U.S. patent application number 11/771192 was filed with the patent office on 2009-01-01 for electromagnetic emissions stimulation and detection system.
This patent application is currently assigned to THE CURATORS OF THE UNIVERSITY OF MISSOURI. Invention is credited to Daryl G. Beetner, Todd H. Hubing, Sarah A. Seguin.
Application Number | 20090006024 11/771192 |
Document ID | / |
Family ID | 40090644 |
Filed Date | 2009-01-01 |
United States Patent
Application |
20090006024 |
Kind Code |
A1 |
Seguin; Sarah A. ; et
al. |
January 1, 2009 |
ELECTROMAGNETIC EMISSIONS STIMULATION AND DETECTION SYSTEM
Abstract
A system and method for detecting and identifying electronic
devices based on their unintended electromagnetic emissions ("UEE")
signals is presented. During device classification, UEE signals are
measured from a plurality of test devices and characteristic data
is obtained from the UEE signal emitted from each test device.
Using the characteristic data, a threshold value and ideal pulse
template can be determined for each test device and stored in a
memory. An ideal stimulation signal is also determined for each
test device and stored in the memory. During device detection, the
ideal stimulation signal is applied to the environment in which a
target device is suspected of being located. Stimulated UEE signals
are measured from the target device and processed. The processed
measurement data is compared to stored power threshold values and
ideal pulse templates to determine if the target device is
present.
Inventors: |
Seguin; Sarah A.; (Rolla,
MO) ; Beetner; Daryl G.; (Rolla, MO) ; Hubing;
Todd H.; (Clemson, SC) |
Correspondence
Address: |
POLSINELLI SHALTON FLANIGAN SUELTHAUS PC
700 W. 47TH STREET, SUITE 1000
KANSAS CITY
MO
64112-1802
US
|
Assignee: |
THE CURATORS OF THE UNIVERSITY OF
MISSOURI
Rolla
MO
|
Family ID: |
40090644 |
Appl. No.: |
11/771192 |
Filed: |
June 29, 2007 |
Current U.S.
Class: |
702/124 |
Current CPC
Class: |
G01S 13/74 20130101;
G01S 7/412 20130101; G01S 13/04 20130101; G01S 7/021 20130101 |
Class at
Publication: |
702/124 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. A computerized system for detecting a target electronic device
emitting an unintentional electromagnetic emission (UEE) signal
within an area, said system comprising: a classification component
for: receiving the UEE signal emitted from each of one or more test
electronic devices while located in each of a noise controlled
environment and a non-noise controlled environment; identifying a
unique characteristic of the UEE signal emitted from each of one or
more test electronic devices; and storing the identified unique
characteristic of the UEE signal from each test device as unique
characteristic data in a memory; and a stimulation component for
transmitting an ideal stimulation signal within the area in
response to input received from an input device, wherein the target
electronic device is responsive to the stimulation signal to emit a
stimulated UEE signal; a detection component for: receiving the
stimulated UEE signal; converting the received stimulated UEE
signal to a digital signal; processing the digital signal to
determine a characteristic of the stimulated UEE signal and
comparing the determined characteristics to characteristic data
stored in the memory; and generating an alert signal when the
determined characteristics of the stimulated UEE signal matches
characteristic data stored in the memory.
2. The system of claim 1, wherein characteristic data includes at
least one or more of the following: a shape of the UEE signal; a
rate at which UEE signals are being emitted from the electronic
device; a frequency or set of frequencies of the UEE signal; a
change in the frequency of the UEE signal over a period of time;
and a change in an emission characteristic of the UEE signal in
response to the stimulation signal.
3. The system of claim 1, wherein the ideal stimulation signal is a
modulated signal having a predetermined frequency.
4. The system of claim 1 further including: an antenna for
capturing the UEE signal being emitted from each test device; and a
signal processing device operatively coupled to the antenna for
measuring time domain and frequency domain characteristic data of
the UEE signal captured by the antenna; wherein the classification
component is operatively coupled to the signal processing device
and is responsive to the measured time domain and frequency domain
characteristic data received from the signal processing device to
store the measured time domain and frequency domain characteristic
data for each test device in the memory.
5. The system of claim 4 further including: a template component
for defining an ideal pulse template for each test device based on
corresponding measured time domain characteristic data stored in
the memory; and the classification component further includes: a
threshold module for determining a threshold value for each test
device based on corresponding measured time domain and frequency
domain characteristic data acquired in the non-noise controlled
environment
6. The system of claim 4 further including: a stimulation component
for generating a stimulation signal at a plurality of different
frequencies for stimulating the UEE signal being emitted from each
test device; wherein the signal processing device measures the time
domain and frequency domain characteristic data of the UEE signal
emitted from each test device after being stimulated at each of the
plurality of different frequencies to identify an ideal stimulation
signal for each test device, the ideal stimulation signal having an
ideal frequency that corresponds to one of the plurality of
different frequencies that optimizes UEE signal being emitted from
the test device, and wherein the stimulation component stores the
ideal frequency for each test device as stimulation signal
characteristic data in the memory.
7. The system of claim 4 further including: a second antenna for
capturing the stimulated UEE signal from the target device; and a
receiver operatively coupled to the second antenna for receiving
the captured stimulated UEE signal and for transmitting the
captured stimulated UEE signal to the detection component.
8. The system of claim 5 wherein the detection component processes
the stimulated UEE signal by: retrieving an ideal pulse template
that correspond to a first test device identified in the memory;
first correlating the digital signal with the retrieved ideal pulse
template;. second correlating the first correlation with a square
wave to determine a normalized measure of similarity comparing the
normalized measure of similarity to a first threshold value stored
in the memory that corresponds to the first test device; generating
the alert signal when the normalized measure of similarity is
greater than or equal to the first threshold value; and retrieving
a different ideal pulse template from the memory that corresponds
to a second test device identified in the memory when the
normalized measure of similarity is less than the first threshold
value.
9. The system of claim 7 wherein the detection component further
determines a distance between the second antenna and the target
electronic device by measuring a first time difference that
corresponds to a period of time between a change in the stimulation
signal and a detected change in measured characteristic data of the
stimulated UEE signal received at the antenna.
10. The system of claim 7 further including: a third antenna
operatively coupled to the signal generator for transmitting the
stimulation signal within the area and for receiving the stimulated
UEE signal; wherein the detection component determines a distance
between the third antenna and the target electronic device by
measuring a second time difference that corresponds to a period of
time between a change in the stimulation signal and a detected
change in the emission characteristic of the UEE signal received at
the additional antenna, and wherein the detection component further
determines a location of the target electronic device as a function
of the measured first time difference and the measured second time
difference.
