U.S. patent application number 11/632340 was filed with the patent office on 2007-10-04 for system and method for collecting, storing, processing, transmitting and presenting very low amplitude signals.
This patent application is currently assigned to NATIVIS, INC.. Invention is credited to Bennett M. Butters, John T. Butters, Patrick Naughton, Miller Puckette.
Application Number | 20070231872 11/632340 |
Document ID | / |
Family ID | 35787792 |
Filed Date | 2007-10-04 |
United States Patent
Application |
20070231872 |
Kind Code |
A1 |
Butters; John T. ; et
al. |
October 4, 2007 |
System and Method for Collecting, Storing, Processing, Transmitting
and Presenting Very Low Amplitude Signals
Abstract
Methods and apparatus inject noise into a substance, detect the
combination of the noise and the signal emitted by the substance,
adjust the noise until the combination signal takes on the
characteristic of the signal generated by the substance through
stochastic resonance, and apply such characteristic signals to
responsive chemical, biochemical, or biological systems. The
generated signal may be stored, manipulated, and/or transmitted to
a remote receiver.
Inventors: |
Butters; John T.; (Del Mar,
CA) ; Butters; Bennett M.; (Lacey, WA) ;
Naughton; Patrick; (Seattle, WA) ; Puckette;
Miller; (Encinitas, CA) |
Correspondence
Address: |
PERKINS COIE LLP;PATENT-SEA
P.O. BOX 1247
SEATTLE
WA
98111-1247
US
|
Assignee: |
NATIVIS, INC.
Suite 150 10975 North Torrey Pines Road
La Jolla
CA
92037
|
Family ID: |
35787792 |
Appl. No.: |
11/632340 |
Filed: |
July 27, 2005 |
PCT Filed: |
July 27, 2005 |
PCT NO: |
PCT/US05/26629 |
371 Date: |
June 22, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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60591549 |
Jul 27, 2004 |
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60593006 |
Jul 30, 2004 |
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60602962 |
Aug 19, 2004 |
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60674083 |
Apr 21, 2005 |
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Current U.S.
Class: |
435/173.1 ;
435/287.1; 702/19 |
Current CPC
Class: |
G01N 37/005 20130101;
G01F 1/56 20130101; G01F 1/66 20130101; A61P 35/00 20180101; A61P
43/00 20180101 |
Class at
Publication: |
435/173.1 ;
435/287.1; 702/019 |
International
Class: |
C12N 13/00 20060101
C12N013/00; G01N 33/48 20060101 G01N033/48 |
Claims
1. An apparatus for providing molecular signals from a sample, the
apparatus comprising: (a) a signal source generated at least in
part from the sample; (b) means for detecting electromagnetic
emission signals positioned near to the sample; (c) a Super
Conducting Quantum Interference Device (SQUID) electrically
connected to the electromagnetic emission detection coil, wherein
the SQUID is positioned within a means for cryogenically cooling;
(d) means for surrounding with noise the signal source and the
means for detecting signals, wherein the means for surrounding with
noise generates noise sufficient to induce stochastic resonance in
the sample so as to amplify the sample characteristic signals; (e)
means for electromagnetically shielding the signal source,
electromagnetic emission detection coil, SQUID, and noise means
from external electromagnetic radiation; (f) means for controlling
the SQUID; (g) means for observing and storing the signals detected
by the means for detecting signals; and (h) means for modifying the
stored signal based on user-defined criteria; and (i) means for
wirelessly providing the modified signal to a chemical or
biological system for inducing an effect in the chemical or
biological system.
2. A method for producing an effect of a chemical or biochemical
agent on a system responsive to such agent, comprising: (a)
generating multiple low-frequency time-domain signals by: (i)
placing a sample containing the agent in a container having both
magnetic and electromagnetic shielding, wherein the sample acts as
a signal source for molecular signals, and wherein the magnetic
shielding is external to a cryogenic container; (ii) injecting
noise into the sample in the absence of another signal from another
signal source at a noise amplitude sufficient to generate
stochastic resonance, wherein the noise has a substantially uniform
amplitude over a plurality of frequencies; (iii) recording an
electromagnetic time-domain signal composed of sample source
radiation superimposed on the injected noise in the cryogenic
container and in the absence of the another generated signal; and
(iv) repeating steps (ii)-(iii) at each of multiple noise levels
within a selected noise-level range if the sample source radiation
is not sufficiently distinguishable from the injected noise until
the superimposed signal takes on characteristics of the signal
generated by the signal source through stochastic resonance; (b)
identifying frequencies representing dominant characteristics of
the time-domain signal by analyzing the signal generated in (a);
(c) synthesizing a response-producing signal by: selecting at least
one frequency from the identified frequencies of the sample; or
combining frequencies selected from the identified frequencies of
two or more agent samples; and (d) exposing the agent-responsive
system to the synthesized response-producing signal by placing the
agent-responsive system within a magnetic field of an
electromagnetic transducer, and applying the synthesized signal by
the transducer at a signal amplitude and for a period sufficient to
produce in the agent-responsive system an agent-specific
effect.
3. The method of claim 2, wherein the synthesized
response-producing signal is a combination of: the identified
frequencies of one or more agent samples that represent chemical or
biological effects of the sample; or frequencies selected from
identified frequencies of one or more agent samples that represent
some aspects of chemical or biological effects of each agent
sample.
4. The method of claim 2, wherein the analyzing (b) is carried by
one of: (i) generating a histogram that shows, for each event bin f
over a selected frequency range within a range DC to 8 kHz, a
number of event counts in each bin, where f is a sampling rate for
sampling the time domain signal, assigning to the histogram, a
score related to number of bins that are above a given threshold;
and selecting a time-domain signal based on the score; (ii)
autocorrelating the time domain signal, generating an FFT (Fast
Fourier Transform) of the autocorrelated signal over a selected
frequency range within the range DC to 8 kHz, assigning to the FFT
signal a score related to a number of peaks above a mean average
noise value, and selecting a time-domain signal based on the score;
and (iii) calculating a series of Fourier spectra of the
time-domain signal over each of multiple defined time periods, in a
selected frequency range between DC and 8 kHz, averaging the
Fourier spectra; assigning to the averaged FFT signal a score
related to the number of peaks above a mean average noise value,
and selecting a time-domain signal based on the score.
5. The method of claim 2, wherein the electromagnet transducer
includes an implantable coil that is implanted in a biological
system prior to the exposing, a hand-held mobile device, or both,
and wherein signals arrive at the transducer via wire or
wirelessly, and wherein wireless signals are transmitted directly
or via satellite.
6-10. (canceled)
11. An optimized low-frequency response-producing signal
representing aspects of chemically or biologically active agents,
produced by steps comprising: (a) generating multiple low-frequency
time-domain signals of an agent by: (i) injecting noise into a
sample of the agent at a selected noise amplitude to generate
stochastic resonance; (ii) recording an electromagnetic time-domain
signal composed of sample source radiation superimposed on the
injected noise; and (iii) repeating steps (ii)-(iii) at each of
multiple noise levels within a selected range if the sample source
radiation is not sufficiently distinguishable from the injected
noise; (b) identifying frequencies representing an optimized
agent-specific signal by analyzing a preferred time-domain signal;
and (c) synthesizing a response-producing signal by: providing at
least one frequency from the identified frequencies of an agent
sample; or combining frequencies selected from the identified
frequencies of two or more agent samples, wherein the selected
frequencies represent selected aspects of signals associated with
desired chemical or biological effects.
