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10958 مرتبه مشاهده شده
Digital signal processing: principles, algorithms, and applications
Proakis, John G.
- ISBN:0133943389
- Call Number : TK 5102 .5 .P6773 1996
- Main Entry: Proakis, John G.
- Title:Digital signal processing: principles, algorithms, and applications / John G. Proakis, Dimitris G. Manolakis.
- Edition:3rd ed.
- Publisher:New York : Prentice-Hall,Inc., 1996.
- Physical Description:xv, 968+[53] p.: ill.; 24 cm
- Notes:Also electronic format is avaiable
- Notes:Includes bibliographical references and index
- Subject:Signal processing-- Digital techniques.
- Added Entry:Manolakis, Dimitris G.
- Added Entry:Proakis, John G, Introduction to digital signal processing.
- Added Entry:Title.
Digital signal processing: principles, algorithms, and applications
- Digital Signal Processing: Principles, Algorithms & Applications (3rd Ed.)
- Copyright
- Contents
- Preface
- Ch1 Introduction
- Ch2 Discrete-Time Signals & Systems
- 2.1 Discrete-Time Signals
- 2.2 Discrete-Time Systems
- 2.3 Analysis of Discrete-Time Linear Time-Invariant Systems
- 2.3.1 Techniques for Analysis of Linear Systems
- 2.3.2 Resolution of Discrete-Time Signal into Impulses
- 2.3.3 Response of LTI Systems to Arbitrary Inputs: Convolution Sum
- 2.3.4 Properties of Convolution & Interconnection of LTI Systems
- 2.3.5 Causal Linear Time-Invariant Systems
- 2.3.6 Stability of Linear Time-invariant Systems
- 2.3.7 Systems with Finite-Duration & Infinite-Duration Impulse Response
- 2.4 Discrete-Time Systems described by Difference Equations
- 2.5 Implementation of Discrete-Time Systems
- 2.6 Correlation of Discrete-Time Signals
- 2.7 Summary & References
- Problems
- Ch3 z-Transform & its Application to Analysis of LTI Systems
- 3.1 z-Transform
- 3.2 Properties of z-Transform
- 3.3 Rational z -Transforms
- 3.4 Inversion of z-Transform
- 3.5 One-Sided z-Transform
- 3.6 Analysis of Linear Time-Invariant Systems in z-Domain
- 3.6.1 Response of Systems with Rational System Functions
- 3.6.2 Response of Pole-Zero Systems with Nonzero Initial Conditions
- 3.6.3 Transient & Steady-State Responses
- 3.6.4 Causality & Stability
- 3.6.5 Pole-Zero Cancellations
- 3.6.6 Multiple-Order Poles & Stability
- 3.6.7 Schur-Cohn Stability Test
- 3.6.8 Stability of Second-Order Systems
- 3.7 Summary & References
- Problems
- Ch4 Frequency Analysis of Signals & Systems
- 4.1 Frequency Analysis of Continuous-Time Signals
- 4.2 Frequency Analysis of Discrete-Time Signals
- 4.2.1 Fourier Series for Discrete-Time Periodic Signals
- 4.2.2 Power Density Spectrum of Periodic Signals
- 4.2.3 Fourier Transform of Discrete-Time Aperiodic Signals
- 4.2.4 Convergence of Fourier Transform
- 4.2.5 Energy Density Spectrum of Aperiodic Signals
- 4.2.6 Relationship of Fourier Transform to z-Transform
- 4.2.7 Cepstrum
- 4.2.8 Fourier Transform of Signals with Poles on Unit Circle
- 4.2.9 Sampling Theorem Revisited
- 4.2.10 Frequency-Domain Classification of Signals: Concept of Bandwidth
- 4.2.11 Frequency Ranges of Some Natural Signals
- 4.2.12 Physical & Mathematical Dualities
- 4.3 Properties of Fourier Transform for Discrete-Time Signals
- 4.4 Frequency-Domain Characteristics of LTI Systems
- 4.4.1 Response to Complex Exponential & Sinusoidal Signals: Frequency Response Function
- 4.4.2 Steady-State & Transient Response to Sinusoidal Input Signals
- 4.4.3 Steady-State Response to Periodic Input Signals
- 4.4.4 Response to Aperiodic Input Signals
- 4.4.5 Relationships between System Function & Frequency Response Function
- 4.4.6 Computation of Frequency Response Function
- 4.4.7 Input-Output Correlation Functions & Spectra
- 4.4.8 Correlation Functions & Power Spectra for Random Input Signals
- 4.5 LTI Systems as Frequency-Selective Filters
- 4.6 Inverse Systems & Deconvolution