11. The system of claim 1 further including a sound generator
operatively coupled to the detection component, wherein the sound
generator processes the alert signal and generates a unique sound
indicative of a particular electronic device.
12. A method for detecting a target electronic device emitting an
unintentional electromagnetic emission (UEE) signal within an area,
said method comprising: receiving the UEE signal emitted from each
of one or more test electronic devices while located in each of a
noise controlled environment and a non-noise controlled
environment; identifying a unique characteristic of the UEE signal
emitted from each of one or more test electronic devices; storing
the identified unique characteristic of the UEE signal from each
test device as unique characteristic data in a memory; transmitting
an ideal stimulation signal within the area in response to input
received from an input device, wherein the target electronic device
is responsive to the stimulation signal to emit a stimulated UEE
signal; receiving the stimulated UEE signal; converting the
received stimulated UEE signal to a digital signal; processing the
digital signal to determine a characteristic of the stimulated UEE
signal and comparing the determined characteristics to
characteristic data stored in the memory; and generating an alert
signal when the determined characteristics of the stimulated UEE
signal matches characteristic data stored in the memory.
13. The method of claim 12 wherein characteristic data includes at
least one or more of the following: a shape of the UEE signal; a
rate at which UEE signals are being emitted from the electronic
device; a frequency or set of frequencies of the UEE signal; a
change in the frequency of the UEE signal over a period of time;
and a change in an emission characteristic of the UEE signal in
response to the stimulation signal.
14. The method of claim 12 wherein the ideal stimulation signal is
an amplitude modulated signal having a predetermined frequency.
15. The method of claim 12 further including: capturing the UEE
signal being emitted from each test device with an antenna; and
measuring time domain and frequency domain characteristic data of
the UEE signal captured by the antenna; and storing the measured
time domain and frequency domain characteristic data for each test
device in the memory.
16. The method of claim 15 further including: defining an ideal
pulse template for each test device based on corresponding measured
time domain characteristic data stored in the memory; and
determining a threshold value for each test device based on
corresponding measured time domain and frequency domain
characteristic data acquired in the non-noise controlled
environment
17. The method of claim 15 further including: generating a
stimulation signal at a plurality of different frequencies and a
plurality of different modulations for stimulating the UEE signal
being emitted from each test device; measuring the time domain and
frequency domain characteristic data of the UEE signal emitted from
each test device after being stimulated at each of the plurality of
different frequencies and modulations to identify an ideal
stimulation signal for each test device, the ideal stimulation
signal having an ideal frequency and modulation characteristic that
corresponds to one of the plurality of different frequencies and
modulation characteristics that optimizes the UEE signal being
emitted from the test device; and storing the ideal frequency and
modulation characteristic for each test device as stimulation
signal characteristic data in the memory.
18. The method of claim 15 further including: capturing the
stimulated UEE signal from the target device with a second antenna;
and determining a distance between the second antenna and the
target electronic device by measuring a first time difference that
corresponds to a period of time between a change in the stimulation
signal and a detected change in measured characteristic data of the
stimulated UEE signal received at the antenna.
19. The method of claim 17 wherein processing the stimulated UEE
signal includes: retrieving an ideal pulse template that correspond
to a first test device identified in the memory; first correlating
the digital signal with the retrieved ideal pulse template; second
correlating the first correlation with a square wave to determine a
normalized measure of similarity; comparing the normalized measure
of similarity to a first threshold value stored in the memory that
corresponds to the first test device; generating the alert signal
when the normalized measure of similarity is greater than or equal
to the first threshold value; and retrieving a different ideal
pulse template from the memory that corresponds to a second test
device identified in the memory when the normalized measure of
similarity is less than the first threshold value.
20. The method of claim 18 further including: determining a
distance between the second antenna and the target electronic
device by measuring a first time difference that corresponds to a
period of time between a change in the stimulation signal and a
detected change in measured characteristic data of the stimulated
UEE signal received at the antenna.
21. The method of claim 18 further including: transmitting the
stimulation signal within the area with a third antenna; receiving
the stimulated UEE signal at the third antenna; and determining a
distance between the third antenna and the target electronic device
by measuring a second time difference that corresponds to a period
of time between a change in the stimulation signal and a detected
change in the emission characteristic of the UEE signal received at
the additional antenna, and determining a location of the target
electronic device as a function of the measured first time
difference and the measured second time difference.
22. The method claim of 12 further includes processing the alert
signal to generate a unique sound indicative of a particular
electronic device.
23. A computerized system for detecting a target electronic device
emitting an unintentional electromagnetic emission (UEE) signal
within an area, said system comprising: a classification component
for: receiving the UEE signal emitted from each of one or more test
electronic devices while located in each of a noise controlled
environment and a non-noise controlled environment; identifying a
unique characteristic of the UEE signal emitted from each of one or
more test electronic devices; storing the identified unique
characteristic of the UEE signal from each test device as unique
characteristic data in a memory; and determining a threshold value
for each test device based on corresponding measured time domain
and frequency domain characteristic data acquired in the non-noise
controlled environment a template component for defining an ideal
pulse template for each test device based on corresponding measured
time domain characteristic data stored in the memory; a detection
component for: receiving the UEE signal; converting the received
UEE signal to a digital signal; processing the digital signal to
determine a characteristic of the UEE signal and comparing the
determined characteristics to characteristic data stored in the
memory; wherein the processing includes: retrieving an ideal pulse
template that correspond to a first test device identified in the
memory; first correlating the digital signal with the retrieved
ideal pulse template; second correlating the first correlation with
a square wave to determine a normalized measure of similarity; and
comparing the normalized measure of similarity to a first threshold
value stored in the memory that corresponds to the first test
device; and generating an alert signal when the normalized measure
of similarity is greater than or equal to the first threshold
value; and a sound generator operatively coupled to the detection
component, wherein the sound generator processes the alert signal
and generates a unique sound indicative of a particular electronic
device.
Description
FIELD
[0001] The present document relates to the field of electromagnetic
emissions detection, and more particularly to the field of
detecting unintentional electromagnetic emissions of electronic
devices such as improvised explosive devices.
