U.S. patent application number 11/349857 was filed with the patent office on 2006-07-20 for system and method for characterizing a sample by low-frequency spectra.
Invention is credited to Bennett M. Butters, Michael Leonard, Patrick Naughton.
Application Number | 20060158183 11/349857 |
Document ID | / |
Family ID | 34435399 |
Filed Date | 2006-07-20 |
United States Patent
Application |
20060158183 |
Kind Code |
A1 |
Butters; Bennett M. ; et
al. |
July 20, 2006 |
System and method for characterizing a sample by low-frequency
spectra
Abstract
A method and apparatus for interrogating a sample that exhibits
molecular rotation are disclosed. In practicing the method, the
sample is placed in a container having both magnetic and
electromagnetic shielding, and Gaussian noise is injected into the
sample. An electromagnetic time-domain signal composed of sample
source radiation superimposed on the injected Guassian noise is
detected, and this signal is used to generate a spectral plot that
displays, at a selected power setting of the Gaussian noise source,
low-frequency spectral components characteristic of the sample in a
selected frequency range between DC and 50 kHz. In one embodiment,
the spectral plot that is generated is a histogram of stochastic
resonance events over the selected frequency range. From this
spectrum, one or more low-frequency signal components that are
characteristic of the sample being interrogated are identified.
Inventors: |
Butters; Bennett M.; (Lacey,
WA) ; Naughton; Patrick; (Clinton, WA) ;
Leonard; Michael; (San Diego, CA) |
Correspondence
Address: |
PERKINS COIE LLP;PATENT-SEA
P.O. BOX 1247
SEATTLE
WA
98111-1247
US
|
Family ID: |
34435399 |
Appl. No.: |
11/349857 |
Filed: |
February 7, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10683875 |
Oct 9, 2003 |
6995558 |
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11349857 |
Feb 7, 2006 |
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10112927 |
Mar 29, 2002 |
6724188 |
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10683875 |
Oct 9, 2003 |
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PCT/US03/09544 |
Mar 28, 2003 |
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10683875 |
Oct 9, 2003 |
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PCT/US03/11834 |
Apr 18, 2003 |
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10683875 |
Oct 9, 2003 |
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Current U.S.
Class: |
324/244 |
Current CPC
Class: |
G01N 37/005 20130101;
G01R 33/032 20130101 |
Class at
Publication: |
324/244 |
International
Class: |
G01R 33/02 20060101
G01R033/02 |
Claims
1-22. (canceled)
23. Apparatus for interrogating a sample that exhibits
low-frequency molecular motion, comprising: a container adapted for
receiving the sample, the container having both magnetic and
electromagnetic shielding; an adjustable-power source of Gaussian
noise for directing Gaussian noise to the sample, with the sample
in the container; a detector for detecting an electromagnetic
time-domain signal composed of sample source radiation superimposed
with the directed Gaussian noise; and an electronic computer
adapted to receive the time-domain signal from the detector, and to
process the signal to generate a spectral plot that displays, at a
selected power setting of the Gaussian noise source, low-frequency
spectral components characteristic of the sample in a selected
frequency range between DC and 50 kHz.
24. The apparatus of claim 23, wherein the electronic computer
includes a signal analyzer that functions to (i) calculate a series
of Fourier spectra of the time-domain signal over each of a
plurality of defined time periods, in a selected frequency range
between 100 Hz and 50 kHz, and (ii) average the Fourier
spectra.
25. The apparatus of claim 24, wherein the calculating includes
calculating at least five Fourier spectra, each taken over a 1-5
second time-domain interval.
26. The apparatus of claim 23, wherein the source of Gaussian noise
includes an adjustable-power Gaussian noise generator and a
Helmholz coil which is contained within the magnetic
electromagnetic shielding, and which receives a selected noise
output signal from the noise generator in a range 100 mV to 1
V.
27. The apparatus of claim 26, wherein the generator is designed to
inject Gaussian noise into the sample at a frequency between DC and
2 kHz.
28. The apparatus of claim 23, wherein the detector is a
second-derivative gradiometer which outputs a current signal, and a
SQUID operatively connected to the gradiometer to convert the
current signal to an amplified voltage signal.
29. A method for interrogating a sample that exhibits low-frequency
molecular motion, comprising: placing the sample in a container
having both magnetic and electromagnetic shielding, injecting noise
into the sample at a selected noise amplitude; recording an
electromagnetic time-domain signal composed of sample source
radiation superimposed on the injected noise, generating a spectral
plot that contains, at a selected power setting of the noise
source, low-frequency, sample-dependent spectral components
characteristic of the sample in a selected frequency range between
100 and 50 kHz, and repeating the injecting, recording and
generating at different selected noise amplitudes until a plot
showing a maximum or near maximum number of spectral components
characteristic of the sample is generated.
30. The method of claim 29, wherein the generating includes (i)
calculating a series of Fourier spectra of the time-domain signal
over each of a plurality of defined time periods, in a selected
frequency range between 100 Hz and 50 kHz, and (ii) averaging the
Fourier spectra.
31. A method for interrogating a sample that exhibits low-frequency
molecular motion, comprising: receiving the sample; magnetically
and electromagnetically shielding the sample; directing
adjustable-power Gaussian noise to the sample; detecting an
electromagnetic time-domain signal composed of sample source
radiation superimposed with the directed Gaussian noise; and
receiving the time-domain signal, and processing the signal to
generate a spectral plot that displays, at a selected power setting
of the Gaussian noise source, low-frequency spectral components
characteristic of the sample in a selected frequency range between
DC and 50 kHz.
32. The method of claim 31, wherein the processing includes (i)
calculating a series of Fourier spectra of the time-domain signal
over each of a plurality of defined time periods, in a selected
frequency range between 100 Hz and 50 kHz, and (ii) averaging the
Fourier spectra.
33. The method of claim 32, wherein the calculating includes
calculating at least five Fourier spectra, each taken over a 1-5
second time-domain interval.
34. The method of claim 31, wherein the directing Gaussian noise
includes providing a Helmholz coil contained within magnetic
electromagnetic shielding, which receives a selected noise output
signal in a range 100 mV to 1 V.
35. The method of claim 34, wherein the directing Gaussian noise
includes injecting Gaussian noise into the sample at a frequency
between DC and 2 kHz.
36. The method of claim 31, wherein the detecting is performed
using a second-derivative gradiometer which outputs a current
signal, and a SQUID operatively connected to the gradiometer to
convert the current signal to an amplified voltage signal.
37. An apparatus for interrogating a sample that exhibits
low-frequency molecular motion, comprising: means for placing the
sample in a container having both magnetic and electromagnetic
shielding; means for injecting noise into the sample at a selected
noise amplitude; means for recording an electromagnetic time-domain
signal composed of sample source radiation superimposed on the
injected noise; means for generating a spectral plot that contains,
at a selected power setting of the noise source, low-frequency,
sample-dependent spectral components characteristic of the sample
in a selected frequency range between 100 and 50 kHz; and, means
for repeating the injecting, recording and generating at different
selected noise amplitudes until a plot showing a maximum or near
maximum number of spectral components characteristic of the sample
is generated.
38. The apparatus of claim 37, wherein the means for generating
includes means for calculating a series of Fourier spectra of the
time-domain signal over each of a plurality of defined time
periods, in a selected frequency range between 100 Hz and 50 kHz,
and means for averaging the Fourier spectra.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part, and claims the
benefit of, U.S. patent application Ser. No. 10/112,927, filed Mar.
29, 2002, entitled APPARATUS AND METHOD FOR MEASURING MOLECULAR
ELECTROMAGNETIC SIGNALS WITH A SQUID DEVICE AND STOCHASTIC
RESONANCE TO MEASURE LOW-THRESHOLD SIGNALS (Attorney Docket No.
38547.8007.US00), International Patent Application No.
PCT/US03/09544, filed Mar. 28, 2003, entitled SYSTEM AND METHOD FOR
CHARACTERIZING A SAMPLE BY LOW-FREQUENCY SPECTRA (Attorney Docket
No. 38547.8005.WO00), and International Patent Application No.
PCT/US03/11834, filed Apr. 18, 2003, entitled SYSTEM AND METHOD FOR
SAMPLE DETECTION BASED ON LOW-FREQUENCY SPECTRAL COMPONENTS
(Attorney Docket No. 38547.8006.WO00), all incorporated herein by
reference.
BACKGROUND
[0002] There are a variety of spectroscopic tools for
characterizing atomic or molecular compound. These include, but are
not limited to, x-ray, UV, visible-light, infrared and microwave
spectroscopy, and nuclear and electron spin resonance (NMR and ESR)
spectroscopy. In general, spectroscopic tools are useful for at
least four different type of chemical-analytical problems: first,
to characterize an atomic and molecular compound according to its
spectrographic features, e.g., spectral components; second, to
determine the atomic composition of a compound, according to the
spectral characteristics of atoms making up the compound; third, to
determine 2-D or 3-D conformation of a molecular compound according
to the spectral characteristic of atom-atom interactions in the
compound; and fourth, to detect and identify components, e.g.,
contaminants, in a sample according to the distinguishing spectral
characteristics of the compound being detected.
[0003] Most existing spectroscopic tools provide some unique
advantage(s) in terms of sensitivity, the information gained, ease
of measurement and cost. Because each tool provides information not
otherwise available, it is generally advantageous to be able to
bring to bear on any chemical-analytical, as many pertinent
spectroscopic tools as possible.
SUMMARY
[0004] The invention includes, in one aspect, a method of
characterizing spectral emission features of a sample material,
e.g., low-frequency emissions related to molecular motion within
the sample. The method uses a time-domain signal of the sample over
a sample-duration time T, and a sampling rate F for sampling the
time domain signal, where F*T is the total sample count S, F is
approximately twice the frequency domain resolution f of a Real
Fast Fourier Transform of the time-domain signal sampled at
sampling rate F, and S>f*n, where n is at least 10. The program
selects S/n samples from the stored time domain signal and performs
a Real Fast Fourier Transform (RFFT) on the samples. The RFFT is
then normalized (e.g., by setting the highest value to 1), and an
average power for the signal is calculated from the normalized
signal. Next, the program places an event count in each of f
selected-frequency event bins, where the measured power at the
corresponding selected frequency.gtoreq.average power*.epsilon.
obtains, where 0<.epsilon.<1, and .epsilon. is chosen such
that the total number of counts placed in an event bin is between
about 20-50% of the maximum possible bin counts in that bin. These
steps are repeated n times, generating a histogram that shows, for
each event bin f over a selected frequency range, the number of
event counts in each bin.