12. The signal of claim 11, wherein the signal is directed to a
biological target.
13. The signal of claim 11, wherein the signal is wirelessly
transmitted to a receiver, and wherein the receiver includes an
implantable transducer or a handheld computing or
telecommunications device.
14. (cancelled)
15. A method for generating electromagnetic signals that produce
selected chemical or biological effects derived from aspects of
employed chemical or biological agents, the method comprising:
inserting a sample into a magnetically shielded detection apparatus
to provide molecular signals; injecting noise into the magnetically
shielded detection apparatus; detecting a combination of the
injected noise and the signal emitted by the sample; separating the
agent-specific signal from noise; computing frequency content of
the agent-specific signal; enhancing the frequency content of the
agent-specific signal by scaling or eliminating frequency
components; identifying frequency content representing desired
agent attributes; and synthesizing an electromagnetic
effect-producing signal using selected enhanced frequencies
detected from different agents, wherein the selected frequencies
represent desired portions or totality of the chemical or
biological effects of the agents.
16. The method of claim 15, wherein at least enhancing the
frequency content of the agent-specific signal is performed by a
user utilizing a user interface.
17. An apparatus for generating a signal having at least a subset
of effects of one or more chemical or biochemical agents, the
apparatus comprising: (i) a holder adapted to receive a sample of
an agent; (ii) an adjustable source of noise for applying noise to
the sample in the holder; (iii) a detector for recording a
time-domain signal composed of the sample radiation together with
the noise; (iv) a memory device for storing detected signals; (v) a
computer adapted to: (a) retrieve the stored signals from the
memory device; (b) produce a spectral representation of the
signals, allowing identification of agent-specific time-domain
signals; and (c) modify via a user interface or a software program,
portions of the retrieved signals to emphasize or deemphasize at
least one desired portion of the retrieved signals; and (vi) a
synthesizer to produce signals by utilizing a combination of
selected modified portions of at least one agent signal.
18. The apparatus of claim 17, wherein elements (v), (vi), or both
are remotely located with respect to the other elements of the
apparatus and are wirelessly in communication with the other
elements.
19. A generated signal for affecting biological or chemical
systems, wherein a Fourier transform of the signal comprises
multiple peaks each of which corresponds to a frequency of a
compound-specific stochastic event produced by a compound known to
induce a detectable response in a biological target, and observed
by recording a time-domain signal of a sample of the compound while
injecting noise into the sample at a selected noise amplitude that
allows identification of the peak frequency when the time-domain
signal is transformed to the frequency domain, wherein: the signal
frequency peaks are identified peak frequencies of one or more
compounds and represent chemical or biological effects of the
compounds; or the signal frequency peaks are manipulated
frequencies selected from identified peak frequencies of one or
more compounds and represent enhanced effects of some aspects of
the chemical or biological compounds.
20. The generated signal of claim 19, wherein the signal is
generated by: (i) identifying the peak frequencies for two or more
compounds, each effective to produce a given detectable response in
a given biological target; (ii) identifying those peak frequencies
that are common to the compounds; and (iii) superimposing the
common peak frequencies identified in (ii) to produce the
electromagnetic wave.
21. The generated signal of claim 19, wherein the signal is
generated by: (i) identifying the peak frequencies in a first set
of compounds effective to produce a given or desired detectable
response in a given biological target, and in a second set of
compounds that are ineffective to produce such desired response in
the target; (ii) identifying those peak frequencies that are common
to all of the compounds in the first set, but not common to all of
the compounds of the second set; and (iii) combining at least some
of the common peak frequencies identified in (ii) to produce the
electromagnetic wave.
22. The generated signal of claim 19, wherein the signal is
generated by: (i) identifying the peak frequencies in a first set
of compounds effective to produce a given or desired detectable
response in a given biological target; (ii) identifying the peak
frequencies in a second set of compounds effective to produce
another desired detectable response in the same biological target;
(iii) identifying those peak frequencies that are common to all of
the compounds in the first set, and those that are common to all of
the compounds of the second set; and (iv) superimposing at least
some of frequencies in the two sets of common peak frequencies
identified in (iii) to produce the electromagnetic wave.
23. The generated signal of claim 19, wherein generating the signal
comprises the steps of: (i) identifying the peak frequencies for a
given compound, and (ii) combining the frequencies, at a selected
amplitude that is at least 2.times. over baseline noise
frequencies.
24. The generated signal of claim 23, wherein the steps of
generating the signal further comprise: step (i) includes
identifying the peak frequencies for two or more compounds, each
effective to produce a given detectable response in a given
biological target, and identifying those peak frequencies that are
common to the compounds, and step (ii) includes superimposing the
common peak frequencies so identified to produce the
electromagnetic wave.
25. The generated signal of claim 23, wherein the steps of
generating the signal further comprise: step (i) includes
identifying the peak frequencies in a first set of compounds
effective to produce a given or desired detectable response in a
given biological target, and in a second set of compounds that are
effective to produce such desired response in the target, and
identifying those peak frequencies that are common to all of the
compounds in the first set, but not common to all of the compounds
of the second set, and step (ii) includes combining at least some
of the common peak frequencies so identified to produce the
electromagnetic wave.
26. The generated signal of claim 23, wherein the steps of
generating the signal further comprise: step (i) includes
identifying the peak frequencies in a first set of compounds
effective to produce a given or desired detectable response in a
given biological target, identifying the peak frequencies in a
second set of compounds effective to produce another desired
detectable response in the same biological target, and identifying
those peak frequencies that are common to all of the compounds in
the first set, and those that are common to all of the compounds of
the second set, and step (ii) includes superimposing at least some
of frequencies in the two sets of common peak frequencies so
identified to produce the electromagnetic wave.
27. The generated signals of claim 19, wherein the signal is
transmitted to a remote transducer and is applied to a chemical or
a biological system to induce a response, and wherein the
transducer is implanted within the system, located in the vicinity
of the system, or is a hand-held mobile device.
28. A method of producing an electromagnetic signal signature,
radiated from an excited substance, the method comprising:
injecting controlled electromagnetic noise into a container devoid
of the substance; computing a first frequency spectrum of the
electromagnetic radiation within the container; placing the
substance of interest in the container; injecting the controlled
electromagnetic noise into the container while containing the
substance; computing a second frequency spectrum of the
electromagnetic radiation within the container; obtaining the
frequency spectrum of the substance by comparing the first computed
frequency spectrum with the second computed frequency spectrum; and
enhancing the content of the frequency spectrum of the
substance.
29. The method of claim 28, wherein information about the enhanced
frequency content of the signal is transmitted to a remote
transducer where the transducer is implanted within the biological
entity, located in the vicinity of the biological entity, or is a
hand-held mobile device.
