Note: Descriptions are shown in the official language in which they were submitted.
METHOD AND SYSTEM FOR TESTING USING LOW RANGE ELECTROMAGNETIC WAVES
Cross-reference to other Applications
[0001] This application is a continuation-in-part (CIP) of U.S. Patent
Application No.
17/139,184 filed December 31, 2020 which is a CIP of US Patent Application No.
15/819,833 filed
November 21, 2017 which claims priority from US Provisional Application No.
62/497,430 filed
November 21, 2016; and a CIP of US Patent Application No.17/026,452 filed
September 21, 2020
which is a divisional of US Patent Application No. 15/819,833 filed November
21, 2017 which
claims priority from US Provisional Application No. 62/497,430 filed November
21, 2016 and
claims priority from US Provisional Patent Application No. 63/399,760 filed
August 22, 2022.
Field
[0002] The disclosure is generally directed at sensor systems, and more
specifically, at a
method and system for testing objects using low range electromagnetic (EM)
waves.
Background
[0003] As the food industry continues to grow, food safety and inspection
is becoming
more and more important. Food composition is also important such as with
products, such as
milk, where certain milk products require an expected butterfat concentration.
[0004] During the manufacturing process, composition issues can arise as
a result of
various factors such as physical/liquid contaminants, piping system issues,
products changeover,
accidental flaws, etc., that can result in poor quality products with
potentially massive
consequences on the manufacturing efficiencies and the company's brand.
Unfortunately,
traditional quality control tools do not provide timely information that could
reduce or prevent any
subsequent losses.
[0005] Therefore, there is provided a novel method and system for testing
liquids within a
container using low range electromagnetic waves.
Summary
[0006] In one aspect of the disclosure, there is provided a method for
testing a packaged
item including transmitting a set of low range electromagnetic waves at the
packaged item;
receiving a set of scattered low range electromagnetic waves, wherein the set
of scattered low
range electromagnetic waves are fully correlated to the packaged item;
determining a relative
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Date Recue/Date Received 2023-08-22
complex permittivity of the packaged item; and processing the relative complex
permittivity to
determine a characteristic of the packaged item.
[0007]
In another aspect, the packaged item is a packaged fluid. In yet a further
aspect,
the packaged fluid is milk and the characteristic is one of a butterfat
percentage of the milk, volume
of content or amount of contaminants. In yet another aspect, transmitting a
set of low range
electromagnetic waves includes transmitting electromagnetic waves in a
frequency range of about
1 GHz to about 300 GHz. In yet a further aspect, the method includes, after
receiving a set of
scattered low range electromagnetic waves, determining a dielectric constant
and a dielectric loss
factor for the packaged fluid. In another aspect, determining a relative
complex permittivity of the
packaged fluid includes processing the dielectric constant and the dielectric
loss factor. In another
aspect, processing the dielectric constant and the dielectric loss factor
comprises includes
processing a magnitude and phase of complex scattering data using a machine
learning algorithm
(MLA). In another aspect, the MLA includes a time series random forest (RF),
support vector
machines (SVM), a principal component analysis (PCA), a recurrent neural
network (RNN), a
gated recurrent unit (GRU), long short-term memory models (LSTM), or a complex
neural
network.
[0008]
In a further aspect, the set of scattered low range electromagnetic waves are
a set
of reflected low range electromagnetic waves. In another aspect, the method
includes, after
receiving a set of scattered low range electromagnetic waves, processing the
set of scattered low
range electromagnetic waves via a continuous wavelet transform (CWT), an
empirical mode
decomposition (EMD), a discrete wavelet transform (DWT), a power spectral
density (PSD), a
fast Fourier transform (FFT), or short-time Fourier Transform (STFT).
[0009]
In another aspect of the disclosure, there is provided a glucose monitoring
device
including at least one transmitter for transmitting electromagnetic waves at a
target; at least one
receiver for receiving reflected electromagnetic waves from the target; and a
glucose monitoring
unit for processing the reflected electromagnetic waves.
[0010]
In another aspect, the at least one transmitter and the at least one receiver
are
implemented within a complementary split-ring resonators (CSRR) sensor. In yet
another aspect,
the CSRR sensor is a single pole CSRR sensor, a triple pole CSRR sensor or a
honey-cell CSRR
sensor.
In yet a further aspect, the at least one transmitter and the at least one
receiver are
implemented within a whispering-gallery mode (WGM) sensor. In yet another
aspect, the at least
one transmitter and the at least one receiver are connected to the glucose
monitoring unit via
individual co-axial cables.
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Date Recue/Date Received 2023-08-22
Brief Description of the Drawings
[0011] Various aspects and features of the present disclosure will become
apparent to
those ordinarily skilled in the art upon review of the following description
of specific embodiments
in conjunction with the accompanying figures.
[0012] Embodiments of the present disclosure will now be described, by way
of example
only, with reference to the attached Figures.
[0013] Figure 1 is a schematic diagram of an apparatus for use in
examining a liquid using
microwaves or millimeter waves;
[0014] Figure 2 is a flowchart showing a method of examining a liquid
using microwaves
or millimeter waves;
[0015] Figure 3 is a flowchart showing a method of examining a liquid
using microwaves
or millimeter waves;
[0016] Figure 4 is a photograph of another embodiment of a system for
examining a liquid
using microwaves or millimeter waves;
[0017] Figures 5a and 5b are graphs showing test results;
[0018] Figure 6 is a chart showing tested milk products;
[0019] Figure 7 is a schematic diagram of another embodiment of an
apparatus for use in
examining a liquid using microwaves or millimeter waves;
[0020] Figure 8 is a schematic diagram of a service-oriented architecture
(SOA);
[0021] Figure 9a is a schematic diagram of a ferrite ring resonator (FRR);
[0022] Figure 9b is a drawing of electric field distribution;
[0023] Figure 10a is a graph showing the coupling level variations with
respect to the loss
tangent of FRR with respect to the Transmission coefficient;
[0024] Figure 10b is a graph showing the coupling level variations with
respect to the loss
tangent of FRR with respect to the Transmission phase (EM Simulation);
[0025] Figures ha and lib are diagrams showing FRR properties;
[0026] Figures 12a and 12b are graphs showing EM simulated transmission
responses
for the unloaded FRR at different gap spaces with respect to (a) Transmission
coefficient (b)
Transmission phase;
[0027] Figures 13a and 13b are graphs showing unloaded WGM600response
(magnitude
and phase) of the sensor at both S21 and S12;
[0028] Figure 14 is a flowchart showing a further embodiment of a method
of examining
a liquid using microwaves or millimeter waves;
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Date Recue/Date Received 2023-08-22
[0029] Figures 15a and 15b are graphs showing WGM600 responses to the six
oil samples
in terms of IS121 and IS211;
[0030] Figures 16a and 16b are graphs showing WGM600 responses to the six
oil samples
in terms of LS12, andzSzi;
[0031] Figures 17a to 17c are charts showing testing results;
[0032] Figures 18a and 18b are photographs showing an experimental setup
for testing
a) skim milk and b) 2% milk;
[0033] Figure 18c is a schematic diagram of spacing between milk cartons
for the
experimental setup;
[0034] Figure 19a is a graph showing the radar amplitude measurements in
four receiving
channels for different milk products (skim and 2%);
[0035] Figures 20a and 20b are photographs showing an experimental setup
for testing
a) 1% milk and b) 3.25% milk;
[0036] Figure 21a is a graph showing the radar amplitude measurements in
four receiving
channels for different milk products (skim, 1%, 2%, and 3.25%);
[0037] Figures 22a to 22c are charts showing results from other beverage
testing;
[0038] Figure 23 is a schematic diagram of another system for sensing;
[0039] Figure 24 is a schematic diagram showing further detail of the
system of Figure
23;
[0040] Figures 25 is a flowchart showing another method for sensing;
[0041] Figure 26 are diagrams showing signal processing results;
[0042] Figure 27 is a flowchart of yet another method for sensing;
[0043] Figures 28a and 28b are schematic diagrams showing a portable
glucose
monitoring device;
[0044] Figure 29a is a schematic diagram of a dipole antenna used as a
reader;
[0045] Figure 29b is a schematic diagram of an integrated sensor
structure with a single-
pole CSRR tag;
[0046] Figure 29c is a schematic diagram of an integrated sensor
structure with a triple-
pole CSRR tag;
[0047] Figure 29d is a graph showing a comparison of simulated reflection
coefficient
responses;
[0048] Figure 30a is a simulation model of the glucose samples in the
sensing region of
the TP-CSRR tag;
[0049] Figure 30b is an image of electric field distribution;
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Date Recue/Date Received 2023-08-22
[0050] Figures 31a to 31c are graphs showing simulated reflection
responses at d = 4mm;
[0051] Figures 32a to 32c are graphs showing simulated reflection
responses at d = 5mm;
[0052] Figures 33a and 33b are graphs showing resonant amplitudes;
[0053] Figure 33c is an image of electric field distribution for a SP-
CSRR tag;
[0054] Figure 33d is an image of electric field distribution for a
TP=CSRR tag;
[0055] Figure 34 is a schematic front view of an integrated CSRR sensor;
[0056] Figures 35a to 35c are graphs showing reflection responses;
[0057] Figure 36 is a schematic diagram of another embodiment of a
portable glucose
monitoring device
[0058] Figure 37 is a photograph of a radar board for use with the
glucose monitoring
device of Figure 36;
[0059] Figures 38a to 38c are graphs showing glucose testing results;
[0060] Figures 39a to 39c are graphs comparing blood glucose readings
from a CSRR
device and a glucometer;
[0061] Figure 40 is a schematic diagram of another portable glucose
monitoring device;
[0062] Figure 41a is shows a SP-CSRR sensor and S21 measurements;
[0063] Figure 41b is a set of photographs showing use of Honey-Cell CSRR
sensors in
testing milk samples; and
[0064] Figure 41c is a set of graphs showing S21 measurements (magnitude
and phase)
of different milk products tested on honey-cell CSRR sensors.
Detailed Description
[0065] The disclosure is directed at a method and system for testing an
item within a
container to determine at least one characteristic of the item or container
using low frequency
electromagnetic waves, or microwaves, or high frequency electromagnetic waves,
or millimeter
waves. For simplicity, the combination of the item and container will be
referred to as a product
in the following description. In one embodiment, the electromagnetic waves are
in a frequency
range or about 1 GHz to about 300 GHz.