[0005] The method may further include the step of placing the
normalized power value from the RFFT in f corresponding-frequency
power bins, and, after n cycles of program operation, (a) dividing
the accumulated values placed in each of the f power bins by n, to
yield an average power in each bin, and (b) displaying on the
histogram, the average power in each bin. The method may further
include identifying those bins in the histogram that have an event
count above a given threshold and an average power.
[0006] Also disclosed is a low-frequency spectral signature
associated with a material of interest comprising a list of
frequency components in the DC-50 kHz frequency range that are
generated by the above method. The frequencies in the list may be
identified from a histogram of the number of sample-dependent
stochastic events occurring at each of a plurality of spectral
increments within a selected frequency range between DC and 50
kHz.
[0007] In another aspect, the invention includes an apparatus for
interrogating a sample that exhibits low-frequency molecular
motion. The apparatus includes a magnetically and
electromagnetically shielded container adapted for receiving the
sample, an adjustable-power source of Gaussian noise for injection
into the sample, with the sample in said container, and a detector
for detecting an electromagnetic time-domain signal composed of
sample source radiation superimposed on the injected Gaussian
noise. An electronic computer in the apparatus receives the
time-domain signal from the detector, and processes the signal to
generate a spectral plot that displays, at a selected power setting
of the Gaussian noise source, low-frequency spectral components
characteristic of the sample in a selected frequency range between
DC and 50 kHz.
[0008] In one general embodiment, the electronic computer includes
a signal analyzer that functions to (i) calculate a series of
Fourier spectra of the time-domain signal over each of a plurality
of defined time periods, in a selected frequency range between DC
and 50 kHz, and (ii) average the Fourier spectra.
[0009] Typically, at least five Fourier spectra are calculated,
each taken over a 1-5 second time-domain interval.
[0010] In another general embodiment, the electronic computer
includes machine-readable code operable to carry out the method
described above for generating a histogram of spectral events.
[0011] The source of Gaussian noise in the apparatus may be an
adjustable-power Gaussian noise generator and a Helmholz coil which
is contained within the magnetic cage and the Faraday cage, and
which receives a selected noise output signal from the noise
generator in the range 100 mV to 1 V. The injector is designed to
inject Gaussian noise into the sample at a frequency, for example,
between DC and 8 kHz.
[0012] The detector in the apparatus may be a first-derivative
superconducting gradiometer which outputs a current signal, and a
SQUID operatively connected to the gradiometer to convert the
current signal to an amplified voltage signal.
[0013] The container in the apparatus may include an attenuation
tube having a sample-holding region, a magnetic shielding cage
surrounding the region, and a Faraday cage contained within the
magnetic shielding cage and also surrounding the region. In this
embodiment, the source of Gaussian noise may include a Gaussian
noise generator and a Helmholz coil which is contained within the
magnetic cage and the Faraday cage, and which receives a noise
output signal from the noise generator, and which further includes,
for use in removing stationary noise components in the
time-dependent signal, a signal inverter operatively connected to
the said noise source and to said SQUID, for receiving Gaussian
noise from the noise source and outputting into said SQUID,
Gaussian noise in inverted form with respect to the Gaussian noise
injected into the sample.
[0014] In still another aspect, the invention includes a method for
interrogating a sample that exhibits low-frequency molecular
motion. In practicing the method, the sample is placed in a
container having both magnetic and electromagnetic shielding, and
Gaussian noise is injected into the sample at a selected noise
amplitude. An electromagnetic time-domain signal composed of sample
source radiation superimposed on the injected Gaussian noise, is
recorded, and from this, a spectral plot that contains, at a
selected power setting of the Gaussian noise source, low-frequency,
sample-dependent spectral components characteristic of the sample
in a selected frequency range between DC and 50 kHz is generated.
The steps are repeated at different selected noise amplitudes until
a plot showing a maximum or near maximum number of spectral
components characteristic of the sample are generated.
[0015] In one embodiment, the spectral plot is generated by (i)
calculating a series of Fourier spectra of the time-domain signal
over each of a plurality of defined time periods, in a selected
frequency range between DC and 50 kHz, and (ii) averaging the
Fourier spectra.
[0016] In another general embodiment, the spectral plot is
generated by the histogram method above.
[0017] These and other objects and features of the invention will
become more fully apparent when the following detailed description
of the invention is read in conjunction with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1 is an isometric view of one embodiment of a molecular
electromagnetic signaling detection apparatus formed in accordance
with one embodiment of the present invention;
[0019] FIG. 2 is an enlarged, detail view of the faraday cage and
its contents shown in FIG. 1; and
[0020] FIG. 3 is an enlarged, cross sectional view of one of the
attenuation tubes shown in FIGS. 1 and 2.
[0021] FIG. 4 is a cross-section view of the faraday cage and its
contents shown in FIG. 2.
[0022] FIG. 5 is a cross-section view of an alternative embodiment
of the invention shown in FIGS. 1 through 4.
[0023] FIG. 6 is an enlarged, detail view of the frames supporting
the coils of the Helmholtz transformer described herein.
[0024] FIG. 7 is a diagram of an alternative electromagnetic
emission detection system.
[0025] FIG. 8 diagram of the processing unit included in the
detection system of the above Figures.
[0026] FIG. 9 is a diagram of an alternative processing unit to
that of FIG. 8.
[0027] FIG. 10 is a flow diagram of the signal detection and
processing performed by the present system.
[0028] FIG. 11A is a spectral plot of the emissions of a first
sample.
[0029] FIG. 11B is spectral plot of the emissions of a second
sample.
[0030] FIGS. 12A and 12B are spectral plots, in the spectral region
between 500-530 Hz, for a sample of saturated NaCl, generated by
Fourier transforming a non-correlated time-domain sample signal
(12A), and Fourier transforming a cross-correlated sample spectrum
(12B).
[0031] FIGS. 13A and 13B are spectral plots, in the spectral region
between 500-530 Hz, for a sample of alkyl ether sulfate, generated
by Fourier transforming a non-correlated time-domain sample signal
(13A), and Fourier transforming a cross-correlated sample spectrum
(13B).
[0032] FIGS. 14A-14F are spectral plots, in the spectral region
between 500-530 Hz, for samples of deionized water (14A), a
saturated NaCl solution (14B), a solution of 1% NaCl in deionized
water (14C); a saturated NaBr sample (14D), alkyl ether sulfate in
deionized water (14E), and no sample (14F).
[0033] FIGS. 15A-15F are spectral plots, in the spectral region
between 500 and 535 Hz, of a sample of an amino acid at a 1:100
wt/volume solution (15A) and at increasing w/v dilutions of
1:10,000 (15B), 1:1 million (15C), 1:100 million (15D), 1:10
billion (15E and 15F), where the spectra in FIGS. 15A-15E were
generated with 50 second recordings and 40 minute correlations, and
the spectrum of FIG. 15F was generated with a 4:25 minute recording
with a 12 hour correlation.
[0034] FIG. 16 is a schematic diagram illustrating an alternative
embodiment of a molecular electromagnetic signaling detection
apparatus.
[0035] FIG. 17A is a cross-sectional view of the alternative
embodiment of FIG. 16.
[0036] FIG. 17B is an enlargement of a portion of FIG. 17A.
[0037] FIG. 18 is a cross-sectional isometric view of FIG. 17B.
[0038] FIG. 19 is a diagram of an alternative processing unit to
that of FIG. 9.
[0039] FIG. 20 shows a high-level flow diagram of data flow for the
histogram spectral plot method of the invention;
[0040] FIG. 21 is a flow diagram of the algorithm for generating a
spectral plot histogram, in accordance with the invention, and
[0041] FIGS. 22A-22D are histogram spectra of a sample taken at
four different noise power levels.
[0042] FIGS. 23A-23C are computer screen shots displaying a user
interface for generating and displaying a spectral plot histogram,
with FIG. 23C being in color.
[0043] In the drawings, identical reference numbers identify
identical or substantially similar elements or acts. To easily
identify the disc
[0044] Discussion of any particular element or art, the most
significant digit or digits in a reference number refer to the
Figure number in which that element is first introduced.
DETAILED DESCRIPTION
I. Definitions
[0045] The terms below have the following definitions unless
indicated otherwise.
[0046] "Sample that exhibits molecular rotation" refers to a sample
material, which may be in gaseous, liquid or solid form (other than
a solid metal) in which one or more of the molecular compounds or
atomic ions making up or present in the sample exhibit
rotation.
[0047] "Magnetic shielding" refers to shielding that inhibits or
prevents passage of magnetic flux as a result of the magnetic
permeability of the shielding material.
[0048] "Electromagnetic shielding" refers to, e.g., standard
Faraday electromagnetic shielding.
[0049] "Time-domain signal" or "time-series signal" refers to a
signal with transient signal properties that change over time.
[0050] "Sample-source radiation" refers to magnetic flux emissions
resulting from molecular motion of a sample, such as the rotation
of a molecular dipole in a magnetic field.
[0051] "Gaussian noise" means random noise having a Gaussian power
distribution.
[0052] "Stationary white Gaussian noise" means random Gaussian
noise that has no predictable future components.
[0053] "Frequency-domain spectrum" refers to a Fourier frequency
plot of a time-domain signal.
[0054] "Spectral components" refer to singular or repeating
qualities within a time-domain signal that can be measured in the
frequency, amplitude, and/or phase domains. Spectral components
will typically refer to signals present in the frequency
domain.
[0055] "Similar sample," with reference to a first sample, refers
to the same sample or a sample having substantially the same sample
components as the first sample.
[0056] "Faraday cage" refers to an electromagnetic shielding
configuration that provides an electrical path to ground for
unwanted electromagnetic radiation, thereby quieting an
electromagnetic environment.
II. Apparatus
[0057] Described in detail below is a system and method for
detecting, processing, and presenting low frequency electromagnetic
emissions or signals of a sample of interest. In one embodiment, a
known white or Gaussian noise signal is introduced to the sample.
The Gaussian noise is configured to permit the electromagnetic
emissions from the sample to be sufficiently detected by a signal
detection system. Sets of detected signals are processed together
to ensure repeatability and statistical relevance. The resulting
emission pattern or spectrum can be displayed, stored, and/or
identified as a particular substance.
[0058] The following description provides specific details for a
thorough understanding of, and enabling description for,
embodiments of the invention. However, one skilled in the art will
understand that the invention may be practiced without these
details. In other instances, well-known structures and functions
have not been shown or described in detail to avoid unnecessarily
obscuring the description of embodiments of the invention.
[0059] As explained in detail below, embodiments of the present
invention are directed to providing an apparatus and method for the
repeatable detection and recording of low-threshold molecular
electromagnetic signals. A magnetically shielded faraday cage
shields the sample material and detection apparatus from extraneous
electromagnetic signals. Within the magnetically shielded faraday
cage, a coil injects white or Gaussian noise, a nonferrous tray
holds the sample, and a gradiometer detects low-threshold molecular
electromagnetic signals. The apparatus further includes a
superconducting quantum interference device ("SQUID") and a
preamplifier.