30-33. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 60/593,006, entitled SYSTEM AND METHOD FOR
PRODUCING CHEMICAL OR BIOCHEMICAL SIGNALS, filed Jul. 30, 2004
(attorney docket number 38547.8010); U.S. Provisional Patent
Application No. 60/591,549, entitled SIGNAL PROCESSING SYSTEM, SUCH
AS FOR PRODUCING AND MANIPULATING SIGNALS FROM CHEMICAL OR
BIOCHEMICAL COMPOUNDS OR SAMPLES, filed Jul. 27, 2004 (attorney
docket number 38547.8011 US); U.S. Provisional Patent Application
No. 60/602,962, entitled TRANSDUCING SIGNALS AND METHODS, filed
Aug. 19, 2004 (attorney docket number 38547.8012US); and U.S.
Provisional Patent Application No. 60/674,083, entitled SYSTEM AND
METHOD FOR PRODUCING CHEMICAL OR BIOCHEMICAL SIGNALS, filed Apr.
21, 2005 (attorney docket number 38547.801 OUS).
TECHNICAL FIELD
[0002] Embodiments of the present invention relate to signals
readable by a system for converting or transducing the signal into
electromagnetic waves, and to methods of producing and applying
such signals.
BACKGROUND
[0003] One of the accepted paradigms in the fields of chemistry and
biochemistry is that chemical or biochemical effector agents, e.g.,
molecules, interact with target systems through various
physicochemical forces, such as ionic, charge, or dispersion forces
or through the cleavage or formation of covalent of charge-induced
bonds. These forces may involve vibrational or rotational energy
modes in either the effector agent or target system.
[0004] A corollary of this paradigm is the requirement, in
effector-target systems, of the effector agent in the target
environment. However, what is not known or understood is whether
this requirement is related to the actual presence of the effector,
or whether it may be due, at least as to certain effector
functions, to the presence of energetic modes that are
characteristic of the effector. If effector function can be
simulated, at least in part, by certain characteristic energetic
modes, it may be possible to "simulate" the effect of the effector
agent in a target system by exposing the system to certain
energetic modes that are characteristic of the effector. If so, the
questions that naturally arise are: what effector-molecule energy
modes are effective, how can they be converted or transduced into
the form of measurable signals, and how can these signals be used
to effect a target system, that is, mimic at least some of the
effector functions of the molecule in a target system?
[0005] These questions were addressed in recently filed co-owned
patent applications 60/593,006 and 60/591,549 (attorney docket
numbers 38547-8010 and -8011). Experiments conducted in support of
the invention described in the application demonstrate that certain
effector functions on a target system (in this case, one of a
number of biological systems), can be duplicated by exposing the
target system to electromagnetic waves produced by "transducing" a
time-domain signal of the effector compound. According to the
earlier-described invention, the time-domain signal is produced by
recording a signal produced by the compound in a shielded
environment, while injecting noise into the recording apparatus at
a level that enhances the ability to observe low-frequency
stochastic events produced by the compound. In the
earlier-described application, the transducing signal was the
actual compound time-domain signal of the effector compound.
[0006] The possibility of achieving effector-molecule functions by
exposing a target system to characteristic effector-molecule
signals, without the need for the actual presence of the effector
agent, has a number of important and intriguing applications.
Instead of treating an organism by the application of a drug, the
same effect may be achieved by exposing the organism to
drug-specific signals. In the field of nanofabrication, it might
now be possible to catalyze or encourage self-assembly patterns by
introducing in the assembly system, signals characteristic of a
multivalent effector molecules capable of promoting the desired
pattern of self-assembly.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] 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;
[0008] FIG. 2 is an enlarged, detail view of the faraday cage and
its contents shown in FIG. 1; and
[0009] FIG. 3 is an enlarged, cross sectional view of one of the
attenuation tubes shown in FIGS. 1 and 2.
[0010] FIG. 4 is a cross-section view of the faraday cage and its
contents shown in FIG. 2.
[0011] FIG. 5 is a diagram of an alternative electromagnetic
emission detection system.
[0012] FIG. 6 diagram of the processing unit included in the
detection system of the above Figures.
[0013] FIG. 7 is a diagram of an alternative processing unit to
that of FIG. 6.
[0014] FIG. 8 is a flow diagram of the signal detection and
processing performed by the present system.
[0015] FIG. 9 shows a high-level flow diagram of data flow for the
histogram spectral plot method of the invention;
[0016] FIG. 10 is a flow diagram of the algorithm for generating a
spectral plot histogram, in accordance with the invention, and
[0017] FIG. 11 is a flow diagram of steps in identify optimal
time-domain signals in accordance with a second embodiment of the
method of the invention;
[0018] FIG. 12 is a flow diagram of steps to identify optimal
time-domain signals in accordance with a third embodiment of the
method of the invention;
[0019] FIG. 13 shows the transduction equipment layout in a typical
transduction experiment.
[0020] FIG. 14 shows a transduction coil and container used in a
typical transduction experiment.
[0021] FIGS. 15A-15E show a portion of a time-domain signal for a
sample containing 40% of an herbicide compound (15A), an FFT of
autocorrelated time-domain signals from the sample in 15A, recorded
at a noise levels of 70.9-dbm (15B), 74.8-dbm (15C and 15D), and
78.3 dbm (15E);
[0022] FIG. 15F is a plot of autocorrelation scores vs noise
setting for the sample in FIG. 15;
[0023] FIG. 16 is a block diagram illustrating a process flow for
creating a signal from a sample that may be applied to a biological
system.
[0024] FIG. 17 is a block diagram illustrating a suitable system
for applying to a patient electromagnetic waves that are generated
from signals created from a sample under the inventive system.
[0025] FIG. 18 is a flow diagram illustrating one example of a
signal processing routine for modifying one or more starting
waveforms.
[0026] FIGS. 19A-19D show modification of a spectral plot using a
graphical user interface.
[0027] FIG. 20 is a block diagram illustrating alternatives for
distributing a signal generated and processed by the detection
system and processing unit.
[0028] FIG. 21 is a block diagram illustrating a
transducer-receiver/transceiver for the distribution system of FIG.
20.
[0029] FIG. 22 is a Helmholtz-type induction coil for use with the
present system.
[0030] FIG. 23 is an implantable coil for transducing a sample
under embodiments of the invention.
[0031] The headings provided herein are for convenience only and do
not necessarily affect the scope or meaning of the claimed
invention.
DETAILED DESCRIPTION
I. Definitions
[0032] The terms below have the following definitions unless
indicated otherwise.
[0033] "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.
[0034] "Magnetic shielding" refers to shielding that decreases,
inhibits or prevents passage of magnetic flux as a result of the
magnetic permeability of the shielding material.
[0035] "Electromagnetic shielding" refers to, e.g., standard
Faraday electromagnetic shielding, or other methods to reduce
passage of electromagnetic radiation.
[0036] "Time-domain signal" or "time-series signal" refers to a
signal with transient signal properties that change over time.
[0037] "Sample-source radiation" refers to magnetic flux or
electromagnetis flux emissions resulting from molecular motion of a
sample, such as the rotation of a molecular dipole in a magnetic
field.
[0038] "Gaussian noise" means random noise having a Gaussian power
distribution. "Stationary white Gaussian noise" means random
Gaussian noise that has no predictable future components.
"Structured noise" may contain a logarithmic characteristic which
shifts energy from one region of the spectrum to another, or it may
be designed to provide a random time element while the amplitude
remains constant. These two represent pink and uniform noise, as
compared to truly random noise which has no predictable future
component. "Uniform noise" means noise having a constant
amplitude.
