System and method for characterizing a sample by low-frequency spectra

• US 6,995,558 B2
• Filed: 10/09/2003
• Issued: 02/07/2006
• Est. Priority Date: 03/29/2002
• Status: Active Grant

• Current Assignee: Nativis, Inc.
• Original Assignee: Wavbank Incorporated
• Inventors: Leonard, Michael, Naughton, Patrick, Butters, Bennett M.
• Primary Examiner(s): Lefkowitz, Edward
• Assistant Examiner(s): WHITTINGTON, KENNETH
• Application Number:
  US10/683,875
• Publication Number:
  US 20040183530A1
• Time in Patent Office: 852 Days
• Field of Search: 324/248, 324/249, 324/244, 324/76.19, 324/76.21, 324/76.24, 324/76.38, 324/228, 324/239, 600/409, 326/5, 505/162, 702/75, 702/76, 702/77
• US Class Current: 324/244
• CPC Class Codes: G01N 37/005 Measurement methods not based on established scientific theories
• CPC Class Codes: G01R 33/032 using magneto-optic devices, e.g. Faraday or Cotton-Mouton effect

Abstract

A method and apparatus for interrogating a sample that exhibits molecular rotation includes placing the sample in a container having both magnetic and electromagnetic shielding, where Gaussian noise is injected into the sample. An electromagnetic time-domain signal composed of sample source radiation superimposed on the injected Guassian noise is detected, and this signal is used to generate a spectral plot that displays, at a selected power setting of the Gaussian noise source, low-frequency spectral components characteristic of the sample in a selected frequency range between DC and 50 kHz. In one embodiment, the spectral plot that is generated is a histogram of stochastic resonance events over the selected frequency range. From this spectrum, one or more low-frequency signal components that are characteristic of the sample being interrogated are identified.

Claims

1. Apparatus for interrogating a sample that exhibits low-frequency molecular motion, comprising:

a container adapted for receiving the sample, the container having both magnetic and electromagnetic shielding;

an adjustable-power source of Gaussian noise for directing Gaussian noise to the sample, with the sample in the container;

a detector for detecting an electromagnetic time-domain signal composed of sample source radiation superimposed with the directed Gaussian noise; and

an electronic computer adapted to receive the time-domain signal from the detector, and to process the signal to generate a spectral plot that displays, at a selected power setting of the Gaussian noise source, low-frequency spectral components characteristic of the sample in a selected frequency range between DC and 50 kHz, wherein the electronic computer includes machine readable code operable to;

(i) store the time-domain signal of the sample over a sample-duration time T;

(ii) select a sampling rate F for sampling the time domain signal, where F*T is a total sample count S, F is approximately twice a frequency domain resolution f of a Real Fast Fourier Transform of the time-domain signal sampled at sampling rate F, and S>

f*n, where n is at least 10,

(iii) select S/n samples from the stored time domain signal and perform a Real Fast Fourier Transform (RFFT) on the selected samples to produce an RFFT signal,

(iv) normalize the RFFT signal and calculate an average power for the RFFT signal,

(v) place an event count in each of f selected-frequency event bins where a measured power at a corresponding selected frequency is greater than an average power times a value ε

, where 0<

ε

<

1 and is chosen such that a total number of counts placed in an event bin is between about 20–

50% of a maximum possible bin counts in that bin,

(vi) repeat steps (iii–

v), and

(vii) generate a histogram that shows, for each event bin f over a selected frequency range, a number of event counts in each bin.

2. The apparatus of claim 1, wherein the machine readable code is further operable to, in (iv), place the normalized power value from the RFFT signal in f corresponding-frequency power bins, and in (vii), (a) divide accumulated values placed in each of f power bins by n, to yield an average power in each bin, and (b) display in the histogram, the average power in each bin.

3. The apparatus of claim 1, wherein the machine readable code is further operable to, in (vii), identify those bins in the histogram that have an event count above a given threshold and an average power.