[0066] In some embodiments, the item may be a food item such as, but not
limited to, dry
goods, milk, oil, carbonated drinks, juices, chips and the like. In other
embodiments, the container
may be a carton, a bottle, plastic packaging, foil packaging and the like.
[0067] The disclosure transmits electromagnetic waves at the product and
then receives
reflected electromagnetic waves which are then processed by the disclosure to
determine at least
one characteristics of the item, the container or the product. For example,
the one characteristic
Date Recue/Date Received 2023-08-22
of the item may be the percentage butterfat of milk within a milk carton to
ensure that the milk
within a specific milk carton or container contains a correct amount of
butterfat. In another
example, the one characteristic of the item may be a purity of an olive oil
within an olive oil
container or bottle.
[0068] In one embodiment, the disclosure may be seen as a compact
wireless sensing
system for rapid quality control monitoring of packaged fluids or packaged
food items in
production lines. In one embodiment, the disclosure is a device that uses
advanced radar
technology to test containers, such as, but not limited to, bottles, bags or
cartons, non-invasively
that have food items within in a short time. When installed on a conveyor
belt, the disclosure
scans the product in the short period of time (milliseconds) by sending
electromagnetic waves
that interact with the container and food item and then reflect back to a
receiver. The measured
raw data may then be processed using advanced signal processing and artificial
intelligence (Al)
algorithms to check the quality status (seen as a characteristic) of the food
items against specific
benchmarks and provide a digital assessment. In other embodiments,
characteristics of the
container may also be determined.
[0069] In one embodiment of determining the at least one characteristic,
the raw data may
be streamed to the cloud for data processing. By integrating the disclosure
with applications
stored with cloud storage, may provide a rapid automated quality testing with
smart alerts for
immediate actionable recommendations on product manufacturing.
[0070] Embodiments of the method and system disclosed herein are intended
to use a
wireless transceiver (for example mm-wave; generally, between 30GHz to
300GHz). The
transceiver will have a transmitter(s) sending a sequence of signals. The
signals will be reflected
and scattered from a product. In some embodiments, the signals may be
reflected and scattered
from a body part if the disclosure is being used to test for or monitor a
glucose level within an
individual. The transceiver will have receiver(s) that receive signals
reflected and scattered from
the object. The system will apply different signal processing algorithms to
the received signals in
order to identify the object and/or differentiate between various objects. It
will be understood that
depending on the radar bandwidth and machine learning involved, objects may be
in a range of
distance from the system. In some cases, the objects may be between a few
millimeters to a
hundred meters from the system.
[0071] Embodiments of the system and method detect and collect the
signals. The
collected signals are then processed. The signals depend on the specific
geometries of the
fingerprint/palm, plus the skin electric/magnetic properties and all of the
underlying veins/bones.
Based on the levels of diffraction, refraction, and reflection occurred, a
signal processing algorithm
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Date Recue/Date Received 2023-08-22
is used to classify the data to determine whether the hand detected belongs to
a specific user or
not.
[0072] In a method for sensing, the data may first be generated by the
radar/radio sensor.
The data is sorted, and certain set of algorithms are applied, for example Al,
Machine learning,
or the like. Then, a decision tree is generated and the individual or object
is identified.
[0073] Turning to Figure 1, a schematic diagram of an apparatus for use
in examining a
product (such as a liquid within a container) using microwaves or millimeter
waves is shown. The
apparatus 100 includes a sensing, or sensor, system 102, which may also be a
near-field
resonator or a mm-Wave radar device. The sensing system 102 includes at least
one transmitter
(Tx) 104 and at least one receiver (Rx) 105. The transmitter 104 is configured
to transmit
electromagnetic waves at a low or high frequency, such as a frequency
generally between 1 GHz
and 300 GHz or an appropriate subset of this frequency range, depending on the
required
functionality or the product being tested. In some embodiments, the at least
one transmitter 104
may be a 2-channel transmitter configured to transmit millimeter waves between
about 30 to about
81 GHz. The transmitter 104 transmits electromagnetic waves that are directed
at a plurality of
products 106, such as a container including a liquid or a container (or bag)
including dry goods,
to determine at least one characteristic of interest or to examine the
contents of the product 106.
In some embodiments, the container holding the liquids or solids, in the form
of dry goods, may
be examined by the sensing system 102. In other embodiments, the container or
packaging may
be a non-metallic material such as, but not limited to, plastic, glass or
ceramic whereby only the
item within the container is tested.
[0074] In one embodiment, the sensing system 102 is associated with, or
mounted to, a
testing apparatus 108 (such as, but not limited to, a conveyor belt system)
that includes the
components to enable the products 106 to travel and pass by the sensing system
102 in order for
product (either or both of the item and container) to be examined.
[0075] In the current embodiment, the sensing system 102 is in
communication with user
interface and/or computing devices 110 such as, but not limited to, a
Smartphone 110a, a tablet
110b, a personal computer (PC) 110c or a laptop 110d. It is understood that
other computing
devices may be contemplated. These computing devices may be seen as control
stations.
Communication between the sensing system 102 and any of the user computing
devices 110 may
be through a public network 112, such as the Internet, or may be via a private
network. The
system 100 may further include a gateway 114 and a server 116 for processing
the information
received from the sensing system 102. The user computing devices 100 may
connect with the
network 112 via a WLAN access point 118 although connection methods are
contemplated.
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Date Recue/Date Received 2023-08-22
[0076] In use, as the products (or objects) 106 pass by the sensing
system 102 along the
testing apparatus 108, electromagnetic waves are transmitted (via the at least
one transmitter
104) towards the products 106 by the sensing system 102. These waves are then
rebounded,
scattered and/or reflected, off the product or products 106 and received by
the receiver 105. Once
the electromagnetic waves that have interacted with the product 106 are
received at the receiver
105 (which may be in the form of raw data), the received signals are processed
to generate data
associated with these received signals. The generated data may represent a
determination of at
least one characteristic of at least one component of the product (either the
food item or the
container or both) or may be processed raw data that can then be processed by
a computing
device 110 to determine the at least one characteristic. The generated data is
then transmitted
to at least one of the user computing devices 110 or control stations.
[0077] In other embodiments, the control station may be a computer, a
purpose-built
device, or other device configured to receive and analyze the data. The
control station or
computing device 110 includes at least one processor configured to carry out
computer-readable
instructions with respect to the data received. The generated data may be
reviewed and may
have various processes or algorithms applied to it by the computing device
110. In some
embodiments, a decision tree may be generated to better analyze the
characteristics of the
product based on the generated data. In other embodiments, other types of
machine learning
algorithms may be used such as, random forest, support vector machines (SVM),
principal
component analysis (PCA), recurrent neural network (RNN) or any other complex
neural networks
to determine the at least one characteristic based on the generated data.
[0078] A random forest classifier may be seen as a supervised machine
learning
algorithm that includes a collection of decision trees used to classify data
into discrete categories.
The decision trees work by mapping the observations of the product, such as
the magnitude and
phase of backscattered signals, to predictions about the target value of the
object such as butterfat
percentage concentration. At the end of the random forest process, the most
recurring prediction
reached by all decision trees is outputted as the characteristic's predicted
value.
[0079] The system 100 (or the user computing device 110) may also include
a memory
component used for storing data, computer instructions, programs, machine
learning and the like.
Instead of being integrated within the user computing device 110, the memory
component may
be an external database, cloud storage or the like. The system may also
include a display and/or
other user interface components in order to enable a user to view and/or
interact with results of
the analysis.
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Date Recue/Date Received 2023-08-22
[0080] As discussed above, the system may be used to examine liquids or
solids within a
container, such as a bottle, a carton, a bag and the like. In one embodiment,
the liquid being
examined is milk and the container is a cardboard carton. In other
embodiments, the sensing
system 100 may be used to test for a level of glucose.
[0081] Turning to Figure 2a, a flowchart showing a method of sensing and
characterizing
or determining characteristics of an object is shown. Initially, the sensing
system transmits
electromagnetic waves (via the transmitter) at or towards the object (200) and
then receives
rebounded or reflected electromagnetic waves via the receiver (202). Data
associated with the
received rebounded or reflected electromagnetic waves is then transmitted to
the control station,
such as in the form of a digital signal, digital data, or raw data (204). In
some embodiments, the
data may be the reflected signal or a series of reflected signals. In other
embodiments, the raw
data may be seen as data that is generated after the reflected signal is
processed.
[0082] In one embodiment, a transmission signal is provided to the
transmitter (such as
via a transmitting antenna) via a radio frequency (RF) generator. In other
embodiments, the signal
is passed through a power divider to the transmitter. The reflected signal is
received at the
receiver (such as via a receiving antenna) and provided to a pre-amplifier.
The signal is then
combined with the signal from the transmitter (via the power divider) to
provide for a result, via,
for example, a mixer. The power divider may provide for signal adjustment
prior to providing the
signal to be combined. In some cases, the signal may be reduced/attenuated by
3dB, although
other adjustments/reductions in amplitude may also be used.
[0083] Once the signal results, or reflected signals, are obtained or
received, the signal
results may be filtered by a filter, for example a low pass filter. The signal
may then be amplified
by an amplifier and converted to a digital signal by an analog to digital
converter. Once a digital
signal is generated, it can be further transmitted to and/or processed by the
control station.
[0084] Turning to Figure 3, a flowchart outlining a method of determining
milk fat
percentage in a low GHz band is shown. In one embodiment, the transmitter
transmits
electromagnetic waves in a low GHz band such as between about 1 GHz and about
10 GHz. In
one embodiment, the disclosure is directed at a method and system for
differentiating milk
products of varying fat percentages while verifying their quality metrics
using non-invasive
microwave sensors operating in the low GHz spectrum. In determining the milk
fat percentage
(the at least one characteristic), it is assumed that the expected percentage
of butterfat of milk is
known and the system may be used to confirm that the milk contains the
expected percentage
butterfat. This may be performed with the milk in the container or out of the
container.
9
Date Recue/Date Received 2023-08-22
[0085] Initially, electromagnetic waves in the low GHz band are
transmitted, via the
transmitter, towards the product (300) (such as a milk carton or a milk bottle
containing milk) and
the reflected electromagnetic waves are then received by the receiver (302).
[0086] In one embodiment, this may be performed using the system of
Figure 1 as the
products pass by the sensing system when on the conveyor belt. In another
embodiment, the
transmitter and receiver may be integrated together in the form of a probe
that operates in the low
frequency band therefore covering (300) and (302) by placing the probe into
the milk being tested.
