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1. Modern Signal Analysis and Processing (Zhang Xudong (Communication Technology))
  
  This book systematically and deeply introduces the foundation of modern digital signal analysis and processing and some widely used algorithms. The first four chapters introduce the important foundations for studying and learning modern digital signal processing, including random signal models, an overview of estimation theory, optimal filter theory, least squares filtering and Kalman filtering. These contents are the basic knowledge of statistical methods for signal processing; the four chapters in Part 2 discuss in detail several types of widely used typical algorithms, including adaptive filtering algorithms, power spectrum estimation algorithms, high-order statistics and cyclic statistics, and blind source separation of signals; Part 3 includes time-frequency analysis, wavelet transform principles and applications, and sparse analysis and compressed sensing of signals. This book introduces in detail some cutting-edge topics that have received widespread attention in recent years, such as EM algorithm, particle filtering, independent component analysis, subspace method for blind source separation, sparse representation and compressed sensing, etc. Some preliminary contents of spatial array signal processing will be interspersed in relevant chapters, but not in separate chapters. In writing this book, we pay attention to both the advancement and systematicness of the content and the readability of the content. Chapter 0 Introduction 0.1 The main content of this book 0.2 Several application examples of modern signal processing 0.3 Discussion of some basic issues in signal processing 0.4 A brief historical overview Volume I Statistical methods for signal processing Chapter 1 Random signal foundation and model 1.1 Random signal foundation 1.1.1 Probability density function representation of random process 1.1.2 Basic characteristics of random process 1.2 Matrix characteristics of random signal vector 1.2.1 Autocorrelation matrix 1.2.2 Cross-correlation matrix 1.2.3 Vector signal correlation matrix 1.3 Common signal examples 1.3.1 Independent and identically distributed and white noise 1.3.2 Complex sine plus noise 1.3.3 Real Gaussian process* 1.3.4 Complex Gaussian process* 1.3.5 Mixed Gaussian process 1.3.6 Gauss-Markov process 1.4 Expansion of random signal 1.4.1 Orthogonal expansion of random signal 1.4.2 Orthogonalization of basis vector set 1. 4.3 KL Transformation*1.4.4 Principal Component Analysis1.4.5 Representing a Random Signal by a Set of Orthogonal Random Sequences1.5 Power Spectral Density of Random Signals1.5.1 Definition and Properties of Power Spectral Density1.5.2 Random Signals Passing Through Linear Systems1.5.3 Relationship between Continuous and Discrete Random Signals1.6 Rational Fraction Model of Random Signals1.6.1 Spectral Decomposition Theorem1.6.2 ARMA Model of Random Signals1.6.3 Further Discussion on the Representation of Random Signals1.6.4 Relationship between Autocorrelation and Model Parameters*1.6.5 Extension of ARMA Model - ARIMA Model1.7 Summary and Further Reading Exercises References Chapter 2 Foundations of Estimation Theory2.1 Basic Classical Estimation Problems2.1.1 Basic Concepts and Performance Parameters of Classical Estimation2.1.2 Several Commonly Used Estimators2.2 Cramer-Rao Lower Bound2.3 Maximum Likelihood Estimation (MLE) 2.4 Bayesian Estimation 2.4.1 Minimum Mean Square Error Bayesian Estimation 2.4.2 Other Forms of Bayesian Estimation 2.5 Linear Bayesian Estimator 2.6 Least Squares Estimation 2.6.1 Weighted Least Squares Estimation 2.6.2 Regularized Least Squares Estimation 2.6.3 LS Estimation for Complex Data* 2.7 EM Algorithm 2.7.1 Special Cases and Extensions of EM Algorithm 2.7.2 EM Algorithm for Gaussian Mixture Models 2.8 Summary and Further Reading Exercises References Chapter 3 Optimal Filters 3.1 Wiener Filtering 3.1.1 Wiener Filtering in Practical Problems 3.1.2 Derivation of Wiener Filtering from the Perspective of Estimation Theory 3.1.3 Wiener Filter-Orthogonality Principle 3.1.4 FIR Wiener Filter 3.1.5 IIR Wiener Filter 3.1.6 Application Example - Optimal Linear Equalizer for Communication Systems* 3.2 Array Beamforming and Wiener Filtering 3.2.1 Basics of Array Beamforming 3.2.2 Wiener Filtering and Beamforming 3.2.3 MVDR Beamformer 3.3 Optimal Linear Prediction 3.3.1 Forward Linear Prediction 3.3.2 Backward Linear Prediction 3.3.3 Levinson-Durbin Algorithm 3.3.4 Lattice Prediction Error Filter 3.3.5 Properties of Prediction Error Filter * 3.4 Generalization of Lattice Filter Structure 3.4.1 AR Model and All-Pole Lattice 3.4.2 Cholesky Decomposition 3.4.3 Lattice Structure of Wiener Filter 3.5 Least Squares Filter 3.5.1 Boundary Problem of LS Filter 3.5.2 Orthogonality Principle of LS 3.5.3 Several Properties of Least Squares Filter 3.5.4 Least Squares Linear Prediction 3.5.5 Regularized Least Squares Filter * 3.5.6 Least Squares Filter Based on Nonlinear Function 3.6 Singular Value Decomposition to Compute LS Problem * 3.7 Total Least Squares (TLS) 3.8 Summary and Further Reading Chapter 3 Appendix Continuous-Time Wiener Filter Exercises References Chapter 4 Kalman Filter and Its Extensions 4.1 Scalar Kalman Filter 4.1.1 Optimal Recursive Estimation of Scalar Random States 4.1.2 Comparison with Wiener Filter 4.2 Standard Kalman Filter in Vector Form 4.2.1 Vector Kalman Filter Model 4.2.2 Derivation of Vector Kalman Filter * 4.3 Some Variations of Kalman Filter 4.3.1 Kalman Filters for Different Forms of State Equations 4.3.2 Kalman Predictor 4.3.3 Kalman Information Filter 4.3.4 