Probability and Random Processes - CERN Document Server
It introduces the reader to the basics of probability theory and explores topics ranging from random variables, distributions and density functions to operations on a single random variable. There are also discussions on pairs of random variables; multiple random variables; random sequences and series; random processes in linear systems; Markov processes; and power spectral density. This book is intended for practicing engineers and students in graduate-level courses in the topic. The book includes unique chapters on narrowband random processes and simulation Probability and Random Processes: With Applications to Signal Processing and Communications.
Scott Miller , Donald Childers. Exceptional exposition and numerous worked out problems make the book extremely readable and accessible The authors connect the applications discussed in class to the textbook The new edition contains more real world signal processing and communications applications Includes an entire chapter devoted to simulation techniques. Introduction to Probability Theory.
Random Variables Distributionsand Density Functions. Operations on a Single Random Variable. Pairs of Random Variables.
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Random Sums and Sequences. This technology mainly discusses the modeling of linear time-invariant continuous system, integral of the system's zero-state response, setting up system function and the continuous time filtering of deterministic signals. Discrete-time signal processing is for sampled signals, defined only at discrete points in time, and as such are quantized in time, but not in magnitude. Analog discrete-time signal processing is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers , analog delay lines and analog feedback shift registers.
This technology was a predecessor of digital signal processing see below , and is still used in advanced processing of gigahertz signals.
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The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration. Digital signal processing is the processing of digitized discrete-time sampled signals. Processing is done by general-purpose computers or by digital circuits such as ASICs , field-programmable gate arrays or specialized digital signal processors DSP chips.
Typical arithmetical operations include fixed-point and floating-point , real-valued and complex-valued, multiplication and addition.
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Other typical operations supported by the hardware are circular buffers and lookup tables. Nonlinear signal processing involves the analysis and processing of signals produced from nonlinear systems and can be in the time, frequency, or spatio-temporal domains. Statistical signal processing is an approach which treats signals as stochastic processes , utilizing their statistical properties to perform signal processing tasks. Statistical techniques are widely used in signal processing applications.
For example, one can model the probability distribution of noise incurred when photographing an image, and construct techniques based on this model to reduce the noise in the resulting image. From Wikipedia, the free encyclopedia. It is not to be confused with Signalling theory or Signalling economics. This article has multiple issues. Please help improve it or discuss these issues on the talk page.
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