Data processing: measuring – calibrating – or testing – Measurement system – Remote supervisory monitoring
Reexamination Certificate
2001-04-03
2004-07-27
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Remote supervisory monitoring
C324S076110
Reexamination Certificate
active
06768969
ABSTRACT:
COMPUTER PROGRAM LISTING APPENDIX
A computer program listing appendix containing the source code of a computer program that may be used with the present invention is incorporated herein by reference and appended hereto as one (1) original compact disk, and an identical copy thereof, containing a total of forty-one (41) files as follows:
Filename
Date of Creation
Size (Bytes)
AMPLDIST.M
Mar. 12, 1999 12:23p
2,736
APTF.C
Aug. 06, 1999 12:33p
8,146
DIGITI~1.C
Jun. 26, 1999 02:38p
5,520
DIGITI~1.M
Jun. 26, 1999 02:39p
627
DISTR1D.M
Jun. 17, 1999 12:45p
3,984
DISTR2D.M
Jun. 26, 1999 02:40p
5,702
DISTRI~1.C
Oct. 13, 1999 02:24p
8,218
EST_ER~1.M
Mar. 27, 2001 04:34p
330
FGTOBG~1.C
Oct. 22, 1999 09:45a
9,730
FGTOBG~2.C
Oct. 22, 1999 10:15a
9,917
KSEEG2.C
Oct. 22, 1999 11:59a
6,606
KSSAPP~1.C
Oct. 19, 1999 03:32p
11,997
LOCATE.M
Jul. 16, 1999 10:02a
542
PTCFIL~1.C
Aug. 04, 1999 08:42a
8,707
PTCFIL~1.M
Jul. 24, 1999 10:42a
935
PTF.C
Oct. 06, 1999 12:22p
7,337
PTF1.M
Sep. 07, 2000 01:32p
847
PTF2.M
Sep. 07, 2000 01:33p
734
PTFHIL~1.C
Oct. 06, 1999 04:08p
7,361
PTFILTER.C
Aug. 04, 1999 08:43a
7,481
PTFILTER.M
Jul. 24, 1999 10:48a
801
PTFSQR.C
Oct. 15, 1999 11:56a
6,901
PTF_CHI.CPP
Feb. 14, 2001 05:58p
3,829
PTF_CHI.M
Feb. 13, 2001 06:02p
477
PTF_DEMO.M
Apr. 02, 2001 12:32a
1,101
PTF_NOR.CPP
Feb. 16, 2001 05:26p
3,618
PTF_NOR.M
Nov. 20, 2000 01:28p
532
PTF_TRI.CPP
Mar. 05, 2001 01:12p
3,387
PTF_TRI.M
Feb. 16, 2001 05:33p
548
PTF_UNI.CPP
Feb. 20, 2001 06:47p
3,110
PTF_UNI.M
Oct. 30, 2000 11:25a
451
QKS.C
Oct. 11, 1999 02:16p
3,959
REXPMEAN.C
Aug. 09, 1999 11:40a
3,544
RMEAN.C
Sep. 13, 1999 01:48p
3,532
RMEAN.M
Jul. 27, 1999 03:35p
588
RMEANM~1.C
Sep. 13, 1999 09:10p
5,464
THRESH~1.C
Jul. 06, 1999 11:11a
8,932
THRESH~1.M
Mar. 03, 1999 04:50p
1,028
THRESH~2.M
Mar. 04, 1999 12:00p
3,218
THRESH~3.M
Jun. 16, 1999 06:03p
1,386
UNI_2_~1.M
Mar. 05, 2001 01:27p
417
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to methods, computer programs and systems for automated signal analysis providing rapid and accurate detection, prediction, or quantification of changes in one or more signal features, characteristics, or properties as they occur. More particularly, the present invention relates to a method, computer program, or system for automated real-time signal analysis providing characterization of temporally-evolving densities and distributions of signal features of arbitrary-type signals in a moving time window by tracking output of order statistic filters (also known as percentile, quantile, or rank-order filters).