Steady-State Kalman Filter 4.3.5 Kalman QR Decomposition Filter 4.3.6 Simple Unexcited Dynamical System 4.4 Kalman Nonlinear Filter I: Extended Kalman Filter (EKF) * 4.5 Kalman Nonlinear Filter II: Unscented Kalman Filter 4.5.1 Unscented Transform (UT) 4.5.2 UKF for Nonlinear Systems with Additive Noise 4.5.3 UKF for General Nonlinear Systems 4.6 Bayesian Filtering*4.7 Particle Filtering4.7.1 Monte Carlo Simulation and Sequence Importance Sampling4.7.2 Particle Filtering Algorithm4.7.3 Improvement of Particle Filtering - Gaussian Particle Filtering4.8 Chapter Summary and Further Reading ExercisesReferencesChapter 5 Adaptive Filters5.1 Classification and Application of Adaptive Filtering5.2 Steepest Descent Method5.3 LMS Adaptive Filtering Algorithm5.3.1 LMS Algorithm5.3.2 Convergence Analysis of LMS Algorithm5.3.3 Some Improved LMS Algorithms*5.3.4 Sparse LMS Algorithm*5.3.5 Affine Projection Algorithm5.4 Recursive LS Algorithm (RLS) 5.4.1 Basic RLS Algorithm 5.4.2 Convergence Analysis of RLS Algorithm 5.5 Simulation Results of LMS and RLS Algorithms for Adaptive Equalizer 5.6 Projection Operator Recursion and LS Lattice Filter 5.6.1 Representation of LS Filter by Vector Space Operator Method 5.6.2 Order Recursion Formula of Projection Operator 5.6.3 Time Recursion Formula of Projection Operator 5.6.4 Least Squares Lattice (LSL) Algorithm * 5.7 Fast Transverse LS Adaptive Filtering Algorithm (FTF) 5.7.14 basic filters 5.7.2 Update of transverse filter operator 5.7.3 FTF algorithm * 5.8 QR decomposition RLS algorithm 5.8.1 LDU decomposition RLS algorithm 5.8.2 Correspondence between RLS and Kalman filter * 5.9 Adaptive filter with IIR structure * 5.10 Examples of nonlinear adaptive filtering 5.11 Examples of application of adaptive filter 5.11.1 Further discussion of adaptive equalization 5.11.2 Application of adaptive interference cancellation * 5.11.3 Adaptive beamforming algorithm * 5.12 Examples of adaptive filtering algorithms without expected response: Blind equalization 5.12.1 Constant modulus algorithm (CMA) 5.12.2 A type of blind equalization algorithm (Bussgang algorithm) 5.12.3 Introduction to the Blind Deconvolution Algorithm 5.13 Summary and Further Reading Exercises References Chapter 6 Power Spectrum Estimation 6.1 Classical Spectrum Estimation Methods 6.1.1 Periodogram Method 6.1.2 Improved Periodogram 6.1.3 Blackman-Tukey Method 6.2 AR Model Spectrum Estimation 6.2.1 Maximum Entropy Spectrum Estimation 6.2.2 Covariance Method for AR Model Spectrum Estimation 6.2.3 Improved Covariance Method 6.2.4 Autocorrelation Method 6.2.5 Burg Algorithm 6.2.6 Improved AR Model Spectrum Estimation Step-by-step discussion 6.3 System model order selection 6.4 MA model spectrum estimation 6.5 ARMA model spectrum estimation 6.5.1 Improved Yule-Walker equation method * 6.5.2 Akaike\'s nonlinear iterative algorithm * 6.6 Minimum variance spectrum estimation 6.7 Frequency estimation using feature space 6.7.1 Pisarenko spectrum decomposition 6.7.2 MUSIC method 6.7.3 Model order estimation * 6.8 ESPRIT algorithm 6.8.1 Basic ESPRIT algorithm 6.8.2LS?ESPRIT and TLS?ESPRIT algorithms*6.9 DOA estimation of spatial linear arrays6.10 Some experimental results of power spectrum estimation6.10.1 Simulation comparison of classical method and AR model method for different signal types6.10.2 Experimental results of harmonic estimation6.11 Summary and further reading exercises References Chapter 7 Characteristics and applications of signals beyond second-order stationary statistics7.1 High-order statistics and high-order spectra of signals7.1.1 Definition of high-order cumulants and high-order moments7.1.2 Some mathematical properties of high-order cumulants7.1.3 Definition of high-order spectrum7.1.4 High-order spectra of linear non-Gaussian processes7.1.5 High-order spectra of nonlinear processes*7.2 High-order statistics and estimation of high-order spectra7.2.1 Estimation of high-order statistics 7.2.2 B?R estimation of high-order spectra7.2.3 Indirect estimation method of high-order spectra7.2.4 Application of high-order spectra*7.3 Spectral correlation analysis of cyclostationary signals7.3.1 Concept of cyclostationary signals7.3.2 Spectral correlation function of cyclostationary signals7.3.3 Spectral correlation function of common modulated signals in communication engineering7.3.4 Estimation of spectral correlation function*7.4 Entropy characteristics of random signals7.4.1 Definition and basic properties of entropy7.4.2 KL divergence, mutual information and negative entropy7.4.3 Approximate calculation of entropy7.5 Summary of this chapter and further reading exercises References Chapter 8 Hidden variable analysis of signal processing8.1 Online principal component analysis8.1.1 Generalized Hebian algorithm8.1.2 Projection approximate subspace tracking algorithm - PAST 8.2 Whitening and Orthogonalization of Signal Vectors 8.2.1 Whitening of Signal Vectors 8.2.2 Orthogonalization of Vector Sets 8.3 Description of Blind Source Separation Problem 8.4 Independent Component Analysis - ICA 8.4.1 Basic Principles and Criteria of Independent Component Analysis 8.4.2 Fixed Point Algorithm - Fast ICA 8.4.3 Natural Gradient Algorithm 8.4.4 Nonlinear PCA Algorithm *8.5 BSS Using Second-Order Statistics 8.5.1 SOBI algorithm 8.5.2 Introduction to other second-order statistical blind source separation algorithms * 8.6 Convolution mixture blind source separation 8.6.1 Convolution mixture model 8.6.2 Convolution mixture separation model 8.6.3 Introduction to convolution mixture separation algorithm * 8.7 Introduction to other BSS methods * 8.8 Application and simulation experiment examples 8.9 Chapter summary and further reading exercises References Volume 2 Time-frequency analysis and sparse representation Chapter 9 Time-frequency analysis methods 9.1 Preliminary knowledge of time-frequency analysis 9.1.1 Fourier transform and its limitations 9.1.2 Several basic concepts of time-frequency analysis 9.1.3 Framework and Reisz basis 9. 