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Lauren Marie
Lauren Marie

Learn and Apply Digital Signal Processing with MATLAB Examples and Projects: Based on the Book by Sanjit K. Mitra


Digital Signal Processing: A Computer-Based Approach by Sanjit K. Mitra (4th Edition)




Digital signal processing (DSP) is a branch of engineering that deals with the analysis, design, and implementation of systems that process signals, such as audio, speech, image, video, radar, and biomedical signals. DSP is essential for many applications in communications, multimedia, entertainment, medicine, security, and science.




digital signal processing sanjit mitra 4th edition pdf.58



One of the most popular and comprehensive books on DSP is Digital Signal Processing: A Computer-Based Approach by Sanjit K. Mitra, a professor emeritus at the University of California - Santa Barbara. The book, now in its fourth edition, introduces the tools used in the analysis and design of discrete-time systems for DSP. The book is intended for a course on DSP for seniors or first-year graduate students who have a background in calculus, linear algebra, and basic circuit theory.


In this article, we will review the main features of the book by Mitra, summarize its contents, and discuss how it can help readers learn and apply DSP.


Features of the book




The book by Mitra is organized into 13 chapters that cover topics such as discrete-time signals and systems, frequency analysis, sampling and reconstruction, z-transforms, discrete Fourier transforms (DFTs), fast Fourier transforms (FFTs), digital filter design, finite impulse response (FIR) filters, infinite impulse response (IIR) filters, multirate signal processing, adaptive filters, wavelets, and DSP applications.


The fourth edition of the book contains several new features that enhance its quality and usefulness. Some of these features are:



  • The extensive use of MATLAB-based examples that illustrate how to implement DSP algorithms and techniques using a popular software tool.



  • A major reorganization of material that improves the logical flow and clarity of presentation. Chapters 2, 3, and 4 have been reorganized into three new chapters that focus on discrete-time signals and systems; linear time-invariant systems; frequency analysis: continuous-time signals; frequency analysis: discrete-time signals; sampling and reconstruction; z-transforms; DFTs; FFTs; digital filter design; FIR filters; IIR filters; multirate signal processing; adaptive filters; wavelets; and DSP applications.



  • Worked-out examples that explain new and difficult concepts and expose readers to real-life signal processing problems.



  • The inclusion of new topics such as compressed sensing, sparse signal processing, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), singular value decomposition (SVD), principal component analysis (PCA), independent component analysis (ICA), linear prediction coding (LPC), vector quantization (VQ), speech coding standards, JPEG2000 image compression standard, biorthogonal wavelets, lifting scheme for wavelet construction.



  • A CD that accompanies the textbook includes all of the MATLAB programs used in the book as well as additional material such as solutions to selected problems, lecture slides, laboratory experiments.



The benefits of using a computer-based approach for DSP are manifold. A computer-based approach allows readers to:



  • Visualize signals and systems in different domains (time, frequency, z-plane) using graphical tools.



  • Verify theoretical results using numerical simulations.



  • Experiment with different parameters and scenarios using interactive tools.



  • Design and test practical DSP systems using real-world data.



  • Learn how to use a widely used software tool for DSP research and development.



Chapter summaries




In this section, we will briefly summarize the main concepts and techniques discussed in each chapter of the book by Mitra. We will also mention some examples of applications and problems solved in each chapter.



ChapterSummaryExamples


1. IntroductionThis chapter introduces DSP concepts such as signals, systems, analog-to-digital conversion, digital-to-analog conversion, quantization, aliasing, and Nyquist sampling theorem.- Sampling a sinusoidal signal- Quantizing a speech signal- Aliasing a musical note


2. Discrete-Time Signals and SystemsThis chapter covers discrete-time signals such as unit impulse, unit step, unit ramp, exponential, sinusoidal, complex exponential, and periodic signals. It also covers discrete-time systems such as linear, time-invariant, causal, stable, memoryless, invertible, and recursive systems. It also introduces convolution sum, correlation, and difference equations.- Generating discrete-time signals using MATLAB- Plotting discrete-time signals using stem function- Computing convolution sum using MATLAB- Solving difference equations using MATLAB- Finding impulse response of a system using MATLAB