4. The apparatus of claim 1, wherein the detector is a second-derivative gradiometer which outputs a current signal, and a SQUID operatively connected to the gradiometer to convert the current signal to an amplified voltage signal, wherein the container is an attenuation tube having a sample-holding region, a magnetic shielding cage surrounding the region, and a Faraday cage also surrounding the region, wherein the source of Gaussian noise includes a Gaussian noise generator and a Helmholz coil, wherein the Helmholz coil is contained within the magnetic cage and the Faraday cage and receives a noise output signal from the noise generator, and wherein the apparatus further includes, for use in removing stationary noise components, a signal inverter operatively connected to the noise generator and to the SQUID, for receiving Gaussian noise from the noise generator and outputting into the SQUID, Gaussian noise in inverted form with respect to the Gaussian noise injected to the sample.

5. A method for interrogating a sample that exhibits low-frequency molecular motion, comprising:

placing the sample in a container having both magnetic and electromagnetic shielding,

injecting noise into the sample at a selected noise amplitude;

recording an electromagnetic time-domain signal composed of sample source radiation superimposed on the injected noise,

generating a spectral plot that contains, at a selected power setting of the noise source, low-frequency, sample-dependent spectral components characteristic of the sample in a selected frequency range between 100 and 50 kHz, and

repeating the injecting, recording and generating at different selected noise amplitudes until a plot showing a maximum or near maximum number of spectral components characteristic of the sample is generated,

wherein the generating includes (i) calculating a series of Fourier spectra of the time-domain signal over each of a plurality of defined time periods, in a selected frequency range between 100 Hz and 50 kHz, and (ii) averaging the Fourier spectra, and

wherein the calculating includes;

(i) storing a time-domain signal of the sample over a sample-duration time T;

(ii) selecting a sampling rate F for sampling the time-domain signal, where F*T is a total sample count S, F is approximately twice a frequency domain resolution f of a Real Fast Fourier Transform of the time-domain signal sampled at the sampling rate F, and S>

f*n, where n is at least 10,

(iii) selecting S/n samples from the stored time-domain signal and performing a Real Fast Fourier Transform (RFFT) on the selected samples to produce an RFFT signal,

(iv) normalizing the RFFT signal and calculating an average power for the RFFT signal,

(v) placing an event count in each of f selected-frequency event bins where a measured power at a corresponding selected frequency<

average power *ε

, where 0<

ε

<

1 and is chosen such that a total number of counts placed in an event bin is between about 20–

50% of a maximum possible bin count in that bin,

(vi) repeating (iii) through (v), and

(vii) generating a histogram that shows, for each event bin f over a selected frequency range, a number of event counts in each bin.

6. The method of claim 5, which further includes, in (iv) placing the normalized power value from the RFFT signal in f corresponding-frequency power bins, and in (vii):

(a) dividing accumulated values placed in each of the f power bins by n, to yield an average power in each bin, and (b) displaying on the histogram, the average power in each bin.

7. The method of claim 6, which further includes identifying those bins in the histogram that have an event count above a given threshold and an average power.

8. A method for interrogating a sample that exhibits low-frequency molecular motion, comprising:

receiving the sample;

magnetically and electromagnetically shielding the sample;

directing adjustable-power Gaussian noise to the sample;

detecting an electromagnetic time-domain signal composed of sample source radiation superimposed with the directed Gaussian noise; and

receiving the time-domain signal, and processing the signal to generate a spectral plot that displays, at a selected power setting of the Gaussian noise source, low-frequency spectral components characteristic of the sample in a selected frequency range between DC and 50 kHz,

wherein the processing includes;

(i) storing the time-domain signal of the sample over a sample-duration time T;

(ii) selecting a sampling rate F for sampling the time domain signal, where F*T is a total sample count S, F is approximately twice a frequency domain resolution f of a Real Fast Fourier Transform of the time-domain signal sampled at sampling rate F, and S>

f*n, where n is at least 10;

(iii) selecting S/n samples from the stored time domain signal and performing a Real Fast Fourier Transform (RFFT) on the selected samples to produce an RFFT signal;

(iv) normalizing the RFFT signal and calculating an average power for the RFFT signal;

(v) placing an event count in each of f selected-frequency event bins where a measured power at a corresponding selected frequency is greater than an average power times a value ε

, where 0<

ε

<

1 and is chosen such that a total number of counts placed in an event bin is between about 20–

50% of a maximum possible bin counts in that bin;

(vi) repeating (iii)–

(v); and

,

(vii) generating a histogram that shows, for each event bin f over a selected frequency range, a number of event counts in each bin.