2 Short-time Fourier transform 9.2.1 Definition and properties of STFT* 9.2.2 Numerical calculation of STFT 9.3 Gabor expansion 9.3.1 Continuous Gabor expansion 9.3.2 Periodic discrete Gabor expansion* 9.4 Fractional Fourier transform 9.4.1 Definition and properties of FRFT 9.4.2 Numerical calculation of FRFT 9.4.3 Brief introduction to the application of FRFT 9.5 Wigner-Ville distribution 9.5.1 Definition and properties of continuous Wigner-Ville distribution 9.5.2 Some examples and problems of WVD 9.5.3 Calculation of WVD through discrete signals * 9.5.4 Radon-Wigner transform * 9.6 General time-frequency distribution: Cohen class 9.6.1 Fuzzy function 9.6.2 Definition and example of Cohen class* 9.7 Fuzzy function re-discussion 9.8 Summary and further reading exercises References Chapter 10 Introduction to wavelet transform principle and application 10.1 Continuous wavelet transform 10.1.1 Definition of CWT 10.1.2 Properties of CWT 10.1.3 Several wavelet examples 10.1.4 Lipschitz index and wavelet transform 10.2 Wavelet transform with scale and displacement discretization 10.3 Multiresolution analysis and orthogonal wavelet basis 10.3.1 Concept of multiresolution analysis 10.3.2 Construction of wavelet basis 10.3.3 Malla of discrete wavelet transform t algorithm 10.4 Biorthogonal wavelet transform 10.5 Wavelet basis example 10.5.1 Daubechies compactly supported wavelet 10.5.2 Biorthogonal wavelet basis example 10.6 Multidimensional space wavelet transform 10.6.1 Two-dimensional separable wavelet transform 10.6.2 Wavelet transform model for digital images 10.7 Wavelet packet decomposition * 10.8 Boundary problems in discrete wavelet transform * 10.9 Lifting and integer wavelet transform 10.9.1 Basic method of lifting wavelet transform 10.9.2 Lifting method for constructing wavelet basis 10.9.3 Several examples of wavelet transform implemented by lifting 10.9.4 Integer wavelet transform * 10.10 Wavelet transform application examples: Image Compression 10.10.1 Tree Representation and Coding of Images in the Wavelet Transform Domain 10.10.2 Embedded Wavelet Zerotree Coding* 10.11 Other Applications of Wavelet Transform 10.11.1 Wavelet Denoising 10.11.2 Introduction to Other Applications 10.12 Summary and Further Reading Exercises Chapter 10 Appendix Subband Coding References* Chapter 11 Sparse Representation and Compressed Sensing of Signals 11.1 Mathematical Foundations of Sparse Representation of Signals 11.1.1 Convex Sets and Convex Functions 11.1.2 Norms 11.1.3 Null Space and Sparsity of Matrix 11.2 Sparse Model Examples of Signals 11.2.1 Compressed Sensing Problem 11.2.2 Lasso Regression Problem - LASSO 11.2.3 Comparison of Different Sparse Problems 11.3 Sparse Model Representation of Signals 11.4 Basic Theory of Sparse Recovery 11.4.1 Uniqueness of (P0) Solution 11.4.2 Uniqueness of (P1) Solution 11.4.3 Solution of (Pε1) Problem 11.5 Compressed Sensing and Sensing Matrix 11.6 Introduction to Sparse Recovery Algorithms 11.6.1 Greedy Algorithms 11.6.2 LAR Algorithm 11.6.3 Lasso\'s Cyclic Coordinate Descent Algorithm 11.6.4 Nearest Neighbor Method and Iterative Shrinkage Algorithm 11.6.5 Iterative Weighted Least Squares Algorithm - IRLS 11.6.6 Online Sparse Recovery Algorithm 11.7 Several Application Examples of Signal Sparse Recovery 11.8 Chapter Summary and Further Reading Exercises References Appendix A Basics of Matrix Theory Appendix B Summary of Optimization Methods Abbreviation Index9 Chapter Summary and Further Reading Exercises References Volume 2 Time-Frequency Analysis and Sparse Representation Chapter 9 Time-Frequency Analysis Methods 9.1 Preliminary knowledge of time-frequency analysis 9.1.1 Fourier transform and its limitations 9.1.2 Several basic concepts of time-frequency analysis 9.1.3 Frame and Reisz basis 9.2 Short-time Fourier transform 9.2.1 Definition and properties of STFT* 9.2.2 Numerical calculation of STFT 9.3 Gabor expansion 9.3.1 Continuous Gabor expansion 9.3.2 Periodic discrete Gabor expansion* 9.4 Fractional Fourier transform 9.4.1 Definition and properties of FRFT 9.4.2 Numerical calculation of FRFT 9.4.3 Brief introduction to the application of FRFT 9.5 Wigner-Ville distribution 9.5.1 Definition and properties of continuous Wigner-Ville distribution 9.5.2 Some examples and problems of WVD 9.5.3 Calculation of WVD from discrete signals * 9.5.4 Radon-Wigner transform * 9.6 General time-frequency distribution: Cohen class 9.6.1 Fuzzy function 9.6.2 Definition and example of Cohen class* 9.7 Fuzzy function re-discussion 9.8 Summary and further reading exercises References Chapter 10 Introduction to wavelet transform principle and application 10.1 Continuous wavelet transform 10.1.1 Definition of CWT 10.1.2 Properties of CWT 10.1.3 Several wavelet examples 10.1.4 Lipschitz index and wavelet transform 10.2 Wavelet transform with scale and displacement discretization 10.3 Multiresolution analysis and orthogonal wavelet basis 10.3.1 Concept of multiresolution analysis 10.3.2 Construction of wavelet basis 10.3.3 Malla of discrete wavelet transform t algorithm 10.4 Biorthogonal wavelet transform 10.5 Wavelet basis example 10.5.1 Daubechies compactly supported wavelet 10.5.2 Biorthogonal wavelet basis example 10.6 Multidimensional space wavelet transform 10.6.1 Two-dimensional separable wavelet transform 10.6.2 Wavelet transform model for