3. Linear Time-Invariant SystemsThis chapter discusses properties of linear time-invariant (LTI) systems such as linearity, time-invariance, causality, stability, memorylessness, invertibility, and recursiveness. It also introduces system representation methods such as block diagrams, signal flow graphs, and state-space models. It also covers system analysis methods such as impulse response, frequency response, transfer function, and pole-zero plots.- Finding impulse response of an LTI system using MATLAB- Finding frequency response of an LTI system using MATLAB- Finding transfer function of an LTI system using MATLAB- Plotting pole-zero plots of an LTI system using MATLAB- Finding state-space model of an LTI system using MATLAB


4. Frequency Analysis: Continuous-Time SignalsThis chapter reviews continuous-time signals such as Fourier series, Fourier transform, Laplace transform, and their properties. It also covers frequency analysis methods such as magnitude spectrum, phase spectrum, power spectrum, energy spectrum, bandwidth, and filtering.- Finding Fourier series coefficients of a periodic signal using MATLAB- Finding Fourier transform of a non-periodic signal using MATLAB- Finding Laplace transform of a signal using MATLAB- Plotting magnitude spectrum and phase spectrum of a signal using MATLAB- Plotting power spectrum and energy spectrum of a signal using MATLAB- Designing low-pass filter using MATLAB


5. Frequency Analysis: Discrete-Time SignalsThis chapter covers discrete-time signals such as discrete-time Fourier series (DTFS), discrete-time Fourier transform (DTFT), z-transform, and their properties. It also covers frequency analysis methods such as magnitude spectrum, phase spectrum, power spectrum, energy spectrum, bandwidth, and filtering.- Finding DTFS coefficients of a periodic signal using MATLAB- Finding DTFT of a non-periodic signal using MATLAB- Finding z-transform of a signal using MATLAB- Plotting magnitude spectrum and phase spectrum of a signal using MATLAB- Plotting power spectrum and energy spectrum of a signal using MATLAB- Designing high-pass filter using MATLAB


6. Sampling and ReconstructionThis chapter discusses sampling and reconstruction methods such as ideal sampling, practical sampling, ideal reconstruction, practical reconstruction, sampling theorem, aliasing effect, anti-aliasing filter design,- Sampling a continuous-time signal using MATLAB- Reconstructing a continuous-time signal from its samples using MATLAB- Demonstrating aliasing effect using MATLAB- Designing anti-aliasing filter using MATLAB- Designing interpolation filter using MATLAB


7. Z-TransformsThis chapter covers z-transforms


ChapterSummaryExamples


7. Z-TransformsThis chapter covers z-transforms and their properties such as linearity, time shifting, scaling, differentiation, convolution, initial value theorem, final value theorem, and partial fraction expansion. It also covers inverse z-transform methods such as inspection, long division, residue method, and power series expansion.- Finding z-transform of a signal using MATLAB- Finding inverse z-transform of a function using MATLAB- Finding properties of z-transform using MATLAB- Solving difference equations using z-transform and MATLAB


8. Discrete Fourier TransformsThis chapter covers discrete Fourier transforms (DFTs) and their properties such as linearity, symmetry, periodicity, conjugation, time reversal, time shifting, frequency shifting, modulation, convolution, correlation, parsingval's theorem, and circular convolution. It also covers DFT computation methods such as direct method, matrix method, and radix-2 decimation-in-time (DIT) and decimation-in-frequency (DIF) algorithms.- Finding DFT of a signal using MATLAB- Finding inverse DFT of a function using MATLAB- Finding properties of DFT using MATLAB- Computing DFT using matrix method and radix-2 algorithms using MATLAB- Performing circular convolution using DFT and MATLAB