BACKGROUND
[0002] Improvised explosive devices ("IEDs") are insidious tools
used by terrorists and insurgents that can cause injury or death to
armed service personnel and civilians. Typically constructed with
common, inexpensive components, IEDs can be hidden along roads, in
vehicles, in buildings, and many other locations, and, thus can
hinder the ability of troops to maneuver effectively in combat
zones. Most IEDs employ radio receivers and/or low-cost electronic
circuits that allow for remote detonation. Critical to reducing the
threats posed by IEDs is the development of systems and methods
that allow for effective detection and location of IEDs from safe
distances.
[0003] Active electronic devices radiate or emit electromagnetic
energy either intentionally or unintentionally, and these emissions
can be used to detect and locate electronic devices from which
these emissions are being emitted. One conventional method for
detecting the presence of an electronic device involves using a so
called "bug scanner" or non-linear junction detector. The
non-linear junction detector detects non-linear junctions such as
would be found in an electronic device containing a semiconductor.
The non-linear junction detector transmits a signal and receives
harmonics of the transmitted signal that are re-radiated by a
non-linear junction. This technology can be difficult to use in a
noisy environment due to the large number of non-linear junctions
that are responsive to this type of stimulation. For example, a
rusty nail can respond in the same manner as an electronic device.
In addition, such bug scanners must be used in very close proximity
to the device or at an extremely high power level. As a result, bug
scanners are not an effective means for detecting IEDs because the
operator must be in close proximity to the IED, which can place the
operator at risk of injury or death, and because it may be
impractical or impossible to obtain the desired power level
required to operate the bug scanner at safer distances when it is
being used in remote areas.
[0004] Other conventional systems have been designed to determine
if a known electronic device is active. For example, such systems
can be used to determine if a cell phone has been powered on during
a flight on an airplane. However, this detection technology
involves exploiting the knowledge of intentional electromagnetic
emissions to identify the active electronic device and would not
function for a device that does not intentionally emit
electromagnetic emissions like an IED.
[0005] There are various industrial and defense applications that
would greatly benefit from identifying and classifying electronic
devices using their unintended emissions. For example, the
detection of unauthorized devices aboard airplanes or the detection
of improvised explosive devices (IEDs) in noisy ambient
environments are examples of such applications.
SUMMARY
[0006] According to one embodiment, unique unintended emissions
from a specific device may be used for detection. Various
techniques are used to process the unintended electromagnetic
emissions of an electronic device for identification.
[0007] In one aspect, a computerized system for detecting a target
electronic device emitting an unintentional electromagnetic
emission (UEE) signal within an area is provided. The system
includes a classification component that receives the UEE signal
emitted from each of one or more test electronic devices while
located in each of a noise controlled environment and a non-noise
controlled environment, identifies a unique characteristic of the
UEE signal emitted from each of one or more test electronic
devices, and stores the identified unique characteristic of the UEE
signal from each test device as unique characteristic data in a
memory. The system further includes a stimulation component for
transmitting an ideal stimulation signal within the area in
response to input received from an input device, wherein the target
electronic device is responsive to the stimulation signal to emit a
stimulated UEE signal. The system also includes a detection
component that receives the stimulated UEE signal, converts the
received stimulated UEE signal to a digital signal, processes the
digital signal to determine a characteristic of the stimulated UEE
signal and to compare the determined characteristics to
characteristic data stored in the memory, and generates an alert
signal when the determined characteristics of the stimulated UEE
signal matches characteristic data stored in the memory.
[0008] According to another aspect, a method is provided for
detecting a target electronic device emitting an unintentional
electromagnetic emission (UEE) signal within an area. The method
includes receiving the UEE signal emitted from each of one or more
test electronic devices while located in each of a noise controlled
environment and a non-noise controlled environment. The method
further includes identifying a unique characteristic of the UEE
signal emitted from each of one or more test electronic devices.
The method also includes storing the identified unique
characteristic of the UEE signal from each test device as unique
characteristic data in a memory. The method further includes
transmitting an ideal stimulation signal within the area in
response to input received from an input device. The target
electronic device is responsive to the stimulation signal to emit a
stimulated UEE signal. The method further includes receiving the
stimulated UEE signal. The method further includes converting the
received stimulated UEE signal to a digital signal. The method
further includes processing the digital signal to determine a
characteristic of the stimulated UEE signal and comparing the
determined characteristics to characteristic data stored in the
memory. The method further includes generating an alert signal when
the determined characteristics of the stimulated UEE signal matches
characteristic data stored in the memory.
[0009] According to another aspect, a computerized system for
detecting a target electronic device emitting an unintentional
electromagnetic emission (UEE) signal within an area is provided.
The system includes a classification component that receives the
UEE signal emitted from each of one or more test electronic devices
while located in each of a noise controlled environment and a
non-noise controlled environment, identifies a unique
characteristic of the UEE signal emitted from each of one or more
test electronic devices, stores the identified unique
characteristic of the UEE signal from each test device as unique
characteristic data in a memory, and determines a threshold value
for each test device based on corresponding measured time domain
and frequency domain characteristic data acquired in the non-noise
controlled environment. The system includes a template component
that defines an ideal pulse template for each test device based on
corresponding measured time domain characteristic data stored in
the memory. The system includes a detection component that:
receives the UEE signal, converts the received UEE signal to a
digital signal, processes the digital signal to determine a
characteristic of the UEE signal and to compare the determined
characteristics to characteristic data stored in the memory. The
processing includes retrieving an ideal pulse template that
correspond to a first test device identified in the memory, first
correlating the digital signal with the retrieved ideal pulse
template, second correlating the first correlation with a square
wave to determine a normalized measure of similarity, comparing the
normalized measure of similarity to a first threshold value stored
in the memory that corresponds to the first test device, and
generating an alert signal when the normalized measure of
similarity is greater than or equal to the first threshold value.
The system further includes a sound generator operatively coupled
to the detection component that processes the alert signal and
generates a unique sound indicative of a particular electronic
device.
