Probability and Random Processes: With Applications to Signal Processing and Communications

Signal processing

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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.

    Other typical operations supported by the hardware are circular buffers and lookup tables.

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    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.

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    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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    This article is in a list format that may be better presented using prose. You can help by converting this article to prose, if appropriate. Editing help is available. The signal on the left looks like noise, but the signal processing technique known as the Fourier transform right shows that it contains five well defined frequency components. Detection theory Discrete signal Estimation theory Nyquist—Shannon sampling theorem.

    Audio signal processing Digital image processing Speech processing Statistical signal processing.

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    With Applications to Signal Processing and Communications. Miller and Childers have focused on creating a clear presentation of foundational concepts with specific applications to signal processing and communications, clearly the two areas of most interest to students and instructors in this course. This involves linear electronic circuits as well as non-linear ones. 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. Pairs of Random Variables 6. 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.

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    It includes unique chapters on narrowband random processes and Buy Direct from Elsevier Bolero Ozon. Probability and Random Processes: With Applications to Signal Processing and Communications. Scott Miller , Donald Childers. It is meant for practicing engineers as well as graduate students.

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    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. Chapter 2 Introduction to Probability Theory. Chapter 4 Operations on a Single Random Variable.

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    Chapter 5 Pairs of Random Variables. Chapter 6 Multiple Random Variables. Chapter 7 Random Sequences and Series. Chapter 8 Random Processes.