In this manner, the milk may be placed in a dish or bowl or the like. In yet
another embodiment,
the transmitter and receiver may be connected to a near-field resonator/sensor
(e.g., split ring
resonator, or its complementary) via coaxial cables. In this manner, the milk
product may be tested
on top of a specific sensing region by perturbing the induced/coupled electric
field and modulating
the received signal. In other embodiments, the at least one transmitter and at
least one receiver
is placed in a predetermined position with respect to the product being
tested.
[0087] The received signals can then be processed (possibly with the
transmitted signals)
to generate a digital signal relating to a dielectric constant (E') and
dielectric loss factor (E") of the
milk product sample (304) in the low frequency band. The received signals may
be processed by
any one of the control stations or may be processed in the cloud and the
resulting digital signal
transmitted to the control station. The dielectric constant (E') and
dielectric loss factor (E") provide
an understanding of the dielectric dispersive behaviour of the milk at varying
butterfat
concentrations. The dielectric measurements may also be used to locate a
region of most
sensitivity to fat detection. In one embodiment, this may be achieved using a
measurement setup
such as shown in Figure 4.
[0088] Figure 4 provides a schematic diagram of another embodiment of an
apparatus for
testing using low range electromagnetic waves, for example to examine
characteristics of a liquid.
The current embodiment may be used to determine milk fat percentage.
[0089] The apparatus 400 includes a co-axial probe 402 (used for the
measurement in
(300 and 302)) that is connected to a port of a vector network analyzer (VNA)
404. The apparatus
400 further includes a central processing unit (CPU), such as a computer, 406
which includes a
display. One example of a VNA is a Keysight Technologies VNA N5227A. In the
current
embodiment, the VNA 404 is connected to the CPU 406 via an Ethernet/LAN cable
and the probe
402 is connected to the CPU 406 via a USB cable. Other setups are
contemplated. For one
experiment, calibration of the apparatus 400 was performed at 20 C in the low
frequency range
using distilled water and an embedded Open-Short-Load methodology.
Date Recue/Date Received 2023-08-22
[0090] This apparatus 400 may be seen as a non-invasive testing
methodology using
complementary split-ring resonators (CSRRs) where testing is performed using
resonant sensors
in a sensitive narrow-band. In the current embodiment, four types of advances
CSRRs were used
with a vector network analyzer to non-invasively test and measure S21 of the
milk products.
[0091] In using the apparatus of Figure 4, to obtain the dielectric
constant (E') and
dielectric loss factor (e), the probe is pressed against the milk product
sample. In one specific
embodiment, a 50 0 coaxial probe is pressed against the milk product sample
that was poured
into a metallic petri-dish on top of an aluminum plate in the sample platform.
A relative complex
permittivity, which is a property for characterizing different materials, is
then determined or
calculated (306) from the measured reflection coefficient Sii, calibrated
sample thickness, probe
diameter, and bead permittivity using a built-in numerical algorithm. The
dielectric constant (E'),
dielectric loss factor (E") and complex permittivity may then be further
processed by a control
station.
[0092] For experimental purposes, this was performed multiple times to
verify
repeatability of the dielectric measurements. The average of the extracted E'
and E" for all trials
is plotted in Figures 5a and 5b, respectively. As can be seen in the graphs of
Figures 5a and 5b,
for all fat contents, the dielectric constant E' decreases with increasing
frequency almost linearly
over the 10 GHz measured bandwidth. However, the dielectric loss factor E" has
a decreasing
pattern up to 1.3 GHz and then increases gradually with the increased
frequency. Higher losses
are expected towards higher frequencies as depicted in Figure 5b. The changing
trends of e and
E" with frequency is not influenced by the concentration of the butterfat,
however, noticeable
contrast in the properties values was observed between the varying-fat milks
where both the
dielectric constant and loss factor tended to decrease with increased fat
content at any given
frequency. El was observed to change in larger resolution compared to those
spotted in the E"
trend. The 1 - 6 GHz frequency band was identified as a promising region for
sensitive milk fat
detection as demonstrated by the percent change in E' (.1-- 2.0% for lob
butterfat change at 4 GHz).
Characterization was also performed in the high frequency band 50 - 67 GHz
with similar patterns
identified in both E' and E", and therefore considered another promising
sensitive region for milk
fat detection. Milk characterization was performed in a controlled temperature
environment of
-20 1 C, yet both E' and E" are expected to decrease proportionally with
increasing temperature.
[0093] Using the method of Figure 3 and the system of Figure 4, the
dielectric properties
of four products of white milk NeilsonTM dairy of the Microfiltered class:
skim, 1%, 2%, and 3.25%,
were investigated using a wide-band characterization system. The tested milk
products are shown
in Figure 6 along with their sizes, butterfat%, and total solid
target/content.
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Date Recue/Date Received 2023-08-22
[0094] The fat-based differentiability was verified on four prototypes of
advanced resonant
sensors where the transmitter and receiver of the radar board were connected
to a near-field
resonator/sensor (e.g., split ring resonator or its complementary) via coaxial
cables as
schematically shown in Figures 41a and 41b. The samples of milk products were
tested on top
of the designed sensing region through perturbing the coupled electric field
and thereby
modulating the received signal of the sensor. As illustrated in Figure 41c,
the results of the
measurements retrieved via the compact and dispersed honey-cell CSRR
integrated sensor are
shown.
[0095] In another example using the testing apparatus of Figures 41a and
41b, 2 ml (by
volume) milk samples (in a circular container) were placed on top of the CSRR
and then tested
via the apparatus 400 three times to determine an average S21 magnitude.
During testing, the
milk samples perturb the electric-field induced over the circular sensing
region or the CSRR and
modulates the S21 resonance profile depending on the electromagnetic (EM)
properties of the milk
fat composition so that distinct amplitude and frequency variations near the
bare resonance of
the device were observed demonstrating the electromagnetic identification of
the milk at varying
fat%.
[0096] One of the types of CSRRs may be a single pole CSRR which includes
a single
pole of two concentric split-rings loaded in a microstrip substrate having
specific geometrical
parameters. Another type of CSRR that was used was a triple pole CSRR. In
testing, similar
transmission parameters were collected between 3.8 ¨4.7 GHz by testing the
milk samples again
at 600 uL volume, but this time inside a rectangular container integrated on
top of a CSRR of
triple integrated poles. The CSRR caused the coupled electric-field to spread
over a larger region
due to mutual coupling between the three adjacent cells. The S21 resonance
profile was observed,
and it was noted that it changed in both amplitude and frequency following the
milk fat% of the
tested milk samples. Another type of CSRR is a honey-cell CSRR which may be
seen as a honey-
cell configuration of a set, such as four, hexagonal CSRRs in a compact or
dispersed formations.
The milk samples (in small vials) were tested first on top of the compact
formation CSRR and the
S21 was observed to significantly change both in co-efficient (between 2.5 ¨ 3
GHz) and in phase
(between 2.6 ¨ 2.9 GHz). The milk products were tested again in larger vials
on top of the
dispersed formation CSRR sensor, and the same changing trends were observed in
the
coefficient (between 2.4 ¨ 3.5 GHz) and in the phase (between 2.6 ¨ 2.9 GHz).
These results
confirmed the ability of these sensors to non-invasively identify the milk
samples at varying fat%.
[0097] In another embodiment, the disclosure is directed at a system and
method of
characterizing and/or sensing of oil characteristics in the mm-wave band. In
this embodiment,
12
Date Recue/Date Received 2023-08-22
the disclosure is directed at a compact, low-cost, miniaturized, and non-
invasive mm-wave sensor
to be integrated into an Internet of Things (loT) based real-time sensing
system.
[0098] Turning to Figure 7, a schematic diagram of an embodiment of a
system for
determining oil characteristics is shown. The sensing system 700 includes a
VNA device 702 that
is in communication with a sensor, such as, but not limited to, a whispering-
gallery-mode (WGM)
sensor 704. The system 700 further includes apparatus that is capable of
transmitting and
receiving electromagnetic waves such as in the form of a transmitter and a
receiver as discussed
above. As shown in the current embodiment, the system may include a plurality
of testing
apparatus, or sensing systems 700 that may operate in parallel in order to
save time although, it
is understood, that there may be a fewer or a larger number of testing
apparatus. The other
components of Figure 7 may be identical to the components discussed in Figure
1 and will be
understood by one skilled in the art. It is understood that the system shown
in Figure 1 may also
be used for determining oil characteristics.
[0099] In one embodiment, a sensitive WGM technique is used to implement
the sensing
platform in the mm-wave range of about 22 to about 32 GHz to induce high 0-
factor resonances
adequate for monitoring the oil quality and determining oil characteristics,
such as, identifying its
brand. In one embodiment, the core sensing structure couples the microwave
power from a
microstrip line to a ring resonator made of ferrite material of high
resistivity and low loss. Its
magnetic anisotropy is exploited to engender a non-reciprocal effect on the
induced modal fields
in the presence of a bias magnetic field. The acquired non-reciprocity feature
is favorable to allow
for checking the oil samples at multiple sensing instances of highly sensitive
WGM modes at
distinct frequencies in both S12 and S21 transmission signals. Particularly,
sensing information is
collected from three distinctive features of each excited WGM mode: 1)
resonant amplitude; 2)
resonant frequency; and 3) phase transition occurring near resonance.
Combining these sensing
parameters enable a robustness and reliability of the measured data by
minimizing, or reducing
the associated uncertainties received from the background noise, ambient
environment,
interconnected instrument, etc. The functionality of the system is practically
demonstrated by
identifying edible oils of different types and brands whose electromagnetic
differences were
imprinted in the S12 and 521 signals of the sensor.
[0100] Given its miniaturized sizing (approximately 6 cm3), the WGM
sensor may be easily
adapted as a low-cost portable tool for rapid real-time identification of the
oil type, on-site EM
analysis, and quality checking for regulations compliance and food quality
control purposes.
Figure 7 depicts the implementation of the sensor in three sensing nodes,
where in each sensing
system, the sensor is installed at one hot spot a few millimeters underneath
the production line
13
Date Recue/Date Received 2023-08-22
(or testing apparatus) where all the oil products pass through. The
interaction of the
electromagnetic field generated by the sensing system with the oil material on
top of the ring
resonator allows for a recording of its scattering response in a short time
frame (such as a few
seconds) by the VNA. The scattering response may also be analyzed and compared
against a
reference response using an artificial intelligence (AO-based software.