digital images 10.7 Wavelet packet decomposition * 10.8 Boundary problems in discrete wavelet transform * 10.9 Lifting and integer wavelet transform 10.9.1 Basic method of lifting wavelet transform 10.9.2 Lifting method for constructing wavelet basis 10.9.3 Several examples of wavelet transform implemented by lifting 10.9.4 Integer wavelet transform * 10.10 Wavelet transform application examples: Image Compression 10.10.1 Tree Representation and Coding of Images in the Wavelet Transform Domain 10.10.2 Embedded Wavelet Zerotree Coding* 10.11 Other Applications of Wavelet Transform 10.11.1 Wavelet Denoising 10.11.2 Introduction to Other Applications 10.12 Summary and Further Reading Exercises Chapter 10 Appendix Subband Coding References* Chapter 11 Sparse Representation and Compressed Sensing of Signals 11.1 Mathematical Foundations of Sparse Representation of Signals 11.1.1 Convex Sets and Convex Functions 11.1.2 Norms 11.1.3 Null Space and Sparsity of Matrix 11.2 Sparse Model Examples of Signals 11.2.1 Compressed Sensing Problem 11.2.2 Lasso Regression Problem - LASSO 11.2.3 Comparison of Different Sparse Problems 11.3 Sparse Model Representation of Signals 11.4 Basic Theory of Sparse Recovery 11.4.1 Uniqueness of (P0) Solution 11.4.2 Uniqueness of (P1) Solution 11.4.3 Solution of (Pε1) Problem 11.5 Compressed Sensing and Sensing Matrix 11.6 Introduction to Sparse Recovery Algorithms 11.6.1 Greedy Algorithms 11.6.2 LAR Algorithm 11.6.3 Lasso\'s Cyclic Coordinate Descent Algorithm 11.6.4 Nearest Neighbor Method and Iterative Shrinkage Algorithm 11.6.5 Iterative Weighted Least Squares Algorithm - IRLS 11.6.6 Online Sparse Recovery Algorithm 11.7 Several Application Examples of Signal Sparse Recovery 11.8 Chapter Summary and Further Reading Exercises References Appendix A Basics of Matrix Theory Appendix B Summary of Optimization Methods Abbreviation Index9 Summary of this chapter and further reading Exercises References Volume 2 Time-Frequency Analysis and Sparse Representation Chapter 9 Time-Frequency Analysis Methods 9.1 Preliminary knowledge of time-frequency analysis 9.1.1 Fourier transform and its limitations 9.1.2 Several basic concepts of time-frequency analysis 9.1.3 Frame and Reisz basis 9.2 Short-time Fourier transform 9.2.1 Definition and properties of STFT* 9.2.2 Numerical calculation of STFT 9.3 Gabor expansion 9.3.1 Continuous Gabor expansion 9.3.2 Periodic discrete Gabor expansion* 9.4 Fractional Fourier transform 9.4.1 Definition and properties of FRFT 9.4.2 Numerical calculation of FRFT 9.4.3 Brief introduction to the application of FRFT 9.5 Wigner-Ville distribution 9.5.1 Definition and properties of continuous Wigner-Ville distribution 9.5.2 Some examples and problems of WVD 9.5.3 Calculation of WVD from discrete signals * 9.5.4 Radon-Wigner transform * 9.6 General time-frequency distribution: Cohen class 9.6.1 Fuzzy function 9.6.2 Definition and example of Cohen class* 9.7 Fuzzy function re-discussion 9.8 Summary and further reading exercises References Chapter 10 Introduction to wavelet transform principle and application 10.1 Continuous wavelet transform 10.1.1 Definition of CWT 10.1.2 Properties of CWT 10.1.3 Several wavelet examples 10.1.4 Lipschitz index and wavelet transform 10.2 Wavelet transform with scale and displacement discretization 10.3 Multiresolution analysis and orthogonal wavelet basis 10.3.1 Concept of multiresolution analysis 10.3.2 Construction of wavelet basis 10.3.3 Malla of discrete wavelet transform t algorithm 10.4 Biorthogonal wavelet transform 10.5 Wavelet basis example 10.5.1 Daubechies compactly supported wavelet 10.5.2 Biorthogonal wavelet basis example 10.6 Multidimensional space wavelet transform 10.6.1 Two-dimensional separable wavelet transform 10.6.2 Wavelet transform model for digital images 10.7 Wavelet packet decomposition * 10.8 Boundary problems in discrete wavelet transform * 10.9 Lifting and integer wavelet transform 10.9.1 Basic method of lifting wavelet transform 10.9.2 Lifting method for constructing wavelet basis 10.9.3 Several examples of wavelet transform implemented by lifting 10.9.4 Integer wavelet transform * 10.10 Wavelet transform application examples: Image Compression 10.10.1 Tree Representation and Coding of Images in the Wavelet Transform Domain 10.10.2 Embedded Wavelet Zerotree Coding* 10.11 Other Applications of Wavelet Transform 10.11.1 Wavelet Denoising 10.11.2 Introduction to Other Applications 10.12 Summary and Further Reading Exercises Chapter 10 Appendix Subband Coding References* Chapter 11 Sparse Representation and Compressed Sensing of Signals 11.1 Mathematical Foundations of Sparse Representation of Signals 11.1.1 Convex Sets and Convex Functions 11.1.2 Norms 11.1.3 Null Space and Sparsity of Matrix 11.2 Sparse Model Examples of Signals 11.2.1 Compressed Sensing Problem 11.2.2 Lasso Regression Problem - LASSO 11.2.3 Comparison of Different Sparse Problems 11.3 Sparse Model Representation of Signals 11.4 Basic Theory of Sparse Recovery 11.4.1 Uniqueness of (P0) Solution 11.4.2 Uniqueness of (P1) Solution 11.4.3 Solution of (Pε1) Problem 11.5 Compressed Sensing and Sensing Matrix 11.6 Introduction to Sparse Recovery Algorithms 11.6.1 Greedy Algorithms 11.6.2 LAR Algorithm 11.6.3 Lasso\'s Cyclic Coordinate Descent Algorithm 11.6.4 Nearest Neighbor Method and Iterative Shrinkage Algorithm 11.6.5 Iterative Weighted Least Squares Algorithm - IRLS 11.6.6 Online Sparse Recovery Algorithm 11.7 Several Application Examples of Signal Sparse Recovery 11.8 Chapter Summary and Further Reading Exercises References Appendix A Basics of Matrix Theory Appendix B Summary of Optimization Methods Abbreviation Index
  
