Digital Signal Processing Algorithms
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Digital Signal Processing
What are the relations between continuous-time and discrete-time/sampled-data systems, signals, and their spectra? How can digital systems be designed to replace existing analog systems? What is the reason for having so many transforms, and how do you know which one to use? What do s and z really means and how are they related? How can you use the fast Fourier transform (FFT) and other digital signal processing (DSP) algorithms to successfully process sampled signals? Inside, you'll find the answers to these and other fundamental questions on DSP. You'll gain a solid understanding of the key principles that will help you compare, select, and properly use existing DSP algorithms for an application. You'll also learn how to create original working algorithms or conceptual insights, design frequency-selective and optimal digital filters, participate in DSP research, and select or construct appropriate hardware implementations. Key Features * MATLAB graphics are integrated throughout the text to help clarify DSP concepts. Complete numerical examples clearly illustrate the practical uses of DSP. * Uniquely detailed coverage of fundamental DSP principles provides the rationales behind definitions, algorithms, and transform properties. * Practical real-world examples combined with a student-friendly writing style enhance the material. * Unexpected results and thought-provoking questions are provided to further spark reader interest. * Over 525 end-of-chapter problems are included, with complete solutions available to the instructor (168 are MATLAB-oriented).
Digital Signal Processing Algorithms
"Digital Signal Processing Algorithms describes computational number theory and its applications to deriving fast algorithms for digital signal processing. It demonstrates the importance of computational number theory in the design of digital signal processing algorithms and clearly describes the nature and structure of the algorithms themselves. The book has two primary focuses: first, it establishes the properties of discrete-time sequence indices and their corresponding fast algorithms; and second, it investigates the properties of the discrete-time sequences and the corresponding fast algorithms for processing these sequences. Digital Signal Processing Algorithms examines three of the most common computational tasks that occur in digital signal processing; namely, cyclic convolution, acyclic convolution, and discrete Fourier transformation. The application of number theory to deriving fast and efficient algorithms for these three and related computationally intensive tasks is clearly discussed and illustrated with examples. Its comprehensive coverage of digital signal processing, computer arithmetic, and coding theory makes Digital Signal Processing Algorithms an excellent reference for practicing engineers. The authors' intent to demystify the abstract nature of number theory and the related algebra is evident throughout the text, providing clear and precise coverage of the quickly evolving field of digital signal processing."--Provided by publisher.
Digital Signal Processing
Digital signal processing (DSP) is used in a wide range of applications including voice processing, image processing, digital communications, the transfer of data over the Internet and image and data compression. This text covers all these topics at a level appropriate for senior undergraduates or first year graduate students.