacent such as contacting) another
device that is not a
sensor.
[0129] In some embodiments, the ensemble of sensors is disposed in a housing.
The
housing may comprise one or more circuit boards. The housing my comprise a
processor or an
emitter. The housing may comprise a temperature exchanging component (e.g.,
heat sink,
cooler, and/or flow of gas). The temperature adjusting component can be active
or passive. The
processor may comprise a GPU or CPU processing unit. The circuitry may be
programmable.
The circuity boards may be disposed in a manner that will permit temperature
exchange, e.g.,
though another medium. The other medium may include a temperature conductive
metal (e.g.,
elemental metal or metal alloy. For example, comprising copper and/or
aluminum. The housing
may comprise a polymer or a resin. The housing may include a plurality of
sensors, emitters,
temperature adjusters, and/or processors. The housing may comprise any device
disclosed
herein. The housing (e.g., container or envelope) may comprise a transparent
or non-
transparent material. The housing may comprise a body and a lid. The housing
may comprise
one or more holes. The housing may be operatively coupled to a power and/or
communication
network. The communication may be wired and/or wireless. Examples of sensor
ensemble,
housing, control, and coupling to the network can be found in U.S. Provisional
Patent
Application Serial No. 63/079,851, filed September 17, 2020, titled, "DEVICE
ENSEMBLES
AND COEXISTENCE MANAGEMENT OF DEVICES," which is incorporated herein by
reference
in its entirety.
[0130] Sensors of a sensor ensemble may collaborate with one another. A sensor
of one type
may have a correlation with at least one other type of sensor. A situation in
an enclosure may
affect one or more of different sensors. Sensor readings of the one or more
different sensors
may be correlated and/or affected by the situation. The correlations may be
predetermined. The
correlations may be determined over a period of time (e.g., using a learning
process). The
period of time may be predetermined. The period of time may have a cutoff
value. The cutoff
value may consider an error threshold (e.g., percentage value) between a
predictive sensor
data and a measured sensor data, e.g., in similar situation(s). The time may
be ongoing. The
correlation may be derived from a learning set (also referred to herein as
"training set"). The
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learning set may comprise, and/or may be derived from, real time observations
in the enclosure.
The observations may include data collection (e.g., from sensor(s)). The
learning set may
comprise sensor(s) data from a similar enclosure. The learning set may
comprise third party
data set (e.g., of sensor(s) data). The learning set may derive from
simulation, e.g., of one or
more environmental conditions affecting the enclosure. The learning set may
compose detected
(e.g., historic) signal data to which one or more types of noise were added.
The correlation may
utilize historic data, third party data, and/or real time (e.g., sensor) data.
The correlation
between two sensor types may be assigned a value. The value may be a relative
value (e.g.,
strong correlation, medium correlation, or weak correlation). The learning set
that is not derived
from real-time measurements, may serve as a benchmark (e.g., baseline) to
initiate operations
of the sensors and/or various components that affect the environment (e.g.,
HVAC system,
and/or tinting windows). Real time sensor data may supplement the learning
set, e.g., on an
ongoing basis or for a defined time period. The (e.g., supplemented) learning
set may increase
in size during deployment of the sensors in the environment. The initial
learning set may
increase in size, e.g., with inclusion of additional (i) real time
measurements, (ii) sensor data
from other (e.g., similar) enclosures, (iii) third party data, and/or (iv)
other and/or updated
simulation.
[0131] In some embodiments, data from sensors may be correlated. Once a
correlation
between two or more sensor types is established, a deviation from the
correlation (e.g., from the
correlation value) may indicate an irregular situation and/or malfunction of a
sensor of the
correlating sensors. The malfunction may include a slippage of a calibration.
The malfunction
may indicate a requirement for re-calibration of the sensor. A malfunction may
comprise
complete failure of the sensor. In an example, a movement sensor may
collaborate with a
carbon dioxide sensor. In an example, responsive to a movement sensor
detecting movement
of one or more individuals in an enclosure, a carbon dioxide sensor may be
activated to begin
taking carbon dioxide measurements. An increase in movement in an enclosure,
may be
correlated with increased levels of carbon dioxide. In another example, a
motion sensor
detecting individuals in an enclosure may be correlated with an increase in
noise detected by a
noise sensor in the enclosure. In some embodiments, detection by a first type
of sensor that is
not accompanied by detection by a second type of sensor may result in a sensor
posting an
error message. For example, if a motion sensor detects numerous individuals in
an enclosure,
without an increase in carbon dioxide and/or noise, the carbon dioxide sensor
and/or the noise
sensor may be identified as having failed or as having an erroneous output. An
error message
may be posted. A first plurality of different correlating sensors in a first
ensemble may include
one sensor of a first type, and a second plurality of sensors of different
types. If the second
plurality of sensors indicate a correlation, and the one sensor indicates a
reading different from
the correlation, there is an increased likelihood that the one sensor
malfunctions. If the first
plurality of sensors in the first ensemble detect a first correlation, and a
third plurality of
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correlating sensors in a second ensemble detect a second correlation different
from the first
correlation, there is an increased likelihood that the situation to which the
first ensemble of
sensors is exposed to is different from the situation to which the third
ensemble of sensors are
exposed to.
[0132] Sensors of a sensor ensemble may collaborate with one another. The
collaboration
may comprise considering sensor data of another sensor (e.g., of a different
type) in the
ensemble. The collaboration may comprise trends projected by the other sensor
(e.g., type) in
the ensemble. The collaboration may comprise trends projected by data relating
to another
sensor (e.g., type) in the ensemble. The other sensor data can be derived from
the other sensor
in the ensemble, from sensors of the same type in other ensembles, or from
data of the type
collected by the other sensor in the ensemble, which data does not derive from
the other
sensor. For example, a first ensemble may include a pressure sensor and a
temperature
sensor. The collaboration between the pressure sensor and the temperature
sensor may
comprise considering pressure sensor data while analyzing and/or projecting
temperature data
of the temperature sensor in the first ensemble. The pressure data may be (i)
of a pressure
sensor in the first ensemble, (ii) of pressure sensor(s) in one or more other
ensembles, (iii)
pressure data of other sensor(s) and/or (iv) pressure data of a third party.
[0133] In some embodiments, sensor ensembles, are distributed throughout an
enclosure.
Sensors of a same type may be dispersed in an enclosure, e.g., to allow
measurement of
environmental parameters at various locations of an enclosure. Sensors of the
same type may
measure a gradient along one or more dimensions of an enclosure. A gradient
may include a
temperature gradient, an ambient noise gradient, or any other variation (e.g.,
increase or
decrease) in a measured parameter as a function of location from a point. A
gradient may be
utilized in determining that a sensor is providing erroneous measurement
(e.g., the sensor has a
failure). Fig. 12 shows an example of a diagram 1290 of an arrangement of
sensor ensembles
in an enclosure. In the example of Fig. 12, sensor ensemble 1292A is
positioned at a distance
D1 from vent 1296. Sensor ensemble 1292B is positioned at a distance D2 from
vent 1296.
Sensor ensemble 1292C is positioned at a distance D3 from vent 1296.
Temperature and noise
measurements made by sensor ensemble 1292A are shown by output reading profile
1294A.
Output reading profile 1294A indicates a relatively low temperature and a
significant amount of
noise. Temperature and noise measurements made by sensor ensemble 1292B are
shown by
output reading profile 1294B. Output reading profile 1294B indicates a
somewhat higher
temperature, and a somewhat reduced noise level. Temperature and noise
measurements
made by sensor ensemble 12920 are shown by output reading profile 1294C.
Output reading
profile 1294C indicates a temperature somewhat higher than the temperature
measured by
sensor ensemble 1292B and 1292A. Noise measured by sensor ensemble 1292C
indicates a
lower level than noise measured by sensor ensemble 1292A and 1292B. In an
example, if a
temperature measured by sensor ensemble 1292C indicates a lower temperature
than a
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temperature measured by sensor ensemble 1292A, one or more processors and/or
controllers
may identify sensor ensemble 1292C sensor as providing erroneous data.
[0134] In another example of a temperature gradient, a temperature sensor
installed near a
window may measure increased temperature fluctuations with respect to
temperature
fluctuations measured by a temperature sensor installed at a location opposite
the window. A
sensor installed near a midpoint between the window and the location opposite
the window may
measure temperature fluctuations in between those measured near a window with
respect to
those measured at the location opposite the window. In an example, an ambient
noise sensor
installed near an air conditioning (or near a heating vent) may measure
greater ambient noise
than an ambient noise sensor installed away from the air conditioning or
heating vent.