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2. Modern Special-Purpose Operational Amplifiers
  
  Modern Special-Purpose Operational Amplifiers ...
  
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3. Fast Analysis Technology of Linear Circuit Transfer Function
  
  This book explains the rapid analysis technology of linear circuit transfer function step by step from simple to complex, and systematically analyzes the circuit transfer function in order to design a stable and reliable control system. Chapter 1 Electrical Analysis - Terminology and Theorems 1.1 Overview of Transfer Function 1.1.1 Input and Output Ports 1.1.2 Different Types of Transfer Functions 1.2 General Tools and Theorems 1.2.1 Voltage Divider 1.2.2 Current Divider 1.2.3 Thevenin Theorem 1.2.4 Norton Theorem 1.3 Key Points in This Chapter 1.4 Appendix 1A - Calculating Output Impedance/Resistance 1.4.1 Output Voltage Divided by 2 1.4.2 Dynamic Output Resistance 1.4.3 Voltage Source Zeroing 1.4.4 Short-Circuit Current 1.4.5 Controlled Source 1.4.6 Transistor Circuits 1.4.7 Verification of Calculation Results Using SPICE 1.5 Appendix 1B - Exercises 1.5.1 Exercise Contents 1.5.2 Exercise Answers References Chapter 2 Transfer Functions 2.1 Linear Systems 2.1.1 Linear Time-Invariant Systems 2.1.2 Necessity for Linearization 2.2 Time Constants 2.2.1 Time Constants of Capacitors 2.2.2 Time Constants of Inductors 2.3 Circuit System Transfer Functions 2.3.1 Low-Entropy Expressions 2.3.2 High-Order Expressions 2.3.3 2-Order Multi-Order Systems 2.3.4 Low-Q approximation of 2nd-order polynomials 2.3.5 Approximation of 3rd-order polynomials 2.3.6 Determination of system order 2.3.7 Network zeros 2.4 Preliminary study of generalized 1st-order transfer function 2.4.1 Solving 1st-order circuits through examples 2.4.2 Calculating zeros by empty double injection method 2.4.3 Calculating zeros obtained by empty double injection using SPICE 2.4.4 Network excitation 2.5 Key points of this chapter 2.6 Exercises 2.6.1 Exercise content 2.6.2 Exercise answers References Chapter 3 Superposition Theorem and Extra Element Theorem 3.1 Superposition Theorem 3.1.1 Dual-input/dual-output system 3.2 Extra Element Theorem 3.2.1 EET Example 1 3.2.2 EET Example 2 3.2.3 EET Example 3 3.2.4 EET Example 4 3.2.5 EET Example 5 3.2.6 EET Example 6 3.2.7 Inverted Poles and Zeros 3.31 Generalized Transfer Functions for 1-Order Systems 3.3.1 Generalized Transfer Function Example 1 3.3.2 Generalized Transfer Function Example 2 3.3.3 Generalized Transfer Function Example 3 3.3.4 Generalized Transfer Function Example 4 3.3.5 Generalized Transfer Function Example 5 3.4 Further Reading 3.5 Chapter Highlights 3.6 Appendix 3A - Exercises 3.6.1 Exercise Contents 3.6.2 Exercise Answers References Chapter 4 Second-Order Transfer Function 4.1 Applying the Extra Element Theorem Again 4.1.1 Low-Entropy Second-Order Expression 4.1.2 Zero Determination 4.1.3 Rearrange Expression and Plot 4.1.4 Example 1 - Low-Pass Filter 4.1.5 Example 2 - Dual-Capacitor Filter 4.1.6 Example 3 - Dual-Capacitor Band-Reject Filter 4.1.7 Example 4 - LC Notch Filter 4.2 Generalized Transfer Function of Second-Order System 4.2.1 Circuit Zero Calculation 4.2.2 Generalized Second-Order Transfer Function Example 1 4.2.3 Generalized Second-Order Transfer Function Example 2 4.2.4 Generalized Second-Order Transfer Function Example 3 4.2.5 Generalized Second-Order Transfer Function Example 4 4.3 Chapter Focus 4.4 Appendix 4A - Exercises 4.4.1 Exercise Contents 4.4.2 Exercise Answers References Chapter 5 n-Order Transfer Function 5.1 From 2EET to nEET 5.1.1 3rd-Order Transfer Function Example 5.1.2 Transfer Function Zeros 5.1.3 Generalized n-Order Transfer Function 5.2 Higher-Order Transfer Function Examples 5.2.1 Example 1 - 3rd-Order Circuit 5.2.2 Example 2 - 3rd-Order Active Notch Filter 5.2.3 Example 3 - 4th-Order LC Passive Filter 5.2.4 Example 4 - 4th-Order Bandpass Active Filter 5.2.5 Example 5 - 3rd-Order Lowpass Active GIC Filter 5.3 Chapter Focus 5.4 Appendix 5A - Exercises 5.4.1 Exercise Contents 5.4.2 Exercise Answers References Terminology Conclusion
  