[0010] Additional objectives, advantages and novel features will be
set forth in the description which follows or will become apparent
to those skilled in the art upon examination of the drawings and
detailed description that follows.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1A is a simplified block diagram illustrating a
suitable operating environment in which embodiments of the
unintended electromagnetic emission detection system may be
implemented;
[0012] FIGS. 1B and 1C are plots of unintended electromagnetic
emission signals radiating from a toy truck in the frequency and
time domains, respectively;
[0013] FIG. 1D depicts a single electromagnetic pulse radiating
from a toy truck on a shorter timescale;
[0014] FIG. 1E is a time-frequency plot of two electromagnetic
pulses radiating from a toy truck;
[0015] FIG. 2A is a simplified block diagram illustrating
executable component modules according to one embodiment of the
unintended electromagnetic emission detection system;
[0016] FIGS. 2B depicts a series of connected square waves;
[0017] FIG. 3 is a plot of an unintended electromagnetic emission
signal emitted from a toy truck before and after transmitting a
stimulation signal;
[0018] FIG. 4 is a plot of pulse repetition rate vs. stimulation
signal power;
[0019] FIGS. 5A depicts a frequency domain plot and a time domain
plot of unintended electromagnetic emission signals prior to
stimulation;
[0020] FIG. 5B depicts a frequency domain plot and a time domain
plot of unintended electromagnetic emission signals after
stimulation;
[0021] FIG. 6 is a plot of ideal pulse template for a toy truck
according to one embodiment of the unintended electromagnetic
emission detection system;
[0022] FIG. 7 is a flow chart illustrating a method for collecting
characteristic data for a particular device based on its unintended
electromagnetic emissions signal;
[0023] FIG. 8 is a flow chart illustrating a method for defining a
pulse template for a particular device according to one aspect of
the unintended electromagnetic emission detection system;
[0024] FIG. 9 is a flow chart illustrating a method for detecting a
particular device based on its unintended electromagnetic
emissions;
[0025] FIG. 10A depicts the amplitude of a possible a correlation
sine wave over time; and
[0026] FIG. 10B depicts a modulated unintended electromagnetic
emissions signal.
[0027] Corresponding reference characters indicate corresponding
elements among the several views. The headings used in the figures
should not be interpreted to limit the scope of the figures.
DETAILED DESCRIPTION
[0028] Referring to the drawings, a system and method for
implementing an unintentional electromagnetic emissions detection
("UEED") system is generally indicated as 100 in FIG. 1. As
described above, all active electronic devices emit electromagnetic
energy, either intentionally or unintentionally. It has been
discovered that the unintentional electromagnetic emissions of
electronic devices have specific qualities that can be used for
detection. Although the UEED system 100 is described herein as
being used to detect devices equipped with regenerative receivers,
it is contemplated that the UEED can be used to detect unintended
emissions from other devices that are not equipped with
regenerative receivers.
[0029] Electronic devices can emit short electromagnetic pulses
with unique characteristics. For example, characteristics of the
short electromagnetic pulses emitted from a particular device, or
class of device, can depend on the characteristics of the
regenerative receiver used in the device, the internal electronics
of the device, and internal signals within the device. The class of
device refers to a group of devices that have substantially the
same internal electronics and/or regenerative receiver. The
individual short pulse for a regenerative receiver has an unusual
time-frequency characteristic essentially amounting to a unique
frequency modulation of the individual pulse. In other words, the
frequency content of the pulse changes over time. This unique
frequency modulation can distinguish a particular device that is
radiating the short electromagnetic pulse from other devices. As
such, precise detection of individual devices can be achieved by
analyzing the unique frequency modulation.
[0030] As shown in FIG. 1A, a simplified block diagram illustrates
a target electronic device ("target device") 102 equipped with a
regenerative receiver that emits an unintentional electromagnetic
emission ("UEE") signal, as indicated by reference character 104.
According to an aspect of the UEED system 100, the presence and
location of the target device 102 can be detected based on the
emitted UEE signal 104.
[0031] The unique UEE signal 104 for a given electronic target
device 102 tends to be periodic and have a consistently shaped
waveform in the time and frequency domains. For example, the UEE
signal 104 emitted by a specific regenerative receiver, such as a
radio controlled toy truck ("toy truck"), may consist of a short
pulse containing high frequency elements around 50 MHz, and other
frequencies, that repeats at a 200 kHz repetition rate. FIG. 1B
shows a frequency plot 150 of the UEE signal 104 from a toy truck
in the frequency domain, and FIG. 1C shows a time plot 155 of the
UEE signal 104 from the toy truck in the time domain. FIG. 1D shows
a single pulse 160 on a shorter timescale and corresponds to the
third pulse shown in the time plot 155 in FIG. 1C.
[0032] In this instance, FIG. 1A diagrammatically shows a computing
device 105 that comprises various operational components for
facilitating device classification and device detection in
accordance with aspects of the UEED system 100. Prior to detecting
a particular target device 102, a classification process is
performed to collect and store time and frequency domain
measurement data for one or more test devices (e.g., toy trucks,
walkie-talkies, cell phones, computers, etc.) 103. For example, a
classification component 106 of the UEED system 100 can be executed
by the computing device 105 to classify the test devices 103. The
classification component 106 initially collects a series of time
and frequency domain measurements of the UEE signal 104 produced by
each of the test devices 103 in a low noise environment. Next, the
collected time domain and frequency domain measurement data can be
processed and key characteristic data of UEE signals 104 for each
of the test devices 103 are determined and stored in a memory 108.
Key characteristics of the UEE signal 104 can include the shape of
the emission pulse, the rate of the emission pulse, and the
frequency content of the emission pulse, and the frequency content
of the signal over time. Any changes in UEE signal 104
characteristics that occur when the test devices 103 are subject to
different noise conditions and environments can be determined and
stored in the memory 108.
[0033] Due to drifting frequency, changing pulse shape, and other
changing characteristics, the UEE signal 104 emitted from a
particular target device 102 may not be consistent enough for
processing, and, as a result detection, capabilities may be
limited. For example, the pulse repetition rate for the inexpensive
and widely used regenerative receiver found in the toy truck can
vary by as much as twenty (20) percent to twenty-five (25) percent.
In addition, other factors such as battery charge, temperature, and
ambient noise levels can also alter the UEE signal 104. However, it
has been observed that UEE signals 104 from devices 102 equipped
with regenerative receivers respond to stimulation predictably, and
that UEE signals 104 can be stimulated to be more consistent and,
thus, easier to detect from greater distances.
[0034] A stimulation component 110 of the UEED system 100 can be
executed by the computing device 105 to apply a stimulation signal,
as indicated by arrow 112, to the target device 102 to stimulate
the UEE signal 104 radiating from the target device 102. During the
classification process, each of the test devices 103 is
individually placed in a low noise environment and radiated with
stimulation signals 112 at various known frequencies. The
stimulation signal frequency that produce the best response in the
UEE signal 104 can be observed and stored in the memory 108, and
can later be used to modulate the UEE signal 104 emitted from that
particular target device 102 during device detection.