[0101] In another embodiment, the oil sample may be exposed to the sensor
before
packaging to monitor its quality and thereafter upon regulation to report any
fraudulent brands
and/or producers. The sensor could also be used for identifying the
adulteration in virgin olive oil
and distinguish similar oils of notable differences in quality. The
electromagnetic resonance profile
of the pure extra virgin olive oil would be slightly different from those been
adulterated as detected
by the device circuitry.
[0102] Some advantages of this embodiment include the fact that the
sensing structure
enjoys many features of low power consumption, affordable cost, and high
sensitivity, thereby
making it attractive not only for development as an independent quality
detection platform but
rather as a complete autonomous system based on loT for implementation as
online, rapid,
noninvasive, and cost-efficient measurement system in the oil processing
industry where no such
system is yet implemented or commercialized.
[0103] In another embodiment, the sensing system may be seen as an
integrated IOT
system, such as schematically shown in either Figure 1 or Figure 7. The system
may be seen as
a service-oriented architecture (SOA) that demonstrates higher effectiveness
for implementation
in smart systems, featuring advantages in defining a simple ecosystem where
all entities are well
defined. Figure 8 shows one structure of a SOA in terms of four consecutive
layers starting at the
sensing layer where the WGM microwave sensor operates. The other three layers
include
network, service, and interface layers.
[0104] With respect to a sensing layer, the sensor may include a ferrite
ring resonator
(FRR) and a microstrip guiding structure (MTL) both combined on top of a
dielectric PCB as shown
in Figure 9a. The FRR may be installed within a few micrometers (seen as gap
g) of the MTL
edges to realize a desired coupling. Consequently, the MTL links the modal
fields to the adjacent
FRR to excite the WGM modes where the coupled waves propagate azimuthally
around the FRR
in multiple rotations that are phase shifted by 2-rrn, where n is an integer
representing the number
of rotations. The resulted traveling waves experience repeated reflections
from the inner rims of
the FRR, and thereby, the E-fields remain confined and highly concentrated
toward the outer
boundary as depicted in Figure 9b for the electric field distribution of the
sensor.
14
Date Recue/Date Received 2023-08-22
[0105] In use, the sensor attains a high degree of sensitivity to changes
in loss property
of its FRR component, which vary its coupling level accordingly. This-behavior
of high sensitivity
to changes in the loss property will yield the sensor very responsive (in
terms of resonance
characteristics) to the little perturbations in the electromagnetic properties
of various oils loaded
on top of the FRR very close to its boundaries as shown in Figures 11a and
11b.
[0106] As shown during experimentation, the diverse oils contain several
fatty acids that
differ in relative percentages depending on their type and origin. In fact,
each edible oil is
essentially a mixture of triacylglycerides (TAGs), which are fatty acids
esters of the trihydric
alcohol glycerol with three alkyl chains contained in each molecule. The
physical and chemical
attributes of each oil are mostly affected by the C18 unsaturated fatty acids
(UFAs) in their
composition. The dielectric constants El of oils were shown to be
significantly determined by their
UFAs composition where z' increases with increasing the relative degree of
oils unsaturation (i.e.,
the number of double bonds in carbon chain) as shown by the iodine values
(IVs). Therefore,
minimal differences are expected within the electromagnetic (EM) properties of
each oil sample.
Oil and water were found to have quite different values of dielectric
constants (--:-- 3.1 and 77.0,
respectively, at 1 MHz and 25 C). Apparently, water polar molecules have
energy storage that is
much greater in magnitude than oil molecules. This energy is stored due to the
orientation and
polarization of the polar molecules under the exposure of the applied E-field.
Therefore, it is
expected that E' of oils would significantly increase with increasing the
moisture content. The
frequency, temperature, and other chemical characteristics, such as volatile
ratio, solid-fat index,
etc., would also impact the EM profile for oils. Analyzing the scattering
response of the WGM
sensor will benefit a sensitive identification of various oil types and the
detection of any impurity
imbedded in the loaded oil samples where the EM properties are slightly
modulated.
[01071 The sensor of this embodiment was designed to operate in 22-32 GHz
to keep a
size of the sensor compact with a wavelength resolution in the range 0.9-1.4
cm that is sufficient
or adequate for more sensitive interaction of the WGM waves with the loaded
oils. However, it
may be desirable to enhance the structure sensitivity through a stronger
coupling between the
MTL and FRR. In some embodiments, the sensor may be designed to operate at
higher
frequencies such as up to about 70 GHz. In other embodiments, the WGM
resonator could also
be interconnected to a radar printed circuit board as a driving source instead
of the VNA.
[0108] In some embodiments, the nonreciprocal operation of the FRR was
triggered to
acquire more sensitive instances of WGM resonances in both the Si2 and S21
signals. To do so,
a permanent magnet (PM) was attached beneath the substrate to induce the
necessary biasing
magnetic field perpendicular to the plane in the z direction as portrayed in
Figure 11b. When such
Date Recue/Date Received 2023-08-22
a biasing field is present, the magnetic dipoles inside the ferrite resonator
are aligned in either
clockwise (CW) or counter-OW (COW) direction to produce non-zero magnetic
dipole moment
that motivates dipoles precession at a frequency controlled by the strength of
the applied
magnetic field, thereby turning the singular permeability of the ferrite
resonator into a
nonsymmetrical tensor due to its activated magnetic anisotropy. The effect of
this tensor will be
seen as two effective permeabilities 14+ and /1-, for the (CW/+) and (CCW/-)
travelling WGM
modes of similar radial "n," azimuthal "m," and axial "I" variation. As a
result, they would exist at
two different resonance frequencies in the S21 and S12 signals.
[0109] The final layout of the sensing structure integrates an MTL of
width Whne=0.28 mm
and thickness t= 0.017 mm on top of a Rogers 4360G2 substrate (E'= 6.15, tan =
38x104) of
length L=30 mm, width W=20 mm, and thickness T=0.2 mm. A ferrite material
(E'=13.2, tan=
4x10-4) was used to design the ring resonator of radius R=5.13 mm and height
h=1.44 mm. A PM
(Samarium-Cobalt) of height H=3 mm and diameter D=9.8 mm was integrated in the
ground plane
of the microstrip substrate. Other magnets of varying sizes depicted could
also be used with the
FRR to realize a compact layout.
[0110] Other devices could be used in this sensing layer (e.g., FMCW mm-
wave radar,
split ring resonator (SRR), CSRR, antenna array, or combination of them for
enhanced sensitivity
performance)
[0111] The network layer provides the necessary wireless network
connectivity to acquire
the oil sensing data from each sensing node, process, and share it among
different units in the
decision level. Applied communication technologies should satisfy the
requirements of flexibility,
compactness, widespread compatibility, reasonable data rate, low cost, and
energy consumption.
Radio-frequency identification (RFID) is one technology that delivers M2M
communication with
passive tags of favorable specifications; however, it is more suit-able for
other applications that
are identification oriented. ZigBee (based on IEEE 802.15.4 standard) is
another option that suits
applications of low energy consumption, low data rate, and long transmission
coverage. The
wireless local area network (WLAN) or WiFi operating in the frequency bands of
2.4 and 5 GHz
is among the candidates that provide higher data rates (-150 Kbps) and longer
coverage (up to
100 m) especially in the new variants of the IEEE 802.11 standards [34].
However, it requires
much higher power for RF transmission when integrated with the sensing
platform. Among all the
aforesaid technologies, Bluetooth low energy (BLE) is shown to have a match
for the intended
industrial application for many favorable features, such as lower energy
consumption, lower
sleeping interval (-10.0 s), moderate data rate, available firmware, and broad
compatibility with
different operating systems.
16
Date Recue/Date Received 2023-08-22
[0112] Every node represents the sensing operation performed at one PL
using at least
one WGM sensor connected to an RF network analyzer or possibly a radar printed
circuit board
(PCB) through a pair of coaxial cables. The function of the analyzer is to
inject the microwave
signal into the sensor and read out the output scattering signal (i.e.,
response). The raw sensing
data are to be sent over the BLE radio network module to the gateway using a
BLE-adapter and
firmware that is compatible with the employed analyzer. The gateway component
collects the oil
sensing data over the BLE radio network and then sends it over the Ethernet
interface to the
Internet cloud. In fact, it is pivotal to secure the sensing data before its
delivery to the cloud.
Therefore, appropriate policies in encryption, authorization, and
authentication are all applied at
the gateway level to enable the data access only for authorized users. The
gateway could be
implemented using a Raspberry Pi platform of low cost, small size, high
integration, and good
performance.
[0113] The service layer demands much of the resources in the SOA
architecture, such
as power consumption, processing time, etc., to perform the necessary tasks of
highly complex
computations. It is desired to select an efficient approach that assures an
independent local
operation yet features a global functionality to allow for real-time
automation. The workstation also
incorporates advanced data analytics modules where some machine learning
algorithms, such
as, but not limited to, support vector machine (SVM), principal component
analysis (PCA),
convolutional neural network (CNN), etc. may be effectively used to enhance
the productivity of
the manufactured oil products while satisfying the regulatory requirements.
Other machine
learning algorithms may include time series random forest (TSF), a recurrent
neural network
(RNN), a gated recurrent unit (GRU), long short-term memory models (LSTM), or
a complex
neural network. In an actual implementation, this layer would be implemented
completely in the
cloud.
[0114] The end users could interact with the sensing system or apparatus
through a
system interface that is easy to use, such as, but not limited to, Laboratory
Virtual Instrument
Engineering Workbench (LabView) to develop a customize application that
presents the
measured sensing data collected from the noninvasive sensing nodes in visual
plots and
enumerated tables. Using the system interface, the raw data could also be
processed, analyzed,
and shared over the Internet to remote users accessing the system. To maintain
the system
integrity, the proxy modality could be employed for managing the accessibility
of authorized users
only at one centralized point where the user authentication, authorization,
and security
frameworks are strictly imposed.
17
Date Recue/Date Received 2023-08-22
[0115] In experimentation, to probe into the feasibility of the oil
quality monitoring system
for industrial implementation, the developed prototype was experimented in the
microwave lab
environment for identifying commercial oil products of different brands and
types, namely,
selection sunflower (A), selection canola (B), selection vegetables (C),
selection peanut (D),
mazola canola (E), and colavita olive (F), which were all purchased from one
grocery store. The
names and labels will be used interchangeably in the following demonstration.
