ADVANCED PATTERN RECOGNITION SYSTEMS FOR SPECTRAL ANALYSIS

• US 20070211248A1
• Filed: 01/17/2007
• Published: 09/13/2007
• Est. Priority Date: 01/17/2006
• Status: Abandoned Application

• Current Assignee: Innovative American Technology, Inc.
• Original Assignee: Innovative American Technology, Inc.
• Inventors: Frank, David, Caulfield, H.J., Seter, Jamie
• Application Number:
  US11/624,121
• Publication Number:
  US 20070211248A1
• Time in Patent Office: Days
• US Class Current: 356/301
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01N 21/25 Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

Abstract

A process of rapid and highly accurate analysis of spectral data, includes both a linear scanning (LINSCAN) method and an advanced peak detection method for pattern recognition. One or both of the methods are used to support the detection and identification of chemical, biological, radiation, nuclear and explosive materials. The spectra of various targets can be analyzed by the two spectral analysis methods. These two methods can be combined for dual confirmation, greater accuracy, and to reduced false positives and false negatives, relative to what can be accomplished by either alone.

Claims

1. A process of smoothing, resampling, and adaptive curve fitting to each peak initially indicated by some simpler curve fitting operation such as convolution of a spectrum with a peaked function such as a Gaussian or Lorentzian.

2. The process of claim 1, wherein the smoothing is done by convolution.

3. The process of claim 1, wherein the smoothing is done by curve fitting.

4. The process of claim 1, wherein a final curve fitting process for a specific peak is done by gradient descent or ascent, depending on whether a figure of merit is to be maximized or minimized.

5. The process of claim 1, wherein a final curve fitting for a specific peak is done by evolutionary methods.

6. The process of claim 1, wherein a final curve fitting for a specific peak is done by simulated annealing.

7. The process of claim 1, wherein a peak detection is used to identify a reference signal position for calibration of a detector used to provide the spectra for analysis.

8. A computer readable medium including software instructions for an information processing system, the software instructions comprising:

a sequence of software operations designed to identify and quantify the intensity of various isotopes contributing to an observed energy spectrum, where the sequence includes;

a preprocessing step that removes noise and minimizes the effects of Compton scattering;

followed by a fit of a resulting spectrum-derived signal as a linear sum of contributions from a prescribed set of isotopes and expected noise spectra; and

followed by an analysis of weights determined by a fit to determine whether an isotope should be reported and whether there may be need for one more stage in which effects from very high radiation levels are reduced and mistakes that nonlinearity can cause are mitigated.

9. The computer readable medium of claim 8, wherein a background subtraction normalizes a magnitude of subtracted spectrum according to a time taken to make a signal-plus-noise measurements.

10. The computer readable medium of claim 8, wherein a background subtraction normalizes a magnitude of subtracted spectrum according to a cross-correlation between a noise spectrum and a measured signal-plus-noise spectrum.

11. The computer readable medium of claim 8, wherein a Compton scattering mitigation process is implemented by differentiation of the observed energy spectrum.

12. The computer readable medium of claim 8, wherein a Compton scattering mitigation is implemented by differentiation of the observed energy spectrum followed by taking at least one of an absolute value of a differentiated signal and a function of an absolute value of a differentiated signal.

13. The computer readable medium of claim 8, wherein a Compton scattering mitigation is implemented by applying unsharp masking to the spectrum.

14. The computer readable medium of claim 8, wherein a Compton scattering mitigation is implemented by applying unsharp masking to the observed energy spectrum.

15. The computer readable medium of claim 8, wherein a Compton scattering mitigation is implemented by applying unsharp masking to the observed energy spectrum and taking at least one of an absolute value of an unsharp masking signal and the square of an absolute value of an unsharp masking signal.

16. The computer readable medium of claim 8, wherein a Compton scattering mitigation is implemented by applying convolution with an edge enhancing kernel such as the Sobel kernel to the observed energy spectrum.

17. The computer readable medium of claim 8, wherein a Compton scattering mitigation is implemented by applying smoothing before enhancing sharp lines.

18. The computer readable medium of claim 17, wherein the smoothing is done by convolution.

19. The computer readable medium of claim 17, wherein the smoothing is done by at least one of rank order filtering and median filtering.

20. The computer readable medium of claim 17, wherein the smoothing is done by convolution by mathematical morphology.

21. The computer readable medium of claim 8, wherein a curve fitting to isotopes and expected noise spectra occurs using Gram-Schmidt orthonormalization.

22. The computer readable medium of claim 8, wherein a curve fitting to isotopes and expected noise spectra occurs using Caulfield-Maloney orthonormalization.

23. The computer readable medium of claim 8, wherein the weights determined by curve fitting are thresholded at values designed to meet a false-positive versus false-negative decision criterion.

24. The computer readable medium of claim 8, wherein the weights are examined to determine if any are high enough to indicate a likely presence of a nonlinearity-induced error.

25. The computer readable medium of claim 24, wherein effects of any indicated nonlinearity on the weights are computed and subtracted to correct for the nonlinearity.

26. The computer readable medium of claim 24, wherein effects of any indicated nonlinearity are linearized by computing and subtracting corrections to the spectrum before an analysis of concentrations is done.

27. The computer readable medium of claim 8, wherein the sequence of software operations are used by the information processing system to detect, identify, and quantify any one or more of chemical, biological, radiation, nuclear, and explosive materials.

28. An information processing system including computer readable medium containing computer instructions comprising instructions for:

(a) a process of smoothing, resampling, and adaptive curve fitting to each peak initially indicated by some simpler curve fitting operation such as convolution of a spectrum with a peaked function such as a Gaussian or Lorentzian; and

(b) a sequence of software operations designed to identify and quantify the intensity of various isotopes contributing to an observed energy spectrum, where the sequence includes;

a preprocessing step that removes noise and minimizes the effects of Compton scattering;

followed by a fit of a resulting spectrum-derived signal as a linear sum of contributions from a prescribed set of isotopes and expected noise spectra; and

followed by an analysis of weights determined by a fit to determine whether an isotope should be reported and whether there may be need for one more stage in which effects from very high radiation levels are reduced and mistakes that nonlinearity can cause are mitigated, and

wherein both (a) and (b) are used as a dual confirmation method to enable greater accuracy.

29. The information processing system of claim 28, wherein both (a) and (b) are used to create greater accuracy by using (a) to optimize false negatives and (b) to further optimize false positives for an overall effect of reducing both false negatives and false positives.