[0035] A template component 114 can be executed by the computing
device 105 to develop a template, or expected signal signature, for
each of the test devices 103 based on the time domain measurements
of their corresponding UEE signals 104. The template component 114
defines, for example, an ideal pulse template that contains
information about the pulse shape and the changing frequency of the
UEE signal 104 over time for each of the test devices 103.
[0036] A detection component 116 can be can be responsive to input
received from an operator of an input device 117 of the computing
device 105 to generate the known stimulation signal 112 to apply to
the surrounding environment and can be configured to measure
ambient noise levels in the environment. The measured ambient data
can be digitized and processed by the computing device 105 or a
digital signal processor (not shown). Processing can include
comparing the digitized ambient data with measurement data and
templates stored in the memory 108 to identify matching data, and,
thus indicate whether a target device 102 is present. The detection
component 116 can be responsive to identified matching data to
generate an alert signal, as indicated by arrow 120, that is
provided to an alert device 121 to notify the operator that the
target device 102 is located within the vicinity. The alert device
121 can be a display 122 operatively coupled to the computing
device 105 for providing a visual alert to the operator. The alert
device 121 can also be a sound generator 124 operatively coupled to
the computing device 105 for providing an audible alert to the
operator. In one aspect of the UEED system 100, the sound generator
124 processes the alert signal 120 to produce an audible alert that
the operator, if trained properly, can identify the type device 102
by listening to the audible alert. In other words, different
devices 102 can each have a unique sound associated therewith such
that the operator can be trained to identify the type of device
based on the generated unique sound. For example, the measured UEE
signal 104 from the toy truck 155 might be processed and played
through a speaker so that the UEE signal 104 captured in 20
microseconds is played back in 2 seconds. In other words, the 50
Mhz UEE signal 104 becomes a 500 Hz audio signal.
[0037] The UEED system 100 illustrated in FIG. 1A may include a
general purpose computing device (e.g., computing device 105), such
as a computer executing computer-executable instructions. The
computing device typically has at least some form of computer
readable media (e.g., CRM 130). Computer readable media 130, which
include both volatile and nonvolatile media, removable and
non-removable media, can be any available medium that may be
accessed by the general purpose computing device. By way of example
and not limitation, computer readable media 130 may include
computer storage media and communication media. Computer storage
media 130 may further include volatile and nonvolatile, removable
and non-removable media implemented in any method or technology for
storage of information such as computer readable instructions, data
structures, program modules or other data. Communication media may
typically embody computer readable instructions, data structures,
program modules, or other data in a modulated data signal, such as
a carrier wave or other transport mechanism and include any
information delivery media. Those skilled in the art will be
familiar with the modulated data signal, which may have one or more
of characteristics set or changed in such a manner that permits
information to be encoded in the signal. The computing device 105
may include or be capable of accessing computer storage media in
the form of removable and/or non-removable, volatile and/or
nonvolatile memory. A user may enter commands and information into
the computing device through the input device 117. Other input
devices (not shown) may also be connected to the computing device
105. The computing device 105 may also operate in a networked
environment using logical connections to one or more remote
computers.
[0038] Other embodiments of the UEED system 100 are operational
with numerous other general purpose or special purpose computing
system environments or configurations. The UEED system 100
illustrated in FIG. 1A, is not intended to suggest any limitation
as to the scope of use or functionality of the various embodiments
of the UEED system 100. Moreover, the UEED system 100 should not be
interpreted as having any dependency or requirement relating to any
one or combination of components illustrated in the operating
environment 100. Examples of well known operating systems,
environments, and/or configurations that may be suitable for use in
embodiments of the UEED system include, but are not limited to,
personal computers, server computers, hand-held or laptop devices,
multiprocessor systems, microprocessor-based systems, set top
boxes, programmable consumer electronics, mobile telephones,
network PCs, minicomputers, mainframe computers, distributed
computing environments that include any of the above systems or
devices, and the like.
[0039] For purposes of illustration, the UEED system 100 is
described herein as collecting classification information from
various devices and initiating device detection at a single
computing device. However, in operation, it is contemplated that
separate computing devices can be used for device classification
and device detection."
[0040] Referring to FIG. 2A, each of the operational components
comprises one or more modules for facilitating device
classification and device detection. As described above,
classification involves acquiring a series of time and frequency
domain measurements of UEE signals 104 for various devices. In
order to obtain accurate measurements of UEE signal 104, the target
device 102 can be placed in a low ambient noise controlled
environment such as a semi-anechoic chamber (not shown).
[0041] Prior to acquiring time and frequency domain measurements of
the UEE signal 104, the UEE signal 104 must be captured. An antenna
202 such as a Sunol Sciences JB5 biconnilog antenna can be used to
capture the UEE signal 104 from the target device 102. The antenna
202 can be operatively coupled to a signal processing device 204
such an Agilent Infinium 54855A DSO oscilloscope and a Rohde and
Schwarz FSEB spectrum analyzer that measure time domain and
frequency domain radiation characteristics, respectively, of the
UEE signal 104. The frequency domain radiation characteristics can
be acquired over a range of frequencies for which the particular
controlled environment is designed. For example, frequency domain
radiation characteristics for the toy truck were acquired over a
range from 30 MHz to 5 GHz. As shown in FIG. 1B, the UEE signal 104
is not discernible (over the noise level) above 300 MHz. The time
domain radiation signature was sampled at a rate of
2.times.10.sup.9 samples per second for varying time periods. For
the toy truck, 100 microseconds of data, containing 20 pulses that
repeat at a rate of 200 kHz, was initially acquired. After
extensive examination of the data it was determined that twenty
(20) microseconds of data could efficiently and accurately detect
the toy truck.
[0042] Referring back to FIG. 2A, key signal characteristics can be
determined from the frequency and time domain measurements of UEE
signals for each of the test devices 103. For example, as described
above, the shape of the emissions pulse, the rate of emissions
pulse, the frequency content of the emissions pulses, the change in
frequency content over time, and the change in emissions
characteristics when subject to different noise conditions and
environments can be determined based on the frequency and time
domain measurements. The signal processing device 204 can be
operatively coupled to the computing device 105 by a standard
communication interface 206, such as the General Purpose Interface
Bus ("GPIB") 206.