[0060] The apparatus is used by placing a sample within the
magnetically shielded faraday cage in close proximity to the noise
coil and gradiometer. White noise is injected through the noise
coil and modulated until the molecular electromagnetic signal is
enhanced through stochastic resonance. The enhanced molecular
electromagnetic signal, shielded from external interference by the
faraday cage and the field generated by the noise coil, is then
detected and measured by the gradiometer and SQUID. The signal is
then amplified and transmitted to any appropriate recording or
measuring equipment.
[0061] Referring to FIG. 1, there is shown a shielding structure 10
which includes, in an outer to inner direction, a conductive wire
cage 16 which is a magnetic shield and inner conductive wire cages
18 and 20 which provide electromagnetic shielding. In another
embodiment, the outer magnetic shield is formed of a solid aluminum
plate material having an aluminum-nickel alloy coating, and the
electromagnetic shielding is provided by two inner wall structures,
each formed of solid aluminum.
[0062] Referring to FIG. 2, the faraday cage 10 is open at the top,
and includes side openings 12 and 14. The faraday cage 10 is
further comprised of three copper mesh cages 16, 18 and 20, nestled
in one another. Each of the copper mesh cages 16, 18 and 20 is
electrically isolated from the other cages by dielectric barriers
(not shown) between each cage.
[0063] Side openings 12 and 14 further comprise attenuation tubes
22 and 24 to provide access to the interior of the faraday cage 10
while isolating the interior of the cage from external sources of
interference. Referring to FIG. 3, attenuation tube 24 is comprised
of three copper mesh tubes 26, 28 and 30, nestled in one another.
The exterior copper mesh cages 16, 18 and 20 are each electrically
connected to one of the copper mesh tubes 26, 28 and 30,
respectively. Attenuation tube 24 is further capped with cap 32,
with the cap having hole 34. Attenuation tube 22 is similarly
comprised of copper mesh tubes 26, 28 and 30, but does not include
cap 32.
[0064] Referring again to FIG. 2, a low-density nonferrous sample
tray 50 is mounted in the interior of the faraday cage 10. The
sample tray 50 is mounted so that it may be removed from the
faraday cage 10 through the attenuation tube 22 and side opening
12. Three rods 52, each of which is greater in length than the
distance from the center vertical axis of the faraday cage 10 to
the outermost edge of the attenuation tube 22, are attached to the
sample tray 50. The three rods 52 are adapted to conform to the
interior curve of the attenuation tube 22, so that the sample tray
50 may be positioned in the center of the faraday cage 10 by
resting the rods in the attenuation tube. In the illustrated
embodiment, the sample tray 50 and rods 52 are made of glass fiber
epoxy. It will be readily apparent to those skilled in the art that
the sample tray 50 and rods 52 may be made of other nonferrous
materials, and the tray may be mounted in the faraday cage 10 by
other means, such as by a single rod.
[0065] Referring again to FIG. 2, mounted within the faraday cage
10 and above the sample tray 50 is a cryogenic dewar 100. In the
disclosed embodiment, the dewar 100 is adapted to fit within the
opening at the top of faraday cage 10 and is a Model BMD-6 Liquid
Helium Dewar manufactured by Tristan Technologies, Inc. The dewar
100 is constructed of a glass-fiber epoxy composite. A gradiometer
110 with a very narrow field of view is mounted within the dewar
100 in position so that its field of view encompasses the sample
tray 50. In the illustrated embodiment, the gradiometer 110 is a
first order axial detection coil, nominally 1 centimeter in
diameter, with a 2% balance, and is formed from a superconductor.
The gradiometer can be any form of gradiometer excluding a planar
gradiometer. The gradiometer 110 is connected to the input coil of
one low temperature direct current superconducting quantum
interference device ("SQUID") 120. In the disclosed embodiment, the
SQUID is a Model LSQ/20 LTS dc SQUID manufactured by Tristan
Technologies, Inc. It will be recognized by those skilled in the
art that high temperature or alternating current SQUIDs can be used
without departing from the spirit and scope of the invention. In an
alternative embodiment, the SQUID 120 includes a noise suppression
coil 124.
[0066] The disclosed combination of gradiometer 110 and SQUID 120
have a sensitivity of 5 microTesla/ Hz when measuring magnetic
fields.
[0067] The output of SQUID 120 is connected to a Model SP Cryogenic
Cable 130 manufactured by Tristan Technologies, Inc. The Cryogenic
Cable 130 is capable of withstanding the temperatures within and
without the dewar 100 and transfers the signal from the SQUID 120
to Flux-Locked Loop 140, which is mounted externally to the faraday
cage 10 and dewar 100. The Flux-Locked Loop 140 in the disclosed
embodiment is an iFL-301-L Flux Locked Loop manufactured by Tristan
Technologies, Inc.
[0068] Referring to FIG. 1, the Flux Locked Loop 140 further
amplifies and outputs the signal received from the SQUID 120 via
high-level output circuit 142 to an iMC-303 iMAG.RTM. SQUID
controller 150. The Flux-Locked Loop 140 is also connected via a
model CC-60 six-meter fiber-optic composite connecting cable 144 to
the SQUID controller 150. The fiber-optic connecting cable 144 and
SQUID controller 150 are manufactured by Tristan Technologies, Inc.
The controller 150 is mounted externally to the magnetic shielding
cage 40. The fiber-optic connecting cable 144 carriers control
signals from the SQUID controller 150 to the Flux Locked Loop 140,
further reducing the possibility of electromagnetic interference
with the signal to be measured. It will be apparent to those
skilled in the art that other Flux-Locked Loops, connecting cables,
and Squid controllers can be used without departing from the spirit
and scope of the invention.
[0069] The SQUID controller 150 further comprises high resolution
analog to digital converters 152, a standard GP-IB bus 154 to
output digitalized signals, and BNC connectors 156 to output analog
signals. In the illustrated embodiment, the BNC connectors are
connected to a dual trace oscilloscope 160 through patch cord
162.
[0070] Referring to FIG. 2, a two-element Helmholtz transformer 60
is installed to either side of the sample tray 50 when the sample
tray is fully inserted within the faraday cage 10. In the
illustrated embodiment, the coil windings 62 and 64 of the
Helmholtz transformer 60 are designed to operate in the direct
current to 50 kilohertz range, with a center frequency of 25
kilohertz and self-resonant frequency of 8.8 megahertz. In the
illustrated embodiment, the coil windings 62 and 64 are generally
rectangular in shape and are approximately 8 inches tall by 4
inches wide. Other Helmholtz coil shapes may be used but should be
shaped and sized so that the gradiometer 110 and sample tray 50 are
positioned within the field produced by the Helmholtz coil. Each of
coil windings 62 and 64 is mounted on one of two low-density
nonferrous frames 66 and 68. The frames 66 and 68 are hingedly
connected to one another and are supported by legs 70. Frames 66
and 68 are slidably attached to legs 70 to permit vertical movement
of the frames in relation to the lower portion of dewar 100.
Movement of the frames permits adjustment of the coil windings 62
and 64 of the Helmholtz transformer 60 to vary the amplitude of
white noise received at gradiometer 110. The legs 70 rest on or are
epoxied onto the bottom of the faraday cage 10. In the illustrated
embodiment, the frames 66 and 68 and legs 70 are made of glass
fiber epoxy. Other arrangements of transformers or coils may be
used around the sample tray 50 without departing from the spirit
and scope of the invention.
[0071] Referring to FIG. 4, there is shown a cross-sectional view
of the faraday cage and its contents, showing windings 62 of
Helmholtz transformer 60 in relation to dewar 100 and faraday cage
10. Note also in FIG. 4 the positioning of sample tray 50 and
sample 200.
[0072] Referring to FIG. 5, there is shown an alternative
embodiment in which the Helmholtz coil windings 62 and 64 are fixed
in a vertical orientation and an additional noise coil 300 is
positioned below sample tray 50. The windings of the additional
noise coil 300 are substantially perpendicular to the vertical
windings 62 and 64 of Helmholtz transformer 60, and the windings of
the additional noise coil 300 are thus substantially in parallel
orientation to the bottom of faraday cage 10.
[0073] In this alternative embodiment, noise would be fed to noise
coil 300 from an identical twisted pair wire (not shown) as that
supplying the Helmholtz coil. The noise source would originate with
the same noise generator used to supply noise to the Helmholtz
coil. Noise would be sampled either at the noise generator via an
additional noise output connection, or via a balanced splitter from
an output connection to the noise generator. Attenuation of the
noise signal at additional noise coil 300 would be through an
adjustable RF signal attenuation circuit, of which many are
available commercially, or via a suitable series of fixed value RF
attenuation filters.
[0074] Referring to FIG. 6, a detail of the frames supporting the
coils of Helmholtz transformer 60 may be seen; the reference point
of FIG. 6 is 90 degrees from the view of FIG. 4, and omits the
faraday cage 10. Frames 66 and 68 are disposed to show the coil
windings of the Helmholtz coil in a substantially vertical position
and parallel to one another. Frames 66' and 68' illustrate the
rotation of said frames about the axis of the hinged connection
joining said frames, so as to dispose the coil windings of the
Helmholtz transformer in an non-parallel relationship with one
another.
[0075] Referring again to FIG. 1, an amplitude adjustable white
noise generator 80 is external to magnetic shielding cage 40, and
is electrically connected to the Helmholtz transformer 60 through
filter 90 by electrical cable 82. Referring to FIG. 3, cable 82 is
run through side opening 12, attenuation tube 24, and through cap
32 via hole 34. Cable 82 is a co-axial cable further comprising a
twisted pair of copper conductors 84 surrounded by interior and
exterior magnetic shielding 86 and 88, respectively. In other
embodiments, the conductors can be any nonmagnetic electrically
conductive material, such as silver or gold. The interior and
exterior magnetic shielding 86 and 88 terminates at cap 32, leaving
the twisted pair 84 to span the remaining distance from the end cap
to the Helmholtz transformer 60 shown in FIG. 1. The interior
magnetic shielding 86 is electrically connected to Faraday cage 16
through cap 32, while the exterior magnetic shielding is
electrically connected to the magnetically shielded cage 40 shown
in FIG. 1.
[0076] Referring to FIG. 1, the white noise generator 80 can
generate nearly uniform noise across a frequency spectrum from zero
to 100 kilohertz. In the illustrated embodiment, the filter 90
filters out noise above 50 kilohertz, but other frequency ranges
may be used without departing from the spirit and scope of the
invention.
