20 April 2015 Adaptive threshold and error-correction coding for robust data retrieval in optical media
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Thresholding techniques that account for noise are essential for the efficiency and accuracy of an optical communication or optical data storage system. Various types of noise in the system can result in error. To recover the data from the noisy signal, the error must be corrected by a fast and accurate signal processing algorithm. By considering the crosstalk effect of the neighboring channels, we have devised a multi-level thresholding method to set the threshold values based on the neighboring channel values. We compare the binary characterization performance of a neural network and the local multi-level adaptive thresholding method for decoding noisy transmission images. We show that the multi-thresholding implementation results in an average of 57.42% less binary characterization errors than the artificial neural network across twenty unique mixed noise optical conditions.
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Thomas Lu, Thomas Lu, Colin Costello, Colin Costello, Matthew Ginley-Hidinger, Matthew Ginley-Hidinger, Tien-Hsin Chao, Tien-Hsin Chao, } "Adaptive threshold and error-correction coding for robust data retrieval in optical media", Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770O (20 April 2015); doi: 10.1117/12.2180402; https://doi.org/10.1117/12.2180402

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