[0043] The classification component 106 is responsive to measured
time domain and frequency domain radiation characteristic data
received from the signal processing device 204 to store the
measured time domain and frequency domain radiation characteristic
data in the memory 108. A storage module 207 of the classification
component is operatively coupled to the signal processing device
204 and is responsive to measured characteristic data to store the
measured characteristic data in an updatable database 208 located,
for example, in the memory 108. The memory 108 can be readily
updated as needed, for example as devices of interest change.
[0044] A digital filter module 210 of the classification component
106 can be executed to design a digital filter for each test device
103 based on the stored time domain and frequency domain radiation
characteristic data to maximize the detection of each test device
103 when located in a noisy environment. As known to those skilled
in the art, digital filters perform mathematical operations on an
intermediate form of a signal to achieve a desired filtering
effect. In this case, the digital filter module 210 designs a
digital filter to maximize the capture of the radiation signature
of the UEE signal 104 in the time domain for each test device 103.
For example, the toy truck generates a pulse signal with a rate of
emissions of 200 kHz. The primary frequency content of the
generated pulse signal is about 50 MHz (FIG. 1B). The frequency
content of this pulse varies consistently by ten (10) percent of 50
MHz, first starting slower, then increasing in frequency and,
finally, decreasing in frequency. This results in a relatively
broadband radiation at 50 MHz. As such, the digital filter designed
to produce a filtered signal that is centered at 50 MHz with a
bandwidth of 25 MHz to maximize the capture most of radiation
signature of the UEE signal 104 in the time domain for the toy
truck. The identified digital filter is stored in the database 208
and can be used later during device detection.
[0045] A threshold module 212 of the classification component 106
can be executed to determine a power threshold value for each test
device 103. Initially, ambient condition measurement data is
acquired in a non-noise controlled environment. In other words,
each target device 102 being classified is placed outside of the
low noise environment (e.g., outside of the semi-anechoic chamber)
and measurement data is collected. Thereafter, the threshold module
212 uses measurement data collected under ambient conditions to
develop a power threshold value for each test device 103 based on
the power in the corresponding UEE signal 104. For example, a
threshold value is calculated by running a statistically
significant number of sample sets of data through the detection
algorithm. These data consist of measurements that were acquired
while the target device 102 was placed in an ambient noise
environment and measurements acquired without the device present.
The threshold value is determined from the power in the UEE signal
104 after executing a series of cross correlations, first with an
ideal pulse and second with a series of square waves. This
threshold value is a measure of the similarity between the measured
data and each test device 103. Comparing the threshold calculated
from these two sets of data (containing the target device and only
ambient noise) a threshold is determined so that the false alarm
rate is acceptably low while the detection rate is acceptably high.
The computing device 105 stores the expected power threshold value
in the database 208 for reference during detection.
[0046] Under certain conditions, UEE signals 104 being emitted from
a device 104 may not be consistent enough for signal processing.
However, by transmitting the stimulation signal 112 with a known
magnitude and frequency within the vicinity of the target device
102 it is possible to stimulate the UEE signal 104 radiating from
the target device 102 so that it can be analyzed by digital signal
processing. When the target device 102 is a regenerative receiver,
the frequency of the stimulation signal 112 is generally near the
transmission frequency the receiver is designed to receive. Varying
the frequency and/or a modulation characteristic of the stimulation
signal 112 and measuring the affects on the UEE signal 104 can
identify an ideal stimulation signal 112 that produces the best
response in the UEE signal 104 (i.e., optimized UEE signal) for a
particular device. For example, the optimized UEE signal may
correspond to the stimulated UEE signal that has the most
consistent pulse repetition rate. In one embodiment of the UEED
system 100, the stimulation component 110 can be operatively
coupled to a signal generator 214 such as a Hewlett Packard signal
generator for generating a sinusoidal signal (i.e. stimulation
signal 112) at various frequencies. Furthermore, the signal
generator 214 can be operatively coupled to a transmitter 216 such
as an unbalanced monopole antenna to transmit the sinusoidal signal
to the test device 103.
[0047] In the example of the toy truck, consider that it has been
determined that a 50 MHz sinusoidal signal produces the best
response on the UEE signal 104 (i.e., optimizes the UEE signal 104)
being emitted from the toy truck. FIG. 3 shows the UEE signal 104
of a toy truck before and after transmitting a 50 MHz sinusoidal
signal. Line 302 (shown in phantom) corresponds to the frequency
domain representation of the UEE signal 304 before stimulation and
line 504 corresponds to the frequency domain representation of the
UEE signal 104 after stimulation. As shown, the UEE signal 104
emitted from the toy truck is noticeably different when the 50 MHz
sinusoidal signal is applied. In the time domain, this corresponds
to a more consistent pulse repetition rate of about 200 kHz. In
contrast, prior to stimulation, the 200 kHz pulse repetition rate
of the UEE signal 104 from the toy truck varied by as much ten (10)
percent.
[0048] FIG. 4 depicts a plot 402 of pulse repetition rate versus
stimulation signal power. It can be seen that the pulse repetition
rate of the UEE signal 104 from toy truck also varies with respect
to changes in magnitude (e.g. power) of the stimulation signal 112.
FIG. 5A depicts a frequency domain plot 502 and a time domain plot
504 of UEE signal 104 prior to stimulation, FIG. 5B depicts a
frequency domain plot 506 and a time domain plot 508 after
stimulation. By comparing the frequency domain plots 502, 506 of
the toy truck's UEE signal 104, it can be seen that the UEE signal
104 is altered after stimulation. Notably, the time domain plots
504, 508 shown in FIGS. 5A and 5B, respectively, are not actual
measurements but are exaggerated diagrams used to depict the
affects of an applied stimulation signal on the toy truck's UEE
signal 104. The ability of the stimulation unit to control the UEE
signal 104 from the device can be used to improve the ability to
detect the device over passive detection and, in fact, to generally
detect a class of devices (for example, regenerative receivers)
based on their response to the stimulation.
[0049] Referring back to FIG. 2A, by observing the affects on the
UEE signals 104 in response to changes in the magnitude and
frequency of the stimulation signal 112, an ideal stimulation
signal 12 can be identified for each test device 103, and the
characteristics (e.g., frequency and magnitude) of the ideal
stimulation signal 112 can be stored in database 208 via the
computing device 105. For example, as described above, the
frequency of the stimulation signal 112 that produces the best
response in the UEE signal 104 being emitted from each test target
device 102 is stored in the database 208 and can be used to
modulate that particular device's UEE signal 104 during device
detection. Notably, the antenna 202 and processing device 204 used
to capture and measure, respectively, the frequency domain and time
domain characteristics of the UEE signal 104 before stimulation can
also be used to identify the stimulation signal 112 that produces
the best response in the UEE signal 104. For example, the Sunol
Sciences JB5 biconnilog antenna can be used to receive the UEE
signal 104 after stimulation and the Agilent Infinium 54855A DSO
oscilloscope and the Rohde and Schwarz FSEB spectrum analyzer can
be used to measure time domain and frequency domain radiation
characteristics, respectively, of the stimulated UEE signal 104. By
analyzing the time domain and frequency domain measurements of the
UEE signals 104 after applying stimulation signals 112 at various
frequencies, the ideal stimulation signal 112 can be identified.