- 4.7 Summary & References
- Problems
- Ch5 Discrete Fourier Transform: its Properties & Applications
- Ch6 Efficient Computation of DFT: Fast Fourier Transform Algorithms
- Ch7 Implementation of Discrete-Time Systems
- Ch8 Design of Digital Filters
- 8.1 General Considerations
- 8.2 Design of FIR Filters
- 8.2.1 Symmetric & Antisymmetric FIR Filters
- 8.2.2 Design of Linear-Phase FIR Filters using Windows
- 8.2.3 Design of Linear-Phase FIR Filters by Frequency-Sampling Method
- 8.2.4 Design of Optimum Equiripple Linear-Phase FIR Filters
- 8.2.5 Design of FIR Differentiators
- 8.2.6 Design of Hilbert Transformers
- 8.2.7 Comparison of Design Methods for Linear-Phase FIR Filters
- 8.3 Design of IIR Fllters from Analog Filters
- 8.3.1 IIR Filter Design by Approximation of Derivatives
- 8.3.2 IIR Filter Design by Impulse lnvariance
- 8.3.3 IIR Filter Design by Bilinear Transformation
- 8.3.4 Matched-z Transformation
- 8.3.5 Characteristics of Commonly Used Analog Filters
- 8.3.6 Some Examples of Digital Filter Designs based on Bilinear Transformation
- 8.4 Frequency Transformations
- 8.5 Design of Digital Filters based on Least-Squares Method
- 8.6 Summary & References
- Problems
- Ch9 Sampling & Reconstruction of Signals
- Ch10 Multirate Digital Signal Processing
- 10.1 Introduction
- 10.2 Decimation by Factor D
- 10.3 Interpolation by Factor I
- 10.4 Sampling Rate Conversion by Rational Factor 1/D
- 10.5 Filter Design & Implementation for Sampling-Rate Conversion
- 10.6 Multistage Implementation of Sampling-Rate Converslon
- 10.7 Sampling-Rate Conversion of Bandpass Signals
- 10.8 Sampling-Rate Conversion by Arbitrary Factor
- 10.9 Applicatiows of Multirate Signal Processing
- 10.9.1 Design of Phase Shifters
- 10.9.2 Interfacing of Digital Systems with Different Sampling Rates
- 10.9.3 Implementation of Narrowband Lowpass Filters
- 10.9.4 Implementation of Digital Filter Banks
- 10.9.5 Subband Coding of Speech Signals
- 10.9.6 Quadrature Mlrror Filters
- 10.9.7 Transmultiplexers
- 10.9.8 Oversampling A/D & D/A Conversion
- 10.10 Summary & References
- Problems
- Ch11 Linear Prediction & Optimum Linear Filters
- 11.1 Innovations Representation of Statlonary Random Process
- 11.2 Forward & Backward Linear Prediction
- 11.3 Solution of Normal Equations
- 11.4 Properties of Linear Prediction-Error Filters
- 11.5 AR Lattice & ARMA Lattice-Ladder Filters
- 11.6 Wiener Filters for Filtering & Prediction
- 11.7 Summary & References
- Problems
- Ch12 Power Spectrum Estimation
- 12.1 Estimation of Spectra from Finite-Duration Observatlons of Signals
- 12.2 Nonparametric Methods for Power Spectrum Estimation
- 12.2.1 Bartlett Method: Averaging Periodograms
- 12.2.2 Welch Method: Averaging Modified Periodograrns
- 12.2.3 Blackman & Tukey Method: Smoothing Periodogram
- 12.2.4 Performance Characteristics of Nonparametric Power Spectrum Estimators
- 12.2.5 Computational Requirements of Nonpararnetric Power Spectrum Estimates
- 12.3 Parametric Methods for Power Spectrum Estimation
- 12.3.1 Relationships between Autocorrelation & Model Parameters
- 12.3.2 Yule-Walker Method for AR Model Parameters
- 12.3.3 Burg Method for AR Model Parameters
- 12.3.4 Unconstrained Least-Squares Method for AR Model Parameters
- 12.3.5 Sequential Estimation Methods for AR Model Parameters
- 12.3.6 Selection of AR Model Order
- 12.3.7 MA Model for Power Spectrum Estimation
- 12.3.8 ARMA Model for Power Spectrum Estimation
- 12.3.9 Some Experimental Results
- 12.4 Minimum Variance Spectral Estimation
- 12.5 Eigenanalysis Algorithms for Spectrum Estimation
- 12.6 Summary & References
- Problems
- AppA Random Signals, Correlation Functions & Power Spectra
- AppB Random Number Generators
- AppC Tables of Transition Coefficients for Design of Linear-Phase FIR Filters
- AppD List of MatLab Functions
- References & Bibliography
- Index