
Proceedings Paper
Passive IR flexi-scope with two spectral colors for household screening of gastrointestinal disordersFormat | Member Price | Non-Member Price |
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$17.00 | $21.00 |
Paper Abstract
According to our generalized Shannon Sampling Theorem of developmental system biology, we should
increase the sampling frequency of the passive Infrared (IR) spectrum ratio test, (I8~12mm / I3~5mm). This
procedure proved to be effective in DCIS using the satellite-grade IR spectrum cameras for an early
developmental symptom of the "angiogenesis" effect. Thus, we propose to augment the annual hospital
checkup of, or biannual Colonoscopy, with an inexpensive non-imaging IR-Flexi-scope intensity
measurement device which could be conducted regularly at a household residence without the need
doctoral expertise or a data basis system. The only required component would be a smart PC, which would
be used to compute the degree of thermal activities through the IR spectral ratio. It will also be used to
keep track of the record and send to the medical center for tele-diagnosis. For the purpose of household
screening, we propose to have two integrated passive IR probes of dual-IR-color spectrum inserted into the
body via the IR fiber-optic device. In order to extract the percentage of malignancy, based on the ratio of
dual color IR measurements, the key enabler is the unsupervised learning algorithm in the sense of the
Duda & Hart Unlabelled Data Classifier without lookup table exemplars. This learning methodology
belongs to the Natural Intelligence (NI) of the human brain, which can effortlessly reduce the redundancy
of pair inputs and thereby enhance the Signal to Noise Ratio (SNR) better than any single sensor for the
salient feature extraction. Thus, we can go beyond a closed data basis AI expert system to tailor to the
individual ground truth without the biases of the prior knowledge.
Paper Details
Date Published: 17 April 2006
PDF: 17 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470E (17 April 2006); doi: 10.1117/12.668656
Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)
PDF: 17 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470E (17 April 2006); doi: 10.1117/12.668656
Show Author Affiliations
Kenneth Byrd, Howard Univ. (United States)
Harold Szu, The George Washington Univ. (United States)
Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)
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