[0135] In some embodiments, a sensor of a first type cooperates with a sensor
of a second
type. In an example, an infrared radiation sensor may cooperate with a
temperature sensor.
Cooperation among sensor types may comprise establishing a correlation (e.g.,
negative or
positive) among readings from sensors of the same type or of differing types.
For example, an
infrared radiation sensor measuring an increase in infrared energy may be
accompanied by
(e.g., positively correlated to) an increase in measured temperature. A
decrease in measured
infrared radiation may be accompanied by a decrease in measured temperature.
In an example,
an infrared radiation sensor measuring an increase in infrared energy that is
not accompanied
by a measurable increase in temperature, may indicate failure or degradation
in operation of a
temperature sensor.
[0136] In some embodiments, one or more sensors are included in an enclosure.
For
example, an enclosure may include at least 1, 2, 4, 5, 8, 10, 20, 50, or 500
sensors. The
enclosure may include a number of sensors in a range between any of the
aforementioned
values (e.g., from about Ito about 1000, from about Ito about 500, or from
about 500 to about
1000). The sensor may be of any type. For example, the sensor may be
configured (e.g., and/or
designed) to measure concentration of a gas (e.g., carbon monoxide, carbon
dioxide, hydrogen
sulfide, volatile organic chemicals, or radon). For example, the sensor may be
configured (e.g.,
and/or designed) to measure ambient noise. For example, the sensor may be
configured (e.g.,
and/or designed) to measure electromagnetic radiation (e.g., RE, microwave,
infrared, visible
light, and/or ultraviolet radiation). For example, the sensor may be
configured (e.g., and/or
designed) to measure security-related parameters, such as (e.g., glass)
breakage and/or
unauthorized presence of personnel in a restricted area. Sensors may cooperate
with one or
more (e.g., active) devices, such as a radar or lidar. The devices may operate
to detect physical
size of an enclosure, personnel present in an enclosure, stationary objects in
an enclosure
and/or moving objects in an enclosure.
[0137] In some embodiments, the sensor is operatively coupled to
at least one controller. The
coupling may comprise a communication link. A communications link (e.g., Fig.
11, 1108) may
comprise any suitable communications media (e.g., wired and/or wireless). The
communication
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link may comprise a wire, such as one or more conductors arranged in a twisted-
pair, a coaxial
cable, and/or optical fibers. A communications link may comprise a wireless
communication link,
such as Wi-Fi, Bluetooth, ZigBee, cellular, or optical. One or more segments
of the
communications link may comprise a conductive (e.g., wired) media, while one
or more other
segments of a communications link may comprise a wireless link.
[0138]
In some embodiments, the enclosure is a facility (e.g., building). The
enclosure may
comprise a wall, a door, or a window. In some embodiments, at least two
enclosures of a
plurality of enclosures are disposed in the facility. In some embodiments, at
least two
enclosures of a plurality of enclosures are disposed different facilities. The
different facilities
may be a campus (e.g. and belong to the same entity). At least two of the
plurality of enclosures
may reside in the same floor of the facility. At least two of the plurality of
enclosures may reside
in different floors of the facility. Enclosures of shown in Fig. 12, such as
enclosures A, B, C, and
Z, may correspond to enclosures located on the same floor of a building, or
may correspond to
enclosures located on different floors of the building. Enclosures of Fig. 12
may be located in
different buildings of a multi-building campus. Enclosures of Fig. 12 may be
located in different
campuses of a multi-campus neighborhood.
[0139]
In some embodiments, following installation of a first sensor, a sensor
performs self-
calibration to establish an operating baseline. Performance of a self-
calibration operation may
be initiated by an individual sensor, a nearby second sensor, or by one or
more controllers. For
example, upon and/or following installation, a sensor deployed in an enclosure
may perform a
self-calibration procedure. A baseline may correspond to a lower threshold
from which collected
sensor readings may be expected to comprise values higher than the lower
threshold. A
baseline may correspond to an upper threshold, from which collected sensor
readings may be
expected to comprise values lower than the upper threshold. A self-calibration
procedure may
proceed beginning with sensor searching for a time window during which
fluctuations or
perturbations of a relevant parameter are nominal. In some embodiments, the
time window is
sufficient to collect sensed data (e.g., sensor readings) that allow
separation and/or
identification of signal and noise form the sensed data. The time window may
be predetermined.
The time window may be non-defined. The time window may be kept open (e.g.,
persist) until a
calibration value is obtained.
[0140] In some embodiments, a sensor may search for an optimal time to measure
a
baseline (e.g., in a time window). The optimal time (e.g., in the time window)
may be a time
span during which (i) the measured signal is most stable and/or (ii) the
signal to noise ratio is
highest. The measured signal may contain some level of noise. A complete
absence of noise
may indicate malfunction of the sensor or inadequacy for the environment The
sensed signal
(e.g., sensor data) may comprise a time stamp of the measurement of the data.
The sensor may
be assigned a time window during which it may sense the environment. The time
window may
be predetermined (e.g., using third party information and/or historical data
concerning the
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property measured by the sensor). The signal may be analyzed during that time
window, and an
optimal time span may be found in the time window, in which time span the
measured signal
ism most stable and/or the signal no noise ratio is highest. The time span may
be equal to, or
shorter than, the time window. The time span may occur during the entire, or
during part of the
time window. Fig. 13E shows an example of a time windows 1353 is indicated
having a start
time 1351 and an end time 1352. In the time window 1353, a time span 1354 is
indicated,
having a start time 1355 and an end time 1356. The sensor may sense a property
which it is
configured to sense (e.g., VOC level) during the time window 1353 for the
purpose of finding a
time span during which an optimal sensed data (e.g., optimal sensed data set)
is collected,
which optimal data (e.g., data set) has the highest signal to noise ratio,
and/or indicates
collection of a stable signal. The optimal sensed data may have a (e.g., low)
level of noise (e.g.,
to negate a malfunctioning sensor). For example, a time window may be 12 hours
between 5
PM and 5 AM. During that time span, sensed VOC data is collected. The
collected sensed data
set may be analyzed (e.g., using a processor) to find a time span during the
12h, in which there
is a minimal noise level (e.g., indicating that the sensor is functioning) and
(i) a highest signal to
noise ratio (e.g., the signal is distinguishable) and/or (ii) the signal is
most stable (e.g., has a low
variability). This time may be of a lh duration between 4 AM and 5 AM. In this
example, the time
window is 12h between 5 PM and 5 AM, and the time span is 1h between 4 AM and
5 AM.
[0141] In some embodiments, finding the optimal data (e.g., set)
to be used for calibration
comprises comparing sensor data collected during time spans (e.g., in the time
window). In the
time window, the sensor may sense the environment during several time spans of
(e.g.,
substantially) equal duration. A plurality of time spans may fit in the time
window. The time
spans may overlap, or not overlap. The time spans may contract each other.
Data collected by
the sensors in the various time spans may be compared. The time span having
the highest
signal to noise and/or having the most stable signal, may be selected as
determining the
baseline signal. For example, the time window may include a first time span
and a second time
span. The first time span (e.g., having a first duration, or a first time
length) may be shorter than
the time windows. The second time span (e.g., having a second duration) may be
shorter than
the time windows. In some embodiments, evaluating the sensed data (e.g., to
find the optimal
sensed data used for calibration) comprises comparing a first sensed data set
sensed (e.g., and
collected) during the first time span, with a second sensed data set sensed
(e.g., and collected)
during the second time span. The length of the first time span may be
different from the length
of the second time span. The length of the first time span may be equal (or
substantially equal)
to the length of the second time span. The first time span may have a start
time and/or end time,
different than the second time span. The start time and/or end time of the
first time span and of
the second time span may be in the time window. The start time of the first
time span and/or of
the second time span, may be equal to the start time of the time window. The
end time of the
first time span and/or of the second time span, may be equal to the end time
of the time window.
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Fig. 13D shows an example of a time window 1343 having a start time 1340 and
an end time
1349, a first time window 1341 having a start time 1345 and an end time 1346,
and a second
time window 1342 having a start time 1347 and an end time 1348. In the example
shown in Fig.