[0116] The oil measurements were performed when operating the sensor in
the WGM600
mode. Figures 13a and 13b depict the unloaded WGM600response (magnitude and
phase) of the
sensor at both S21 and S12. The resonance frequency and Q-factor for the
operating WGM600 is
highest when no oil sample is yet introduced in the sensing zone of the FRR
(i.e., baseline). The
change in this reference response of the sensor at the two resonant peaks in
S12 /S21 and phases
were used to track the quality of the tested oils.
[0117] The six different oil products were measured on top of each sensor
at very close
proximity d = 1 mm from the FRR. The baseline response in Figure 13 was
retrieved after
measuring each oil bottle to ensure reliable comparison and repeatability. The
WGM600 responses
to the six oil samples are compared in Figures 15a and 15b and Figures 16a and
16b, in terms of
the 'Sul, 1S211, LS12, and LS21, respectively. Each plotted response is an
average of three
repeatable measurements for the specific oil under test.
[0118] With their slight contrast in permittivity and losses, the oil
samples perturb the
coupled FRR evanescent fields differently. Remarkably, each oil sample is
captured in the 'Sid
and 1S211 responses with distinctive shift in resonance frequency and
amplitude/depth as depicted
in Figures 15a and 15b, respectively. A significant difference of about 8 MHz
was detected
between the S12 resonances of the Canola oil from two different brands
(Selection and Mazola).
It was also seen as 9 MHz difference between their corresponding S21
resonances. Additionally,
the oil sensing information are considerably expanded by the scattering phases
of S12 and S21 to
allow for more precise identification of the oil samples. Figure 15a shows
that S12 phase responses
to various oils have distinct frequencies where the phase slopes abruptly
change.
[0119] Turning to Figure 14, another method of testing using low range
microwaves is
shown. In some embodiments, a single product is placed directly in front of a
sensing system. In
other embodiments, the product (combination of liquid and container) being
tested is placed on a
conveyor belt that moves the container past a sensing system that is mounted
or positioned for
transmitting and receiving electromagnetic waves at and from the liquid and
container being
tested. In some embodiments, this method may be used with respect to the setup
of Figure 18
below.
18
Date Recue/Date Received 2023-08-22
[0120] Initially, a sensing system, such as in the form of a radar or
radar chipset, is
installed, such as on a rod or fixture (1400). In one embodiment, the
installation is at a
predetermined height in accordance with a position of the object being tested.
The radar chipset
may include at least one transmitter and at least one receiver for
transmitting EM waves and for
receiving reflected EM waves, respectively. Alternatively, the object may be
placed on top of the
radar where the object is in contact with a surface of the radar or a short
distance above the
surface of the radar. If a conveyor belt is being used in the testing, the
product may travel on the
conveyor belt in front of the sensing system and, in other embodiments, the
product may travel
over the sensing system whereby the sensing system may be mounted within or
integrated with
the conveyor belt.
[0121] In other embodiments, multiple sensing systems may be installed at
different
positions and angles with respect to the container being tested and/or the
conveyor belt, such as
for quality monitoring, full scanning and/or sensing. In yet another
embodiment, such as for food
manufacturing production lines, positioning of the one or more sensing systems
may be selected
to scan packaged products when passing by on conveyor belts.
[0122] After installation of the sensing system, the object being tested
is placed at a
predetermined distance away from the sensing system (1402) or the product is
moved past the
sensing system by the conveyor belt. In some embodiments, the predetermined
distance may be
handled by the location of the sensing system with respect to the testing
apparatus. In other
embodiments, the object being tested may be manually placed the predetermined
distance away
from the sensing system. In other embodiments, the object, such as a milk
carton, is placed
inside a 3D-printed fixture to help maintain a stable position for the object
during radar
measurements (or when the EM waves are transmitted towards the object). In
further
embodiments, the conveyor belt is started so that the products that are
located atop the conveyor
belt travel past the sensing system.
[0123] The sensing system then illuminates the product with a high
frequency modulated
continuous wave (FMCW) (1404) or EM waves. In one embodiment, the sensing
system
transmits FMCW chirps that are radiated periodically at high frame rates. In
some embodiments,
the number of transmitting and receiving antennas (or transmitters and
receivers) may vary and
in different array installations (e.g., 3x4, 1x3, or other larger arrays) to
increase the sensitivity
resolution. The sensing system may also be integrated with off-board antennas,
passive/active
resonator, or dielectric lens to boost the detection sensitivity of the
targeted objects (or fluid
packages).
19
Date Recue/Date Received 2023-08-22
[0124]
The reflected signals are then received or sensed by the receiver (1406). In
some
embodiments, the reflected signals (or reflections) from the product are
continuously received by
one of the receivers within the sensing system. These reflected signals may
represent a store,
or cache, of information that describe various attributes (thickness, volume,
internal
synthesis/composition, etc.) with respect to the object(s) (i.e., packaged
product) based on the
radar transmitted EM waves. The reflected signals may be received over a
predetermined time
window or time period.
[0125]
The received EM signals are then filtered to retrieve raw data from each of
the
receiving channels during the time window (1408). The raw data may include
information relating
to various attributes of the liquid within the container. These attributes may
include, but are not
limited to, thickness, volume and/or internal synthesis/composition. In some
experiments that
were performed using the method of Figure 14, milk samples of homogenous
composition but
with different amounts of butterfat percentages were tested in order to
determine butterfat
percentages of the milk within the container to confirm that the packaging
label was correct. In
these experiments, all the samples had a similar shape, volume, placement, and
almost the same
composition except for the concentrations of the butterfat that have tiny
variations from one
sample to another.
The variation in butterfat percentage modifies the dielectric properties of
the product, thereby making it possible for the radar signals to capture the
unique signature of
any deviations from the benchmark or target butterfat percentage.
Particularly, tiny changes in
compositions are perceived as changes in the amplitude and phase of the radar
echo signals. In
other studies, physical and liquid contaminants were added to different
products in order to
determine a purity of the liquid within the package.
[0126]
In the specific embodiment of testing butterfat in milk samples, the variation
in
butterfat percentage modifies the dielectric properties of the sample/product
in place, thereby
making it possible for the radar signals to capture the unique signature of
any deviations from the
benchmark percentage Particularly, tiny changes in compositions are perceived
as changes in
the amplitude and phase of the radar echo signals.
[0127]
The raw data is then further processed using signal processing methodologies
(1408). This processing may be performed using sample gating, continuous
wavelet transforms
(CWT), empirical mode decomposition (EMD), discrete wavelet transform (DWT),
short-time
Fourier transform (STFT), fast Fourier transform (FFT), power spectral density
(PSD) or other
known signal processing methodologies. In one embodiment, the processing
denoises the echo
signals to extract a peak zone of each of the received reflected signals to
capture material
dependent properties of each of the scanned products. In other embodiments,
the peak zone that
Date Recue/Date Received 2023-08-22
is extracted is around the maximum received signal strength (RSS) which is
then filtered and
processed to extract further features of the liquid. In other embodiments, the
signal processing is
used to de-noise the echo signals so that the EM properties of the reflected
signals can be purely
captured.
[0128] In some embodiments, the signals may then be further processed to
mitigate or
remove location interference (1409) such as by using the range and the angle
of arrival (AoA) of
the sensing systems. This is shown in dotted lines indicating that this may
not need to be
performed in each embodiment.
[0129] Properties of interest may include, but are not limited to,
predicting milk fat
percentage or any deviations. For the testing of milk, by recording the multi-
channel raw radar
signals that are unique to various products/samples, and analyzing all using
signal processing
and machine learning algorithms, the system of the disclosure demonstrated the
radar sensor
capability to identify different milk products, detect any deviation from the
quality benchmark,
predict the milk fat percentage, detect any volume variation, detect any
liquid contaminants mixed
with milk and detect any physical contaminants inside milk. The method of
Figure 3b may be
used for packaged fluids such as, but not limited to, milk, edible oil,
carbonated beverages or
juices.
[0130] The further signal processed signal can then be processed or
applied to machine
and/or deep learning models (1410) to predict and/or determine characteristics
of the liquid or
object being tested or identify the property of interest. Properties of
interest may include, but are
not limited to, predicting milk fat percentage or any deviations. In one
embodiment, with respect
to testing of butterfat percentage in milk, by recording the multi-channel raw
radar (or reflected)
signals that are unique to various milk products/samples, and analyzing them
using signal
processing and machine learning algorithms, the radar sensor capability to
identify different milk
products, detect any deviation from the quality benchmark, predict the milk
fat percentage, detect
any volume variation, detect any liquid contaminants mixed with milk and
detect any physical
contaminants inside milk was demonstrated.
[0131] Example models may include support vector machine learning,
convolutional
neural networks, recurrent neural networks or long short-term memory models.
In some
embodiments, a relative complex permittivity may be processed to determine a
characteristic of
the packaged milk. As will be understood, the method of Figure 14 may be
applied to all packaged
fluids (milk, edible oil, carbonated beverages, etc.).
[0132] In a further embodiment, results from the method of Figure 14 may
be checked
against specific benchmarks to provide a digital assessment and immediate
actionable
21
Date Recue/Date Received 2023-08-22
recommendations. The latter may be used to quickly synchronize a main fluid
tank in a facility to
ensure a rapid automated quality testing on product manufacturing. That would
substantially
reduce the products processing losses, labour shifts, operational costs, and
effectively improve
product quality and supply-chains.
[0133] In one specific embodiment, a radar sensor operating between 58 ¨
63 GHz was
developed and initially deployed to test milk cartons at known fat
percentages. Each carton was
measured by the sensor 3 times to confirm the repeatability of the device. The
collected sensor
responses have demonstrated the differentiation between tested cartons of
varying fat
concentrations from 0.8 to 3.25%. Ultimately, the system to be installed
should be able to detect
any deviation from the specific fat concentration of the milk cartons in that
line. Results are shown
in Figures 17a to 17c. Figure 17a shows results for milk fat variation
testing, Figure 17b shows
results for sides of cartons testing and Figure 17c shows results for volume
and contaminants
testing.
[0134] In further experimentation, testing of 1% and 3.25% milk cartons
was performed
along with the testing of skim and 2% milk cartons. In the experiment, three
separate trials were
performed with three different cartons such that there were fifteen (15) tests
for each type of milk.
The top nine (9) tests were recorded (as outlined below).
