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Advanced Signal Processing: A Concise Guide
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Advanced Signal Processing: A Concise Guide

Book Details

Format Paperback / Softback
ISBN-10 1260458938
ISBN-13 9781260458930
Publisher McGraw-Hill Education
Imprint McGraw-Hill Education
Country of Manufacture GB
Country of Publication GB
Publication Date Sep 29th, 2020
Print length 352 Pages
Weight 708 grams
Dimensions 19.30 x 24.20 x 2.40 cms
Product Classification: Electrical engineeringSignal processing
Ksh 13,500.00
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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A comprehensive introduction to the mathematical principles and algorithms in statistical signal processing and modern neural networks. This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting school program for professionals with students from electrical engineering, physics, computer and data science, and mathematics backgrounds.  It covers the theory underlying applications in statistical signal processing including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays.  Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series prediction and image classification.    Coverage includes:Mathematical structures of signal spaces and matrix factorizationslinear time-invariant systems and transformsLeast squares filtersRandom variables, estimation theory, and random processesSpectral estimation and autoregressive signal modelslinear prediction and adaptive filtersOptimal processing of linear arraysNeural networks

Publisher''s Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.

A comprehensive introduction to the mathematical principles and algorithms in statistical signal processing and modern neural networks. 

This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting school program for professionals with students from electrical engineering, physics, computer and data science, and mathematics backgrounds.  It covers the theory underlying applications in statistical signal processing including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays.  Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series prediction and image classification.    

Coverage includes:

  • Mathematical structures of signal spaces and matrix factorizations
  • linear time-invariant systems and transforms
  • Least squares filters
  • Random variables, estimation theory, and random processes
  • Spectral estimation and autoregressive signal models
  • linear prediction and adaptive filters
  • Optimal processing of linear arrays
  • Neural networks





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