13D, start times 1345 and 1347 are in the time window 1343, and end times 1346
and 1348 are
in the time window 1343.
[0142] Figs. 13A-13D show examples of various time windows that
include time spans. Fig.
13A depicts a time lapse diagram in which a time window 1310 is indicated
having a start time
1311 and an end time 1312. In the time window 1310, various time spans 1301-
1307 are
indicated, which time spans overlap each other. The sensor may sense a
property which it is
configured to sense (e.g., humidity, temperature, or CO2 level) during at
least two of the time
spans (e.g., of 1301-1307), e.g., for the purpose of comparing the signal to
find at time at which
the signal is most stable and/or has a highest signal to noise ratio. For
example, the time
window (e.g., 1301) may be a day, and the time span may be 50 minutes. The
sensor may
measure a property (e.g., CO2 level) during overlapping periods of 50 minutes
(e.g., during the
collective time 1301-1307), and the data may later on be divided into distinct
(overlapping) 50
minutes, e.g., by using the time stamped measurements. The 50 minutes that
indicates the
stables CO2 signal (e.g., at night) and/or having the highest signal to noise,
may be designated
as an optimal time for measuring a baseline CO2 signal. The signal measured
may be selected
as a baseline for the sensor. Once the optimal time span has been selected,
other CO2 sensors
(e.g., in other locations) can utilize this time span for baseline
determination. Finding of the
optimal time for baseline determination can speed up the calibration process.
Once the optimal
time has been found, other sensors may be programmed to measure signal at the
optimal time
to record their signal, which may be used for baseline calibration. Fig. 13B
depicts a time lapse
diagram in which a time window 1323 is indicated, during which two time spans
1321 and 1322
are indicated, which time spans overlap each other. Fig. 13C depicts a time
lapse diagram in
which a time window 1333 is indicated, during which two time spans 1331 and
1332 are
indicated, which time spans contact each other, that is, ending of the first
time span 1331 is the
beginning of the second time span 1332. Fig. 13D depicts a time lapse diagram
in which a time
window 1343 is indicated, during which two time spans 1341 and 1342 are
indicated, which time
spans are separate by a time gap 1344.
[0143] In an example, for a carbon dioxide sensor, a relevant parameter may
correspond to
carbon dioxide concentration. In an example, a carbon dioxide sensor may
determine that a
time window during which fluctuations in carbon dioxide concentration could be
minimal
corresponds to a two-hour period, e.g., between 5:00 AM and 7:00 AM. Self-
calibration may
initiate at 5:00 AM and continue while searching for a duration within these
two hours during
which measurements are stable (e.g., minimally fluctuating). In some
embodiments, the duration
is sufficiently long to allow separation between signal and noise. In an
example, data from a
carbon dioxide sensor may facilitate determination that a 5-minute duration
(e.g., between 5:25
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AM and 5:30 AM) within a time window between 5:00 AM and 7:00 AM forms an
optimal time
period to collect a lower baseline. The determination can be performed at
least in part (e.g.,
entirely) at the sensor level. The determination can be performed by one or
more processors
operatively couple to the sensor. During a selected duration, a sensor may
collect readings to
establish a baseline, which may correspond to a lower threshold.
[0144]
In an example, for gas sensors disposed in a room (e.g., in an office
environment), a
relevant parameter may correspond to gas (e.g., CO2) levels, where requested
levels are in a
range of about 1000 ppm or less. In an example, a CO2 sensor may determine
that self-
calibration should occur during a time window where CO2 levels are minimal
such as when no
occupants are in the vicinity of the sensor (e.g. see CO2 levels before 18000
seconds in FIG.
14). Time windows during which fluctuations in CO2 levels are minimal, may
correspond to, e.g.,
a one-hour period during lunch from about 12:00 PM to about 1:00, and during
closed business
hours. Fig. 15 shows a contour map example of a horizontal (e.g., top) view of
an office
environment depicting various levels of CO2 concentrations. The gas (CO2)
concentrations may
be measured by sensors placed at various locations of the enclosure (e.g.,
office). The office
environment may include a first occupant 1501, a second occupant 1502, a third
occupant
1503, a fourth occupant 1504, a fifth occupant 1505, a sixth occupant 1506, a
seventh occupant
1507, an eighth occupant 1508, and a ninth occupant 1509. The gas (002)
concentrations may
be measured by sensors placed at various locations of the enclosure (e.g.,
office).
[0145] In some examples, a source chemical component(s) of the atmosphere
material (e.g.,
VOC) is located using a plurality of sensors in the room. A spatial profile
indicating distribution of
the chemical(s) in the enclosure may indicate various (e.g., relative or
absolute) concentrations
of the chemical(s) as a function of space. The profile may be a two or three
dimensional profile.
The sensors may be disposed in different locations of the room to allow
sensing of the
chemical(s) in different room locations. Mapping the (e.g., entire) enclosure
(e.g., room) may
require (i) overlap of sensing regions of the sensors and/or (i) extrapolating
distribution of the
chemical(s) in the enclosure (e.g., in regions of low or absence of sensor
coverage (e.g.,
sensing regions)). For example, Fig. 15 shows an example of relatively steep
and high
concentration of carbon dioxide towards the location of occupant 1505,
relative to low
concentration 1510 in an unoccupied region of the enclosure. This can indicate
that in position
of the occupant 1505 there is a source of carbon dioxide expulsion. Similarly,
one can find a
location (e.g., source) of chemical removal by finding a (e.g., relatively
steep) low concentration
of a chemical in the environment. Relative is with respect to the general
distribution of the
chemical(s) in the enclosure.
[0146] In some examples, one or more sensors in the enclosure are VOC sensors.
A VOC
sensor can be specific for a VOC compound (e.g., as disclosed herein), or to a
class of
compounds (e.g., having similar chemical characteristic). For example, the
sensor can be
sensitive to aldehydes, esters, thiophenes, alcohols, aromatics (e.g.,
benzenes and/or
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toluenes), or olefins. In some example, a group of sensors (e.g., sensor
array) sensed VOCs
(e.g., having different chemical characteristics). The group of compound may
comprise
identified or non-identified compounds. The chemical sensor(s) can output a
sensed value of a
particular compound, class of compounds, or group of compounds. The sensor
output may be
of a total (e.g., accumulated) measurements of the class, or group of
compounds sensed. The
sensor output may be of a total (e.g., accumulated) measurements of multiple
sensor outputs of
(i) individual compounds, (ii) classes of compounds, or (iii) groups of
compounds. The one or
more sensors may output a total VOC output (also referred to herein as TVOC).
Sensing can be
over a period of time. The VOCs may derive from human or other sources, e.g.,
perspiration,
aldehydes from carpet/furnishing, etc.
