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Digital Signal Processing by Ramesh Babu is a popular textbook that covers the fundamentals and applications of digital signal processing (DSP) in various domains such as speech and image processing, digital communications, and biomedical engineering. The book provides a comprehensive and systematic introduction to the theory and design of discrete-time systems, frequency domain analysis, discrete Fourier transform, z-transform, digital filter design, fast Fourier transform, and sampling theorem. The book also includes numerous solved examples, exercises, and MATLAB programs to illustrate the concepts and techniques of DSP.
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Digital Signal Processing by Ramesh Babu is a suitable textbook for undergraduate and postgraduate students of engineering and science who are studying DSP as a core or elective subject. The book can also be used as a reference by researchers and professionals who are working in the field of DSP or related areas. The book covers the latest developments and trends in DSP such as adaptive filters, wavelets, multirate signal processing, and digital signal processors.
The book is divided into 14 chapters that cover the following topics:
Introduction: This chapter gives an overview of the basic concepts and applications of DSP.
Application of Digital Signal Processing (Speech and Image Processing): This chapter discusses the use of DSP techniques in speech and image processing such as speech synthesis, speech recognition, speech enhancement, image compression, image enhancement, and image segmentation.
Discrete Time Systems: This chapter introduces the concept of discrete-time signals and systems and their properties such as linearity, time-invariance, causality, stability, convolution, correlation, and impulse response.
Frequency Domain Characterization or Discrete-Time System: This chapter explains the frequency domain representation and analysis of discrete-time signals and systems using Fourier series, Fourier transform, discrete-time Fourier transform, and discrete Fourier transform.
Discrete Fourier Transform: This chapter describes the computation and properties of discrete Fourier transform (DFT) and its applications such as spectrum analysis, filtering, and correlation.
z-Transform: This chapter introduces the concept of z-transform and its properties such as region of convergence, inverse z-transform, pole-zero plot, and transfer function. It also discusses the relationship between z-transform and DFT.
Digital Filter Design: This chapter presents the design methods and techniques for digital filters such as finite impulse response (FIR) filters and infinite impulse response (IIR) filters. It also covers the topics such as filter specifications, frequency response, phase response, group delay, filter structures, filter realization, filter stability, filter implementation, and filter comparison.
Fast Fourier Transform: This chapter explains the fast algorithms for computing DFT such as radix-2 decimation-in-time (DIT) algorithm, radix-2 decimation-in-frequency (DIF) algorithm, radix-4 algorithm, split-radix algorithm, chirp-z algorithm, and prime factor algorithm. It also discusses the applications of fast Fourier transform (FFT) such as fast convolution, fast correlation, spectral analysis, and power spectrum estimation.
Sampling Theorem: This chapter describes the sampling process and its effects on continuous-time signals such as aliasing and reconstruction. It also covers the topics such as sampling rate conversion, oversampling, undersampling, interpolation, decimation, multirate signal processing, polyphase decomposition, and filter banks.
Adaptive Filters: This chapter introduces the concept of adaptive filters and their applications such as noise cancellation, echo cancellation 061ffe29dd