17 April 2006 Passive IR flexi-scope with two spectral colors for household screening of gastrointestinal disorders
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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.
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Kenneth Byrd, Kenneth Byrd, Harold Szu, Harold Szu, } "Passive IR flexi-scope with two spectral colors for household screening of gastrointestinal disorders", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470E (17 April 2006); doi: 10.1117/12.668656; https://doi.org/10.1117/12.668656

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