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Informationen zum Autor André Quinquis received the M.S. degree in 1986 and the Ph.D. degree in 1989 in signal processing, both from the University of Brest. Between 1989 and 1992 he taught and developed research activities in signal and image processing at the Naval Academy in Brest. In 1992 he joined the Engineering School ENSIETA of Brest, where he held the positions of Senior Researcher and Head of the Electronics and Informatics Department. Since 2001 he has been Scientific Director of ENSIETA. He is mainly interested in signal processing, time-frequency methods, and statistical estimation and decision theory. Prof. Quinquis is author of 8 books and of more than 100 papers (international journals and conferences) in the area of signal processing. Klappentext This book uses MATLAB as a computing tool to explore traditional DSP topics and solve problems. This greatly expands the range and complexity of problems that students can effectively study in signal processing courses. A large number of worked examples, computer simulations and applications are provided, along with theoretical aspects that are essential in order to gain a good understanding of the main topics. Practicing engineers may also find it useful as an introductory text on the subject. Zusammenfassung This book uses MATLAB as a computing tool to explore traditional DSP topics and solve problems. This greatly expands the range and complexity of problems that students can effectively study in signal processing courses. Inhaltsverzeichnis Chapter 1. Introduction. Chapter 2. 1-D and 2-D discrete-time signals. Chapter 3. Discrete-time random processes. Chapter 4. Statistics for signal processing. Chapter 5. Convolution of 1-D and 2-D signals. Chapter 6. Discrete Fourier transform of 1-D and 2-D discrete signals. Chapter 7. Linear and invariant disctrete-time systems. Chapter 8. Infinite impulse response filters. Chapter 9. Finite impulse response filters. Chapter 10. Digital modulations. Chapter 11. Statistical signal processing: estimation. Chapter 12. Power spectrum density estimation. Chapter 13. Time-frequency spectral estimation. Chapter 14. Model-based time-frequency spectral estimation. Chapter 15. Statistical signal processing: classification. Chapter 16. Data compression. Chapter 17. Programming digital signal processors with MATLAB. List of Authors. Index. ...
