Digital Signal Processing: Principles, Algorithms, and Applications

NEW—reorganizes coverage of DFT and FFT algorithm for greater clarity—now introduces the DFT and describes its efficient computation immediately following the treatment of Fourier analysis. Describes the operations and techniques involved in the analog-to-digital conversion of analog signals. Studies the characterization and analysis of linear time-invariant discrete-time systems and discrete- time signals in the time domain. Considers both the bilateral and the unilateral z-transform, and describes methods for determining the inverse z-transform. Analyzes signals and systems in the frequency domain, and presents Fourier series and Fourier transform in both continuous-time and discrete-time signals. Treats the realization of IIR and FIR systems, including direct-form, cascade, parallel, lattice and lattice-ladder realizations. Looks at the basics of sampling rate conversion and presents systems for implementing multirate conversion. Offers a detailed examination of power spectrum estimation, with discussions on nonparametric and model-based methods, as well as eigen-decomposition- based methods, including MUSIC and ESPRIT. Includes many examples throughout the book and with approximately 500 workable problems.

Giving students a sound balance of theory and practical application, this no-nonsense text presents the fundamental concepts and techniques of modern digital signal processing with related algorithms and applications. Covering both time-domain and frequency- domain methods for the analysis of linear, discrete-time systems, the book offers cutting-edge coverage on such topics as sampling, digital filter design, filter realizations, deconvolution, interpolation, decimation, state-space methods, spectrum analysis, and more. Rigorous and challenging, it further prepares students with numerous examples, exercises, and experiments emphasizing software implementation of digital signal processing algorithms integrated throughout.