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Synopsis
This book addresses one of the key problems in signal processing, the problem of identifying statistical properties of excursions in a random process in order to simplify the theoretical analysis and make it suitable for engineering applications. Precise and approximate formulas are explained, which are relatively simple and can be used for engineering applications such as the design of devices which can overcome the high initial uncertainty of the self-training period. The information presented in the monograph can be used to implement adaptive signal processing devices capable of detecting or recognizing the wanted signals (with a priori unknown statistical properties) against the background noise. The applications presented can be used in a wide range of fields including medicine, radiolocation, telecommunications, surface quality control (flaw detection), image recognition, thermal noise analysis for the design of semiconductors, and calculation of excessive load in mechanics. New & Used
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What Reviewers Are Saying
"...unique in its description of some of the aspects of the theory of excursions for random processes and its applications...equally useful for senior university students as well as for scientists and engineers."--Zentralblatt MATH, Applications of Random Process Excursion Analysis
"...addresses one of the key problems in signal processing, the problem of identifying statistical properties of excursions in a random process in order to simplify the theoretical analysis and make it suitable for engineering applications. Precise and approximate formulas are explained, which are relatively simple and can be used for engineering applications such as the design of devices which can overcome the high initial uncertainty of the self-training period."--zbMATH.org, June 19, 2014 "Brainina shares the results of her many years studying the theory of excursions as it applies to adaptive radio communications systems. A highlight is that she manages to apply calculation methods chosen for Gaussian processes to a larger class of non-Gaussian random processes."--Reference & Research Book News, October 2013
"...addresses