Thereafter, the stimulation component 110 can store the measured
characteristic data for the ideal stimulation signal 112 in the
database 208.
[0050] The template component 114 executes a correlation module 218
to develop a pulse template for each target device 102. The
correlation module 218 retrieves a random sampling of a
statistically significant number of pulses obtained from all
available time domain measurements in the database 208 for a
particular electronic target device 102. Each of the retrieved
pulses can be combined through a progressive cross-correlation
process. A single correlation process is defined for discrete time
as:
(f*g).sub.i=.SIGMA.f.sub.j*g.sub.i+j (1).
The progressive cross-correlation process used to develop a pulse
template involves iteratively correlating each pulse with the
result of the previous correlation. After a statistically
significant number of single pulses are randomly selected from all
sets of measurement data collected during classification for the
particular device, the first correlation combines two of the
initially selected pulses out of the sample of pulses and
normalizes the result. Thereafter, the result from the first
correlation is successively correlated with another pulse and
normalized until all selected pulses have been correlated and
normalized. These correlations may be calculated in the frequency
or time domains. The result of the correlations is referred to
herein as an "ideal" pulse that is normalized to produce an ideal
pulse template. The correlation module 218 stores the ideal pulse
template in the database 208 for recall during device
detection.
[0051] The ideal pulse template contains information about both the
pulse shape of the UEE signal 104 and its changing frequency
characteristic over time. For example, a single pulse for a
regenerative receiver has an unusual time-frequency characteristic
essentially amounting to a frequency modulation of an individual
pulse (that is, the frequency content of the pulse changes over
time). FIG. 1E is shows a time-frequency plot 160 of two pulses. As
described above, this frequency modulation further distinguishes it
from other devices allowing for more precise detection. As a
result, the correlation process can effectively capture the unique
frequency modulation of a specific regenerative receiver. For any
target device 102 that emits an UEE signal 104, it is therefore
possible to find a single pulse or "ideal pulse template" for use
in a time domain correlation with measured emissions. A single
"ideal" pulse template 602 for the toy truck attained from the
correlate-shift-average procedure is shown in FIG. 6.
[0052] Referring again to FIG. 2A, the detection component 116
includes one or more modules for facilitating the detection of the
target device 102. A detection module 220 of the detection
component 116 can be responsive to a detection command, as
indicated by 222, to identify a stimulation signal 112 to apply to
the environment in an area in which a target device 102 is
suspected of being located. The detection command 222 includes
detection preference data and can be generated by an operator via
the input device 117 operatively coupled to the computing device
105 or can be automatically generated by the computing device 105
at predetermined intervals. According to one aspect of the UEED
system 100, the preference data included in the detection command
222 can correspond to a particular target device 102 for which
detection is desired. The detection module 220 queries the database
208 to identify characteristic data of the ideal stimulation signal
112 that corresponds to the target device 102 identified in the
detection command 222. For example, if the preference data
indicates a toy truck, the frequency of the stimulation signal 112
is identified as 50 MHz from the stimulation signal characteristic
data stored in the database 208. The detection module 220 is
operatively coupled to the stimulation component 110 to cause the
stimulation component to generate the ideal stimulation signal 112.
While the stimulation signal 112 is applied, detection module 220
measures ambient noise levels using a receiver 224 attached to an
antenna 226.
[0053] A receiving module 228 of the detection component 116 is
operatively coupled to the detection module 220 to receive the
stimulated UEE signal 104 and measure characteristic data included
in the UEE signal 104. The receiving module 228 digitizes the
measured data to generate a digital measurement signal, as
indicated by arrow 230.
[0054] A processing module 232 of the detection component 116 is
operatively coupled to the receiving module 224 and processes the
digital measurement signal 230. Notably, the processing module 232
can be executed on the computing device 105 or a digital signal
processor (not shown). Processing the digital measurement signal
230 can involve retrieving the ideal pulse template from the
database 208 and information about the periodicity of the pulse
that corresponds to the particular device identified in the
detection command 222. Thereafter, the measurement signal 230 is
correlated with the retrieved ideal pulse template in an attempt to
bring the stimulated UEE signal 104 out of the noise. The
periodicity of the UEE signal 104 can then be utilized by
correlating with a single square wave having repetition rate that
matches the expected repetition rate found during classification.
Many devices have a repetition rate that varies. To capture this
variation, several squares waves can be correlated, with the output
of the correlation with the ideal pulse. A series of connected
square waves 233 with periods T1 and T2 are shown in the FIG. 2B.
The square waves are chosen to capture all the expected pulse
repetition rates of the device. For example, the toy truck is
correlated with at least three square waves having frequencies of
190 kHz, 200 kHz and 210 kHz or periods of 5.26 microseconds, 5
microseconds and 4.75 microseconds respectively.
[0055] A detection threshold module 234 operatively coupled to the
processing module 232 uses the information obtained from these two
correlations to calculate a normalized measure of similarity that
is compared to a power threshold value. As described above, the two
correlations provide information about the pulse shape of the UEE
signal 104 for the particular device and the changing frequency of
UEE signal 104 over time. The detection threshold module 234 can
retrieve the expected threshold value that corresponds to the
particular target device 102 from the database 208. If the
calculated normalized threshold is greater than the expected
threshold value, the target device 102 that corresponds to the
retrieved ideal pulse template is deemed to be present and the
computing device generates the alert 120 (FIG. 1A). In contrast, if
the normalized threshold is not greater than the expected threshold
value, the target device 102 that corresponds to the retrieved
ideal pulse template is determined not to be present and the
detection module retrieves a different ideal pulse template that
corresponds to the next device to be tested. The correlation and
threshold comparison process continues until a target device 102 is
determined present, or the detection module has cycled through each
of the test devices identified in the database 208.