[0039] "Frequency-domain spectrum" refers to a Fourier frequency
plot of a time-domain signal.
[0040] "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.
[0041] "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.
[0042] "Faraday cage" refers to an electromagnetic shielding
configuration that provides an electrical path to ground for
unwanted electromagnetic radiation, thereby quieting an
electromagnetic environment.
[0043] A "spectral-features score" refers to a score based on the
number and/or amplitude of agent-specific spectral peaks observed
over a selected low-frequency range, e.g., DC to 1 kHz or DC to 8
kHz, in a time-domain signal recorded for an agent or sample that
has been processed by a suitable method, such as one of the three
methods described herein, to reveal identifiable spectral features
that are specific to the agent or sample.
[0044] An "optimized agent-specific time-domain signal" refers to a
time-domain signal having a maximum or near-maximum
spectral-features score.
II. Suitable Apparatus for Producing and Processing Time-domain
Signals
[0045] 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.
[0046] Some embodiments of the present invention describe signals
for use with a transducing system for producing compound-specific
electromagnetic waves that can act on target systems placed in the
field of the waves, and methods of producing such signals. Other
embodiments, relate to generating and distributing such
signals.
[0047] 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.
[0048] 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 for later, remote use. 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.
[0049] 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.
[0050] 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.
[0051] 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.
[0052] 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.
[0053] 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.
[0054] 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.
[0055] The disclosed combination of gradiometer 110 and SQUID 120
have a sensitivity of 5 microTesla/ Hz when measuring magnetic
fields.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] White noise generator 80 is also electrically connected to
the other input of dual trace oscilloscope 160 through patch cord
164.
[0064] 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.
[0065] 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.
[0066] 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 Helmholz 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.
[0067] 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.
[0068] 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.
[0069] Referring now to FIG. 5, 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.
[0070] The detection unit 702, which is shown in a cross-sectional
view in FIG. 5, includes multiple 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.
[0071] 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 may be configured to maintain a certain temperature inside the
sample chamber 706.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] Referring to FIG. 6, 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.
[0082] 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 Helmholz 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.
[0083] In FIG. 7, 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."
[0084] 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.
[0085] 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.
[0086] 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).
[0087] 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.
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] The Helmholz coil may have a sweet spot of about one cubic
inch with a balance of 1/.sub.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.
[0098] 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.
[0099] 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.
[0100] "A flow diagram of the signal detection and processing
performed by the system 100 is shown in FIG. 8. 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.).
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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).
[0106] 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.
[0107] 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.
[0108] 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.
[0109] If noise cancellation is provided at the SQUID 206, then the
use of thresholds and averaging correlations are not necessary.
[0110] 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).
[0111] 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.
III. Method of Producing an Optimized Time-domain Signal
[0112] According to one aspect of the invention, it has been
discovered that sample-dependent spectral features in a
low-frequency time-domain signal obtained for a given sample can be
optimized by recording time-domain signals for sample over a range
of noise levels, that is power gain on the noise injected into the
sample during signal recording. The recorded signals are then
processed to reveal spectral signal features, and the time domain
signal having an optimal spectral-features score, as detailed
below, is selected. The selection of optimized or near-optimized
time-domain signals is useful because it has been found, also in
accordance with the invention, that transducing a chemical or
biological system with an optimized time-domain signal gives a
stronger and more predictable response than with a non-optimized
time-domain signal. Viewed another way, selecting an optimized (or
near-optimized) time-domain signal is useful in achieving reliable,
detectable sample effects when a target system is transduced by the
sample signal.
[0113] In general, the range of injected noise levels over which
time-domain signals are typically recorded between about 0 to 1
volt, typically, or alternatively, the noise injected is preferably
between about 30 to 35 decibels above the molecular electromagnetic
emissions sought to be detected, e.g., in the range 70-80-dbm. The
number of samples that are recorded, that is, the number of
noise-level intervals over which time-domain signals are recorded
may vary from 10-100 or more, typically, and in any case, at
sufficiently small intervals so that a good optimum signal can be
identified. For example, the power gain of the noise generator
level can be varied over 50 20 mV intervals. As will be seen below,
when the spectral-feature scores for the signals are plotted
against level of injected noise, the plot shows a peak extending
over several different noise levels when the noise-level increments
are suitable small.
[0114] The present invention contemplates three different methods
for calculating spectral-feature scores for the recorded
time-domain signals. These are (1) a histogram bin method, (2)
generating an FFT of autocorrelated signals, and (3) averaging of
FFTs, and each of these is detailed below.
[0115] Although not specifically described, it will be appreciated
that each method may be carried out in a manual mode, where the
user evaluates the spectra on which a spectral-feature score is
based, makes the noise-level adjustment for the next recording, and
determines when a peak score is reached, or it may be carried out
in an automated or semi-automated mode, in which the continuous
incrementing of noise level and/or the evaluation of
spectral-feature score, is performed by a computer-driven
program.
A. Histogram Method of Generating Spectral Information
[0116] FIG. 9 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. 10,
and indicated by the box 2014 labeled Fourier Analysis. The
histogram can be displayed at 2016. Alternatively, and as will be
described below, the converted data may be passed to one of two
additional al
[0117] With reference to FIG. 10, 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.
[0118] 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.
[0119] 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.
[0120] 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.
[0121] 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.
[0122] 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).
[0123] 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.
[0124] 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.
[0125] 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.
[0126] 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.
[0127] The relevant settings in this method are noise gain and 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.
[0128] Therefore, one can systematically increase the power gain on
the noise input, e.g., in 50 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.
[0129] 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.
[0130] 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.
[0131] 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.
[0132] 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.
B. FFT of Autocorrelated Signals
[0133] In a second general method for determining spectral-feature
scores, time-domain signals recorded at a selected noise are
autocorrelated, and a fast Fourier transform (FFT) of the
autocorrelated signal is used to generate a spectral-features plot,
that is, a plot of the signal in the frequency domain. The FFTs are
then used to score the number of spectral signals above an average
noise level over a selected frequency range, e.g., DC to 1 kHz or
DC to 8 kHz.
[0134] FIG. 11 is a flow diagram of steps carried out in scoring
recorded time-domain signals according to this second embodiment.
Time-domain signals are sampled, digitized, and filtered as above
(box 402), with the gain on the noise level set to an initial
level, as at 404. A typical time domain signal for a sample
compound 402 is autocorrelated, at 408, using a standard
autocorrelation algorithm, and the FFT of the autocorrelated
function is generated, at 410, using a standard FFT algorithm.
[0135] An FFT plot is scored, at 412, by counting the number of
spectral peaks that are statistically greater than the average
noise observed in the autocorrelated FFT and the score is
calculated at 414. This process is repeated, through steps 416 and
406, until a peak score is recorded, that is, until the score for a
given signal begins to decline with increasing noise gain. The peak
score is recorded, at 418, and the program or user selects, from
the file of time-domain signals at 422, the signal corresponding to
the peak score (box 420).
[0136] As above, this embodiment may be carried out in a manual
mode, where the user manually adjusts the noise setting in
increments, analyzes (counts peaks) from the FFT spectral plots by
hand, and uses the peak score to identify one or more optimal
time-domain signals. Alternatively, one or more aspects of the
steps can be automated.