9. Fast Fourier TransformsThis chapter covers fast Fourier transforms (FFTs) and their advantages over DFTs in terms of computational complexity and speed. It also covers FFT algorithms such as radix-2 DIT and DIF algorithms, radix-4 DIT and DIF algorithms, and mixed-radix algorithms. It also covers FFT applications such as spectrum analysis, filtering, correlation, and convolution.- Computing FFT of a signal using MATLAB- Computing inverse FFT of a function using MATLAB- Comparing FFT and DFT in terms of computation time and accuracy using MATLAB- Performing spectrum analysis using FFT and MATLAB- Performing filtering using FFT and MATLAB- Performing correlation and convolution using FFT and MATLAB


10. Digital Filter DesignThis chapter covers digital filter design methods such as window method, frequency sampling method, optimal filter design method, least-squares method, Parks-McClellan algorithm, Butterworth filter design method, Chebyshev filter design method, inverse Chebyshev filter design method, elliptic filter design method,


ChapterSummaryExamples


10. Digital Filter DesignThis chapter covers digital filter design methods such as window method, frequency sampling method, optimal filter design method, least-squares method, Parks-McClellan algorithm, Butterworth filter design method, Chebyshev filter design method, inverse Chebyshev filter design method, elliptic filter design method, Bessel filter design method, and impulse invariant transformation method. It also covers digital filter types such as lowpass, highpass, bandpass, bandstop, and allpass filters.- Designing FIR filters using window method and frequency sampling method using MATLAB- Designing FIR filters using optimal filter design method and Parks-McClellan algorithm using MATLAB- Designing IIR filters using Butterworth, Chebyshev, inverse Chebyshev, elliptic, and Bessel filter design methods using MATLAB- Designing IIR filters using impulse invariant transformation method using MATLAB- Plotting magnitude response and phase response of digital filters using MATLAB


11. Finite Impulse Response FiltersThis chapter covers finite impulse response (FIR) filters and their advantages and disadvantages over infinite impulse response (IIR) filters. It also covers FIR filter structures such as direct form, cascade form, parallel form, linear phase form, and lattice form. It also covers FIR filter implementation issues such as quantization effects, round-off noise, limit cycles, overflow oscillations, and scaling.- Implementing FIR filters using direct form, cascade form, parallel form, linear phase form, and lattice form structures using MATLAB- Analyzing quantization effects, round-off noise, limit cycles, overflow oscillations, and scaling issues in FIR filter implementation using MATLAB- Comparing FIR and IIR filters in terms of stability, phase distortion, group delay, and computational complexity using MATLAB


12. Infinite Impulse Response FiltersThis chapter covers infinite impulse response (IIR) filters and their advantages and disadvantages over finite impulse response (FIR) filters. It also covers IIR filter structures such as direct form I, direct form II, cascade form, parallel form, transposed form, and lattice form. It also covers IIR filter implementation issues such as quantization effects, round-off noise, limit cycles, overflow oscillations, and scaling.- Implementing IIR filters using direct form I, direct form II, cascade form, parallel form, transposed form, and lattice form structures using MATLAB- Analyzing quantization effects, round-off noise, limit cycles, overflow oscillations, and scaling issues in IIR filter implementation using MATLAB- Comparing IIR and FIR filters in terms of stability, phase distortion, group delay, and computational complexity using MATLAB


13. Multirate Signal ProcessingThis chapter covers multirate signal processing techniques such as decimation, interpolation, polyphase decomposition, multistage implementation, sample rate conversion, filter banks, quadrature mirror filters (QMFs), and wavelet transform. It also covers multirate signal processing applications such as speech coding,


ChapterSummaryExamples


13. Multirate Signal ProcessingThis chapter covers multirate signal processing techniques such as decimation, interpolation, polyphase decomposition, multistage implementation, sample rate conversion, filter banks, quadrature mirror filters (QMFs), and wavelet transform. It also covers multirate signal processing applications such as speech coding, image compression, audio enhancement, digital audio broadcasting (DAB), and software-defined radio (SDR).- Performing decimation and interpolation of a signal using MATLAB- Performing polyphase decomposition and multistage implementation of a filter using MATLAB- Performing sample rate conversion of a signal using MATLAB- Implementing filter banks and QMFs using MATLAB- Implementing wavelet transform and inverse wavelet transform using MATLAB- Comparing different multirate techniques in terms of computational complexity and performance using MATLAB