[0135] In the conveyor belt experiment setup, the radar 1800 was set up
in proximity to a
conveyor belt 1802 such that when milk cartons 1804 passed the radar, there
was about a two
(2) cm radial distance between the radar 1800 and the carton 1804. This is
schematically shown
in Figures 18a and 18b. Figure 18a shows the position of skim milk cartons
1804 on the conveyor
belt 1802 with respect the radar 1800 and the Figure 18b shows the position of
2% milk cartons
1804 on the conveyor belt 1802 with respect to the radar 1800. Figure 18c is a
schematic diagram
showing a distance of each milk carton with respect to the installed radar.
Using a conveyor belt
speed of 20 (which results in a linear speed of 0.143 m/s), carton 1 passes
the radar at t1 = 874
ms, carton 2 passes the radar at t2 = 2000 ms and carton 3 passes the radar at
t3 = 3111ms.
[0136] Results are shown in Figures 19a which provides a comparison of
the testing
results between the skim milk cartons (orange color) and the 2% milk cartons
(blue color). Along
the x-axis is radar channel bins (in the fast-time) and along the y-axis is
the signals magnitude
[0137] Testing for the 1% and 3.25% milk cartons was set up in an
identical manner as
discussed above with respect to the skim and 2% milk cartons. Figure 21a
provides a chart
comparing all of the different milk and milk cartons that were tested. As
shown, the radar would
identify different milk products with unique amplitude variations for each
milk.
22
Date Recue/Date Received 2023-08-22
[0138] In reviewing the different data that was received during a 1st
pass of the
experiment, 36 measured samples were reviewed resulting from 9 trials for each
milk product.
This was received via 256 data features from 4 receiving channels. There was a
random split of
80% (twenty-eight samples) of the samples were for training and 20% (eight
samples) of the
samples were for testing.
[0139] As can be seen in Table 1 (which represents the data from a 1st
pass or shuffle):
Test Sample True Label Prediction
1 2% 2%
2 1% 1%
3 2% Skim
4 Skim Skim
1% 1%
6 3.25% 3.25%
7 Skim Skim
8 Skim Skim
[0140] As can be seen, during the 1st pass the radar was correct 88% of
the time with its
testing whereby only one sample was misclassified.
[0141] In a second pass or shuffle of the experiment, 36 measured samples
were used,
and 9 trials preformed for each milk product. The data was received via 20 PCA
features from 4
receiving channels (where there was a dimensionality reduction from 64 to 5
for each reaction).
There was a random split of 80% (twenty-eight samples) of the samples were for
training and
20% (eight samples) of the samples were for testing.
[0142] As can be seen in Table 2 (which represents the data from a 2nd
pass or shuffle):
Test Sample True Label Prediction
1 =1% 1 %
2 3.25% 3.25%
3 3.25% 3.25%
4 1% Skim
5 Skim Skim
6 2% 2%
7 2% 2%
8 Skim Skim
23
Date Recue/Date Received 2023-08-22
[0143] As can be seen, during the 2nd pass the radar was correct 88% of
the time with its
testing whereby only one sample was misclassified.
[0144] In a third pass or shuffle of the experiment, 36 measured samples
were used, and
9 trials preformed for each milk product. This was received via 256 data
features from 4 receiving
channels. There was a random split of 70% (twenty-five samples) of the samples
were for training
and 30% (eleven samples) of the samples were for testing.
[0145] As can be seen in Table 3 (which represents the data from a 3rd
pass or shuffle):
Test Sample True Label Prediction
1 3.25% 3.25%
2 3.25% 3.25%
3 2% 2%
4 Skim Skim
1% %
6 1% %
7 Skim Skim
8 Skim Skim
9 3.25% 1%
2% 2%
11 1% %
[0146] As can be seen, during the 3rd pass the radar was correct 91% of
the time with its
testing whereby only one sample was misclassified.
[0147] In summary, the experiments showed the effectiveness of the radar
system to
measure milk fat percentage. Radar raw data show distinguishable scattering
patterns for the
four milk products (Skim, 1%, 2%, and 3.25%), especially at RX3 and RX4. The
machine learning
component shows great potential for learning the extracted signatures and
further predict the
milk% of any tested product. While there was misclassification for 1 carton in
each pass, however
practically a re-work flag is raised only if back-to-back cartons are
classified incorrectly.
Therefore, the system will forgive the 1% milk classification in a 3.25%
product line. However, if
back-to-back cartons are classified as 1%, this will trigger a warning and the
milk line will be
stopped. The machine learning models herein used are simplistic to allow for
quick classification
in this POC given the small amount of measured data. Advanced models (e.g.,
deep neural
networks) are more powerful (on larger datasets) and worth investigating to
ensure real-time
detection.
24
Date Recue/Date Received 2023-08-22
[0148] Turning to Figures 22a to 22c, different results from testing of
other liquids is
shown. Figure 22a shows a measured parameter after passing mm-Wave radar
through three
types of Coke TM ; Figure 22b shows a second parameter for the same
measurements; and Figure
22c shows a confusion matrix generated after processing the measurements
through the system.
[0149] Figure 23 illustrates another embodiment of a sensing system. The
sensing
system 1000 for sensing biometric and environmental characteristics. In
particular, the system
1000 includes at least one transmitter 1010 and at least one receiver 1020.
The transmitter 1010
is configured to transmit electromagnetic waves at a frequency generally
between 30 GHz and
300 GHz or an appropriate subset of this frequency range depending on the
required functionality.
In some cases, the at least one transmitter 1010 may be a 2-channel
transmitter configured to
transmit between 30 to 67 GHz. The transmitter is intended to transmit
electromagnetic waves at
an object 1030 to determine a characteristic of interest. In some cases, the
characteristic may be
a biometric characteristic, for example, a fingerprint, a palm print, a
respiration rate, a heart rate,
a glucose level, a gait velocity, a stride length or the like. In other cases,
the characteristic may
be environmental, for example, presence of impurities, air quality, explosive
detection, or the like.
In yet another embodiment, the characteristics may be characteristics relating
to a liquid such as
a butterfat percentage of the milk, volume of content or amount of
contaminants within the liquid.
The characteristics may also relate to food packaging.
[0150] Once the electromagnetic waves that have interacted with the
object 1030 are
received at the receiver 1020, the data may be transmitted to a control
station 1040. The control
station 1040 may be, for example, a computer, a purpose-built device, or other
device configured
to receive and analyze the data. The control station 1040 includes at least
one processor 1050
configured to carry out computer-readable instructions with respect to the
data received. The data
received may be reviewed and may have various processes or algorithms applied
to it. In some
cases, a decision tree may be generated to better analyze the characteristics
of the object in
question.
[0151] The system 1000 may also include a memory component 1060 used for
example,
for storing data, computer instructions, programs, machine learning and the
like. The memory
component 1060 may also or alternatively be an external database, cloud
storage or the like. The
system 1000 may also include a display 1070 and/or other user interface
components in order to
view and/or interact with results of the analysis. In other cases, for example
in fingerprint
detection, the result may simply be the unlocking or granted access for the
individual and no
display may be included in the system.
Date Recue/Date Received 2023-08-22
[0152] Figure 24 provides further detail with respect to the system 1000.
In some
embodiments, a signal is provided to the transmitter 1010 (shown as a
transmitting antenna) via
an RF Generator 1080. In some cases, the signal is passed through a power
divider 1090 to the
transmitter 1010. The reflected signal is received at the receiver 1020 (shown
as a receiving
antenna) and provided to a pre-amplifier 1100. The signal is then combined
with the signal from
the transmitter (via the power divider 1090) to provide for a result, via for
example a mixer 1110.
The power divider 1090 may provide for signal adjustment prior to providing
the signal to be
combined. In some cases, the signal bay be reduced/attenuated by 3dB, although
other
adjustments/reductions in amplitude may also be used.
[0153] Once the signal results are obtained, the signal results may be
filtered by a filter
1120, for example a low pass filter. The signal may then be amplified by an
amplifier 1130 and
converted to a digital signal by an analog to digital converter 1140. Once a
digital signal, it can be
further processed by the control station 1040.
[0154] Figure 25 illustrates a method 1200 for sensing characteristics of
an object of
interest. The sensing system 1000 may be populated or pre-populated with data
related to the
characteristic of interest, at 1210. For example, if the system 1000 is
intended to sense
fingerprints to allow authorization to certain individuals, results for the
individuals may be pre-
populated to the system 1000. Alternatively, the characteristic may relate to
the composition of
liquids and therefore, composition ranges may be pre-populated to the system.
It will be
understood that this may occur during a setup of the system 1000 or may re-
occur when other
data becomes relevant to the system 1000. The system 1000 is unlikely to be
populated each
time the method 1100 is run by the system.
[0155] At 1220, the transmitter transmits electromagnetic waves to an
object to determine
a characteristic of interest. It is intended that the electromagnetic waves
are between 30 GHz and
300 GHz. At 1230, the waves are then received by the at least one receiver
configured to receive
the electromagnetic waves from the transmitter. The transmitter and receiver
are positioned in
relation to an object to be scanned such that the receiver receives
electromagnetic waves (for
example, reflected) in order to determine the characteristic of interest of
the object.
[0156] At 1240, the results are analyzed. In some cases, the results may
be analyzed
using machine learning. In other cases, other analysis may be performed to
determine whether
the characteristic of interest is present in the object r the characteristic
itself.
[0157] At 1250, the system 1000 makes a decision as to whether the
characteristic of
interest is present. For example, in detecting ammonia, the system 1000 may
determine whether
there is presence of ammonia to a predetermined threshold or if there is no
ammonia detected.
26
Date Recue/Date Received 2023-08-22
[0158] Figure 26 illustrates various signal processing
techniques/approaches that can be
used by the system to analyze the data received. It will be understood that
depending on the
particular application, the history and the processing of the data, various
differing results may be
obtained.
[0159] Figure 27 illustrates a method 1300 for sensing biometrics, and in
particular a palm
print using an embodiment of the system detailed herein. At 1310, the signal
is transmitted by a
transmitter, the signal reflects off a hand, and at 1320 the signal is
received by a receiver. At
1330, the signal is provided to the system for analog pre-processing. At 1340
the analog signal
has been converted to a digital signal (see D/A converter above) and is
digital pre-processed by
the system. At 1350 signal transformations are completed by the system. At
1360, the system
may perform feature extraction in relation to characteristics of interest with
respect to the palm
print. At 1370, the system provides recognition/non-recognition with respect
to the characteristics
of interest and is able to determine whether the palm print is, for example,
an authorized palm
print. At 1380, the system provides the results to the application, for
example, opening a door,
turning on a phone, opening secure software, or the like.
