ADVANCED SIGNAL PROCESSING A CONCISE GUI

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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 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

Nota editor: Produsele cumparate de la vanzatori terti nu sunt garantate de editor pentru calitate, autenticitate sau acces la orice drepturi online incluse in produs. O introducere cuprinzatoare la principiile matematice si algoritmi in procesarea statistica a semnalului si retelele neuronale moderne. Acest text este o versiune extinsa a unui curs postuniversitar de prelucrare avansata a semnalului la programul scolar Johns Hopkins University Whiting pentru profesionisti cu studenti din ingineria electrica, fizica, informatica si stiinta datelor si matematica.