[0056] Moreover, the detection component 116 can be configured to
determine the distance between the antenna 226 and the target
device 102 by measuring a time difference that corresponds to a
period of time between a change in the stimulation signal 112 and a
detected change in the emission characteristic of the stimulated
UEE signal 104 received at the antenna 226. Notably, it is
contemplated that an additional antenna 236 (shown in phantom) can
be operatively coupled to the detection module 220 such that the
target device 102 can be located with more precision. For example,
a second time difference can be determined that corresponds to a
period of time between a change in the stimulation signal and a
detected change in the emission characteristic of the UEE signal
104 received at the additional antenna. The detection module 220
can determine a more precise location of the electronic device as a
function of the measured first time difference based on the UEE
signal 104 received at antenna 226 and the measured second time
difference based on the UEE signal 104 received at an additional
antenna 236.
[0057] In another aspect of the UEED system 100, device detection
is implemented without the use of the stimulation component 110.
For example, receiving module 228 receives a non-stimulated UEE
signal 104 and measures characteristic data included in the UEE
signal 104. The receiving module 228 digitizes the measured data to
generate the digital measurement signal 230. The processing module
232 then processes the digital measurement signal 230 as described
above.
[0058] Referring to FIG. 7, a method for classifying one or more
test devices 103 according to an aspect of the UEED system 100 is
illustrated. At step 702, a test device 103 is placed in a low
noise environment such as an anechoic chamber. The UEE signal 104
radiating from the test device 103 is measured and key
characteristics of the UEE signal 104 such as pulse shape, pulse
size, and pulse repetition rate are determined at step 704. At step
706, the key characteristic data for that device 103 (or class of
devices) are stored in memory 108. At step 708, a stimulation
signal 112 is radiated at various known frequencies, and the
response on the UEE signal 104 radiating from the test device 103
is observed. The frequency (or frequencies) at which the simulation
signal 102 produces the greatest response in the UEE signal 104 is
stored in the memory at 710. At step 712, the test device 103 is
placed outside of the low noise environment and the UEE signal 104
radiating from the test device 103 is measured under ambient
conditions. An expected power threshold value is determined based
on the measurement data collected under ambient conditions and
stored in the memory at step 714. At step 716, a progressive
cross-correlation process is performed using a statistically
significant number of single pulses randomly selected from stored
measurement data for the target device 102 to define an ideal pulse
template that is also stored in the memory 108.
[0059] Referring to FIG. 8, a method for creating an ideal pulse
template for each test device 103 is illustrated. At step 802,
sample measurements are collected from the UEE signal 104 being
emitted from the test device 103. A statistically significant
number of random pulses are selected from the measurements at step
804. At step 806, a correlation process cross-correlates two of the
selected pulses. Another pulse is selected at step 808, and the
selected pulse is cross-correlated with the output of the prior
correlation at step 810. The steps performed at 808 and 810 are
repeated, or looped, until all pulses are correlated. At step 812,
the result of the correlations is normalized to obtain the ideal
pulse template. The ideal pulse template is saved in the memory 108
for use during device detection at step 814.
[0060] Referring to FIG. 9, a method for detecting a target device
102 according to an aspect of the UEED system 100 is illustrated. A
stimulation signal 112 is applied to the environment in an area in
which the target device 102 is suspected of being located at step
902. The stimulation signal 112 can consist of a single sine wave,
a modulated sine wave, a square wave, or some other periodic
signal. For example, FIG. 10A shows the modulated amplitude 1002 of
a sine wave stimulation. FIG. 10B is a plot 1004 of how the UEE
signal may react to the sine wave 1002. The sine wave effectively
modulates the device's radiation signal in frequency. In other
words, the period of the unintended emissions follows the amplitude
of the stimulation signal as shown in the figure.
[0061] Referring again to FIG. 9, stimulated electromagnetic
emissions (i.e., stimulated UEE signals 104) from the target device
102 can be detected via an antenna (e.g., antenna 226) at step 904.
The detected UEE signal 104 is received and digitized to generate a
digital measurement signal 230 at step 906. At step 908, an ideal
pulse template is retrieved from the memory 108. The retrieved
ideal pulse template can correspond to a particular test device 103
identified in the memory 108. The measurement signal 230 is
correlated with the retrieved ideal pulse template in an attempt to
separate the stimulated UEE signal 104 from noise at step 910. At
step 912, a second correlation is performed with a square wave. A
normalized measure of similarity is determined from the two
correlations and compared to an expected threshold value at step
914. At decision point 916, if the normalized measure of similarity
is greater than the expected threshold value, the presence of
target device 102 is stated at step 918. In contrast, if the
normalized threshold is not greater than the expected threshold
value at decision point 916, a different ideal pulse template is
selected from the memory at step 908. The different ideal pulse
template corresponds to a next test device 103 identified in the
memory 108. This process continues until a target device 102 is
determined present, or an ideal pulse template has been selected
and correlated for each test device 103 identified in the memory
108.
[0062] In operation, a computer readable medium (e.g., CRM)
executes computer-executable instructions such as those illustrated
in the FIGS. 7 and 8 to implement the UEED system 100.
[0063] The order of execution or performance of the operations in
embodiments of the UEED system 100 illustrated and described herein
is not essential, unless otherwise specified. That is, the
operations may be performed in any order, unless otherwise
specified, and embodiments of the UEED system 100 may include
additional or fewer operations than those disclosed herein. For
example, it is contemplated that executing or performing a
particular operation before, contemporaneously with, or after
another operation is within the scope of embodiments of the UEED
system 100.
[0064] Embodiments of the UEED system 100 may be implemented with
computer-executable instructions. The computer-executable
instructions may be organized into one or more computer-executable
components or modules. Aspects of the invention may be implemented
with any number and organization of such components or modules. For
example, aspects of the UEED system 100 are not limited to the
specific computer-executable instructions or the specific
components or modules illustrated in the figures and described
herein. Other embodiments of the UEED system 100 may include
different computer-executable instructions or components having
more or less functionality than illustrated and described
herein.
[0065] When introducing elements of aspects of the invention or the
embodiments thereof, the articles "a," "an," "the," and "said" are
intended to mean that there are one or more of the elements. The
terms "comprising," "including," and "having" are intended to be
inclusive and mean that there may be additional elements other than
the listed elements.
[0066] As various changes could be made in the above constructions,
products, and methods without departing from the scope of aspects
of the invention, it is intended that all matter contained in the
above description and shown in the accompanying drawings shall be
interpreted as illustrative and not in a limiting sense.
* * * * *