2. Description of the Prior Art
It is often desirable to detect and quantify feature changes in an evolving signal, and there have been numerous attempts to develop automated signal analysis means operable to do so. One well-known approach, for example, is based upon analysis of the signal's mean value, which is typically a well known, well understood, and easily computed property. Other well-known techniques look for changes in signal variance or standard deviation over time.
Unfortunately, these commonly used approaches have significant drawbacks, including lack of robustness in the presence of signal outliers. Furthermore, in all but a few ideal cases, monitoring these individual parameters does not enable detection of all types of changes in feature distribution. This is because the mean and standard deviation rarely completely describe the signal distribution. Another problem that plagues many existing analysis techniques is that they are unable to deal adequately with real-world problems in which the analyzed signal is often highly complex, non-stationary, non-linear, and/or stochastic.
Another well-known approach, one more suited to practical situations than the above-mentioned methods, uses order statistics (e.g., the median or other percentile or quantile values). Order statistics are advantageous because they are directly related to the underlying distribution and are robust in the presence of outliers. For example, a method of signal analysis that enables the detection of state changes in the brain through automated analysis of recorded signal changes is disclosed in U.S. Pat. No. 5,995,868. This method addresses the problem of robustness in the presence of outliers through novel use of order-statistic filtering. Additionally, given information from a moving time window of a certain time scale, referred to as the “foreground”, this method provides for real-time comparison thereof with a reference obtained from past data derived, e.g., from a longer time scale window, referred to as the “background.” This approach thereby addresses some of the normalization problems associated with complex, non-stationary signals.
Although the prior invention disclosed in U.S. Pat. No. 5,995,868 has successfully addressed many of the above-mentioned limitations, including normalization problems associated with complex non-stationary signals, it is lacking in breadth of scope. Detection of changes, for example, is limited to a particular order statistic of the signal. Additionally, the order statistic filter employed to detect signal changes requires large amounts of processing ability, memory, and power when used on digital signals for which sorting procedures are performed at each point in time. Furthermore, the method does not enable full analog implementation.
Most work on order statistic filters, such as median filters, and their implementation is in the areas of digital signal and image processing, which, as mentioned, requires large amounts of processing ability, memory, and power not practical or cost-effective for some applications. Work on analog median filters is limited to situations where the input is provided as parallel lines of data, and a program or circuit that implements the filter outputs a value that is equal to the median of the data on different input lines. Work on analog median filtering for continuous-time signals is not extensive, and no realizable implementations exist able to track a percentile (e.g., median) of a continuous-time signal. One reason for this is that the operation of finding the rank or order is non-linear, making modeling the procedure using an ordinary differential equation so complicated that it has not yet been addressed.
Due to the above-described and other problems, a need exists for a more general, powerful, and broad method for automated analysis of signals of any degree of complexity and type.
SUMMARY OF THE INVENTION
The present invention solves the above-described and other problems to provide a distinct advance in the art of automated signal analysis. More specifically, the present invention comprises a method, computer program, and system for real-time signal analysis providing characterization of temporally-evolving densities and distributions of signal features of arbitrary-type signals in a moving time window by tracking output of order statistic filters (also called percentile, quantile, or rank-order filters).
The present invention is operable to analyze input signals of arbitrary type, origin and scale, including, for example, continuous-time or discrete-time, analog or digital, scalar or multi-dimensional, deterministic or stochastic (i.e., containing a random component), stationary (i.e., time invariant) or non-stationary (i.e., time varying), linear or nonlinear. Thus, the present invention has broad applicability to analysis of many different types of complex signals and sequences of data, including but not limited to biological signals such as those produced by brain, heart, or muscle activity; physical signals such as seismic, oceanographic, or meteorological; financial signals such as prices of various financial instruments; communication signals such as recorded speech or video or network traffic signals; mechanical signals such as jet engine vibration; target tracking and recognition; signals describing population dynamics, ecosystems or bio-systems; signals derived from manufacturing or other queuing systems; chemical signals such as spectroscopic signals; and sequences of
Bhavaraju Naresh C.
Frei Mark G.
Nikitin Alexei V.
Osorio Ivan
Chase Law Firm, L.C.
Flint Hills Scientific, L.L.C.
Hoff Marc S.
Raymond Edward
LandOfFree
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