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4. Phase Change Memory Research Collective
  
  Phase Change Memory Research CollectivePhase Change Memory Research CollectivePhase Change Memory Research Collective
  
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5. Small Compiler Design Practice (Compiled by Su Mengjin)
  
  Author profile: Su Mengjin was born in Shanghai and his ancestral home is Changzhou, Jiangsu. In 1978, he was admitted to Zhejiang University to study computer science and received a bachelor\'s degree in 1982. In the same year, he began to work in the computer science department of Tongji University in Shanghai and was awarded the title of lecturer in 1987. In 1990, he went to the United States to study and received a master\'s degree in 1992. Contents Part I Design of PIC16Fxxxx Compiler (cc16e.exe) Chapter 1 Tool Preparation and System Settings 2 1.1 Selection of GNUC/C++ Compiler Tools 2 1.1.1 MinGW 2 1.1.2 DJGPP 2 1.1.3 Cygwin 2 1.2 Parsing Tool Constructor 3 1.3 Tool Installation 3 1.4 System Settings Before the Target Compiler Runs 4 Chapter 2 Design of Preprocessor 5 2.1 Preprocessor (C/C++ Version) 6 2.1.1 Project Files and Settings 6 2.1.2 Tasks and Algorithms 7 2.2 Source Program Preprocessor (Flex Version) 11 2.2.1 Introduction to Regular Expressions 12 … Contents Summary As one of the core software of computer technology, compilers are a topic of concern to industry insiders and an important tool in daily work. Compiler design and theory are compulsory contents for relevant majors in colleges and universities. This book takes the PIC16F series processors designed and produced by Microchip as the target object, describes the steps and details of compiler design in a practical form, and provides all the design source code. The content of this book focuses on the specific implementation process of compiler design rather than theory. It is mainly intended for enthusiasts in the computer industry who are interested in compiler design. It can also be used as a reference for teachers and students in related majors in colleges and universities.
  
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6. Core power devices
  
  Core Energy Power Devices Core Energy Power Devices Core Energy Power Devices
  
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7. Chip dynamic threshold static power consumption optimization technology
  
  Optimization technology of chip dynamic threshold static power consumption Optimization technology of chip dynamic threshold static power consumption
  
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8. Chip-Scale Power Transistor Packaging
  
  Chip-level power transistor packagingChip-level power transistor packagingChip-level power transistor packagingChip-level power transistor packagingChip-level power transistor packaging
  
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9. Introduction to New Energy Vehicles (Hu Ping)
  
  Based on the development trend of new energy vehicle technology and the actual needs of skilled talents in the field of new energy vehicle application and maintenance technology in my country, \"Introduction to New Energy Vehicles\" draws on the research results of the new energy vehicle industry at home and abroad, takes project teaching and task-driven as the guide, and focuses on introducing the basic knowledge about new energy vehicles, mainly including the understanding of new energy vehicles, pure electric vehicles, hybrid vehicles, fuel electric vehicles, and the combination of new energy and intelligent networked vehicles. The main features of \"Introduction to New Energy Vehicles\" are as follows: 1. In terms of writing theory, it is written according to the training objectives and cognitive characteristics of vocational education students. The text of the book is easy to understand, with rich pictures and vivid images, which stimulates students\' interest in learning and improves learning efficiency. 2. In terms of content arrangement, it adheres to the project-task style, takes projects as the carrier and tasks as the guide, from easy to difficult, step by step, closely following the theme, and accurately positioning. 3. In terms of teaching content, it combines theory with practice, understands students\' mastery from actual hands-on experience, and checks for omissions in a timely manner. Project 1 New energy and intelligent networked vehicles Task 1 New energy vehicles Task 2 Intelligent networked vehicles Project 2 New energy vehicle structure recognition Task 1 Pure electric vehicle structure recognition Task 2 Hybrid vehicle recognition Task 3 Fuel cell vehicle structure recognition Project 3 Power battery system structure recognition Task 1 Power battery system recognition Task 2 Power battery structure Task 3 Power battery management system control principle Project 4 Motor and transmission system structure recognition Task 1 Motor recognition Task 2 Motor structure Task 3 Motor control system working principle Task 4 Transmission system structure Project 5 Principle and detection of vehicle control system Task 1 Vehicle control system recognition Task 2 Vehicle control system structure and maintenance
  
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10. Introduction to New Energy Vehicles (Li Kai)
  
  This book starts with the concept of new energy vehicles, and introduces the development and technology of new energy vehicles step by step from the shallow to the deep, and explains the electric vehicle companies and typical models. Chapter 1 introduces the concept and development of new energy vehicles, and Chapters 2 to 4 respectively describe the structure and principle of pure electric vehicles, hybrid electric vehicles and fuel cell electric vehicles. Li Kai, male, graduated from Dalian Maritime University, majoring in marine engineering. In 2010, he joined the Navigation Department of Shandong Transportation School as a teacher. In March 2014, he was transferred to the Automobile Department, mainly responsible for teaching new energy vehicles.
  
