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Download e-book Random Signals and Noise: A Mathematical Introduction

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Before launching into the particulars of random signals and noise, the author outlines the elements of probability that are used throughout the book and includes an appendix on the relevant aspects of linear algebra.


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He offers a careful treatment of Lagrange multipliers and the Fourier transform, as well as the basics of stochastic processes, estimation, matched filtering, the Wiener-Khinchin theorem and its applications, the Schottky and Nyquist formulas, and physical sources of noise. Along with these traditional topics, the book includes a chapter devoted to spread spectrum techniques.

Random signals and noise : a mathematical introduction

A self-contained primer for solving real problems, Random Signals and Noise presents a complete set of tools and offers guidance on their effective application. Search all titles.


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In signal processing , noise is a general term for unwanted and, in general, unknown modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. Sometimes the word is also used to mean signals that are random unpredictable and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise.

Noise reduction , the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters. The mathematical limits for noise removal are set by information theory , namely the Nyquist—Shannon sampling theorem.

Shlomo Engelberg - Google Scholar Citations

Signal processing noise can be classified by its statistical properties sometimes called the " color " of the noise and by how it modifies the intended signal:. My profile My library Metrics Alerts. Sign in. Verified email at jct.

Introduction to Random Signals and Noise

Articles Cited by. Indiana University Mathematics Journal, , The American Mathematical Monthly 6 , , Articles 1—20 Show more.

Lecture 11 - Random Signals and Noise

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