[0077] White noise generator 80 is also electrically connected to
the other input of dual trace oscilloscope 160 through patch cord
164.
[0078] Referring to FIGS. 1, 2 and 3, a sample of the substance 200
to be measured is placed on the sample tray 50 and the sample tray
is placed within the faraday cage 10. In the first embodiment, the
white noise generator 80 is used to inject white noise through the
Helmholtz transformer 60. The noise signal creates an induced
voltage in the gradiometer 110. The induced voltage in the
gradiometer 110 is then detected and amplified by the SQUID 120,
the output from the SQUID is further amplified by the flux locked
loop 140 and sent to the SQUID controller 150, and then sent to the
dual trace oscilloscope 160. The dual trace oscilloscope 160 is
also used to display the signal generated by white noise generator
80.
[0079] The white noise signal is adjusted by altering the output of
the white noise generator 80 and by rotating the Helmholtz
transformer 60 around the sample 200, shown in FIG. 2. Rotation of
the Helmholtz transformer 60 about the axis of the hinged
connection of frames 66 and 68 alters its phasing with respect to
the gradiometer 110. Depending upon the desired phase alteration,
the hinged connection of frames 66 and 68 permits windings 62 and
64 to remain parallel to one another while rotating approximately
30 to 40 degrees around sample tray 50. The hinged connection also
permits windings 62 and 64 to rotate as much as approximately 60
degrees out of parallel, in order to alter signal phasing of the
field generated by Helmholtz transformer 60 with respect to
gradiometer 110. The typical adjustment of phase will include this
out-of-parallel orientation, although the other orientation may be
preferred in certain circumstances, to accommodate an irregularly
shaped sample 200, for example. Noise is applied and adjusted until
the noise is 30 to 35 decibels above the molecular electromagnetic
emissions sought to be detected. At this noise level, the noise
takes on the characteristics of the molecular electromagnetic
signal through the well-known phenomenon of stochastic resonance.
The stochastic product sought is observed when the oscilloscope
trace reflecting the signal detected by gradiometer 110 varies from
the trace reflecting the signal directly from white noise generator
80. In alternative embodiments, the signal can be recorded and or
processed by any commercially available equipment.
[0080] In an alternative embodiment, the method of detecting the
molecular electromagnetic signals further comprises injecting noise
180.degree. out of phase with the original noise signal applied at
the Helmholtz transformer 60 through the noise suppression coil 124
of the SQUID 120. The stochastic product sought can then be
observed when the oscilloscope trace reflecting the signal detected
by gradiometer 110 becomes non-random.
[0081] Regardless of how the noise is injected and adjusted, the
stochastic product can also be determined by observing when an
increase in spectral peaks occurs. The spectral peaks can be
observed as either a line plot on oscilloscope 160 or as numerical
values, or by other well known measuring devices.
[0082] Embodiments of the present invention provide a method and
apparatus for detecting extremely low-threshold molecular
electromagnetic signals without external interference. They further
provide for the output of those signals in a format readily usable
by a wide variety of signal recording and processing equipment.
[0083] Referring now to FIG. 7, an alternative embodiment to the
molecular electromagnetic emission detection and processing system
of the above Figures is shown. A system 700 includes a detection
unit 702 coupled to a processing unit 704. Although the processing
unit 704 is shown external to the detection unit 702, at least a
part of the processing unit can be located within the detection
unit.
[0084] The detection unit 702, which is shown in a cross-sectional
view in FIG. 7, includes a plurality of components nested or
concentric with each other. A sample chamber or faraday cage 706 is
nested within a metal cage 708. Each of the sample chamber 706 and
the metal cage 708 can be comprised of aluminum material. The
sample chamber 706 can be maintained in a vacuum and may be
temperature controlled to a preset temperature. The metal cage 708
is configured to function as a low pass filter.
[0085] Between the sample chamber 706 and the metal cage 708 and
encircling the sample chamber 706 are a set of parallel heating
coils or elements 710. One or more temperature sensor 711 is also
located proximate to the heating elements 710 and the sample
chamber 706. For example, four temperature sensors may be
positioned at different locations around the exterior of the sample
chamber 706. The heating elements 710 and the temperature sensor(s)
711 are configured to maintain a certain temperature inside the
sample chamber 706.
[0086] A shield 712 encircles the metal cage 708. The shield 712 is
configured to provide additional magnetic field shielding or
isolation for the sample chamber 706. The shield 712 can be
comprised of lead or other magnetic shielding materials. The shield
712 is optional when sufficient shielding is provided by the sample
chamber 706 and/or the metal cage 708.
[0087] Surrounding the shield 712 is a cryogen layer 716 with G10
insulation. The cryogen may be liquid helium. The cryogen layer 716
(also referred to as a cryogenic Dewar) is at an operating
temperature of 4 degrees Kelvin. Surrounding the cryogen layer 716
is an outer shield 718. The outer shield 718 is comprised of nickel
alloy and is configured to be a magnetic shield. The total amount
of magnetic shielding provided by the detection unit 702 is
approximately -100 dB, -100 dB, and -120 dB along the three
orthogonal planes of a Cartesian coordinate system.
[0088] The various elements described above are electrically
isolated from each other by air gaps or dielectric barriers (not
shown). It should also be understood that the elements are not
shown to scale relative to each other for ease of description.
[0089] A sample holder 720 can be manually or mechanically
positioned within the sample chamber 706. The sample holder 720 may
be lowered, raised, or removed from the top of the sample chamber
706. The sample holder 720 is comprised of a material that will not
introduce Eddy currents and exhibits little or no inherent
molecular rotation. As an example, the sample holder 720 can be
comprised of high quality glass or Pyrex.
[0090] The detection unit 702 is configured to handle solid,
liquid, or gas samples. Various sample holders may be utilized in
the detection unit 702. For example, depending on the size of the
sample, a larger sample holder may be utilized. As another example,
when the sample is reactive to air, the sample holder can be
configured to encapsulate or form an airtight seal around the
sample. In still another example, when the sample is in a gaseous
state, the sample can be introduced inside the sample chamber 706
without the sample holder 720. For such samples, the sample chamber
706 is held at a vacuum. A vacuum seal 721 at the top of the sample
chamber 706 aids in maintaining a vacuum and/or accommodating the
sample holder 720.
[0091] A sense coil 722 and a sense coil 724, also referred to as
detection coils, are provided above and below the sample holder
720, respectively. The coil windings of the sense coils 722, 724
are configured to operate in the direct current (DC) to
approximately 50 kilohertz (kHz) range, with a center frequency of
25 kHz and a self-resonant frequency of 8.8 MHz. The sense coils
722, 724 are in the second derivative form and are configured to
achieve approximately 100% coupling. In one embodiment, the coils
722, 724 are generally rectangular in shape and are held in place
by G10 fasteners. The coils 722, 724 function as a second
derivative gradiometer.
[0092] Helmholtz coils 726 and 728 may be vertically positioned
between the shield 712 and the metal cage 708, as explained herein.
Each of the coils 726 and 728 may be raised or lowered
independently of each other. The coils 726 and 728, also referred
to as a white or Gaussian noise generation coils, are at room or
ambient temperature. The noise generated by the coils 726, 728 is
approximately 0.10 Gauss.
[0093] The degree of coupling between the emissions from the sample
and the coils 722, 724 may be changed by repositioning the sample
holder 720 relative to the coils 722, 724, or by repositioning one
or both of the coils 726, 728 relative to the sample holder
720.
[0094] The processing unit 704 is electrically coupled to the coils
722, 724, 726, and 728. The processing unit 704 specifies the white
or Gaussian noise to be injected by the coils 726, 728 to the
sample. The processing unit 104 also receives the induced voltage
at the coils 722, 724 from the sample's electromagnetic emissions
mixed with the injected Gaussian noise.
[0095] Referring to FIG. 8, a processing unit employing aspects of
the invention includes a sample tray 840 that permits a sample 842
to be inserted into, and removed from, a Faraday cage 844 and
Helmholtz coil 746. A SQUID/gradiometer detector assembly 848 is
positioned within a cryogenic dewar 850. A flux-locked loop 852 is
coupled between the SQUID/gradiometer detector assembly 848 and a
SQUID controller 854. The SQUID controller 854 may be a model
iMC-303 iMAG multichannel controller provided by Tristan.
[0096] An analog noise generator 856 provides a noise signal (as
noted above) to a phase lock loop 858. The x-axis output of the
phase lock loop is provided to the Helmholtz coil 846, and may be
attenuated, such as by 20 dB. The y-axis output of the phase lock
loop is split by a signal splitter 860. One portion of the y-axis
output is input the noise cancellation coil at the SQUID, which has
a separate input for the gradiometer. The other portion of the
y-axis signal is input oscilloscope 862, such as an analog/digital
oscilloscope having Fourier functions like the Tektronix TDS 3000b
(e.g., model 3032b). That is, the x-axis output of the phase lock
loop drives the Helmholz coil, and the y-axis output, which is in
inverted form, is split to input the SQUID and the oscilloscope.
Thus, the phase lock loop functions as a signal inverter. The
oscilloscope trace is used to monitor the analog noise signal, for
example, for determining when a sufficient level of noise for
producing non-stationary spectral components is achieved. An analog
tape recorder or recording device 864, coupled to the controller
854, records signals output from the device, and is preferably a
wideband (e.g. 50 kHz) recorder. A PC controller 866 may be an MS
Windows based PC interfacing with the controller 854 via, for
example, an RS 232 port.
[0097] In FIG. 9, a block diagram of another embodiment of the
processing unit is shown. A dual phase lock-in amplifier 202 is
configured to provide a first signal (e.g., "x" or noise signal) to
the coils 726, 728 and a second signal (e.g., "y" or noise
cancellation signal) to a noise cancellation coil of a
superconducting quantum interference device (SQUID) 206. The
amplifier 202 is configured to lock without an external reference
and may be a Perkins Elmer model 7265 DSP lock-in amplifier. This
amplifier works in a "virtual mode," where it locks to an initial
reference frequency, and then removes the reference frequency to
allow it to run freely and lock to "noise."
[0098] An analog noise generator 200 is electrically coupled to the
amplifier 202. The generator 200 is configured to generate or
induce an analog white Gaussian noise at the coils 726, 728 via the
amplifier 202. As an example, the generator 200 may be a model 1380
manufactured by General Radio.
[0099] An impedance transformer 204 is electrically coupled between
the SQUID 206 and the amplifier 202. The impedance transformer 204
is configured to provide impedance matching between the SQUID 206
and amplifier 202.