[0160] Embodiments of the system and method of the disclosure may also
find benefit
from use in monitoring blood glucose levels. In one embodiment, the blood
glucose levels may
be monitored with respect to diabetes non-ionizing electromagnetic radiations
in order to reduce
or eliminate hazards when penetrating the body. The sensors of the disclosure
were coupled with
frequency-compatible radar boards to realize small mobile glucose sensing
systems of reduced
cost.
[0161] Turning to Figure 28, a first embodiment of a wearable version of
a glucose
monitoring device is shown. The glucose monitoring device of Figure 28
includes the CSRR
sensor as discussed above. The device 2800 includes a flexible antenna 2802
and a TP-CSRR
sensor 2804 that includes a transmitter and a receiver for transmitting and
receiving
electromagnetic waves. The device 2800 is connected (such as via cables 2808)
to a glucose
measuring unit 2806 that processes the reflected or received electromagnetic
waves to determine
the individual's glucose level. The results may then be transmitted to a
mobile device 2810 so
that the information can be displayed to a user and/or stored for future
reference. The TP-CSRR
biosensor in the tag/reader format enables non-invasive blood glucose
monitoring.
[0162] The current embodiment provides a sensing distance between the
communicating
reader and tag that enables the device to be used as a wearable. The passive
tag is based on
the CSRR technology that offers multiple features when used for sensing. The
sensing structure
27
Date Recue/Date Received 2023-08-22
includes a groundless resonator that serves as a passive tag and a simple
flexible antenna that
works as a reader.
[0163] In one embodiment, the tag sensing includes three similar cells of
circular CSRRs
patterned horizontally on the top layer of an FR4 dielectric substrate (Er' =
4.4 and tano = 0.02) as
schematically shown in Figure 28b. In this specific embodiment, the sensing
elements (three
CSRRs) in the passive tag are coupled to the remote interrogator antenna at
the operating
frequency f = 2.3 GHz. The three CSRRs in the sensing tag are configured to
realize a larger
sensing region of concentrated electric field, thus enabling a higher
sensitivity for glucose
detection. The device of Figure 28a provides a wearable on the finger of an
individual for
continuous blood glucose level monitoring. In other embodiments, the device
may also be
adapted as a wearable around a wrist of an individual.
[0164] The reader portion of the device coupled with the sensing tag
could be of any
antenna type that conforms to the wearable standards including, but not
limited to, a low-profile,
low-cost, simple-geometry, and directional electromagnetic radiation pattern
to enhance the
performance efficiency of the integrated sensor when attached to the finger
part. When the tag is
electrically coupled by the antenna radiation at the resonance frequency, an
electric field of high
localization and concentration will be generated along the tag surface in the
near-field region,
thus allowing the sensor to detect small variations in the electromagnetic
properties that
characterize the varying glucose levels in the underlying tissue. The attached
finger would
consequently perturb the distribution of the highly concentrated electric
field in the tag, and further
induce noticeable changes in the scattering response at the antenna port. The
variations in the
reflected signals are further analyzed to extract the signature of the
measured blood glucose level.
[0165] Experiments were performed on the device of Figure 28a using a
HFSS FEM
simulator. First, a A/2 dipole antenna was designed using a perfect electric
conductor (PEC) to
couple the passive TP-CSRR tag at the operating frequency f = 2.3 GHz as shown
in Figure 29a.
The passive resonators in the tag were electrically excited from distance d as
shown in Figures
29b and 29c. Accordingly, the dipole antenna acted as an active reader that
communicates from
this sensing distance with the TP-CSRR tag when used for glucose sensing.
[0166] Before loading any glucose sample, the performance of the
integrated sensor was
compared to that of a bare dipole antenna (Figure 29a) and when a single-pole
CSRR is patterned
in the tag (Figure 29b). Figure 29d depicts the simulated reflection
coefficients S11 (return loss)
over the frequency range 1 ¨ 4 GHz for the three respective cases. The bare
antenna has a
wideband reflection response that resonates around f = 2.32 GHz with -7.23 dB
return loss. The
Sii is shifted slightly towards lower frequencies when the passive tags are
attached at distance d
28
Date Recue/Date Received 2023-08-22
= 4 mm from the dipole. In particular, the scattering response is shifted to f
= 2.27 GHz and f =
2.22 GHz for the SP-CSRR and TP-CSRR tags, respectively. Additionally,
attaching the passive
tag would strengthen the resonance perceived at the antenna port and narrow
its 3-dB bandwidth.
It is also observed that the TP-CSRR tag exhibits a steeper resonance
peak/depth of about -12.18
dB compared to -11.36 dB for the case of SP-CSRR. Having three cells
integrated together on
the tag surface will relatively enhance the resonance strength and confine the
resonating electric
fields over a larger sensing region via exploiting the mutual coupling
originated between the three
resonating cells.
[0167] The sensor performance was analyzed when the glucose
concentrations of
interest, 60 ¨ 500 mg/dL relevant to different diabetes conditions (normal,
hypoglycemia, and
hyperglycemia), were introduced in the sensing area on top of the tag as shown
in Figure 30a.
The glucose samples were modelled in a rectangular shape of 39 x 13 mm2 and 2
mm height
inside a plexiglass container as shown in Figure 30a. The sensing parameter
used for tracking
the glucose level variations was the reflection coefficient S11 which
represents the amplitude ratio
of the reflected wave to the incident wave at the antenna port written with
respect to frequency.
[0168] A first-order Debye model with the coefficients was used to
approximate the
dielectric properties of the blood mimicking samples at disparate glucose
concentrations 60 ¨500
mg/dL. The extracted model was integrated into the FEM simulator over the
operating frequency
range 1 ¨4 GHz. The glucose samples were first simulated in proximity of the
bare dipole antenna
at d = 4 mm without the TP-CSRR tag to study the effect of the glucose level
variations on the
electric field induced by the interrogating antenna and its scattering
response. The parametric
sweep function in HFSS was used to vary the Er' and tan5 parameters for the
glucose samples
G1 ¨ G8. As expected, no significant change in the antenna Sii was observed in
reaction to the
varying dielectric parameters of the glucose samples in the vicinity of the
radiator. It was
determined that detecting the small changes in the electromagnetic properties
of the glucose
samples requires a highly confined and concentrated electric field that is not
possible with a bare
antenna setup wherein the radiated fields are dispersed around in the near-
and far-field regions.
[0169] The full sensor structure with the TP-CSRR tag was analyzed for
sensing the
glucose concentrations inherent in the loaded samples G1 ¨ G8 as shown in
Figure 30a. The
dipole antenna was placed at two sensing distances above the tag, 4 and 5 mm.
The TP-CSRR
has shown to be sensitive to the dielectric property changes of various
glucose samples placed
nearby the passive CSRR cells. This sensitivity to glucose dielectric contrast
is translated to
variations in the antenna reflection coefficient via the electromagnetic
coupling between the dipole
and the passive tag. As depicted in Figure 30b, the electromagnetic field
variations around the
29
Date Recue/Date Received 2023-08-22
passive TP-CSRR when loaded with different glucose concentrations will affect
the input
impedance of the antenna, and hence its reflection coefficient.
[0170] Figure 31a shows the simulated reflection coefficient Sii of the
dipole in the
frequency range 1 ¨ 4 GHz when the TP-CSRR tag was placed at d = 4 mm. Two
resonances
were observed around f = 2 GHz and f = 3.44 GHz. The resonant amplitude at
both is varying in
response to the glucose concentration changes G1 ¨ G8 as depicted in Figures
31b and 31c,
respectively. However, the second resonance has shown more sensitivity with
higher resolution
to glucose level changes. Similarly, Figure 32a shows the reflection responses
for the case of d
= 5 mm with two resonances at f = 2.04 GHz and f = 3.32 GHz. Notably, the
first resonance
strength has significantly increased at this distance, and therefore brings a
higher amplitude
resolution for identifying different glucose concentrations.
[0171] The glucose samples G1 ¨ G8 were also numerically simulated on top
of a passive
tag of a single CSRR cell at d = 4 mm from the dipole to compare the glucose
detection sensitivity
to that of our proposed sensor that uses TP-CSRR tag. Figures 33a and 33b
depict the resonant
amplitude resolution at the varying glucose levels for both sensors. The
minimum or low Sii at
the first resonance around f = 2.06 GHz and f = 2.0 GHz for different glucose
samples introduced
onto the SP-CSRR and TP-CSRR tag, respectively, are shown in Figure 33a.
Figure33b shows
the same results at the second resonance around f = 3.19 GHz and f = 3.44 GHz,
for the SP-
CSRR and TP-CSRR, respectively. Clearly, the TP-CSRR tag realizes a larger
amplitude
resolution for tracking the glucose level changes. This is shown in Figure 33c
and 33d which show
that the electric field distribution across the G1 glucose sample at f = 2.3
GHz when loaded onto
each sensor. As shown in Figure 33d, the electric field is highly concentrated
in the TP-CSRR tag
with higher magnitudes along a larger sensing region wherein the glucose
samples are loaded.
However, the electric field in Figure 33c is relatively restricted in a
smaller region that corresponds
to one cell of the SP-CSRR tag.
[0172] The sensitivity of the sensor was further studied when a skin
layer (Er' = 38.1 and
tan 6 = 0.28) of thickness 1 mm was introduced between the tag and the glucose
samples G1 ¨
G8 as shown in Figure 34. This simulation model provided insights for the
sensor performance
in a practical scenario when the sensor is attached to the finger. The sensor
response in the
updated model was simulated over the frequency range 1 ¨4 GHz as depicted in
Figure 35a, with
two resonances induced at f = 1.918 GHz (Figure 35b) and f = 3.45 GHz (Figure
35c). Both
resonances exhibit remarkable amplitude variations for the glucose level
changes, however, the
detection sensitivity is much higher (larger amplitude resolution) at the
second resonance
compared to that of the first resonance as shown in Figure 35c and Figure 35b,
respectively. The
Date Recue/Date Received 2023-08-22
sensor preserved the high sensitivity performance despite the inclusion of
another lossy layer of
the skin tissue.