C. Averaged FFTs
[0137] In another embodiment for determining spectral-peak scores,
an FFT of many, e.g., 10-20 time domain signals at each noise gain
are averaged to produce a spectral-peaks plot, and scores are
calculated as above.
[0138] FIG. 12 is a flow diagram of steps carried out in scoring
recorded time-domain signals according to this third embodiment.
Time-domain signals are sampled, digitized, and filtered as above
(box 424), with the gain on the noise level set to an initial
level, as at 426. The program then generates a series of FFTs for
the time domain signal(s) at each noise gain, at 428, and these
plots are averaged at 430. Using the averaged FFT plot, scoring is
done by counting the number of spectral peaks that are
statistically greater than the average noise observed in the
averaged FFT, as at 432, 434. This process is repeated, through the
logic of 436 and 437, until a peak score is recorded, that is,
until the score for a given signal begins to decline with
increasing noise gain. The peak score is recorded, at 438, and the
program or user selects, from the file of time-domain signals at
442, the signal corresponding to the peak score (box 440).
[0139] As above, this method may be carried out in a manual,
semi-automated, or fully automated mode.
IV. Forming Transducing Signals
[0140] Signals for various therapeutic uses, or for uses to
otherwise effect biological systems, may be generated directly from
processed time-domain signals. Signals may also be formed by
constructing a signal having specific identified peak frequencies.
For example, the system can take advantage of "signal-activity
relationship" in which molecular signal features, e.g.,
characteristic peak frequencies of a compound, are related to
actual chemical activity for the compound, analogous to
structure-activity relationships used in traditional drug design.
In one general application, signal-activity relationships are used
for drug screening, following, in one example, the following
method.
[0141] First, one or more compounds having desired activity are
identified, e.g., compounds capable of producing a desired response
in a biological system. The system records a time-series signal for
one of these compounds, and the wave form is processed or otherwise
optimized to identify low-frequency peaks for that compound.
("Low-frequency" in this case refers to peaks at or below 10 kHz.)
The steps are repeated for each of a group of structurally related
compounds. The structurally related compounds include those that
are active (produce a desired response), and some that are inactive
for the tested biological response. The spectral components of the
two groups of compounds are compared to identify those spectral
components that are uniquely associated with compound activity. For
example, by analyzing forms from three active and two inactive
compounds, one may identify those peaks in the signal found in the
active compounds, and not in the inactive compounds, some of which
are presumed to provide the desired biological response.
[0142] In like manner, the system may record and optimize any
unknown compound. One may then analyze the resulting wave form with
signals associated with known compounds to see if the unknown
compound displays structural features associated the desired
activity, and lack components associated with inactive components
to help identify an active compound. Rules derivable from
signal-structure relationships are more accessible and more
predictive than rules derived from structure-activity
relationships, since activity can be correlated with a relatively
small number of peak frequencies, rather than a large number of
structural variables. Thus, tor use in drug design, one can use the
presence or absence of certain peak frequencies to guide synthesis
of drugs with improved pharmacokinetic or target activity. For
example, if poor pharmacokinetic properties, or an undesired side
effect, can be correlated with certain peak frequencies, novel
compounds that lack or have reduced amplitudes in these frequencies
would be suggested. As a result, the inventive system greatly
simplifies the task of formulating useful drug-design rules, since
the rules can be based on the relatively small number of peak
frequencies.
[0143] A large database of spectral peak frequencies representing
numerous compounds would allow one to combine signal features to
"synthesize" virtually any drug or drug-combination property
desired. By combining this database with a chemical compound
database, one may generate chemical structures that display a
desired peak-frequency set. This approach would be similar to
current computer-assisted chemical-synthesis programs used to
generate compound syntheses for novel compounds of interest.
[0144] The system can employ numerous signal processing techniques,
as described herein. For example, signals from two or more
structurally-related compounds can be compared with one or more
signals from a structurally-related, but inactive or undesirable
compound to identify only the desired frequency components between
the signals. A resulting signal may thus be constructed that
includes only the desired peaks. By then generating a time-domain
signal, that time-domain signal may be used for therapeutic
purposes.
[0145] Of course, a time-domain signal may be generated from the
processed frequency-domain signal of a single compound. For
example, one may obtain the frequency-domain signal for a desired
sample, and produce a processed, desired signal. From the processed
signal, a time-domain signal may be generated using known
techniques, which can then be employed for therapeutic or other
uses as an analog to the compound itself.
[0146] FIG. 15A shows a typical time domain signal for a sample
compound, in this case the herbicide glyphosphate (RoundupR). The
segment shown here is taken over the time interval 14.08 to 14.16
seconds. The time-domain signal is then autocorrelated using a
standard autocorrelation algorithm, and the FFT of the
autocorrelated function is generated using a standard FFT
algorithm.
[0147] Using the FFT plot, such as shown in FIGS. 15B-15E, the plot
is scored by counting the number of spectral peaks that are
statistically greater than the average noise observed in the
autocorrelated FFT. This process is repeated until a peak score is
recorded, that is, until the score for a given signal begins to
decline with increasing noise gain. The peak score is recorded and
the program or user selects, from the file of time-domain signals,
the signal corresponding to the peak score.
[0148] The series of autocorrelated FFT plots in FIGS. 15B-15E
illustrate the signal analysis involved in this method. At a noise
level of 70.9-dbm (FIG. 15B), very few peaks above background noise
are observed (the highest spike represents 60 cycle noise). At the
optimum noise level of 74.8-dbm (FIGS. 15C and 15D), which
represent different recordings at the same noise level), numerous
peaks statistically greater than average noise are observed
throughout the frequency range of DC-8 kHz. Several of these peaks
are less prominent or have disappeared at the higher noise gain of
78.3-dbm (FIG. 15E).
[0149] When the spectral-features scores for these signals are
plotted as a function of noise setting, as shown in FIG. 15F, the
peak score in the noise setting of about 75-dbm is observed. From
this plot, the time-domain signals corresponding to one or the peak
score is selected.
V. Transduction Apparatus and Protocols
[0150] This section describes equipment and methodology for
transducing a sample with signals formed in accordance with aspects
of the present invention, and summarizes experiments that
demonstrate the response of various biological systems to
time-domain signals of the present invention. The signals employed
in these experiments, which are optimized time-domain signals
formed in accordance with the method described above demonstrate
the ability of signals in accordance with the invention to produce
a compound-specific response in various biological systems.
[0151] FIG. 13 shows the layout of equipment for transducing a
sample with an agent-specific signal, in accordance with the
invention. The particular layout accommodates five different
samples, including three samples 444, 446, and 448 which are held
within transductions coils, and exposed to electromagnetic signals,
a sample 450 that serves as a control, and a sample 452 that serves
as a chemical-induction control. The system of FIG. 13 may be used
for experimentation; if used for treating a patient, then some
elements may be omitted, such as 448, 450, 452, etc.
[0152] Transduction by an agent-specific signal is carried out by
"playing" the optimized agent-specific signal to the sample, using,
where the signal is recorded on a CD, and is played on a CD
recorded 454 through a preamplifier 456 and an audio amplifier 458.