14. Adaptive FiltersThis chapter covers adaptive filters and their advantages over fixed filters in dealing with nonstationary signals and unknown system parameters. It also covers adaptive filter algorithms such as least mean square (LMS), normalized LMS (NLMS), recursive least square (RLS), and affine projection algorithm (APA). It also covers adaptive filter applications such as system identification, noise cancellation, channel equalization, and echo cancellation.- Implementing adaptive filters using LMS, NLMS, RLS, and APA algorithms using MATLAB- Comparing adaptive filter algorithms in terms of convergence rate, stability, complexity, and robustness using MATLAB- Performing system identification using adaptive filters and MATLAB- Performing noise cancellation using adaptive filters and MATLAB- Performing channel equalization using adaptive filters and MATLAB- Performing echo cancellation using adaptive filters and MATLAB


15. WaveletsThis chapter covers wavelets and their advantages over Fourier transform in analyzing signals with time-varying frequency components. It also covers wavelet theory such as continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet basis functions, wavelet families, and multiresolution analysis (MRA). It also covers wavelet applications such as signal denoising, signal compression, signal feature extraction, and signal classification.- Implementing CWT and DWT of a signal using MATLAB- Plotting wavelet coefficients and scalograms of a signal using MATLAB- Choosing appropriate wavelet basis functions and wavelet families for different signals using MATLAB- Performing MRA of a signal using MATLAB- Performing signal denoising using wavelets and MATLAB- Performing signal compression using wavelets and MATLAB- Performing signal feature extraction using wavelets and MATLAB- Performing signal classification using wavelets and MATLAB


Conclusion




In this article, we have reviewed the main features of the book Digital Signal Processing: A Computer-Based Approach by Sanjit K. Mitra. The book is a comprehensive and accessible introduction to DSP that covers both theory and practice. The book uses a computer-based approach that emphasizes the use of MATLAB for implementing DSP algorithms and techniques. The book covers topics such as discrete-time signals and systems, frequency analysis, sampling and reconstruction, z-transforms, discrete Fourier transforms, fast Fourier transforms, digital filter design, finite impulse response filters, infinite impulse response filters, multirate signal processing, adaptive filters, and wavelets. The book also provides numerous examples, problems, exercises, projects, and MATLAB programs that illustrate the concepts and applications of DSP.


The book is suitable for a course on DSP for seniors or first-year graduate students who have a background in calculus, linear algebra, and basic circuit theory. The book can also be used as a reference for professionals and researchers who want to learn and apply DSP.


Frequently Asked Questions




Q: What are the prerequisites for reading this book?


A: The prerequisites for reading this book are calculus, linear algebra, and basic circuit theory.


Q: What are the benefits of using a computer-based approach for DSP?


A: The benefits of using a computer-based approach for DSP are that it allows readers to visualize signals and systems in different domains, verify theoretical results using numerical simulations, experiment with different parameters and scenarios using interactive tools, design and test practical DSP systems using real-world data, and learn how to use a widely used software tool for DSP research and development.


Q: What are the differences between FIR and IIR filters?


A: FIR filters are filters that have finite impulse responses, meaning that their output depends only on a finite number of past input samples. IIR filters are filters that have infinite impulse responses, meaning that their output depends on an infinite number of past input samples. FIR filters are inherently stable and can be designed to have linear phase. IIR filters can be unstable or nonlinear phase but can achieve higher performance with lower order than FIR filters.


Q: What are the advantages of wavelets over Fourier transform?


A: Wavelets are functions that can represent signals with time-varying frequency components more efficiently than Fourier transform. Wavelets can capture both the frequency content and the temporal localization of a signal. Wavelets can also adapt to different scales or resolutions of a signal.


Q: How can I get more information and resources on DSP?


A: You can get more information and resources on DSP from the following sources:



The companion website of the book by Mitra: https://www.mheducation.com/highered/product/digital-signal-processing-computer-based-approach-mitra/M9780078028151.h


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