[0100] The noise cancellation feature of the SQUID 206 can be
turned on or off. When the noise cancellation feature is turned on,
the SQUID 206 is capable of canceling or nullifying the injected
noise component from the detected emissions. To provide the noise
cancellation, the first signal to the coils 726, 728 is a noise
signal at 20 dB or 35 dB above the molecular electromagnetic
emissions sought to be detected. At this level, the injected noise
takes on the characteristics of the molecular electromagnetic
signal through stochastic resonance. The second signal to the SQUID
206 is a noise cancellation signal and is inverted from the first
signal at an amplitude sufficient to null the noise at the SQUID
output (e.g., 180 degrees out of phase with respect to the first
signal).
[0101] The SQUID 206 is a low temperature direct element SQUID. As
an example, the SQUID 206 may be a model LSQ/20 LTS dC SQUID
manufactured by Tristan Technologies, Inc. Alternatively, a high
temperature or alternating current SQUID can be used. The coils
722, 724 (e.g., gradiometer) and the SQUID 206 (collectively
referred to as the SQUID/gradiometer detector assembly) combined
has a magnetic field measuring sensitivity of approximately 5
microTesla/ Hz. The induced voltage in the coils 722, 724 is
detected and amplified by the SQUID 206. The output of the SQUID
206 is a voltage approximately in the range of 0.2-0.8
microVolts.
[0102] The output of the SQUID 206 is the input to a SQUID
controller 208. The SQUID controller 208 is configured to control
the operational state of the SQUID 206 and further condition the
detected signal. As an example, the SQUID controller 208 may be an
iMC-303 iMAG multi-channel SQUID controller manufactured by Tristan
Technologies, Inc.
[0103] The output of the SQUID controller 208 is inputted to an
amplifier 210. The amplifier 210 is configured to provide a gain in
the range of 0-100 dB. A gain of approximately 20 dB is provided
when noise cancellation node is turned on at the SQUID 206. A gain
of approximately 50 dB is provided when the SQUID 206 is providing
no noise cancellation.
[0104] The amplified signal is inputted to a recorder or storage
device 212. The recorder 212 is configured to convert the analog
amplified signal to a digital signal and store the digital signal.
In one embodiment, the recorder 212 stores 8600 data points per Hz
and can handle 2.46 Mbits/sec. As an example, the recorder 212 may
be a Sony digital audiotape (DAT) recorder. Using a DAT recorder,
the raw signals or data sets can be sent to a third party for
display or specific processing as desired.
[0105] A lowpass filter 214 filters the digitized data set from the
recorder 212. The lowpass filter 214 is an analog filter and may be
a Butterworth filter. The cutoff frequency is at approximately 50
kHz.
[0106] A bandpass filter 216 next filters the filtered data sets.
The bandpass filter 216 is configured to be a digital filter with a
bandwidth between DC to 50 kHz. The bandpass filter 216 can be
adjusted for different bandwidths.
[0107] The output of the bandpass filter 216 is the input to a
Fourier transformer processor 218. The Fourier transform processor
218 is configured to convert the data set, which is in the time
domain, to a data set in the frequency domain. The Fourier
transform processor 218 performs a Fast Fourier Transform (FFT)
type of transform.
[0108] The Fourier transformed data sets are the input to a
correlation and comparison processor 220. The output of the
recorder 212 is also an input to the processor 220. The processor
220 is configured to correlate the data set with previously
recorded data sets, determine thresholds, and perform noise
cancellation (when no noise cancellation is provided by the SQUID
206). The output of the processor 220 is a final data set
representative of the spectrum of the sample's molecular low
frequency electromagnetic emissions.
[0109] A user interface (UI) 222, such as a graphical user
interface (GUI), may also be connected to at least the filter 216
and the processor 220 to specify signal processing parameters. The
filter 216, processor 218, and the processor 220 can be implemented
as hardware, software, or firmware. For example, the filter 216 and
the processor 218 may be implemented in one or more semiconductor
chips. The processor 220 may be software implemented in a computing
device.
[0110] This amplifier works in a "virtual mode," where it locks to
an initial reference frequency, and then removes the reference
frequency to allow it to run freely and lock to "noise." The analog
noise generator (which is produced by General Radio, a truly analog
noise generator) requires 20 dB and 45-dB attenuation for the
Helmholz and noise cancellation coil, respectively.
[0111] The Helmholz coil may have a sweet spot of about one cubic
inch with a balance of 1/100.sup.th of a percent. In an alternative
embodiments, the Helmholtz coil may move both vertically,
rotationally (about the vertical access), and from a parallel to
spread apart in a pie shape. In one embodiment, the SQUID,
gradiometer, and driving transformer (controller) have values of
1.8, 1.5 and 0.3 micro-Henrys, respectively. The Helmholtz coil may
have a sensitivity of 0.5 Gauss per amp at the sweet spot.
[0112] Approximately 10 to 15 microvolts may be needed for a
stochastic response. By injecting noise, the system has raised the
sensitivity of the SQUID device. The SQUID device had a sensitivity
of about 5 femtotesla without the noise. This system has been able
to improve the sensitivity by 25 to 35 dB by injecting noise and
using this stochastic resonance response, which amounts to nearly a
1,500% increase.
[0113] After receiving and recording signals from the system, a
computer, such as a mainframe computer, supercomputer or
high-performance computer does both pre and post processing, such
by employing the Autosignal software product by Systat Software of
Richmond Calif., for the pre-processing, while Flexpro software
product does the post-processing. Flexpro is a data (statistical)
analysis software supplied by Dewetron, Inc. The following
equations or options may be used in the Autosignal and Flexpro
products. Forward .times. .times. Transform ##EQU1## .times. X n =
k = 0 N - 1 .times. e i .times. .times. 2 .times. .pi. .times.
.times. kn N k ##EQU1.2## Reverse .times. .times. Transform
##EQU1.3## .times. X k = 1 .times. v / n = 0 N - 1 .times. X n
.times. e - i .times. .times. 2 .times. .pi. .times. .times. kn N
##EQU1.4## FFT Algorithm: Best Exact N using Temperton's Prime
Factor FFT (C. Temperton, "Implementation of a Self-Sorting
In-Place Prime Factor FFT Algorithm, Journal of Computation
Physics, v. 58, p. 283, 1985). Data Tapering Windows: [0114] [cs4
BHarris min]
0.35875-0.48829*cos(2*Pi*i/(n-1))+0.14128*cos(4*Pi*i/(n-1))-0.01168*(6*Pi-
*i/(n-1)), i=0.n-1 [0115] [Rectangular] No fixed shape tapering
available (Oscilloscope) [0116] Magnitude: sqrt(Re*Re+Im*Im)
[Re=real component, Im=imaginary component] [0117] Amplitude:
2.0*sqrt(Re*Re+Im*Im)/n [0118] db, decibels:
10.0*log10(Re*Re+Im*Im) Averaging Replicates:
[0119] Replicates are based on the X-values coinciding to within
1e-8 fractional precision.
Reference Subtraction:
[0120] Reference Signal Subtraction (baseline noise) is performed
on Y axis (amplitude) at each point (channel) along the X (time)
axis. Negative Y values are then zeroed.
Cross-Correlation:
[0121] The function calculates the cross correlation function using
summation and integration. Since the signal is transient, the
correlation function is calculated using direct multiplication and
integration. All of the values required for the calculation which
lie outside the source channels (data series) are taken to be 0.
The points for which t<0 are also calculated.
Fourier Significance Levels:
[0122] Monte Carlo data is fitted to parametric models. Where data
size N is the only factor, univariate TableCurve 2D parametric
models are used. For a segmented FFT where segment size and overlap
are additional influences, trivariate Chebyshev polynominals are
implemented. These are options selected under Autosignal. One could
have data sets that analyze individually, or could be analyzed in
an overlapping fashion where data set one would be analyzed, then
the second half of data set one and the first half of data set two,
then data set two, then the second half.
[0123] A flow diagram of the signal detection and processing
performed by the system 100 is shown in FIG. 10. When a sample is
of interest, at least four signal detections or data runs are
performed: a first data run at a time t.sub.1 without the sample, a
second data run at a time t.sub.2 with the sample, a third data run
at a time t.sub.3 with the sample, and a fourth data run at a time
t.sub.4 without the sample. Performing and collecting data sets
from more than one data run increases accuracy of the final (e.g.,
correlated) data set. In the four data runs, the parameters and
conditions of the system 100 are held constant (e.g., temperature,
amount of amplification, position of the coils, the noise signal,
etc.).
[0124] At a block 300, the appropriate sample (or if it's a first
or fourth data run, no sample), is placed in the system 100. A
given sample, without injected noise, emits electromagnetic
emissions in the DC-50 kHz range at an amplitude equal to or less
than approximately 0.001 microTesla. To capture such low emissions,
a white Gaussian noise is injected at a block 301.
[0125] At a block 302, the coils 722, 724 detect the induced
voltage representative of the sample's emission and the injected
noise. The induced voltage comprises a continuous stream of voltage
values (amplitude and phase) as a function of time for the duration
of a data run. A data run can be 2-20 minutes in length and hence,
the data set corresponding to the data run comprises 2-20 minutes
of voltage values as a function of time.
[0126] At a block 304, the injected noise is cancelled as the
induced voltage is being detected. This block is omitted when the
noise cancellation feature of the SQUID 206 is turned off.
[0127] At a block 306, the voltage values of the data set are
amplified by 20-50 dB, depending on whether noise cancellation
occurred at the block 304. And at a block 308, the amplified data
set undergoes analog to digital (A/D) conversion and is stored in
the recorder 212. A digitized data set can comprise millions of
rows of data.
[0128] After the acquired data set is stored, at a block 310 a
check is performed to see whether at least four data runs for the
sample have occurred (e.g., have acquired at least four data sets).
If four data sets for a given sample have been obtained, then
lowpass filtering occurs at a block 312. Otherwise, the next data
run is initiated (return to the block 300).
[0129] After lowpass filtering (block 312) and bandpass filtering
(at a block 314) the digitized data sets, the data sets are
converted to the frequency domain at a Fourier transform block
316.
[0130] Next, at a block 318, like data sets are correlated with
each other at each data point. For example, the first data set
corresponding to the first data run (e.g., a baseline or ambient
noise data run) and the fourth data set corresponding to the fourth
data run (e.g., another noise data run) are correlated to each
other. IF the amplitude value of the first data set at a given
frequency is the same as the amplitude value of the fourth data set
at that given frequency, then the correlation value or number for
that given frequency would be 1.0. Alternatively, the range of
correlation values may be set at between 0-100. Such correlation or
comparison also occurs for the second and third data runs (e.g.,
the sample data runs). Because the acquired data sets are stored,
they can be accessed at a later time as the remaining data runs are
completed.