[0173] Turning to Figure 36, another embodiment of a device for testing
glucose using
low frequency electromagnetic waves. The device 3600 includes a CSRR sensor
3602 which
includes a sensing region 2604 for an individual to place a tip of their
finger. A pair of smart metal
alloy connectors 3606 are located at each end of the CSRR sensor 3602. The
sensor 3602 is
connected via the connectors 3606 to a radar printed circuit board (PCB) 3608
or an associated
radar with one connector connected to a transmitter 3610 of the PCB 3608 via a
transmitter
coaxial cable 3612 and the other connector connected to a receiver 3614 of the
PCB 3608 via a
receiver coaxial cable 3616. The radar PCB 3608 is connected to a processing
machine 3618
(such as a computer) to transmit the received signals (or real-time data) so
that the processing
machine 3618 can process the real-time data to determine glucose sensing
results 3620.
[0174] In a specific embodiment of Figure 36, the radar is a low-cost
2.45 GHz ISM band
radar for measuring glucose level non-invasively in the blood tissue. The
compact honey-cell
CSRR sensor 3602 is interconnected to the small low-cost and -power radar
module 3608 as a
driving source. Open-source QM-RDKIT, which supports the frequency modulated
continuous
wave (FMCW) functionality, was utilized to couple the CSRR sensor at the ISM
frequency range
(2.4 ¨ 2.5 GHz) via the coaxial cables.
[0175] Figure 37 provides a schematic diagram of the radar PCB. In this
embodiment,
the PCB 3608 includes a lightbar, a BluetoothTM radio, a digital area, a USB
port, a speaker, a
filter prototyping area, and a radio frequency area. The radar PCB may include
other components
or sections as will be understood. The RF section or area generates and
outputs the transmitted
signal and down-converts the received signal to a frequency range that can be
easily digitized
using an onboard Analog-to-Digital converter (ADC). Specifically, the onboard
Voltage Controlled
Oscillator (VCO) and Phase Lock Loop (PLL) are used to generate the
transmitted signal of
defined frequency. The PLL serves to frequency lock the output of the VCO
using the onboard
reference to provide a stable and repeatable output frequency. The output of
the VCO is amplified
before being passed to the input port of the interconnected sensor. The
corresponding signal from
the sensor output port is first mixed with a sample of the transmitted signal
to produce a frequency
offset (beat frequency), then it is filtered to remove any unwanted signals
developed from the
mixing process. Afterwards, the signal is passed to the ADC for digitization,
and either stored in
memory or streamed over the USB/Bluetooth connection. In addition to the ADC,
the digital
section also contains the PIC microcontroller, USB, and power interfaces. The
PIC microcontroller
coordinates all functions of the radar board, responds to all control commands
and data requests
31
Date Recue/Date Received 2023-08-22
received through the USB/Bluetooth connections. Figures 38a to 38c show
results from in-vitro
experiments using the sensor of Figure 36 where Figure 38a is the raw data for
tested glucose
samples as collected on the receiving channel, Figure 38b is a comparison of
energy density and
Figure 38c are PCA processed results.
[0176] For in-vivo experiments using the sensor of Figure 37, the sensor
was tested for a
simple in-vivo experiment as a proof-of-concept for this technology when
revised for intermittent
or continuous blood glucose level monitoring. For this purpose, the CSRR
compact sensor was
attached to a fixture structure suitable for finger placement. The structure
was designed to enable
users to precisely place their finger onto the sensor for accurate
measurements. Another
advantage of the sensor is that it is portable and has more advantages when
compared to other
current portable blood glucose level measuring instruments.
[0177] Tests were performed on a healthy male volunteer before and after
having the
lunch meal while comparing the non-invasive measurements against a standard
glucometer used
as a reference for comparison. This testing recipe was guided by the fact
that, in healthy non-
diabetic people, the blood glucose should measure between 72 ¨ 99 mg/dL before
a meal and
should be less than 140 mg/dL two hours after a meal. Therefore, a pre-
prandial test was first
performed for the tested subject by placing his fingertip suitably in the
sensing region inside the
fixture.
[0178] In use, the finger should be in contact with the sensing region
(firmly attached to
the fixture) to perturb the electromagnetic fields and induce noticeable
changes in the sensor
transmission response. The sensing process from the fingertip would take a
short time of about
one-minute during which no changes in the temperature status of the subject
finger is expected.
The corresponding raw data in response of a one-way single-sweep transmission
was collected
from the radar receiving channel using the featured graphical user interface.
The same test was
repeated three times for repeatability verification and the average of the
three readings (with 0.03
Volts error max) is plotted in Figure 39a (black curve). Afterwards, the
individual's blood glucose
level was measured using the commercial invasive glucometer to get the actual
pre-prandial blood
glucose level of about 4.4 mmol/L (---,, 80 mg/dL). Similarly, a second test
was performed for the
tested subject two hours after having a lunch meal for normal diet. The test
on the non-invasive
sensor was repeated for three consecutive times and the average voltage signal
is plotted in
Figure 39a (blue curve). The post-prandial blood glucose level was measured at
6.9 mmol/L (--x124
mg/dL) on the glucometer.
[0179] Following these measurements, the transmission results of the CSRR
sensor were
observed to be reliably consistent and aligned with the glucometer readings
for the individual BGL
32
Date Recue/Date Received 2023-08-22
variations before and after the meal. Particularly, the sensor transmitted
signal exhibits a change
in amplitude and a shift in time domain in response to the varying blood
glucose level of the tested
subject. The black curve corresponds to 80 mg/dL blood glucose level while the
blue one
represents the 124 mg/dL reading that leveled up two hours after the meal
intake.
[0180] To better understand the blood glucose level detection, the
measured sensor data
was further analyzed and processed using the Discrete Fourier Transform (DFT)
algorithm. The
consequent energy density has shown to be varying for the two processed data
corresponding to
the two different blood glucose level readings, 80 and 124 mg/dL, as depicted
in the enclosed plot
in Figure 39a, that shows an energy density of about 1207 and 1122 Volts',
respectively. This
would also imply that the sensitivity to glucose variation is slightly reduced
when compared to that
of the samples in glassy container. In fact, the coupled electric field in the
sensing region has less
interaction with the glucose molecules in this case given the lossy nature of
the fingertip biological
structure including the cornified skin layer. This is seen very clearly when a
skin layer model was
introduced in the numerical simulations showing the field intensity with
decaying magnitudes
halfway through the glucose-contained layer. However, the sensitivity could be
enhanced by
modifying the design specifications through incorporating a flexible substrate
of smaller loss
tangent or utilizing a more powerful driving circuit (> 1Watt output power)
instead of the RDKIT
used in this preliminary prototype as a proof-of-concept.
[0181] To confirm the correlation of the sensor readings to that of the
actual blood glucose
level in real-time setting, another experiment was performed while
continuously monitor a
volunteer's blood glucose level over a course of 30 minutes before and after a
meal. First, the
pre-prandial test was conducted, and the corresponding sensor data were
collected every 10
minutes resulting into four distinct readings. At each trial instant, the
measurement was repeated
for three times while placing the fingertip and the average of was plotted in
Figure 39b in terms of
peak amplitudes (blue curve). The invasive readings were collected accordingly
using the
Glucometer and plotted Figure 39b (red curve). The sensor measurements follow
the trend of the
reference blood glucose level that increases slightly in the range 93.7- 101
mg/dL. In this narrow
range of blood glucose level variation, the sensor readings exhibited a
repeatability error of about
0.0198 Volts max.
[0182] The post-prandial test was performed similarly right after the
meal (-10 mins) by
collecting four distinct readings over a period of 30 minutes. The average of
three repeatable
sensing trials was plotted in Figure 39c in terms of the peak amplitudes with
a repeatability error
of about 0.019 Volts. The invasive measurements revealed a significant jump
to 165.7 mg/dL 10
mins after the meal intake, then dropped slightly to 155, 146, then 110 mg/dL
by the fourth check
33
Date Recue/Date Received 2023-08-22
performed 40 mins after the meal. The sensor readings follow this descending
pattern as depicted
in Figure 39c. The sensor results for both pre- and post-prandial measurements
shows no delay
compared to the reference blood glucose level, thus indicating the direct
blood glucose level
monitoring from blood.
[0183] Turning to Figure 40, a schematic diagram of another embodiment of
a sensor for
testing glucose levels is shown. The current embodiment may be seen as a
portable high-band
glucose sensor. The sensor 4000 includes a radar, seen as a radar PCB 4002
that includes a
receiver component 4004 and a transmitter component 4006. The radar PCB 4002
is connected
to a WGM sensor 4008 which includes a sensing region 4010 for testing the
fingertip. The
receiver component 4004 is connected to one end of the WGM sensor 4008 via a
receiver cable
and the transmitter component 4006 is connected to an opposite end of the WGM
sensor 4008
via a transmitter cable.
[0184] Ina specific embodiment, the sensor 4008 is a mm-wave WGM sensor
with the
radar board 4002 operating in 60 ¨ 64 GHz. In the current embodiment, a radar
board that
supports FMCW functionality in the mm-wave range was used to couple the WGM
sensor at the
input and output ports to the radar board.
[0185] In the preceding description, for purposes of explanation,
numerous details are set
forth in order to provide a thorough understanding of the embodiments.
However, it will be
apparent to one skilled in the art that these specific details may not be
required. In other instances,
well-known structures may be shown in block diagram form in order not to
obscure the
understanding.
[0186] Embodiments of the disclosure or elements thereof may be
represented as a
computer program product stored in a machine-readable medium (also referred to
as a computer-
readable medium, a processor-readable medium, or a computer usable medium
having a
computer-readable program code embodied therein). The machine-readable medium
can be any
suitable tangible, non-transitory medium, including magnetic, optical, or
electrical storage medium
including a diskette, compact disk read only memory (CD-ROM), memory device
(volatile or non-
volatile), or similar storage mechanism. The machine-readable medium can
contain various sets
of instructions, code sequences, configuration information, or other data,
which, when executed,
cause a processor to perform steps in a method according to an embodiment of
the disclosure.
Those of ordinary skill in the art will appreciate that other instructions and
operations necessary
to implement the embodiments can also be stored on the machine-readable
medium. The
instructions stored on the machine-readable medium can be executed by a
processor or other
suitable processing device and can interface with circuitry to perform the
described tasks.
34
Date Recue/Date Received 2023-08-22
[0187]
The above-described embodiments are intended to be examples only. Alterations,
modifications and variations can be affected to the particular embodiments by
those of skill in the
art without departing from the scope, which is defined solely by the claims
appended hereto.
Date Recue/Date Received 2023-08-22
