Samuel D. Stearns, Donald R. Hush

Digital Signal Processing with Examples in MATLAB (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Updated and expanded, the second edition of this bestselling text introduces the fundamentals of DSP. Along with discussing current DSP applications, this edition includes new chapters on analog systems models and pattern recognition using support vector machines as well as new sections on the chirp z-transform, resampling, waveform reconstruction, discrete sine transform, and logarithmic and nonuniform sampling. It also contains a more comprehensive table of transforms. Numerous exercises and examples harness the power of MATLAB and MATLAB functions and examples are available for download online.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 516
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781000755633
• Artikelnr.: 58255461

Autorenporträt

Samuel D. Stearns is a professor emeritus at the University of New Mexico, where has been involved in adjunct teaching and research since 1960. An IEEE fellow, Dr. Stearns was also a distinguished member of the technical staff at Sandia National Laboratories for 27 years. His principal technical areas are DSP and adaptive signal processing.

Don R. Hush is a technical staff member at the Los Alamos National Laboratory. An IEEE senior member, Dr. Hush was previously a technical staff member at Sandia National Laboratories and a professor at the University of New Mexico. He was also an associate editor for IEEE Transactions on Neural Networks and IEEE Signal Processing Magazine.

Inhaltsangabe

Introduction. Least Squares
Orthogonality
and the Fourier Series. Correlation
Fourier Spectra
and the Sampling Theorem. Linear Systems and Transfer Functions. Finite Impulse Response Filter Design. Infinite Impulse Response Filter Design. Random Signals and Spectral Estimation. Least-Squares System Design. Adaptive Signal Processing. Signal Information
Coding
and Compression. Models of Analog Systems. Pattern Recognition with Support Vector Machines. Appendix. Index.