[0131] When the SQUID 206 provides no noise cancellation, then
predetermined threshold levels are applied to each correlated data
set to eliminate statistically irrelevant correlation values. A
variety of threshold values may be used, depending on the length of
the data runs (the longer the data runs, greater the accuracy of
the acquired data) and the likely similarity of the sample's actual
emission spectrum to other types of samples. In addition to the
threshold levels, the correlations are averaged. Use of thresholds
and averaging correlation results in the injected noise component
becoming very small in the resulting correlated data set.
[0132] If noise cancellation is provided at the SQUID 206, then the
use of thresholds and averaging correlations are not necessary.
[0133] Once the two sample data sets have been refined to a
correlated sample data set and the two noise data sets have been
refined to a correlated noise data set, the correlated noise data
set is subtracted from the correlated sample data set. The
resulting data set is the final data set (e.g., a data set
representative of the emission spectrum of the sample) (block
320).
[0134] Since there can be 8600 data points per Hz and the final
data set can have data points for a frequency range of DC-50 kHz,
the final data set can comprise several hundred million rows of
data. Each row of data can include the frequency, amplitude, phase,
and a correlation value.
[0135] In FIGS. 11A and 11B, there are shown examples of sample
emission spectrums. A Fourier plot 400 shown in FIG. 11A
corresponds to a spectrum of a sample of saturated sodium chloride
solution. A Fourier plot 500 shown in FIG. 11B corresponds to a
spectrum of a sample of an enzyme.
[0136] Referring to FIG. 16, another alternative embodiment to the
systems described above will now be described as a system 1600. In
general, alternatives alternative embodiments described herein are
substantially similar to previously described embodiments, and the
same reference numbers often identify common elements and
functions. Only significant differences in construction or
operation are described in detail.
[0137] A second derivative gradiometer is shown as 1602, where the
target sample is positioned between upper and lower pairs of coils.
Two inner coils on opposite sides of the sample complement each
other, while two outer coils (top and bottom coils) each complement
each other, and oppose the two inner coils. Such an arrangement
allows for greater signal extraction from the sample and improved
noise rejection.
[0138] While shown in the Figures and described in greater detail
below, the system 1600 employs a concentric series of elements and
an arrangement along a central axis extending into the dewar. A
stepper motor 1604 allows the sample to be positioned axially
within this arrangement of concentric elements. In particular, the
sample may be positioned at a desired location within a middle of
the gradiometer 1602.
[0139] Likewise, a micrometer adjustment mechanism 1606, such as a
mechanical micrometer or stepper motor, allows the Helmholtz coils
to be aligned with respect to elements in the system (such as the
sample and gradiometer). Such an adjustment of the Helmholtz coil
aids in manufacture and calibration of the system 1600, as well as
allowing precise alignment of fields within the system, such as
providing a uniform field with respect to the gradiometer 1602. It
may be useful to also provide a field off set or change in field
gradient to produce a better stochastic result, to offset noise in
the system, or to provide other benefits.
[0140] FIGS. 17A, 17B, and 18 show more clearly the concentric
arrangement of elements within the system 1600, wherein the sample
tube extends axially through a center of a low pass filtering metal
shield 1802 (such as a stainless steel alloy) to pass signals below
2 kHz. An outer magnetic (MU) shield surrounds the gradiometer,
Helmholtz coils and sample. The arrangement of system 1600 is
generally self-explanatory with respect to the Figures.
[0141] The random white noise generator, model 1381, manufactured
by General Radio and described above, may be replaced by a
programmable Gaussian white noise generator manufactured by
Noise/Com. Such a generator employs two outputs, one inverted from
the other. One output may be connected to the Helmholtz coil, with
the other (inverted) output connected to the SQUID noise
cancellation coil noted above.
[0142] Likewise, as shown in FIG. 19, the Tektronix digital
oscilliscope noted above may be replaced by a two-channeled dynamic
signal analyzer 1902, model SR 785, manufactured by Stanford
Research Systems. Such a signal analyzer may process incoming
signals by sampling multiple time domain signals and averaging them
across multiple frequency domain FFT's. This may result in a full
spectrum frequency domain record of all non-random signal
components. Other changes that may be made include replacing the
digital audio tape storage system with a digital versatile disk
(DVD) recorder 1904. Further, a data acquisition board 1906
manufactured by Keithley, model 3801, may be used, which works with
software for generating histograms, as described below.
[0143] In the alternative embodiment shown in FIG. 19, a noise
cancellation coil 1908 is connected between the gradiometer and
SQUID. (While a first derivative gradiometer is shown, a second
derivative gradiometer, such as that shown in FIG. 16, may be
used.) While not shown in FIG. 19, an inverted noise channel
(inverted with respect to noise applied to the Helmholtz coils) may
be applied to the noise cancellation coil 1908 (and may first pass
through an impedance transformer that attenuates the noise signal
by, for example, 45 dB). In an alternative embodiment, not shown,
the noise cancellation coil may be positioned within the SQUID 120,
between the SQUID input and output coils.
III. Histogram Method of Generating Spectral Information
[0144] FIG. 20 is a high level data flow diagram in the histogram
method for generating spectral information. Data acquired from the
SQUID (box 2002) or stored data (box 2004) is saved as 16 bit WAV
data (box 2006), and converted into double-precision floating point
data (box 2008). The converted data may be saved (box 2010) or
displayed as a raw waveform (box 2012). The converted data is then
passed to the algorithm described below with respect to FIG. 21,
and indicated by the box 2014 labeled Fourier Analysis. The
histogram can be displayed at 2016.
[0145] With reference to FIG. 21, the general flow of the histogram
algorithm is to take a discrete sampled time-domain signal and use
Fourier analysis to convert it to a frequency domain spectrum for
further analysis. The time-domain signals are acquired from an ADC
(analog/digital converter) and stored in the buffer indicated at
2102. This sample is SampleDuration seconds long, and is sampled at
SampleRate samples per second, thus providing SampleCount
(SampleDuration*SampleRate) samples. The FrequencyRange that can be
recovered from the signal is defined as half the SampleRate, as
defined by Nyquist. Thus, if a time-series signal is sampled at
10,000 samples per second, the FrequencyRange will be 0 Hz to 5
kHz. One Fourier algorithm that may be used is a Radix 2 Real Fast
Fourier Transform (RFFT), which has a selectable frequency domain
resolution (FFTSize) of powers of two up to 2.sup.16. An FFTSize of
8192 is selected, to provide provides enough resolution to have at
least one spectrum bin per Hertz as long as the FrequencyRange
stays at or below 8 kHz. The SampleDuration should be long enough
such that SampleCount>(2*) FFTSize*10 to ensure reliable
results.
[0146] Since this FFT can only act on FFTSize samples at a time,
the program must perform the FFT on the samples sequentially and
average the results together to get the final spectrum. If one
chooses to skip FFTSize samples for each FFT, a statistical error
of 1/FFTSize 0.5 is introduced. If, however, one chooses to overlap
the FFT input by half the FFTSize, this error is reduced to
1/(0.81*2*FFTSize) 0.5. This reduces the error from 0.0110485435 to
0.0086805556. Additional information about errors and correlation
analyses in general, consult Bendat & Piersol, "Engineering
Applications of Correlation and Spectral Analysis", 1993.
[0147] Prior to performing the FFT on a given window, a data
tapering filter may be applied to avoid spectral leakage due to
sampling aliasing. This filter can be chosen from among Rectangular
(no filter), Hamming, Hanning, Bartlett, Blackman and
Blackman/Harris, as examples.
[0148] In an exemplary method, and as shown in box 2104, we have
chosen 8192 for the variable FFTSize, which will be the number of
time-domain samples we operate on at a time, as well as the number
of discrete frequencies output by the FFT. Note that FFTSize=8192
is the resolution, or number of bins in the range which is dictated
by the sampling rate. The variable n, which dictates how many
discrete RFFT's (Real FFT's) performed, is set by dividing the
SampleCount by FFTSize*2, the number of FFT bins. In order for the
algorithm to generate sensible results, this number n should be at
least 10 to 20 (although other valves are possible), where more may
be preferred to pick up weaker signals. This implies that for a
given SampleRate and FFTSize, the SampleDuration must be long
enough. A counter m, which counts from 0 to n, is initialized to
zero, also as shown in box 2104.
[0149] The program first establishes three buffers: buffer 2108 for
FFTSize histogram bins, that will accumulate counts at each bin
frequency; buffer 2110 for average power at each bin frequency, and
a buffer 2112 containing the FFTSize copied samples for each m.
[0150] The program initializes the histograms and arrays (box 2113)
and copies FFTSize samples of the wave data into buffer 2112, at
2114, and performs an RFFT on the wave data (box 2115). The FFT is
normalized so that the highest amplitude is 1 (box 2116) and the
average power for all FFTSize bins is determined from the
normalized signal (box 2117). For each bin frequency, the
normalized value from the FFT at that frequency is added to each
bin in buffer 2108 (box 2118).
[0151] In box 2119 the program then looks at the power at each bin
frequency, relative to the average power calculated from above. If
the power is within a certain factor epsilon (between 0 and 1) of
the average power, then it is counted and the corresponding bin is
incremented in the histogram buffer at 16. Otherwise it is
discarded.
[0152] Note that the average power it is comparing to is for this
FFT instance only. An enhanced, albeit slower algorithm might take
two passes through the data and compute the average over all time
before setting histogram levels. The comparison to epsilon helps to
represent a power value that is significant enough for a frequency
bin. Or in broader terms, the equation employing epsilon helps
answer the question, "is there a signal at this frequency at this
time?" If the answer is yes, it could due be one of two things: (1)
stationary noise which is landing in this bin just this one time,
or (2) a real low level periodic signal which will occur nearly
every time. Thus, the histogram counts will weed out the noise
hits, and enhance the low level signal hits. So, the averaging and
epsilon factor allow one to select the smallest power level
considered significant.
[0153] Counter m is incremented at box 2120, and the above process
is repeated for each n set of WAV data until m is equal to n (box
2121). At each cycle, the average power for each bin is added to
the associated bin at 2118, and each histogram bin is incremented
by one when the power amplitude condition at 2114 is met.
[0154] When all n cycles of data have been considered, the average
power in each bin is determined by dividing the total accumulated
average power in each bin by n, the total number of cycles (box
2122) and the results displayed (box 2123). Except where structured
noise exists, e.g., DC=0 or at multiples of 60 Hz, the average
power in each bin will be some relatively low number. This is
indicated in the plots shown at FIGS. 22A-D (the histograms
generated at 400, 600, 700, and 900 mV). The plots of FIGS. 22A-22D
show only a portion of the histogram bins, namely a spectrum from
7953 Hz through 8533 Hz. As shown in FIGS. 22A and 22B, no
stochastic event is visible at 400 mV or 600 mV of injected noise,
respectively. However, as shown in FIG. 22C, at 700 mV, a visible
stochastic event is evident. Thereafter, as shown in FIG. 22D, at
900 mV, the stochastic event is lost.