[0147] In some embodiments, at least one of the atmospheric components is a
VOC. The
atmospheric component (e.g., VOC) may include benzopyrrole volatiles (e.g.,
indole and skatole), ammonia, short chain fatty acids (e.g., having at most
six carbons), and/or
volatile sulfur compounds (e.g., Hydrogen sulfide, methyl mercaptan (also
known
as methanethiol), dimethyl sulfide, dimethyl disulfide and dimethyl
trisulfide). The atmospheric
component (e.g., VOC) may include 2-propanone (acetone), 1-butanol, 4-ethyl-
morpholine,
Pyridine, 3-hexanol, 2-methyl-cyclopentanone, 2-hexanol, 3-methyl-
cyclopentanone, 1-methyl-
cyclopentanol, p-cymene, Octanal, 2-methyl-cyclopentanol, Lactic acid, methyl
ester, 1,6-
heptadien-4-ol, 3-methyl-cyclopentanol, 6-methyl-5-hepten-2-one, 1-methoxy-
hexane, Ethyl (-)-
lactate, Nonanal, 1-octen-3-ol, Acetic acid, 2,6-dimethyl-7-octen-2-ol
(dihydromyrcenol), 2-ethyl
hexanol, Decanal, 2,5-hexanedione, 1-(2-methoxypropoxy)-2-propanol, 1,7,7-
trimethylbicyclo[2.2.1]heptan-2-one (camphor), Benzaldehyde, 3,7-dimethyl-1,6-
octadien-3-ol
(linalool), 1-methyl hexyl acetate, Propanoic acid, 6-hydroxy-hexan-2-one, 4-
cyanocyclohexene,
3,5,5-trimethylcyclohex-2-en-1-one (isophoron), Butanoic acid, 2-(2-propy1)-5-
methy1-1-
cyclohexanol (menthol), Furfuryl alcohol, 1-phenyl-ethanone (acetophenone),
Isovaleric acid,
Ethyl carbamate (urethane), 4-tert-butylcyclohexyl acetate (vertenex), p-menth-
1-en-8-ol (alpha-
terpineol), Dodecanal, 1-phenylethylester acetic acid, 2(5H)-furanone, 3-
methyl, 2-ethylhexyl 2-
ethylhexanoate, 3,7-dimethyl-6-octen-1-ol (citronellol), 1,1-oxybis-2-
propanol, 3-hexene-2,5-
diol, 3,7-dimethyl-2,6-octadien-l-ol (geraniol), Hexanoic acid,
Geranylacetone, 2,4,6-tri-tert-butyl-
phenol, Unknown, 2,6-bis(1,1-dimethylethyl)-4-(1-oxopropyhphenol, Phenyl ethyl
alcohol,
Dimethylsulphone, 2-ethyl-hexanoic acid, Unknown, Benzothiazole, Phenol,
Tetradecanoic acid,
1-methylethyl ester (isopropyl myristate), 2-(4-tert-butylphenyhpropanal (p-
tert-butyl
dihydrocinnamaldehyde), Octanonic acid, a-methyl-8-(p-tert-butylphenyhpropanal
(lilia!), 1,3-
diacetyloxypropan-2-y1 acetate (triacetin), p-cresol, Cedrol, Lactic acid,
Hexadecanoic acid, 1-
methylethyl ester (isopropyl palnnitate), 2-hydroxy, hexyl ester benzoic acid
(hexyl salicylate),
Palmitic acid, ethyl ester, Methyl 2-penty1-3-oxo-1-cyclopentyl acetate
(methyl dihydrojasmonate
or hedione), 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethyl-cyclopenta-gamma-2-
benzopyran
(galaxolide), 2-ethylhexylsalicylate, Propane-1,2,3-triol (glycerin), Methoxy
acetic acid, dodecyl
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ester, a-hexyl cinnamaldehyde, Benzoic acid, Dodecanoic acid, 5-
(hydroxymethyl)-2-
furaldehyde, Homomethylsalicylate, 4-vinyl imidizole, Methoxy acetic acid,
tetradecyl ester,
Tridecanoic acid, Tetradecanoic acid, Pentadecanoic acid, Hexadecanoic acid, 9-
hexadecanoic
acid, Heptadecanoic acid, 2,6,10,15,19,23-hexamethy1-2,6,10,14,18,22-
tetracosahexaene
(squalene), Hexadecanoic acid, and/or 2-hydroxyethylester.
[0148] In an example, for an ambient noise sensor disposed in a crowded area
such as a
cafeteria, a relevant parameter may correspond to sound pressure (e.g., noise)
level measured
in decibels above background atmospheric pressure. In an example, an ambient
noise sensor
may determine that self-calibration should occur during a time window while
fluctuations in
sound pressure level are minimal. A time window while fluctuations in sound
pressure are
minimal may correspond to a one-hour period from about 12:00 AM to about 1:00
AM. Self-
calibration may continue with the sensor determining a duration within a
window during which
may be made to establish a baseline (e.g., an upper threshold). In an example,
an ambient
noise sensor may determine that a 10-minute duration (e.g., from about 12:30
AM to about
12:40 AM) within a time window of from about 12:00 AM to about 1:00 AM forms
an optimal time
to collect an upper baseline, which may correspond to an upper threshold.
[0149] In some embodiments, one or more shortcomings in sensor
operation may be at least
partially corrected and/or alleviated by leveraging a correlation of sensor
data between two or
more sensor types to synergistically detect an attribute with increased
accuracy, sensitivity,
and/or speed relative to detection using fewer sensor types. Multiple sensors
of one or more of
the correlated sensor types may be used to yet further increase accuracy,
sensitivity, and/or
reliability of attribute detection.
[0150] An attribute detected using sensor synergy as described herein may
comprise a
measurable value obtained by one or more sensors and/or data derived
therefrom, such as a
status or state of an enclosure or environment, and/or activity occurring
therein. Sensor data
processing may be performed as elsewhere herein, including the use of one or
more learning
techniques (machine learning, Al, etc.) and/or rule-based or heuristic
techniques to detect an
attribute. Detection of various attributes (described in more detail
hereafter) using sensor
synergy can lead to, among other things, increased efficiency in facility
management. For
example, the cleaning of a particular restroom in a facility may be
dynamically driven by
detected events (e.g., detected usage of the restroom), rather than by a
static schedule. The
cleaning of the restroom itself may be detected, enabling building management
software to
dynamically (e.g., automatically) track and schedule the cleaning of the
restroom.
[0151] Fig. 16 shows a table 1600 providing example attributes that may be
detected using
sensor synergy, according to some embodiments. In the table 1600, sensors
include sensors
capable of measuring temperature (e.g., a thermometer), relative humidity,
CO2, VOC, lux (e.g.,
light), Correlated Color Temperature (CCT), and sound pressure level (SPL).
Alternative
embodiments may have other sensor types. Different attributes are listed in
each row of the
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table 1600, and cells marked with an "X" indicate which sensor types may be
used to determine
the respective attribute for that row. As shown, different combinations of
sensors can be used to
determine different types of attributes. For example, CO2 and SPL sensors can
be used to
detect an occupancy status (e.g., whether a room or spaces occupied) or
occupancy number.
Lux and SPL sensors can be used to detect noises or lights that are loud
(e.g., having a volume
or brightness exceeding a threshold) or troublesome; CO2, VOC, and SPL sensors
can be used
to determine whether a cleaning is in progress (e.g., by detecting CO2 from a
human occupant,
chemicals from chemical cleaners, noise from a vacuum or other cleaning
device, etc.); and so
forth. Other embodiments may use other sensor combinations. In some
embodiments, for
example, one or more CO sensors can be used to detect the presence of a gas-
fueled
appliance and/or vehicle.
[0152] Under normal operational conditions, environmental factors
in a particular environment
(e.g., room, lobby, or other area related to a facility) that may be detected
by one or more
sensors may have a natural state or operating range (e.g., baseline values).
In some cases, the
environmental factors may also have natural relationships with each other.
Fig. 17A, for
example, shows a graph 1700 that illustrates an inverse relationship that
humidity and
temperature have, in a particular example. Specifically, a rise in relative
humidity 1710 and a fall
in air temperature 1720 are correlated in time, as are a rise in air
temperature 1730 and a fall in
relative humidity 1740. Fig. 17B further illustrates this inverse relationship
between relative
humidity (RH) and temperature by showing a plot of actual measurements 1750 of
humidity and
temperature and a curve 1760 derived from the measurements, which can be used
to model the
relationship of relative humidity and temperature. Because the nature of at
least some types of
such relationships may be at least partially dependent on the type of
environment, a model
(e.g., curve 1760) may be determined on a per-environment basis, based on
sensor data from
the environment and/or similar environments in which the relationship has been
modeled. An
environment from which a relationship has been modeled may be determined to be
"similar"
based on features and/or aspects of the environment that may have an effect on
the
relationship, such as room size, HVAC features (e.g., number or location of
vent(s)), window
information (e.g., size, location, transmissivity, orientation (e.g., with
respect to the sun), or
number), construction material information (e.g., material type or insulation
rating), and the like.
Additional details regarding similar environments are provided hereafter.
Relationships between
more than two sensors may also exist. Thus, in some embodiments, a
relationship is
determined between at least two sensors, three sensors, four sensors, five
sensors, 10 sensors,
or the like, in which case a multi-dimensional model (e.g., similar to curve
1760, using additional
dimensions) may be determined from the relationship of the more than two
sensors.
[0153] Various automated and/or human activities in an environment
may disrupt the natural
state or operating range (baseline) of environmental factors. Thus, according
to some
embodiments, an attribute (e.g., status or activity) may be detected from the
sensor data
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indicative of a departure of an environmental factor from the natural state or
operating range.
