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Abstract

Stochastic processes are classes of signals whose fluctuations in time are partially or completely random. Examples of signals that can be modelled by a stochastic process are speech, music, image, time-varying channels, noise, and any information bearing function of time. Stochastic signals are completely described in terms of a probability model, but they can also be characterised with relatively simple statistics, such as the mean, the correlation and the power spectrum. This chapter begins with a study of the basic concepts of random signals and stochastic processes, and the models that are used for characterisation of random processes. We study the important concept of ergodic stationary processes in which time-averages obtained from a single realisation of a stochastic process can be used instead of the ensemble averages. We consider some useful and widely used classes of random signals, and study the effect of filtering or transformation of a signals on its probability distribution.

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Copyright information

© John Wiley & Sons Ltd. and B.G. Teubner 1996

Authors and Affiliations

  • Saeed V. Vaseghi
    • 1
  1. 1.Queen’s UniversityBelfastUK

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