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 no-nonsense guide to the fundamentals and applications of statistical signal processing Ideal for upper-undergraduate and graduate courses, this engineering textbook offers key signal analysis principles and uses and explains the necessary underlying mathematics. Coverage includes representation and approximation theory in vector spaces, the orthogonality principle, the least squares problem, minimum mean square estimation, and the Wiener-Hopf equation. Signal Analysis: A Concise Guide clearly explains linear systems and signals and the concepts behind them. The book covers matrix factorizations, optimal linear filter theory, classical and modern spectral estimation, adaptive filters, and processing of spatial arrays. You will also explore linear optima filters, eigrn decomposition methods, the singular value decomposition, adaptive linear filters, noise cancellation, and spectral estimation. * Includes exercises for computer implementation using MATLAB * Presents the core material in a succinct format * Written by a team of renowned academics with multiple teaching awards

Introduced nearly three decades ago as a variable resolution alternative to the Fourier transform, a wavelet is a short oscillatory waveform for analysis of transients. The discrete wavelet transform has remarkable multi-resolution and energy-compaction properties. Amir-Homayoon Najmi's introduction to wavelet theory explains this mathematical concept clearly and succinctly. Wavelets are used in processing digital signals and imagery from myriad sources. They form the backbone of the JPEG2000 compression standard, and the Federal Bureau of Investigation uses biorthogonal wavelets to compress and store its vast database of fingerprints. Najmi provides the mathematics that demonstrate how wavelets work, describes how to construct them, and discusses their importance as a tool to investigate and process signals and imagery. He reviews key concepts such as frames, localizing transforms, orthogonal and biorthogonal bases, and multi-resolution. His examples include the Haar, the Shannon, and the Daubechies families of orthogonal and biorthogonal wavelets. Our capacity and need for collecting and transmitting digital data is increasing at an astonishing rate. So too is the importance of wavelets to anyone working with and analyzing digital data. Najmi's primer will be an indispensable resource for those in computer science, the physical sciences, applied mathematics, and engineering who wish to obtain an in-depth understanding and working knowledge of this fascinating and evolving field.