For example, a model (e.g., curve 1760) of a relationship between values
measured by different
sensor types can be made from sensor data of an environment during normal
operating
conditions (e.g., when the environment is unoccupied) and subsequently used as
a reference to
detect an attribute of the environment based on the departure of the measured
values (e.g.,
beyond a threshold) from the model representing the normal operating
conditions. To do so,
sensor data may be obtained from sensors and outlier detection (e.g., outlier
detection as used
for self-calibration described herein) then may be performed in which the
measured values are
compared with the model. To help ensure sensors are properly calibrated, this
outlier detection
may be performed if sensor data is obtained from sensors within a threshold
time and/or
operating range after calibration, if outlier data is confirmed using sensor
data from multiple
sensors of the same type, or if data is obtained from a sensor that does not
otherwise need
calibration. Again, outlier detection can be performed using learning-based
techniques (e.g.,
machine learning or Al) or rule-based techniques (e.g., heuristic analysis).
[0154] Fig. 18 shows an example graph 1800 in which measured values of
relative humidity
and temperature over a period of time are used to determine occupancy in a
room. In particular,
measured values (represented as small circles in figure 18) are compared with
a model 1805
representing a natural state of the room. Outlier detection is used to
determine outlier values
1810 indicating higher values for temperature and/or relative humidity (e.g.,
beyond a threshold)
than those obtained while the room is in a natural state. These outlier values
1810, which are a
result of the presence of one or more human occupants in the room, may be
associated with
occupancy in the room based on a previously-established relationship of such
values (e.g.,
during training of a machine learning model or based on similar values in
other rooms). The
number of occupants in the room may be obtained based on information such as
the degree to
which outlier values 1810 depart from the model 1805 and/or other related
information (e.g.,
time information indicative of a rate of relative humidity and/or temperature
increase).
[0155] Because different sensors have different sampling rates and detention
times, the
detection of different attributes may be made on different timescales. A light
sensor, for
example, may be sampled at a relatively fast rate (e.g., compared with other
sensor types) and
may be capable of determining a change in light at a similarly fast rate
(e.g., on a scale of
milliseconds or seconds). A sensor measuring relative humidity, on the other
hand, may be
sampled at a relatively slow rate and may be capable of determining a change
in humidity at a
similarly slow rate (e.g., on a scale of minutes or tens of minutes). Thus,
detecting an attribute
may occur on a relatively long time scale if detected using a relatively slow
sensor alone.
[0156] Fig. 19 shows a table 1900 of timescales for different sensors in an
example sensing
system. In the table 1900, the sensor response row indicates timescales of
sensor output (e.g.,
periodicity of sensor output) of the example sensing system. Rows for natural
state inputs and
human activity inputs indicate timescales of rates of change of sensor data
under conditions in
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which the environment is in a natural state (e.g., no human activity) and when
human activity is
occurring in the environment, respectively. (For example, a detectable change
in CO2 levels
may occur over the course of 1-5 minutes when human activity is occurring in
the environment,
whereas a similar change in CO2 may take up to an hour if the environment is
in its natural
state). As shown in table 1900, lux, CCT, and SPL sensors have quick sensor
response times
(e.g., 30 seconds) corresponding with quick input timescales (e.g., 1-5 or 1-
10 seconds) relative
to the timescales of response times (e.g., 5-10 minutes) and inputs (e.g., up
to one hour) of
temperature, relative humidity, CO2, and VOC sensors. Thus, detecting an
attribute of an
environment using a temperature, relative humidity, CO2, or VOC sensor may
occur on a
relatively long timescale relative to an attribute of the environment detected
using a lux, COT, or
SPL sensors.
[0157] In some embodiments, one or more sensor types with relatively fast
sensor response
times can be used in a synergistic with one or more sensor types having
relatively slow
response times to reduce a timescale at which an attribute is detected. In
particular, by
leveraging a relationship of environmental factors (e.g., similar to the
relationship shown in
figures 17A and 17B) sensed by fast and slow sensors, embodiments may detect
an attribute
using the fast and slow sensors synergistically on a timescale much faster
than detection of the
attribute using slow sensors alone. In some embodiments, pattern recognition
(e.g., using
machine learning, Al, or the like) may be performed on sensor data from two or
more sensor
types of an environment to determine relationships between sensor types and
model natural
state sensor data (e.g., the in a manner discussed with regard to figures 17A
and 17B).
Embodiments may further use pattern recognition to detect certain attributes
associated with
departures from natural state sensor data. By using sensor data from sensor
types having a
relatively fast response times, some attributes may be detected more quickly
than they would
otherwise be detected utilizing sensor data from sensor types having
relatively slow response
times. According to some embodiments, attributes may be detected with more
certainty using
multiple sensor types than with a single sensor type.
[0158] Fig. 20 shows a graph 2000 in which data from three sensor types in a
room over the
course of one day are plotted, where data is obtained every 30 seconds. The
three sensor types
comprise CO2 (with values shown in parts per million (PPM)), lux, and SPL
(sound, with value
shown in decibels) sensors. Data values 2010 corresponds to values obtained by
sensors when
the room is unoccupied (e.g., in a natural state). As shown, sound and CO2
values fluctuate
when the room is unoccupied, due to, for example, and HVAC system turning on
and off. Thus,
occupancy of the room may be difficult to accurately detect or predict using
data from CO2 and
SPL sensors alone. However, when coupled with a lux sensor, occupancy can be
detected from
values 2020 (e.g., using outlier detection in view of natural state data
values 2010). In particular,
data from the lux sensor can be used to detect when a person turns on a light
when entering a
room, and the sound and CO2 sensors can be used to detect whether the person
stays in the
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room. In the example shown in the graph 2000, the person stays in the room,
which elevates
the CO2 values until they peak at 2030, after which the person turns the light
off when exiting
the room, and the peak CO2 values then begin to drop (as shown by values
2040).
[0159] The data in graph 2000 illustrate how sensor synergy can be used to
increase the
accuracy and reduce detection time for detecting the attribute that the room
is occupied. In
particular, the use of lux sensor alone may result in relatively quick
detection of the turning on of
a light, but an erroneous determination that the room is occupied in cases
where the light is on
but no occupant is present (e.g., when a person turns on a light in the room
and subsequently
leaves without turning the light off). On the other hand, the use of a CO2
sensor alone may allow
for detection of an occupant based on CO2 levels that may exceed CO2 levels
when the room is
unoccupied (e.g., CO2 levels at peak 2030 may exceed CO2 levels in data values
2010), but
may take a long time (relative to the lux sensor) to make the detection.
However, lux and CO2
sensors may be used synergistically to allow for occupant detection with
increased accuracy
and speed over using each sensor alone. For example, detection of an occupant
entering a
room may be based on pattern recognition where the lux sensor detects a light
increase (e.g.,
due to a light in the room being turned on), followed by an increase in CO2
levels (e.g. due to
one or more occupants being in the room). Because the increased CO2 levels are
accompanied
by the increase in light, the determination that an occupant is in the room
may be made at a
faster rate than with a CO2 sensor alone. This is because the lux sensor
readings provide
additional certainty that an occupant is in the room. Otherwise, use of CO2
levels alone would
take longer to reach a similar certainty (waiting, for example, tens of
minutes to ensure that an
increase in CO2 levels to exceed those of natural state data values 2010, or
waiting to ensure
that an increase in CO2 levels is more than just sensor noise). The use of
sound values from an
SPL sensor, which may detect common sounds made by one or more occupants
entering or
exiting the room (e.g., a door opening or closing) can add additional
certainty and/or speed to
the occupancy detection based on readings from the CO2 and lux sensors.
[0160] The use of sensor synergy to increase the accuracy and/or speed of
attribute
detection can be useful in a variety of ways. In the example of Fig. 20, for
instance, faster
detection of the occupancy in a room can be used to trigger an HVAC system in
the room to
help ensure CO2 and temperature levels are comfortable for occupants.
Increased accuracy of
the occupancy detection can be used to determine patterns of occupancy, which
can be used to
accurately predict occupancy and, in turn, operate HVAC and/or other systems
in a predictive
manner.
[0161] Fig. 21 shows another example graph 2100 on which 002, lux, and SPL
(sound)
sensors are used together data from a room. This example includes a restroonn
with ambient
music and the light, where the light changes between bright and dim settings,
resulting in
natural state sensor data values 2110 in which values for sound extend across
a range, and
values for light are generally bimodal. Anomalous values 2120 result primarily
from light
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fluctuations occurring when people enter and exit the restroom, crossing in
front of the light
sensor. However, when synergistically coupled with sound data (e.g., a door
opening or closing)
and CO2 data, detection of these anomalous values 2120 can be indicative of
people entering
and exiting the room with a high degree of certainty.