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11. New Energy Vehicles Overview Full Color Edition
  
  \"Introduction to New Energy Vehicles (Full Color Edition)\" starts with the definition and classification of new energy vehicles, and introduces in detail the definition and classification of new energy vehicles, the structure and principles of hybrid vehicles, the structure and principles of pure electric vehicles, the structure and principles of fuel cell vehicles, new energy vehicle power batteries and management systems, new energy vehicle drive motors and control systems, temperature control systems of power batteries and drive motors, charging systems and vehicle controllers, etc. Chapter 1 Introduction 1.1 The development of automobile energy 1.1.1 Steam cars 1.1.2 Early electric cars 1.1.3 Internal combustion locomotives 1.1.4 Modern electric cars 1.2 Overview of new energy vehicles 1.2.1 Concept and classification of new energy vehicles 1.2.2 Development background of new energy vehicles 1.2.3 Current status of new energy vehicles Chapter 2 Power batteries and management systems for new energy vehicles 2.1 Basics of power batteries for new energy vehicles 2.1.1 Overview 2.1.2 Basic performance of power batteries 2.1.3 Requirements of new energy vehicles for power batteries 2.2 Lead-acid batteries and nickel-metal hydride batteries 2.2.1 Lead-acid batteries 2.2.2 Nickel-metal hydride batteries 2.3 Lithium-ion power batteries 2.3.1 Structure and principle of lithium-ion power batteries 2.3.2 Characteristics of lithium-ion power batteries 2.3.3 Applications of lithium-ion power batteries 2.3.4 Special forms of lithium-ion power batteries 2.4 Power battery management system 2.4.1 Overview of power battery management system 2.4.2 Functions of the battery management system Chapter 3 New energy vehicle drive motor and control system 3.1 Introduction to drive motor 3.1.1 Development history of drive motor 3.1.2 Basic concepts of motors 3.1.3 Types of drive motors 3.2 DC motor 3.2.1 Overview of DC motor 3.2.2 Basic principles of DC motor 3.3 Three-phase AC asynchronous motor 3.3.1 Classification of three-phase AC asynchronous motor 3.3.2 Structure of three-phase AC asynchronous motor 3.3.3 Characteristics of three-phase AC asynchronous motor 3.3.4 Working principle of three-phase AC asynchronous motor 3.4 Permanent magnet synchronous motor 3.4.1 Overview of permanent magnet synchronous motor 3.4.2 Application of permanent magnet synchronous motor 3.5 Switched reluctance motor 3.5.1 Structure and characteristics of switched reluctance motor 3.5.2 Working principle of switched reluctance motor Chapter 4 Pure electric vehicle 4.1 Overview of pure electric vehicle 4.1.1 Characteristics of pure electric vehicle 4.1.2 Types of pure electric vehicle 4.1.3 Extended range electric vehicle 4.2 Basic structure and working principle of pure electric vehicle 4.2.1 4.2.2 Structure and working principle of pure electric vehicles 4.2.3 Structure and working principle of extended-range electric vehicles 4.2.4 Examples of pure electric vehicle models... Chapter 5 Plug-in hybrid electric vehicles Chapter 6 Fuel cell electric vehicles Chapter 7 Other clean energy vehicles Chapter 8 Intelligent network connection system for new energy vehicles Chapter 9 Business model and service system of new energy vehicles References
  
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12. Full-color illustration of the structure and principle of new energy vehicles (Edited by Repper)
  
