The binary derivative test: noise filter, crypto aid, and random-number seed selector

Article Abstract:

Random noise obscuring digitized images or text can be removed by a new technique that recognizes the appearance of randomness in short bit strings. This test makes use of binary derivatives. The derivative of a bit string is formed by recursively XORing the bits pairwise. The measure of randomness depends on the maximum range of the one's ratio (p) among various order derivatives. Random pixel strings in digital images can be zeroed out leaving outlined regions where image details can be recovered by edge-detection or other techniques. Binary derivatives can also assist cryptographers and cryptanalysts. And they can help simulationists avoid selecting random-number generator seeds that would bias their results. (Reprinted by permission of a shameless publisher.)

Product introduction, Image processing, Digital signal processors, Modeling, Data modeling software, Signal processing, Cryptography, Simulation, Scientific Research, New Technique, Filters, Frequency, Recursion, Digital Signal Processor

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Simulation of white noise in dynamical systems

Article Abstract:

We address the problem of how to properly handle the application of white Gaussian process noise to a digital simulation of a continuous-time linear dynamical system, given the standard device of a Gaussian random numbers generator. (Reprinted by permission of the publisher.)

Continuous Simulation, Discrete Simulation, Gaussian Elimination, Linear Integrated Circuits

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