Resumes
Resumes
Marc Boysworth Phones & Addresses
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609 Galveston Rd, Fredericksbrg, VA 22405 (703) 836-6477
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Fredericksburg, VA
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Stafford, VA
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6729 Sunset Woods Ct, Burke, VA 22015 (703) 440-0063
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Alexandria, VA
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Falls Church, VA
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Arlington, VA
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Harrisonburg, VA
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Fairfax, VA
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10002 Georgian Woods Ct, Burke, VA 22015
Work
Position:
Clerical/White Collar
Education
Degree:
High school graduate or higher
Publications
Us Patents
Retro-Regression Residual Remediation For Spectral/Signal Identification
View pageUS Patent:
7127372, Oct 24, 2006
Filed:
Feb 24, 2005
Appl. No.:
11/064290
Inventors:
Marc Kenneth Boysworth - Alexandria VA, US
Assignee:
ITT Manufacturing Enterprises, Inc. - Wilmington DE
International Classification:
G01N 31/00
US Classification:
702179, 702 22, 702 27, 702 30
Abstract:
An improved regression-based qualitative analysis algorithm useful when the mixture to be analyzed contains a compound not in the library spectra, a so-called unknown. A regression of a measured spectrum is computed against the library spectra. This regression is referred to as a “master” regression. Estimated mixing coefficients for an estimated spectrum are computed from the regression. Next, a residual error is computed between the estimated spectrum and the measured spectrum. Peaks in the residual error are identified that extend in a direction opposite to that of peaks in the measured spectrum. These peaks are referred to as “negative” peaks. A regression is performed on the peaks. This is referred to as a “retro-regression” to be distinguished from the master regression performed on the measured spectrum. Using information from the retro-regression, corrected mixing coefficients are computed and the process repeats.
Method And Apparatus For Categorizing Unexplained Residuals
View pageUS Patent:
6961677, Nov 1, 2005
Filed:
Aug 25, 2003
Appl. No.:
10/646774
Inventors:
Marc Kenneth Boysworth - Alexandria VA, US
Assignee:
ITT Manufacturing Enterprises, Inc. - Wilmington DE
International Classification:
G06F011/34
US Classification:
702189, 702183
Abstract:
A method and apparatus for performing continuous variance analysis (CVA) to characterize a data set. Data set values may be associated with any source, including measurements of a received signal and/or measurements of natural and/or man-made phenomena. CVA generates an output matrix that contains a measure of variation for a plurality of ranges (or windows) of data elements within a data set positioned at known locations within the data set. CVA output can be interpreted visually by a technician and/or using automatic numerical analysis. CVA is compatible with any apparatus/approach that uses numerical analysis to generate a predicted model based upon stored library models and/or linear/nonlinear components. CVA is compatible with any programming language and can be readily added to new and/or existing apparatus to compliment existing capabilities. CVA is less complex than conventional techniques, and requires less computer processing capacity, yet results in more readily interpretable results.