[0155] The histogram produced by the above steps contains, in each
bin, a count between 0 and n of the number of times that the power
at that frequency was above (epsilon*the average power for that
whole FFT output). If a bin count is incremented due to
unstructured noise, that noise will be distributed across all the
frequency bins over time, thus not adding up to much in a given
bin. If there is consistent signal at a given frequency, it will be
present at each of the n time slices and thus have a bin count
approaching n. Large amplitude noise, such as sixty hertz and its
harmonics have both high bin counts as well as high average power.
We can differentiate between these frequencies, and the ones we are
interested in that have low average power, but high bin counts.
[0156] FIGS. 22A-22D show histograms generated by the method at
four different noise power inputs. As shown, the program may
display average power at each frequency as a vertical bar. The
histogram bin counts may be represented as a connected upper line.
If the power is considered "low" (e.g. less than average/3), and
the histogram has a certain count, then a connecting line may
become observable between the peak of a power bar and a peak of a
histogram bar. Bins highlighted by the connecting lines are likely
candidates for low energy molecular spectra.
[0157] It can be appreciated from FIGS. 22A-22D and from the above,
that there are two settings of note used in generating a meaningful
histogram, that is, a histogram that shows stochastic resonance
effects related to a sample being interrogated. The first is the
power level of Gaussian white noise supplied to the sample. If this
level is too low, the noise level is not sufficient to create
stochastic resonance and the bin histogram reflects noise only. If
the power input is too high, the average power level calculated for
each bin is high and stochastic events cannot be distinguished.
[0158] The second setting is the value of epsilon. This value
determines a power value that will be used to distinguish an event
over average value. At a value of 1, no events will be detected,
since power will never be greater than average power. As epsilon
approaches zero, virtually every value will be placed in a bin.
Between 0 and 1, and typically at a value that gives a number of
bin counts between about 20-50% of total bin counts for structured
noise, epsilon will have a maximum "spectral character," meaning
the stochastic resonance events will be most highly favored over
pure noise.
[0159] Therefore, in practicing the invention, one can
systematically increase the power gain on the noise input, e.g., in
100 mV increments between 0 and 1 V, and at each power setting,
adjust epsilon until a histogram having well defined peaks is
observed. Where, for example, the sample being processed represents
a 20 second time interval, total processing time for each different
power and epsilon will be about 25 seconds. When a well-defined
signal is observed, either the power setting or epsilon or both can
be refined until an optimal histogram, meaning one with the largest
number of identifiable peaks, is produced.
[0160] Under this algorithm, numerous bins may be filled and
associated histogram rendered for low frequencies due to the
general occurrence of noise (such as environmental noise) at the
low frequencies. Thus, the system may simply ignore bins below a
given frequency (e.g., below 1 kHz), but still render sufficient
bin values at higher frequencies to determine unique signal
signatures between samples.
[0161] Alternatively, since a purpose of the epsilon variable is to
accommodate different average power levels determined in each
cycle, the program could itself automatically adjust epsilon using
a predefined function relating average power level to an optimal
value of epsilon.
[0162] Similarly, the program could compare peak heights at each
power setting, and automatically adjust the noise power setting
until optimal peak heights or character is observed in the
histograms.
[0163] Although the value of epsilon may be a fixed value for all
frequencies, it is also contemplated to employ a
frequency-dependent value for epsilon, to adjust for the higher
value average energies that may be observed at low frequencies,
e.g., DC to 1,000. A frequency-dependent epsilon factor could be
determined, for example, by averaging a large number of
low-frequency FFT regions, and determining a value of epsilon that
"adjusts" average values to values comparable to those observed at
higher frequencies.
[0164] Referring to FIGS. 23A-23C, an example of a user interface
for generating histograms is shown. A slider bar 2302 determines
the length of a sample waveform segment, such as up to 300-600
seconds, and allows a user to effectively scroll within a waveform.
A box 2304 allows the user to set a Nyquist frequency, such as 5,
10 or 20 kHz, and also provided is an adjacent reset button. A
slider bar 2306 allows the user to move the baseline for
histograms, while a 60 Hz checkbox 2308 allows the user to identify
the 60 Hz bin and all related 60 Hz harmonics with vertical lines
(as shown in FIG. 23C). When an acquire button 2312 is selected,
the software generates or acquires a waveform from a sample, such
as that shown in FIG. 23B. When an fft button 2310 is selected, the
software generates a histogram plot, such as that shown in FIG.
23C.
IV. Methods and Applications
[0165] This section describes the use of the apparatus described
above for interrogating a sample, and a variety of applications of
the apparatus in characterizing a sample and in detecting sample
components. Also disclosed, in accordance with the invention, is a
low-frequency spectroscopic signature or data set by which a sample
can be characterized, and a time-domain signal of a sample, used,
for example, in generating the sample spectroscopic signature.
A. Method of Interrogating a Sample
[0166] An objective of the method of the invention is to generate
spectroscopic information relating a sample being interrogated. As
will be seen, the information may be in the form of a spectral
plot, in a selected low-frequency spectral range, or a data set
which identifies low-frequency spectral components characterizing
the sample, or actual identification of a sample or sample
components, based on the characteristic frequencies identified for
the sample.
[0167] The sample may be any material having atomic or molecular
components, e.g., ionic salt components or molecular compound in
ionized or nonionized form, or protonated or non-protonated form,
that has molecular rotation, and preferably a dipole moment such
that molecular rotation in a magnetic field, e.g., the earth's
magnetic field, is effective to produce a low-frequency
electromagnetic emission. The sample is typically a liquid sample,
but may be gaseous or solid or semi-solid as well, as long as at
least one component of the sample has one or more rotational
degrees of freedom. Typical samples are aqueous or organic
solutions having one or more solute components, which may be the
sample material of interest, dissolved in the solvent.
[0168] The sample is placed in a suitable vessel, preferably one
such as Pyrex glass that has little observable low-frequency
spectral components, and the vessel is then positioned in the
apparatus container as described in Section II. With the sample
positioned in the apparatus container, the Gaussian noise generator
is activated to inject Gaussian noise into the sample. The
amplitude (mean amplitude) of Gaussian noise injected is preferably
sufficient to produce non-stationary composite time-domain signal
components. This may be done, for example, using an oscilloscope
with a Fourier transform capability, and observing the
frequency-domain signal in a suitable range, e.g., 200-800 Hz
window. A suitable noise level is selected when detectable
frequency components are first observed.
[0169] During noise injection, the recording device records a
time-domain electromagnetic signal from the detector over a preset
time interval. The recording interval may be relatively short,
e.g., 30-60 seconds, or may be several minutes or more, depending
on the final spectral resolution required. The signals recorded are
stored in a suitable signal storage device, e.g., a tape or hard
disc, for use in later signal processing operations now to be
described.
[0170] In general, it is desirable to enhance sample signal
components by cross-correlating the sample time-domain signal
recorded with a second time-domain signal of the same sample or,
less preferably, an identical sample or a sample having the same
sample components of interest. The recording time for the second
signal is preferably the same as for the first signal. The two
signals are cross-correlated using a standard cross-correlation
algorithm in the time domain. This results in a spreadsheet or
spectrum identifying the signal spectral components that are common
in both signals that hold up over time, and a correlation value for
each component which measures the relationship between spectral
components common to both signals.
[0171] The improvement in spectral resolution obtained by the
signal cross-correlation is seen in the FIGS. 12A and 12B, and
FIGS. 13A and 13B. The Figures are Fast Fourier transforms of a
first time-dependent signal in the frequency domain (FIGS. 12A and
13A) or a fast Fourier transform of the first and second
cross-correlated frequency-domain spectra (the spreadsheet referred
to above) (FIGS. 13B and 13B) to plot the spectral components in
the frequency domain, and in the spectral range of 500-530 Hz.
[0172] Comparing FIGS. 12A and 12B for a NaCl sample, it is seen
that the cross-correlation signal processing significantly enhances
signal-to-noise ratio, brings out much more detail in the
sample-specific spectral component whose peak is at 522.5 Hz, and
also produces a significantly refined peak position. Similar (and
exemplary) results were observed for the alkyl ether sulfate
sample, whose spectral features in the 500-530 Hz range are seen in
FIGS. 13A and 13B for uncorrelated and correlated signals,
respectively. As with the NaCl sample, the spectrum derived from
the correlated signals gave much lower signal-to-noise ratio, much
more detail and information as to sample-specific spectral
components. The signal correlation can also be applied,
conventionally, to produce a spreadsheet relating frequency and
phase (rather than frequency and amplitude).
[0173] The correlated time-domain spectrum from above is plotted in
the frequency domain by applying a fast Fourier transform to the
spectrum, where the spectral correlations values are represented in
the y axis as amplitudes. The plot is within the frequency range DC
to 50 kHz, preferably in the region DC to 6.5 kHz. As will be seen
below, the dominant spectral features of many samples are found in
the 100-1,500 Hz range, particularly the 500-550 Hz range; and thus
the spectrum generated may be confined accordingly, e.g., in the
500-530 Hz range. The FTT is carried out by a well known FTT
algorithm. The correlated time-domain signal may also, or
alternatively, be transformed to phase-domain or amplitude or
magnitude domain signals, to extract signal information related to
phase or amplitude components of the sample spectrum.
[0174] Once the frequency-domain spectrum is generated, either by
the cross-correlating or FFT step, the spectrum is used to identify
one or more low-frequency signal components that are characteristic
of the sample being interrogated. This step may be performed by the
user from direct viewing or by computer analysis of the
spectra.
[0175] FIGS. 14A-14F show spectral features for the samples
deionized water (FIG. 14A), saturated NaCl (FIG. 14B), 1% NaCl in
deionized water (FIG. 14C), saturated NaBr (FIG. 14D), alkyl ether
sulfate (FIG. 14E), and empty sample vessel (FIG. 14F), all in the
spectral range of about 500-530. As seen, each sample has
distinctive spectral components characterized by one or more peaks
at well defined frequencies.
B. Characterizing a Sample
[0176] Accordance with another aspect of the invention, the method
above is used to generate a data set of low-frequency spectral
components of a given sample, also referred to herein as a
low-frequency signature signal of the material.
