Adaptive threshold and error-correction coding for robust data retrieval in optical media

Thomas Lu; Colin Costello; Matthew Ginley-Hidinger; Tien-Hsin Chao

doi:10.1117/12.2180402

20 April 2015 Adaptive threshold and error-correction coding for robust data retrieval in optical media

Thomas Lu, Colin Costello, Matthew Ginley-Hidinger, Tien-Hsin Chao

Author Affiliations +

Proceedings Volume 9477, Optical Pattern Recognition XXVI; 94770O (2015) https://doi.org/10.1117/12.2180402
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

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, Colin Costello, Matthew Ginley-Hidinger, and 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); https://doi.org/10.1117/12.2180402

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