This signal is supplied to the electromagnetic coils 444 and 446
through separate channels, as shown. In one embodiment, a Sony
Model CDP CE375 CD Player is used. Channel 1 of the Player is
connected to CD input 1 of Adcom Pre Amplifier Model GFP 750.
Channel 2 is connected to CD input 2 of Adcom Pre Amplifier Model
GFP 750. CD's are recorded to play identical signals from each
channel. Alternatively, CD's may be recorded to play different
signals from each channel. The coil in sample 448 is used primarily
to produce a white noise field as a control for experiments. For
example, a GR analog noise generator provides a white Gaussian
noise source for this coil. Alternatively, this coil can be used to
play any pre recorded transduction signal via a second Crown
amplifier.
[0153] FIG. 14 shows sample transduction equipment 466 such as
represented by any of samples 444, 446, and 448 in FIG. 13. The
equipment includes a chamber 468 housing an electromagnet 470, and
various probes for monitoring conditions within the chamber, e.g.,
temperature. The electromagnet sits on a base 474, and includes,
conventionally a toroidal ferromagnetic core and wire windings.
[0154] In one embodiment, the coils are engineered and manufactured
by American Magnetics to provide uniform performance between coils.
Each coil consists of 416 turns of #8 gauge (awg) square copper
magnet wire, enamel coated, about a 2'' air core. Each coil can
produce approximately 1500 Gauss in the center at 10 Volts RMS at
10 Amps RMS at 11 Hertz without exceeding a 15 degree Celsius rise
in temperature.
[0155] In operation, the sample, e.g., a human patient or a portion
of the patient's body is place between the centrally between the
coils. Thus, for example, the coils may be at opposite ends of a
support bed, or on opposite sides of the bed, and on opposite sides
of the patient's head. The coil is then activated, using signal
generation equipment like that shown in FIG. 13, for a
predetermined therapeutic period, e.g., 1 to several hours.
[0156] FIG. 16 shows an example of a process for creating and
applying signals under the inventive system. Under block 3102, the
system receives and records a time domain signal from one or more
samples, in a manner described above. Under block 3104, the system
generates a frequency domain signal, and then processes that signal
to isolate the desired frequency components from undesired
components. Under block 3106, the processed frequency domain signal
is converted back into a time domain signal. The time domain signal
may then be applied to a biological system to generate a desired
result, under block 3108.
[0157] Referring to FIG. 18, a method 3300 for modifying waveforms
begins in block 3302 where the user obtains a starting waveform.
For example, the user, using standard user interface techniques,
selects and retrieves from data storage a desired waveform.
Alternatively, the user may obtain a signal during "live"
interrogation of a sample.
[0158] In block 3304, the user can combine the starting waveform
with another waveform and if so desired, the user retrieves another
waveform under block 3306. Of course, the user can simply modify
the starting waveform, if desired.
[0159] Under block 3308, the user modifies the starting waveform
using any of a variety of techniques. FIG. 19C shows an example
where the user may simply employ standard user interface
techniques, such as a mouse, to manipulate a pointer 3404 and
attenuate (or amplify) one or more frequency peaks in the starting
waveform as displayed on a display device. For example, the user
can simply click on a peak 3402 of a displayed portion of a
waveform and, using the mouse, drag the peak down to attenuate its
magnitude, as shown in FIG. 19D.
[0160] Many other techniques may be employed. The user may simply
select a portion of a waveform, cut or copy it, and then paste it
into the starting waveform. For example, referring to FIG. 19A, the
user may move the cursor about a portion of a waveform to select
that portion of the waveform (shown as a dashed line box 3406).
Once selected, the user can select from one of several menu
choices, such as to cut that portion from the waveform.
Alternatively, once selected, the user may modify that portion of
the waveform, such as by replacing it with a flat line, attenuate
it, amplify it, or perform various other signal processing
techniques.
[0161] The system may employ a library of waveforms that can be
inserted or employed as desired by the user. The user can select a
portion of the signal and cause it to filter out all peaks, thereby
eliminating noise or undesired frequency components in the
waveform. For example, FIG. 19B shows an example of a waveform or
filter signal 3408 that may be stored in a library. By applying the
signal 3408 to the waveform of FIG. 19A, the system provides the
resulting, processed waveform shown in FIG. 19C.
[0162] The system may employ various mathematical techniques under
block 3308 to modify the starting waveform. For example, the
starting waveform may be combined using a variety of mathematical
techniques with one or more waveforms retrieved under block 3306.
Examples of such mathematical operations include: addition,
subtraction, multiplication, convolution, cross-correlation,
scaling of starting waveform (SW) as a linear or non-linear
function of other waveforms, etc.
[0163] Under block 3310, the routine 3300 queries the user
regarding whether more modifications to the starting waveform are
desired. If so, the routine loops back to again perform under
blocks 3304 through 3308. If not, then in block 3312, the user may
store the resulting waveform. The stored waveform can then be used
for future modifications to other starting waveforms, used for
therapeutic effect, or a variety of other reasons, described
herein.
[0164] The following are some examples of additional techniques to
shape a waveform or set of waveforms in time series.
[0165] Passive Filters: simple electronic filters are based on
combinations of resistors, inductors and capacitors (or logical or
programmed representations of same). These filters can be used to
shape the waveform prior to recording, prior to processing, or
prior to transduction. Various existing software packages or
routines permit a user to model the responsive electronic filters.
Such software routines may be readily employed under the inventive
system to filter frequency domain waveforms using software modeled
versions of such electronic filters.
[0166] Active Filters: hardware or software filters can also be
implemented using a combination of passive components and
amplifiers to create active filters. These can have high Q, and
achieve resonance without the use of inductors. Ab with passive
filters, software applications or routines exist for modeling the
response of active filters, and such routines may be employed
herein to modify waveforms using one or more active filter models.
The inventive system may employ similar existing software routines
to implement with the filters, processing and shaping described
below.
[0167] Digital Filters: a digital filter is an electronic filter
(usually linear), in discrete time, that is normally implemented
through digital electronic computation. Digital filters are
typically either finite impulse response (FIR) or infinite impulse
response (IIR), though there are others, such as a hybrid class of
filters known as truncated infinite impulse response (TIIR)
filters, which show finite impulse responses despite being made
from IIR components.
[0168] Digital Signal Processing: digital signal processing (e.g.,
executed as a computer program) may simulate, e.g., comb filter
having a tapped delay line. The program selects numbers from a
string of digital values representing the signal, at a spacing that
simulates a comb of a tapped delay line. These numbers are
multiplied by constants, and added together to make the output of
the filter. DSP allows for multiple pass bands or multiple band
gaps, essentially allowing only a select set of frequencies to make
it to an output stage.
[0169] Wave Shaping: many well known methods exist for shaping a
waveform by altering its rise time, sustain time, and decay time,
or otherwise altering a signal from, or to a sine wave using full
wave rectifiers or pulse width modulation (as examples).
[0170] All of the equipment described herein may be scaled to
produce systems of greater or lesser size or intensity for various
applications. For example, if the system to be used to treat the
human patient, then a system having a coil for generating
electromagnetic waves to be directed at a patient may be
constructed. In one example, a bed having embedded therein round or
square Helmholtz coils would receive the time-domain signal created
from the processed frequency-domain signal. The patient would then
receive the resulting electromagnetic wave to induce the desired
biological effect that would otherwise be provided by, for example,
ingesting the compound from which the signals was generated.