[0162] The relative timing of sensor data can be used to determine
correlations between
sensor types in a given environment and/or to detect an attribute (e.g., an
event) of the
environment. Fig. 22, for example, shows timing diagrams illustrating sound
and lux data values
of the graph 2100 of Fig. 21 overtime. The upper diagram 2200 shows lux values
2210 and
sound values 2220 overtime, across 15,000 samples (where samples are taken at
a frequency
of 30 seconds). The lower diagram 2230 shows similar values across 3,500
samples
representing a portion 2240 of the upper diagram 2200. Both diagrams 2200 and
2230 show
downward spikes in lux values 2210 and upward spikes in sound values 2220
which correspond
in time and represent fluctuations in light and sound as people enter and exit
the room. These
spikes correspond with anomalous values 2120 of fig. 21. As another example,
fig. 23 shows
timing diagrams similar to the timing diagrams of Fig. 22, where the upper
diagram 2300 shows
lux values 2310 and sound values 2320 over time, across 15,000 samples (the
same as the
upper diagram 2200 of fig. 22). The lower diagram 2330 shows similar values
across 2,000
samples representing a portion 2340 of the upper diagram 2300 (different than
the portion 2240
of fig. 22). Again, both diagrams 2300 and 2330 show downward spikes in lux
values 2310 and
upward spikes in sound values 2320 that correspond in time and correspond with
anomalous
values 2120 of fig. 21.
[0163] Different sensor combinations may be used synergistically
to detect different attributes
of an environment. As a specific example, the cleaning of a room may be
detected using CO2,
lux, and SPL sensors to detect the entrance and exit of a person into and out
of a room, and
sound related to cleaning (e.g., vacuuming or floor buffering). Additionally,
a VoC sensor may
be used to detect cleaning chemicals. The CO2 sensor may be omitted where
automated (e.g.,
robotic) cleaning occurs without a human or other source of CO2 present. A CO
sensor may be
used where a gas-fueled appliance and/or vehicle is used. The detection of
cleaning can result
in the triggering of an HVAC system to help prevent a spike in VoC levels
and/or harmful gasses
(e.g., CO). Again, examples of different sensor combinations are shown in the
table 1600 of fig.
16. Figures 24 and 25, described below,
[0164] Fig. 24 shows a flowchart for a method 2400 for determining an
attribute, according to
an embodiment. The method of Fig. 24 may be performed by an individual sensor
of a sensor
ensemble. The method of Fig. 24 may be performed by a first sensor coupled to
(e.g., and in
communication with) a second sensor The method of Fig_ 24 may be directed by
at least one
controller (e.g., processor) coupled to (e.g., in communication with) first
and/or second sensors.
The method of Fig. 24 may be performed or facilitated by a system comprising a
network
operatively coupled to first and second sensors. The method of Fig. 24 begins
at 2410, in which
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a first sensor is used to measure a first attribute at a first sampling rate,
the first sampling rate
being faster than a second sampling rate of a second sensor configured to
sense a second
attribute. The first sampling rate may be at least about one order of
magnitude faster than the
second sampling rate. At 2420, the second attribute is determined and/or
predicted at least in part
by using measurements of the first attribute by the first sensor rather than
measurements of the
second sensor, the first sensor and the second sensor being of a facility. The
second sensor and
the first sensor may be at disposed in the facility. The first sensor and the
second sensor may be
at disposed in an enclosure of the facility. The first sensor may be disposed
in a housing enclosing
another sensor, a transceiver, or an emitter. The housing may be disposed in a
fixture of the
facility or is attached to a fixture of the facility. The first sensor may be
utilized to control an
environment of the facility. The second attribute may comprise an activity.
The activity may
comprise (i) cleaning of an enclosure, (ii) movement of one or more personnel
in the enclosure,
(iii) a change in an environmental condition, (iv) one or more personnel
entering into the
enclosure, (v) one or more personnel exiting the enclosure, (vi) activity in
the enclosure, (vii)
exceeding of a maximum occupancy of the enclosure, or (viii) an arrival of a
vehicle at an
transportation hub. The enclosure may comprise a building, room, or any other
confined space.
The environmental condition may comprise a weather condition, a sound level,
an
electromagnetic radiation level, an air quality level, a gas level, a
particulate matter level, or a
volatile organic compound level. The transportation hub may comprise an
airport, a train station,
a bus station, a tram stop, a ferry slip, a pilot station, a sailing station,
or any other transit station;
and wherein optionally the other transit station comprises a rapid transition
station. The vehicle
may comprise an airplane, a train, a bus, a car, a subway car, a light rail
car, a tram, a ferry, a
boat, a ship, a helicopter, or a rocket; and optionally wherein the car
comprises a taxi car. The
second attribute may comprise occupancy status of an enclosure, number of
occupants in the
enclosure, sound, electromagnetic radiation, an indicator of a level of
comfort, an indicator of
energy efficiency, air quality, temperature, gas, particulate matter, or
volatile organic compounds.
The electromagnetic radiation may comprise visible, infrared, ultrasound, or
radio frequency
radiation. The second attribute that comprises the gas, may comprise the
second attribute of one
or more of: gas, comprises gas type, velocity, and pressure. The second
attribute that comprises
gas, they comprise the second attribute of one or more of: humidity, carbon
dioxide, carbon
monoxide, hydrogen sulfide, radon, nitrogen oxides, halogen, organic halogens,
and
formaldehyde. The second attribute may comprise temperature, sound, pressure,
humidity, gas,
particulate matter, volatile organic compound, or electromagnetic radiation.
The gas may
comprise carbon dioxide, carbon monoxide, radon, or hydrogen sulfide. The
second attribute may
be associated with an activity. The activity may comprise a human activity or
a mechanical activity.
[0165] In some embodiments, the method 2400 may further comprise using a third
sensor to
measure a third attribute at a third sampling rate where the third sampling
rate being faster than
the second sampling rate of the second sensor, and determining and/or
predicting the second
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attribute at least in part by using measurements of (i) the first attribute by
the first sensor and (ii)
the third attribute by the third sensor, the third sensor being of a facility.
The first sensor and the
third sensor may be disposed in a housing. The housing may enclose another
sensor, a
transceiver, or an emitter. The housing may enclose at least two or seven
different sensors. The
housing may be disposed in a fixture of the facility, or may be attached to a
fixture of the facility.
The method 2400 may further comprise synergistically and/or symbiotically
evaluating
measurements of the first sensor and measurements of the third sensor to
determine and/or
predict the second attribute. The method 2400 may further comprise using
artificial intelligence to
determine and/or predict the second attribute. The artificial intelligence may
comprise machine
learning. The first sensor may be used to collect first sensed data during a
period of time at the
first sampling rate, the third sensor may be used to collect third sensed data
during the period of
time at the third sampling rate, wherein the third sensor may comprise a
different sensor type than
the first sensor and obtaining data indicative of an occurrence of an activity
during the period of
time, responsive to obtaining the data indicative of the occurrence of the
activity: (i) evaluating
the first sensed data to determine a first correlation between the activity
and a first range of values
of the first sensed data, and (ii) evaluating the third sensed data to
determine a second correlation
between the activity and a third range of values of the third sensed data. The
method 2400 may
further comprise determining a relationship between the activity, the first
range of values, and the
third range of values, wherein determining the relationship is based at least
in part on the first
correlation and the second correlation, and storing data indicative of the
determined relationship.
The first sensed data, the third sensed data, or both the first sensed data
and the third sensed
data may occur prior to and/or during the occurrence of the activity.
[0166] Fig. 25 shows a flowchart for a method 2500 for establishing a
relationship between an
attribute (e.g., an activity of interest) and values of sensor data from first
and second sensors,
according to an embodiment. The method of Fig. 25 may be performed by an
individual sensor of
a sensor ensemble. The method of Fig. 25 may be performed by a first sensor
coupled to (e.g.,
and in communication with) a second sensor. The method of Fig. 25 may be
directed by at least
one controller (e.g., processor) coupled to (e.g., in communication with)
first and/or second
sensors. The method of Fig. 25 may be performed or facilitated by a system
comprising a network
operatively coupled to first and second sensors. The method of Fig. 25 begins
at 2510, where
data of the first sensor is analyzed to identify variation from baseline that
occurs when an attribute
occurs. Baseline may comprise sensor data values of a natural state or mode of
operation.