[0177] The 500-530 spectral range shown for the several samples
above was selected to illustrate having prominent spectral features
in the various samples. In order to obtain a more complete data set
of spectral frequency components of a sample, spectral components
over a wider frequency range, e.g., 100-1,500 Hz, should be
determined. In one aspect, the invention includes a data set of
spectral components associated with a given sample material, e.g.,
a solvent, gas, or solute component of a solution. The data set
includes a list of the low-frequency spectral components of the
sample, e.g., in the 100-1,500 Hz range, whose cross-spectral
correlations have a selected statistical measure above background
spectral noise, or selected ones of these components that are
unique to the sample.
[0178] A variety of signal-analysis methods may be employed in
generating the low-frequency data set for a given sample. In one
exemplary method, a cross-correlated sample signal spectrum is
compared with a cross-correlated noise (no sample) signal. The
algorithm next advances incremental, e.g., in 0.1 Hz intervals
across the cross-correlated sample spectrum and the
cross-correlated noise spectrum, looking at the correlation value
at each frequency point, and subtracts the noise correlation from
the sample correlation at that point, to yield a frequency plot of
corrected correlation values. These values will be relative to a
particular sample, and depend, for example, on the relative
amplitude of any noise component.
[0179] In general, frequency components having a higher correlation
value (relative to the other values in that sample) will tend to
hold up (be observed) over many interrogations of the same sample.
To identify those that do hold up, the frequency components
observed for the sample over two or more sample sets, each obtained
as above, are compared, and only those that are seen in two (or
more, if available) sets are taken as valid components of the data
set for that sample. In the tables below, data sets for several
samples (as identified in the tables) are given along with the
correlations determined from a single sample interrogation. Those
values indicated in italics (typically having the smaller
correlation values) were found not to hold up in multiple data sets
from the same sample material.
[0180] Thus, for example, for the saturated NaCl sample in Table 1,
spectral components at 522.58, 523.12, 523.47, and 523.85 Hz
correlate from sample to sample, and would form a data set for the
sample in the frequency range 500-530 Hz. Additional members of the
data set may be included in an expanded frequency range.
[0181] Similarly, for the amino acid sample of Table 3, the data
set would include components at 262.93, 257.81, 257.23, 536.68,
448.05, 531.37, 528.80, 593.44, 588.68, 583.74, 578.61, 769.59, and
744.14 in the frequency range of between about 250 and 1,400 Hz.
The greater spectral composition of the amino acid sample, relative
to NaCl, presumably reflects in part, the greater complexity of the
sample molecule. TABLE-US-00001 Reversing Noise NaCl (Sat) NaCl
(1%) NaBr (Sat) Frequency / Frequency / Frequency / Correlation
Correlation Correlation A A A 522.58 .3762 521.12 1.4161 520.57
2.0847 523.12 .1732 521.48 1.4100 519.84 2.0704 523.47 .1235 515.99
1.3865 509.37 2.0304 523.85 .1021 520.75 1.3641 513.45 2.0155
507.38 .0832 514.34 1.3735 516.35 1.9950 524.43 .0768 525.86 1.3440
519.46 1.9950 512.71 .0753 523.70 1.3400 518.33 1.9929 -- -- 526.61
1.3364 522.78 1.9635
[0182] TABLE-US-00002 Reversing Noise DI Water Alkyl Ether Sulfate
Noise Frequency / Frequency / Frequency / Correlation Correlation
Correlation A A A 521.12 1.5324 517.81 .3376 514.34 .0734 521.67
1.0818 516.50 .3375 513.79 .0432 520.20 1.0630 517.08 .2776 506.28
.0326 511.23 1.0502 515.46 .2749 512.70 .0277 515.44 1.0457 518.37
.2508 522.58 .0220 513.06 1.0451 519.47 .2425 525.15 .0177 525.51
1.0371 515.44 .2400 516.36 .0149 520.75 1.0301 519.84 .2383 523.13
.0140
[0183] TABLE-US-00003 Spectra Frequency Correlation Number (Hz)
Factor 1 262.93 .139 2 340.39 .134 3 257.81 .126 4 357.23 .114 5
417.48 .110 6 536.68 .101 7 448.05 .096 8 531.37 .096 9 528.80 .077
10 593.44 .071 11 588.68 .065 12 583.74 .058 13 1408.99 .052 14
840.08 .050 15 1393.99 .048 16 578.61 .045 17 1348.99 .044 18
769.59 .042 19 1042.96 .042 20 1238.52 .042 21 1472.16 .042 22
1062.92 .041 23 1281.73 .041 24 744.14 .039
[0184] The data above demonstrates that both simple and more
complex molecular samples can be characterized in terms of unique
low-frequency spectral compounds. The data set associated with a
given sample material may also include (as shown in the tables) the
associated correlations values of the spectral components. The data
set may be used for example, in identifying components in an
unknown sample and/or for estimating the relative concentrations of
a material in a sample. The use of the method for identifying
low-concentration components in a sample is discussed in the next
section.
C. Identifying Components in a Sample
[0185] It is often desirable to detect sample components, e.g.,
trace contaminants, present in a multi-component sample material,
such as a liquid sample with unknown contaminants, or other samples
capable of holding or supporting a contaminant that it is desired
to detect.
[0186] An analytical method for detecting a component of a sample,
in accordance with another aspect of the invention, includes first
identifying the low-frequency sample spectral components of a
sample (i) in a selected frequency range between DC and 50 kHz (ii)
whose cross-spectral correlations have a selected statistical
measure above background spectral noise, as described above.
[0187] The sample spectral components are then compared with
characteristic low-frequency spectral components of known compounds
suspected of being present in the sample. In a typical example, the
sample components are compared against the data set of each of the
components suspected of being in the sample and which one desires
to detect. A components, e.g., compound is identified as being
present in the sample if one or more of its characteristic
low-frequency spectral components correspond to one or more
low-frequency spectral components of a known sample.
[0188] As shown in the set of FIGS. 11A-11F, detection of a
compound (an amino acid) can occur at very low levels, e.g., in the
parts per billion range or lower. In particular, even at a dilution
of 1:10 billion w/v, a characteristic spectral component at about
531 Hz is observed. The Figures demonstrate that signal amplitude,
corresponding spectral component correlation, does decline with
increasing compound dilution. However, the loss in signal amplitude
at low concentration can be compensated for by extending the
recording time, in this example, from 50 seconds for the first
group of Figures to 4.25 minutes for the most dilute sample (FIG.
11F).
[0189] Where, as in the above example, the spectral component
amplitude declines with decreasing concentration, the amount of
compound can be estimated on the basis of signal amplitude,
assuming that the data set for the compound also includes
concentration dependent amplitude information.
[0190] It has also been observed in some cases that the frequency
of the characteristic spectral components may shift by as much as 3
Hz in a systematic fashion with changes in concentration. For such
compounds, the amount of material present in a sample can be
estimated by changes in amplitude and/or frequency shift in one or
more of the spectral components. It will be appreciated for
materials showing a concentration dependent frequency shift that a
data set for that compound could include concentration-dependent
frequencies as well as concentration-dependent amplitudes for
particular components.
D. Time-Domain Signals
[0191] In still another aspect, the invention includes a
time-domain signal associated with a material of interest. The
time-domain signal, and its method of production, have been
discussed above. Briefly, the signal is produced by placing the
sample of interest in a container having both magnetic and
electromagnetic shielding, injecting Gaussian noise into the
sample; and recording an electromagnetic time-domain signal
composed of sample source radiation superimposed on the injected
Gaussian noise.
[0192] The signal may be used to characterize a sample, much as a
spectral component data set is used to characterize a material.
Alternatively, the signal may be used for generating a low
frequency signal signature of spectral components associated with a
material of interest. The signal signature can be generated, also
as described above, by (i) cross correlating the time-domain signal
recorded with a second time domain signal separately recorded from
the same or similar sample, to produce a frequency domain spectrum
in a frequency range within DC to 50 kHz.
CONCLUSION
[0193] Unless the context clearly requires otherwise, throughout
the description and the claims, the words "comprise," "comprising,"
and the like are to be construed in an inclusive sense as opposed
to an exclusive or exhaustive sense; that is to say, in the sense
of "including, but not limited to." Words in the above detailed
description using the singular or plural number may also include
the plural or singular number respectively. Additionally, the words
"herein," "above," "below" words of similar import, when used in
this application, shall refer to this application as a whole and
not to any particular portions of this application. When the claims
use the word "or" in reference to a list of two or more items, that
word covers all of the following interpretations of the word: any
of the items in the list, all of the items in the list and any
combination of the items in the list.
[0194] The above detailed descriptions of embodiments of the
invention are not intended to be exhaustive or to limit the
invention to the precise form disclosed above. While specific
embodiments of, and examples for, the invention are described above
for illustrative purposes, various equivalent modifications are
possible within the scope of the invention, as those skilled in the
relevant art will recognize. For example, while processes or steps
are presented in a given order, alternative embodiments may perform
routines having steps in a different order, and some steps may be
deleted, moved, added, subdivided, combined, and/or modified. Each
of these steps may be implemented in a variety of different ways.
Also, while these steps are shown as being performed in series,
these steps may instead be performed in parallel, or may be
performed at different times.
[0195] The teachings of the invention provided herein can be
applied to other systems, not necessarily the system described
herein. These and other changes can be made to the invention in
light of the detailed description. The elements and acts of the
various embodiments described above can be combined to provide
further embodiments.
[0196] All of the above patents and applications and other
references, including any that may be listed in accompanying filing
papers, are incorporated herein by reference. Aspects of the
invention can be modified, if necessary, to employ the systems,
functions and concepts of the various references described above to
provide yet further embodiments of the invention.
[0197] These and other changes can be made to the invention in
light of the above detailed description. While the above
description details certain embodiments of the invention and
describes the best mode contemplated, no matter how detailed the
above appears in text, the invention can be practiced in many ways.
Details of the signal acquiring and analysis system may vary
considerably in its implementation details, while still be
encompassed by the invention disclosed herein. As noted above,
particular terminology used when describing certain features or
aspects of the invention should not be taken to imply that the
terminology is being re-defined herein to be restricted to any
specific characteristics, features or aspects of the invention with
which that terminology is associated. In general, the terms used in
the following claims should not be construed to limit the invention
to the specific embodiments disclosed in the specification, unless
the above Detailed Description section explicitly defines such
terms. Accordingly, the actual scope of the invention encompasses
not only the disclosed embodiments, but also all equivalent ways of
practicing or implementing the invention under the claims.
[0198] While certain aspects of the invention are presented below
in certain claim forms, the inventors contemplate the various
aspects of the invention in any number of claim forms. For example,
while only one aspect of the invention is recited as embodied in a
method claim format, it may likewise be embodied in a
computer-readable medium claim format. Accordingly, the inventors
reserve the right to add additional claims after filing the
application to pursue such additional claim forms for other aspects
of the invention.
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