The classic signal quantization problem was introduced by Lloyd. We formulate another, similar problem: The optimal mapping of digital fine grayscale images (such as 9-13 bits-per-pixel medical images) to a coarser scale (e.g., 8 bits per pixel on conventional computer monitors). While the former problem is defined basically in the real signal domain with smoothly distributed noise, the latter refers to an essentially digital domain. As we show in this paper, it is this difference that makes the classic quantization methods virtually inapplicable in typical cases of requantization of the already digitized images. We found experimentally that an algorithm based on dynamic programming provides significantly better results than Lloyd's method.

This paper deals with the functionality of a research program complex for processing and analysis of unstructured text data. It provides a thorough description of the implemented algorithms and experimental results obtained in various text samples. With the recent disclose of PRISM and similar governmental data surveillance programs, intelligent surveillance of the open source data that circle the World Wide Web is gaining a special importance. Our results might be used for improving the speed and the mode of the unstructured text data analysis and contribute to tackling the new threats that include international terrorism and espionage (including cyber-espionage) in the globalized world.