9. The method of claim 8, further comprising, in (iv), placing the normalized power value from the RFFT signal in f corresponding-frequency power bins, and in (vii), (a) dividing accumulated values placed in each of f power bins by n, to yield an average power in each bin, and (b) displaying in the histogram, the average power in each bin.

10. The method of claim 8, further comprising, in (vii), identifying those bins in the histogram that have an event count above a given threshold and an average power.

11. The method of claim 8, wherein the detecting is performed using a second-derivative gradiometer which outputs a current signal, and a SQUID operatively connected to the gradiometer to convert the current signal to an amplified voltage signal, wherein the receiving includes providing an attenuation tube having a sample-holding region, a magnetic shielding cage surrounding the region, and a Faraday cage also surrounding the region, and wherein the directing Gaussian noise includes providing a Gaussian noise generator and a Helmholz coil, wherein the Helmholz coil is contained within the magnetic cage and the Faraday cage and receives a noise output signal from the noise generator, and wherein the method further includes, removing stationary noise components via a signal inverter operatively connected to the noise generator and to the SQUID, for receiving Gaussian noise from the noise generator and outputting into the SQUID, Gaussian noise in inverted form with respect to the Gaussian noise injected to the sample.

12. An apparatus for interrogating a sample that exhibits low-frequency molecular motion, comprising:

means for placing the sample in a container having both magnetic and electromagnetic shielding;

means for injecting noise into the sample at a selected noise amplitude;

means for recording an electromagnetic time-domain signal composed of sample source radiation superimposed on the injected noise;

means for generating a spectral plot that contains, at a selected power setting of the noise source, low-frequency, sample-dependent spectral components characteristic of the sample in a selected frequency range between 100 and 50 kHz; and

,

means for repeating the injecting, recording and generating at different selected noise amplitudes until a plot showing a maximum or near maximum number of spectral components characteristic of the sample is generated,

wherein the means for generating includes means for calculating a series of Fourier spectra of the time-domain signal over each of a plurality of defined time periods, in a selected frequency range between 100 Hz and 50 kHz, and means for averaging the Fourier spectra, and

wherein the means for calculating includes;

(i) means for storing a time-domain signal of the sample over a sample-duration time T;

(ii) means for selecting a sampling rate F for sampling the time-domain signal, where F*T is a total sample count S, F is approximately twice a frequency domain resolution f of a Real Fast Fourier Transform of the time-domain signal sampled at the sampling rate F, and S>

f*n, where n is at least 10;

(iii) means for selecting S/n samples from the stored time-domain signal and performing a Real Fast Fourier Transform (RFFT) on the selected samples to produce an RFFT signal;

(iv) means for normalizing the RFFT signal and calculating an average power for the RFFT signal;

(v) means for placing an event count in each of f selected-frequency event bins where a measured power at a corresponding selected frequency>

average power*ε

, where 0<

ε

<

1 and is chosen such that a total number of counts placed in an event bin is between about 20–

50% of a maximum possible bin count in that bin;

(vi) means for repeating the selecting, normalizing, and placing; and

,

(vii) means for generating a histogram that shows, for each event bin f over a selected frequency range, a number of event counts in each bin.

13. The apparatus of claim 12, wherein the means for normalizing includes means for placing the normalized power value from the RFFT signal in f corresponding-frequency power bins, and

wherein the means for generating includes means for dividing accumulated values placed in each of the f power bins by n, to yield an average power in each bin, and means for displaying on the histogram, the average power in each bin.

14. The apparatus of claim 13, which further includes means for identifying those bins in the histogram that have an event count above a given threshold and an average power.