Variation from baseline may be identified using outlier detection techniques,
as described herein
Natural state or mode of operation may correspond to a state in which an
environment (e.g., a
room or facility) is unoccupied. At 2520, data from one or more additional
sensors may be
analyzed to identify values that occur before, or during, the attribute. The
analysis of data from
the first sensor and data from the one or more additional sensors may utilize
a multi-dimensional
data analysis. The analysis of data from the first sensor and data from the
one or more additional
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sensors may learning techniques (e.g. machine learning or Al) or rule-based
techniques (e.g.,
heuristic analysis). For learning techniques, the data from the first sensor
and data from the one
or more additional sensors may represent the positive data set. The attribute
may be verified by
a human user. At 2530, values from the analysis are contrasted with values
that occur when the
attribute does NOT occur. For learning techniques, values that occur when the
attribute does not
occur may comprise a negative data set. The human user may verify that the
attribute does not
take place. The functionality at 2530 can help prevent possible false
positives (e.g., detecting an
activity of interest when no activity of interest is taking place). At 2540, a
range of values of data
from first sensor and any additional sensors that occur (e.g., only or often)
when the attribute
occurs is established. The range may be established in view of the analysis of
data at 2510 and
2520, as well as the functionality at 2530.
[0167] According to some embodiments, once a relationship is established
between an
attribute of an environment (e.g., an activity of interest) and values of
sensor data from two or
more sensors that can be used synergistically (e.g., using the method 2500),
the relationship may
be modeled for a particular type of environment (e.g., a particular type of
room) and utilized in
other environments of a similar type. Because each environment has a unique
combination of
characteristics (e.g., HVAC vents, windows, dimensions, and
insulation/building materials), initial
deployments of sensors in environments using the synergistic sensing
techniques herein may rely
on measured sensor data in the field to establish the relationship between
sensor values and an
attribute. However, as increasingly more data are available across large
number of environments,
the data may be analyzed to establish environment profiles in which values of
sensor data have
a similar relationship with an attribute of the environment. Sensor values of
rooms having similar
combinations of characteristics, for example, may have similar values for
detecting a given
attribute. Once these environment profiles are established, data from these
environment profiles
can be utilized to streamline deployment to a new environment by leveraging
data from an
environment profile applicable to the new environment. According to some
embodiments,
machine learning or Al techniques may be utilized to analyze data from various
environments,
determine environment characteristics, and establish environment profiles
based on sensor data.
[0168] Fig. 26 shows a schematic example of a computer system 2600 that is
programmed or
otherwise configured to one or more operations of any of the methods provided
herein. The
computer system can control (e.g., direct, monitor, and/or regulate) various
features of the
methods, apparatuses and systems of the present disclosure, such as, for
example, control
heating, cooling, lightening, and/or venting of an enclosure, or any
combination thereof. The
computer system can be part of, or be in communication with, any sensor or
sensor ensemble
disclosed herein. The computer may be coupled to one or more mechanisms
disclosed herein,
and/or any parts thereof. For example, the computer may be coupled to one or
more sensors,
valves, switches, lights, windows (e.g., IGUs), motors, pumps, optical
components, or any
combination thereof. The sensor may be integrated in a transceiver.
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[0169] The computer system can include a processing unit (e.g., 2606) (also
"processor,"
"computer" and "computer processor" used herein). The computer system may
include memory
or memory location (e.g., 2602) (e.g., random-access memory, read-only memory,
flash
memory), electronic storage unit (e.g., 2604) (e.g., hard disk), communication
interface (e.g.,
2603) (e.g., network adapter) for communicating with one or more other
systems, and peripheral
devices (e.g., 2605), such as cache, other memory, data storage and/or
electronic display
adapters. In the example shown in Fig. 26, the memory 2602, storage unit 2604,
interface 2603,
and peripheral devices 2605 are in communication with the processor 2606
through a
communication bus (solid lines), such as a motherboard. The storage unit can
be a data storage
unit (or data repository) for storing data. The computer system can be
operatively coupled to a
computer network ("network") (e.g., 2601) with the aid of the communication
interface. The
network can be the Internet, an internet and/or extranet, or an intranet
and/or extranet that is in
communication with the Internet. In some cases, the network is a
telecommunication and/or
data network. The network can include one or more computer servers, which can
enable
distributed computing, such as cloud computing. The network, in some cases
with the aid of the
computer system, can implement a peer-to-peer network, which may enable
devices coupled to
the computer system to behave as a client or a server.
[0170] The processing unit can execute a sequence of machine-readable
instructions, which
can be embodied in a program or software. The instructions may be stored in a
memory
location, such as the memory 2602. The instructions can be directed to the
processing unit,
which can subsequently program or otherwise configure the processing unit to
implement
methods of the present disclosure. Examples of operations performed by the
processing unit
can include fetch, decode, execute, and write back. The processing unit may
interpret and/or
execute instructions. The processor may include a microprocessor, a data
processor, a central
processing unit (CPU), a graphical processing unit (GPU), a system-on-chip
(SOC), a co-
processor, a network processor, an application specific integrated circuit
(ASIC), an application
specific instruction-set processor (ASIPs), a controller, a programmable logic
device (PLD), a
chipset, a field programmable gate array (FPGA), or any combination thereof.
The processing
unit can be part of a circuit, such as an integrated circuit. One or more
other components of the
system 2600 can be included in the circuit.
[0171] The storage unit can store files, such as drivers, libraries and saved
programs. The
storage unit can store user data (e.g., user preferences and user programs).
In some cases, the
computer system can include one or more additional data storage units that are
external to the
computer system, such as located on a remote server that is in communication
with the
computer system through an intranet or the Internet.
[0172] The computer system can communicate with one or more remote computer
systems
through a network. For instance, the computer system can communicate with a
remote
computer system of a user (e.g., operator). Examples of remote computer
systems include
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personal computers (e.g., portable PC), slate or tablet PC's (e.g., Apple
iPad, Samsung
Galaxy Tab), telephones, Smart phones (e.g., Apple iPhone, Android-enabled
device,
Blackberry ), or personal digital assistants. A user (e.g., client) can access
the computer
system via the network.
[0173] Methods as described herein can be implemented by way of machine (e.g.,
computer
processor) executable code stored on an electronic storage location of the
computer system,
such as, for example, on the memory 2602 or electronic storage unit 2604. The
machine
executable or machine-readable code can be provided in the form of software.
During use, the
processor 2606 can execute the code. In some cases, the code can be retrieved
from the
storage unit and stored on the memory for ready access by the processor. In
some situations,
the electronic storage unit can be precluded, and machine-executable
instructions are stored on
memory.
[0174] The code can be pre-compiled and configured for use with a machine have
a
processer adapted to execute the code or can be compiled during runtime. The
code can be
supplied in a programming language that can be selected to enable the code to
execute in a
pre-compiled or as-compiled fashion.
[0175] In some embodiments, the processor comprises a code. The
code can be program
instructions. The program instructions may cause the at least one processor
(e.g., computer) to
direct a feed forward and/or feedback control loop. In some embodiments, the
program
instructions cause the at least one processor to direct a closed loop and/or
open loop control
scheme. The control may be based at least in part on one or more sensor
readings (e.g., sensor
data). One controller may direct a plurality of operations. At least two
operations may be
directed by different controllers. In some embodiments, a different controller
may direct at least
two of operations (a), (b) and (c). In some embodiments, different controllers
may direct at least
two of operations (a), (b) and (c). In some embodiments, a non-transitory
computer-readable
medium cause each a different computer to direct at least two of operations
(a), (b) and (c). In
some embodiments, different non-transitory computer-readable mediums cause
each a different
computer to direct at least two of operations (a), (b) and (c). The controller
and/or computer
readable media may direct any of the apparatuses or components thereof
disclosed herein. The
controller and/or computer readable media may direct any operations of the
methods disclosed
herein.
[0176] In some embodiments a user is able to adjust the
environment, e.g., using a virtual
reality (VR) module (e.g., augmented reality module). The VR module may
receive data from
one or more sensors about various environmental properties (e.g.,
characteristics) sensed by
the one or more sensors. The VR module may receive structural information
regarding the
environment, e.g., to account for any surrounding walls, windows, and/or doors
enclosing the
environment. The VR module may receive visual information about the
environment, e.g., from
one or more sensors (e.g., comprising a camera such as a video camera). The VR
module may
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be operated by a controller (e.g., comprising a processor). The VR module may
be operatively
(e.g., communicatively) coupled to a projection aid. The projection aid may
comprise a screen
(e.g., electronic or non-electronic screen), a projector, or a headset (e.g.,
glasses or goggles).
