Digital signal processing with examples in MATLAB
著者
書誌事項
Digital signal processing with examples in MATLAB
（The electrical engineering and applied signal processing series）
CRC Press, 2011
2nd ed
- : hardback
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today's powerful computing capabilities, they can be used in a wide range of application areas, including engineering, communications, geophysics, computer science, information technology, medicine, and biometrics.
Updated and expanded, Digital Signal Processing with Examples in MATLAB (R), Second Edition introduces the basic aspects of signal processing and presents the fundamentals of DSP. It also relates DSP to continuous signal processing, rather than treating it as an isolated operation.
New to the Second Edition
Discussion of current DSP applications
New chapters on analog systems models and pattern recognition using support vector machines
New sections on the chirp z-transform, resampling, waveform reconstruction, discrete sine transform, and logarithmic and nonuniform sampling
A more comprehensive table of transforms
Developing the fundamentals of DSP from the ground up, this bestselling text continues to provide readers with a solid foundation for further work in most areas of signal processing. For novices, the authors review the basic mathematics required to understand DSP systems and offer a brief introduction to MATLAB. They also include end-of-chapter exercises that not only provide examples of the topics discussed, but also introduce topics and applications not covered in the chapters.
目次
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.
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