[0171] A system for more targeted application of electromagnetic
waves to a patient is of course possible. For example, one or more
coils may be provided within a small device (such as a helmet, or
handheld wand). This output device receives the time-domain signal
produced from a desired frequency-domain signal, as noted above.
Resulting electromagnetic waves can be directed to specific parts
of a patient's body via the output device to produce a desired
effect at a localized portion of the patient.
[0172] FIG. 17 shows an example of such a signal output device. A
database 3202 stores signals from one or more compounds or samples.
Alternatively, the signals may be unprocessed frequency- or
time-domain signals generated as noted above. A computer 3204
retrieves the signal (or signals) and provides it to a signal
generator 3206. For example, the computer retrieves a desired
time-domain signal generated from a processed frequency domain
signal that was created from a specific compound. The computer then
provides the time domain signal to the signal generator 3206 to
simply amplify the signal. Alternatively, the computer may retrieve
processed frequency-domain signals that the signal generator
converts into time-domain signals. The signals output from the
signal generator 3206 may be modified by a signal modifier 3208.
The signal modifier may perform additional amplification,
filtering, and so forth. In an alternative embodiment, the computer
3204 performs the necessary signal generation modification, and
thus separate circuitry for the signal generator 3206 and signal
modifier 3208 may be omitted. Alternatively, the signal generator
3206 or signal modifier 3208 may be eliminated.
[0173] The signal output device 3210 receives the signal and
applies to a patient 3212. As noted above, the signal output device
may be a patient bed having embedded therein one or more coils to
output electromagnetic waves. Alternatively, the signal output
device 3210 can be a small, handheld device, a wearable device
(such as an article of clothing containing a coil), and so
forth.
[0174] The detector 702 obtains a signal from the sample 200, and
that signal is processed by the processing unit 704 to produce a
digital file 1501, such as a .wav file. That file may then be
stored on a storage media 1502 and distributed or transported to a
remote computer or other device. Any of the storage media noted
above may be employed for transporting signals or data files.
[0175] Aspects of the invention may be implemented in
computer-executable instructions, such as routines executed by a
general-purpose computer, e.g., a server computer, wireless device
or personal computer. Those skilled in the relevant art will
appreciate that the invention can be practiced with other
communications, data processing, or computer system configurations,
including: Internet appliances, hand-held devices (including
personal digital assistants (PDAs)), wearable computers, all manner
of cellular or mobile phones, multi-processor systems,
microprocessor-based or programmable consumer electronics, set-top
boxes, network PCs, mini-computers, mainframe computers, and the
like. Indeed, the terms "icomputer," "computing device," and
similar terms are generally used interchangeably herein, and refer
to any of the above devices and systems, as well as any data
processor.
[0176] Aspects of the invention can be embodied in a special
purpose computer or data processor that is specifically programmed,
configured, or constructed to perform one or more of the
computer-executable instructions explained in detail herein.
Aspects of the invention can also be practiced in distributed
computing environments where tasks or modules are performed by
remote processing devices, which are linked through a
communications network, such as a Local Area Network (LAN), Wide
Area Network (WAN), or the Internet. In a distributed computing
environment, program modules may be located in both local and
remote memory storage devices.
[0177] Aspects of the invention, such as data files, may be stored
or distributed on computer-readable media, including magnetically
or optically readable computer discs, hard-wired or preprogrammed
chips (e.g., EEPROM semiconductor chips), nanotechnology memory,
biological memory, or other data storage media. Indeed, computer
implemented instructions, data structures, screen displays,
wave/signal files, and other data under aspects of the invention
may be distributed over the Internet or over other networks
(including wireless networks), on a propagated signal on a
propagation medium (e.g., an electromagnetic wave(s), a sound wave,
etc.) over a period of time, or they may be provided on any analog
or digital network (packet switched, circuit switched, or other
scheme).
[0178] Alternatively, a transmitter 1504 within a signal
collection, processing and transmission system 500 transmits the
file to a network 1506 (e.g., the Internet), either via an
appropriate cable or wire, or wirelessly. The file then may be
transmitted to a computer 1512 (again, wired orwirelessly).
[0179] The file may be transmitted via the network to a remote
location, such as to a transducer-receiver 1508. For example, a
satellite network 1510 could be used to transmit the file to the
transducer-receiver 1508.
[0180] The transducer-receiver 1508 could be a standard receiver
for receiving the file, and include a transducer for transducing
the file as an electromagnetic signal to be applied. In one
embodiment, an implanted transducer-receiver is implanted into a
patient, body or structure. Where the receiver component of the
transducer-receiver 1508 is a wireless receiver, then the
transducer/receiver may receive the file wirelessly via the network
(or satellite). In an alternative embodiment, a cell phone or
mobile device 1514 receives the file from the network and relays it
to the transducer-receiver via any known wireless protocol,
including short range wireless protocols such as Bluetooth, any of
the IEEE802.11 protocols, etc.
[0181] A transducer-transceiver 1516, similar to the
transducer-receiver 1508, has a sensor 1518. Thus, the
transducer-transceiver 1516 can not only similarly receive the
transmitted file 1501 and transduce or apply it to a biological
system, but also obtain data from the sensor 1518 and transmit that
data back to the system 1500 (e.g., via the network).
[0182] According to FIG. 21, an example of the transducer-receiver
1508 and transducer-transceiver 1516 is provided which includes a
power source 1530 for providing power to the device. A
receiver-transceiver 1532 wired or wirelessly receives the file
1501, which may then be transduced or applied to a subject or
sample via a transducer 1534. The file may be amplified by an
amplifier 1536 and/or processed by a processor 1538. Memory 1540
may store the file, or store data obtained from one or more
optional sensors 1518.
[0183] FIGS. 22 and 23 show transduction coils suitable for use in
aspects of the invention. A transducer 494 in FIG. 22 is a long
solenoid, e.g., up to several feet in length. The field inside the
solenoid is parallel to the axis of the solenoid and constant
within the solenoid, going to zero outside the solenoid (in an
approximation of an infinitely long solenoid). This finite length
coil will have a substantially uniform field only near its center.
Thus, by placing the sample or subject at the center of the coil, a
substantially uniform magnetic field is created at the sample when
the coil is energized with the data file 1501 or MIDS signal.
[0184] By adding additional turns to the solenoid, such as
additional turns 500 in solenoid 496 in FIG. 23, additional field
strength can be added at the ends of the coil to compensate for the
fall off of the coil's magnetic fields at its ends.
[0185] With either or additional embodiments, the transduction coil
may be a small implantable ferromagnetic coil, such as a vascular
stent coil capable of receiving transducing signals either by
electrodes attached to opposite ends of the coil, by an implantable
system (like systems 1508, 1516) or by a remote, inductive system
in which an electromagnet is placed near the body surface, against
the patient's chest, and signals are transmitted inductively to the
implanted coil.
[0186] As noted above, the system utilizes, as input, soundfiles
obtained in stochastic resonance experiments and outputs
frequencies, amplitudes, and phases of the content sinusoids. The
system may employ a software routine, dubbed "peakfinder," which in
turn employ other software packages, such as Octave, and Pd, both
of which are open-source and currently supported software
platforms.