The one or more sensors may be disposed on an electrical board (e.g., a
motherboard). The
one or more sensors may be a part of a sensor ensemble. The sensor ensemble
may be a
device ensemble comprising (i) sensors or (ii) a sensor and an emitter. The
enclosure may
comprise sensors of the same type disposed at different locations in the
environment. The
enclosure may comprise ensembles disposed at different locations in the
environment. The VR
module may allow a user to select a type of environmental property (e.g.,
among different
property types) to view and/or control. The VR module may allow emulation of
any variability in
the property in the environment. The property variability may be emulated as a
three-
dimensional map superimposed on any fixtures of the enclosure enclosing the
environment. The
property variability in the environment may change in real time. The VR module
may update the
property variability in real time. The VR module may use data of the one or
more sensors (e.g.,
measuring the requested property in the environment), simulation, and/or third
party data, to
emulate the property variability. The simulation may utilize artificial
intelligence. The simulation
may be any simulation described herein. The VR module may project a plurality
of different
properties in the environment, e.g., simultaneously and/or in real time. A
user may request
alteration of any property displayed by the VR module. The VR module may send
(e.g., directly
or indirectly) commands to one or more components that affect the environment
of the
enclosure (e.g., HVAC, lighting, or tint of a window). Indirect command may be
via one or more
controllers communicatively coupled to the VR module. The VR module may
operate via one or
more processors. The VR module may reside on a network that is operatively
coupled to the
one or more components that affect the environment, to one or more
controllers, and/or to one
or more processors. For example, the VR module may facilitate controlling a
tint of a window
disposed in the enclosure. The VR projection may project the window, as well
as a menu or bar
(e.g., sliding bar) depicting various levels of tint. The menu may be
superimposed on the VR
projection of the enclosure. The user may look at the window and select the
desired level of tint.
Receiving the command (e.g., through the network), the window controller may
direct the user
selected window to alter its tint. For example, the VR module may facilitate
controlling a
temperature in the enclosure. In another example, the VR module may emulate a
temperature
distribution in the enclosure. A user may look at the temperature range
displayed on a menu or
bar (e.g., sliding bar) and select the desired temperature in the enclosure
and/or in a portion of
the enclosure. The request may be directed to a local controller that directs
the HVAC system
(e.g., including any vents) to adjust its temperature according to the
request. Subsequent to the
request, the VR module may emulate a change in the property (e.g., glass tint,
and/or
temperature), e.g., as the change occurs in the enclosure. The user may be
able to view both
temperature distribution and window tint level in the same VR experience
(e.g., projection
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timeframe of the VR environment) or in different VR experiences. The user may
be able to
request both new temperature and new window tint level in the same VR
experience or in
different VR experiences. The user may be able to view a change in both new
temperature and
new window tint level in the same VR experience or in different VR
experiences. At times, a VR
projected update of an alteration of a first property may lag (e.g., due to
processing time of
sensor data) relative to an update of an alteration of at least one second
property, wherein the
user requested a change in both first property and the at least one second
property. At times, a
VR projected update of an alteration of a first property may coincide with an
update of an
alteration of at least one second property, wherein the user requested a
change in both first
property and the at least one second property. The selection may be using any
VR tools and/or
any other user input tool such as a touchscreen, joystick, console, keyboard,
controller (e.g.,
remote controller and/or game controller), digital pen, camera, or microphone.
[0177] In some embodiments, the at least one sensor is operatively coupled to
a control
system (e.g., computer control system). The sensor may comprise light sensor,
acoustic sensor,
vibration sensor, chemical sensor, electrical sensor, magnetic sensor,
fluidity sensor, movement
sensor, speed sensor, position sensor, pressure sensor, force sensor, density
sensor, distance
sensor, or proximity sensor. The sensor may include temperature sensor, weight
sensor,
material (e.g., powder) level sensor, metrology sensor, gas sensor, or
humidity sensor. The
metrology sensor may comprise measurement sensor (e.g., height, length, width,
angle, and/or
volume). The metrology sensor may comprise a magnetic, acceleration,
orientation, or optical
sensor. The sensor may transmit and/or receive sound (e.g., echo), magnetic,
electronic, or
electromagnetic signal. The electromagnetic signal may comprise a visible,
infrared, ultraviolet,
ultrasound, radio wave, or microwave signal. The gas sensor may sense any of
the gas
delineated herein. The distance sensor can be a type of metrology sensor. The
distance sensor
may comprise an optical sensor, or capacitance sensor. The sensor may comprise
an
accelerometer. The temperature sensor can comprise Bolometer, Bimetallic
strip, calorimeter,
Exhaust gas temperature gauge, Flame detection, Gardon gauge, Golay cell, Heat
flux sensor,
Infrared thermometer, Microbolometer, Microwave radiometer, Net radiometer,
Quartz
thermometer, Resistance temperature detector, Resistance thermometer, Silicon
band gap
temperature sensor, Special sensor microwave/imager, Temperature gauge,
Thermistor,
Thermocouple, Thermometer (e.g., resistance thermometer), or Pyrometer. The
temperature
sensor may comprise an optical sensor. The temperature sensor may comprise
image
processing. The sensor may comprise an IR camera, a visible light camera,
and/or a depth
camera. The temperature sensor may comprise a camera (e.g., IR camera, CCD
camera). The
pressure sensor may comprise Barograph, Barometer, Boost gauge, Bourdon gauge,
Hot
filament ionization gauge, Ionization gauge, McLeod gauge, Oscillating U-tube,
Permanent
Downhole Gauge, Piezometer, Pirani gauge, Pressure sensor, Pressure gauge,
Tactile sensor,
or Time pressure gauge. The position sensor may comprise Auxanometer,
Capacitive
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displacement sensor, Capacitive sensing, Free fall sensor, Gravimeter,
Gyroscopic sensor,
Impact sensor, Inclinometer, Integrated circuit piezoelectric sensor, Laser
rangefinder, Laser
surface velocimeter, LIDAR, Linear encoder, Linear variable differential
transformer (LVDT),
Liquid capacitive inclinometers, Odometer, Photoelectric sensor, Piezoelectric
accelerometer,
Rate sensor, Rotary encoder, Rotary variable differential transformer, Selsyn,
Shock detector,
Shock data logger, Tilt sensor, Tachometer, Ultrasonic thickness gauge,
Variable reluctance
sensor, or Velocity receiver. The optical sensor may comprise a Charge-coupled
device,
Colorimeter, Contact image sensor, Electro-optical sensor, Infra-red sensor,
Kinetic inductance
detector, light emitting diode (e.g., light sensor), Light-addressable
potentiometric sensor,
Nichols radiometer, Fiber optic sensor, Optical position sensor, Photo
detector, Photodiode,
Photomultiplier tubes, Phototransistor, Photoelectric sensor, Photoionization
detector,
Photomultiplier, Photo resistor, Photo switch, Phototube, Scintillometer,
Shack-Hartmann,
Single-photon avalanche diode, Superconducting nanowire single-photon
detector, Transition
edge sensor, Visible light photon counter, or Wave front sensor. The one or
more sensors may
be connected to a control system (e.g., to a processor, to a computer).
[0178] While preferred embodiments of the present invention have been shown,
and
described herein, it will be obvious to those skilled in the art that such
embodiments are
provided by way of example only. It is not intended that the invention be
limited by the specific
examples provided within the specification. While the invention has been
described with
reference to the afore-mentioned specification, the descriptions and
illustrations of the
embodiments herein are not meant to be construed in a limiting sense. Numerous
variations,
changes, and substitutions will now occur to those skilled in the art without
departing from the
invention. Furthermore, it shall be understood that all aspects of the
invention are not limited to
the specific depictions, configurations, or relative proportions set forth
herein which depend
upon a variety of conditions and variables. It should be understood that
various alternatives to
the embodiments of the invention described herein might be employed in
practicing the
invention. It is therefore contemplated that the invention shall also cover
any such alternatives,
modifications, variations, or equivalents. It is intended that the following
claims define the scope
of the invention and that methods and structures within the scope of these
claims and their
equivalents be covered thereby.
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