[0187] In addition, two environment variables may be used: PF_TMP
which specifies a temporary directory and PF_BASE which specifies
the location of a peakfinder folder. If PF_BASE is not supplied, a
peakfinder.sh script attempts to infer it from its own invocation
(assuming it is invoked as an absolute pathname). The input file is
a stereo soundfile, assumed to be at a standard sample rate of
44100. The file format may be "wav," "au," or "aiff," in 16, 24, or
32 bit sample frames. The output file is an ASCII file specifying
one sinusoid. For instance: TABLE-US-00001 595 100.095749 0.095624
-0.091218 -0.028693 1487 250.155258 0.100177 0.040727 0.091524
[0188] Here the first field is the frequency in units of the
fundamental analysis frequency, explained below, the second is the
frequency in Hertz, the third is the peak magnitude of the
sinusoid, in the input soundfile native units, and the fourth and
fifth are the amplitudes of the cosine and sine components of the
sinusoid, the real and imaginary parts of the complex amplitude.
The magnitude could, of course, be inferred from the real and
imaginary components. The first field has no physical meaning and
is intended for debugging purposes.
[0189] A technique for determining the amplitude and frequency of a
single sinusoid in white noise is the Maximum Likelihood (ML)
method, which has been extended to multiple sinusoids. This methods
assume that the number of sinusoids is known in advance. The
problem of finding an un-predetermined number of sinusoids is
harder to treat mathematically but can be dealt with assuming that
the sinusoids in question are adequately separated in frequency.
Furthermore, a method is needed to discriminate between the
presence and absence of a sinusoid.
[0190] The following analysis starts by considering a single
sinusoid in white noise and progresses to the problems of multiple
sinusoids and non-white (e.g., pink) noise. Given a measured
signal: x[n], n=0, . . . , N, the (discrete-time) unnormalized
Fourier transform is defined as: FT .times. { x .function. [ n ] }
.times. ( k ) = n = 0 N - 1 .times. .times. e - 2 .times. .pi.
.times. .times. ink / N .times. x .function. [ n ] , ##EQU1## where
k is the frequency in units of the fundamental frequency of the
analysis; 2.pi./N radians per sample. k need not be an integer; in
practice extra values of k can be filled in as needed by
zero-padding the signal. With the assumption that a single sinusoid
is present, its most likely frequency is given by: k=arg
max|FT{x[n]}(k)|. In other words, the best estimate is simply the
value of k at which the Fourier transform's magnitude is the
largest.
[0191] Next, the system determines if the estimated value of k
corresponds to a true sinusoid or simply to random fluctuations.
For this, the null hypothesis is analyzed to determine whether x[n]
only contains white noise, with mean 0 and RMS amplitude .sigma.,
for instance. The Fourier transform at each point k is a sum of N
independent random variables, each equal to a sample x[n] times a
complex number of unit magnitude, so the mean of each point of the
Fourier transform is still zero, and the standard deviation is
.sigma. {square root over (N)}. If the tail behavior of the
individual noise samples is well behaved (which it is for Gaussion
or Uniform noise, for instance) the resulting random variable
FT{x[n]}(k) will be very nearly Gaussian for the values of N used
(on the order of 10.sup.6). So the probability of exceeding more
than about 5.sigma. {square root over (N)} is very small.
[0192] On the other hand, a real-valued sinusoid with peak
amplitude .alpha. and frequency k (in the usual units of 2.pi./N)
has a Fourier transform magnitude of .alpha.N/2. To get a magnitude
of 5.sigma. {square root over (N)}, we only need a to be at least a
.apprxeq. 10 .times. .sigma. N .about. .sigma. 100 ##EQU2##
[0193] The method zero-pads the recorded soundfile (between a
factor of two and four, depending on the next power of two), and
then reports peaks that exceed this amplitude threshold. A peak is
defined as having greater magnitude for the given value of k than
for its neighbors, and also having at least half again the
magnitude of the twenty neighboring values of k (a band of roughly
20.pi./N Hz, or 1/3 Hz. for a one-minute sample.)
[0194] If several sinusoids are present, provided their frequencies
are mutually spaced more than about 20.pi./N, the above method
should resolve them separately; each sinusoid's influence on the
calculated Fourier transform drops off as 2/3.pi.k in amplitude at
k frequency units away from the peak.
[0195] To compensate for the non-white nature of noise signals, the
spectral envelope of the measured signal is estimated. The noise
can be assumed to be locally white in each narrow range of
frequency (20.pi./N as above), with the value of .sigma. varying
gently according to the frequency range chosen. Another issue is to
determine whether the injected noise sample can be subtracted from
the measured output of the experiment. In such a situation, with an
easily measurable transfer function relating the two, even if it is
nonlinear, an estimate of the transfer function is used to remove
the bulk of the noise from the measured signal. This also increases
the sensitivity of the method.
[0196] As can be seen from the description provided above, the
system allows a user to create waveforms that may be used for
therapeutic affect or otherwise induce a reaction in a biological
system. Waveforms or spectral series generated from two or more
compounds may be obtained. These two signals may then be combined
to create a single, combined signal having the properties of the
two individual signals. If, for example, the two original signals
related to two different compounds having two different therapeutic
properties, then the resulting, combined signal would have the
combined therapeutic properties of the two compounds. The combined
signal may then be manipulated to remove unwanted frequency
component that have been found to be associated with side effects
or negative reactions in a biological system.
[0197] Alternatively, if the two compounds produce similar
responses in a biological system, then the two signals generated
from those compounds can be compared to identify common frequency
components associated with generating the biological effect. A
third signal may then be generated that includes only those
frequency components associated with the biological effect. Thus,
for example, signals from certain pain reliever drugs may be
compared to identify common frequency components, and then generate
a resulting signal for use in transmission, storage, or application
to a biological system. Indeed, the system permits a new signal to
be constructed that is not based directly on signals generated from
one or more compounds. Instead, the system permits a signal to be
generated having only peaks at desired frequencies, where such
peaks have a desired result in a biological system. Thus, such a
synthesized signal is independent of existing compounds.
CONCLUSION
[0198] 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." The word "coupled", as
generally used herein, refers to two or more elements that may be
either directly connected, or connected by way of one or more
intermediate elements. Additionally, the words "herein," "above,"
"below," and 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. Where the context
permits, words in the above Detailed Description using the singular
or plural number may also include the plural or singular number
respectively. 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.
[0199] The above detailed description of embodiments of the
invention is 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 blocks
are presented in a given order, alternative embodiments may perform
routines having steps, or employ systems having blocks, in a
different order, and some processes or blocks may be deleted,
moved, added, subdivided, combined, and/or modified. Each of these
processes or blocks may be implemented in a variety of different
ways. Also, while processes or blocks are at times shown as being
performed in series, these processes or blocks may instead be
performed in parallel, or may be performed at different times.
[0200] The teachings of the invention provided herein can be
applied to other systems, not necessarily the system described
above. The elements and acts of the various embodiments described
above can be combined to provide further embodiments.
[0201] 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.
[0202] 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 processing system may vary considerably in
its implementation details, while still being 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
redefined 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.
* * * * *