  This book combines the technical achievements of automobile companies such as BMW, Mercedes-Benz, Volkswagen, Audi and my country\'s BYD, BAIC New Energy, SAIC Roewe and other brands in new energy vehicles, and vividly interprets the various forms and operating principles of new energy vehicles in the form of full-color illustrations. It also focuses on the structure and principle of the core technical components of electric vehicles in the form of \"battery, motor, and electronic control\". The book is divided into ten modules, which introduce the basic knowledge of new energy vehicles, the basic structure and operating principle of hybrid and pure electric vehicles in the form of 31 projects. Among them, modules 4 to 6 focus on the types, characteristics, structures and principles of the three core technical components of batteries, motors and electronic controls; module 7 describes other high-voltage and electrified components, such as electric air-conditioning compressors, electric auxiliary heaters, electric starters, electric power steering pumps, etc.; module 8 explains the types of transmissions commonly used in hybrid vehicles and the structure and function of reducers commonly used in pure electric vehicles; module 9 introduces the data bus used in hybrid and pure electric vehicles; the last module briefly introduces the structure and principle of hydrogen fuel vehicles. This book is suitable for use as a supplementary textbook for new energy majors in various automotive colleges, and can also be used as a self-study introductory reading for practitioners in the field of new energy vehicles and the majority of new energy vehicle owners. This book combines the technical achievements of automobile companies such as BMW, Mercedes-Benz, Volkswagen, Audi and my country\'s BYD, BAIC New Energy, SAIC Roewe and other brands in new energy vehicles, and vividly interprets the various forms and operating principles of new energy vehicles in the form of full-color illustrations. It also focuses on the structure and principle of the core technical components of electric vehicles in an independent chapter of \"battery, motor, and electronic control\". The book is divided into ten modules, and introduces the basic knowledge of new energy vehicles, the basic structure and operating principle of hybrid and pure electric vehicles one by one in the form of 31 projects. Modules 4 to 6 focus on the types, characteristics, structures and principles of the three core technical components of batteries, motors and electronic controls; Module 7 describes other high-voltage and electrified components, such as electric air-conditioning compressors, electric auxiliary heaters, electric starters, electric power steering pumps, etc.; Module 8 explains the types of transmissions commonly used in hybrid vehicles and the structure and function of reducers commonly used in pure electric vehicles; Module 9 introduces the data bus used in hybrid and pure electric vehicles; and the last module briefly introduces the structure and principle of hydrogen fuel vehicles. This book is suitable for use as a supplementary textbook for new energy majors in various automotive colleges, and can also be used as a self-study entry book for practitioners in the field of new energy vehicles and the majority of new energy vehicle owners. This book combines the technical achievements of automobile companies such as BMW, Mercedes-Benz, Volkswagen, Audi and my country\'s BYD, BAIC New Energy, SAIC Roewe and other brands in new energy vehicles, and vividly interprets the various forms and operating principles of new energy vehicles in the form of full-color illustrations, and focuses on the structure and principle of the core technical components of electric vehicles in \"batteries, motors, and electronic controls\" as independent chapters. The book is divided into ten modules, which introduce the basic knowledge of new energy vehicles, the basic structure and operation principle of hybrid and pure electric vehicles in the form of 31 projects. Modules 4 to 6 focus on the types, characteristics, structures and principles of the three core technical components of batteries, motors and electronic controls; Module 7 describes other high-voltage and electrified components, such as electric air-conditioning compressors, electric auxiliary heaters, electric starters, electric power steering pumps, etc.; Module 8 explains the types of transmissions commonly used in hybrid vehicles and the structure and function of reducers commonly used in pure electric vehicles; Module 9 introduces the data bus used in hybrid and pure electric vehicles; and the last module briefly introduces the structure and principle of hydrogen fuel vehicles. This book is suitable for use as a supplementary textbook for new energy majors in various automotive colleges, and can also be used as a self-study introductory reading for practitioners in the field of new energy vehicles and the majority of new energy vehicle owners.
  
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13. New Energy Vehicles and New Technologies (Edited by Jie Shilei and Sun Yufeng)
  
  As the main professional course in the course structure system of this major, the book \"New Energy Vehicles and New Technologies\" mainly studies the classification, basic structure, composition and principle of new energy vehicles, and has a clear understanding of the organic connection between the electric batteries and motors used in new energy vehicles. This book comprehensively and systematically discusses the basic knowledge of new energy vehicles, and introduces the definition and classification of new energy vehicles, pure electric vehicles, hybrid electric vehicles, fuel cell electric vehicles, the foundation of automotive electrification technology, new energy vehicle charging technology, new energy vehicle high-voltage safety and isolation technology, etc. The book is detailed and well-illustrated.
  
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14. New energy vehicle application technology (Shan Guifeng, Li Gou)
  
  New energy vehicle application technology (Shan Guifeng, Li Gou)
  
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15. New Energy Vehicle Control System Inspection and Maintenance (Edited by Xu Jinghui, Xu Zemin, and Peng Peng)
  
  This book is developed based on the work process approach. The content is organized based on typical work tasks. It mainly includes three learning scenarios: vehicle control system recognition, vehicle control system function testing, and vehicle control system inspection and maintenance. Each scenario also contains several learning units. Each learning unit is introduced with actual work tasks. The theoretical knowledge includes common knowledge and individual knowledge. The practical skills section takes BYD E5 450 as an example. This book is suitable for vocational schools that offer new energy vehicle majors, and can also be used by new energy vehicle technical training institutions. It can also be used as a reference book for relevant industry personnel engaged in new energy vehicle maintenance and other work. Learning scenario 1 Recognition of vehicle control system Task 1 Inspection and disassembly of gateway controller 1.1.1 Overview of control system 1.1.2 Composition of vehicle control system 1.1.3 Vehicle control system of BYD E5 1.1.4 Replacement of BYD E5 gateway controller 1.1.5 Inspection of BYD E5 gateway controller Task 2 Reading vehicle control data stream 1.2.1 Functions of vehicle control system 1.2.2 Control strategy of vehicle control system 1.2.3 Data stream/active test Task 3 Disassembly and assembly of high-voltage electronic control assembly 1.3.1 External features of BYD E5 high-voltage electronic control assembly 1.3.2 Internal structure of BYD E5 high-voltage electronic control assembly 1.3.3 Replacement of high-voltage electronic control assembly 1.3.4 Internal structure of high-voltage electronic control assembly Recognition learning scenario 2 Functional test of vehicle control system Task 1 Vehicle stationary state test 2.1.1 Instrument panel indication function 2.1.2 Multi-function information display screen 2.1.3 Infotainment system 2.1.4 Other auxiliary electrical equipment function test2.1.5 Electric vehicle specific fault light information recognition2.1.6 Bluetooth setting and connection task 2 Vehicle drive control function test2.2.1 Electric vehicle drive device2.2.2 Common pure electric vehicle drive system and drive mode2.2.3 Driving mode selection2.2.4 BYD E5 drive protection measures2.2.5 Vehicle drive function test method2.2.6 Anti-slip test2.2.7 Low speed warning tone recognition and setting task3 Vehicle energy management and energy recovery2.3.1 Electric vehicle energy management system2.3.2 Energy recovery system2.3.3 BYD E5 brake energy recovery control2.3.4 Discharge test2.3.5 Braking energy recovery test task4 Protection function test2.4.1 High voltage interlock2.4.2 High voltage self-discharge circuit2.4.3 Leakage protection and potential equalization2.4.4 Insulation resistance detection2.4.5 Short circuit fuse protection circuit2.4.6 Electromagnetic protection2.4.7 BYD E5 high voltage protection2.4.8 High voltage interlock circuit recognition and testing2.4.9 Charging port insulation resistance measurementTask 5 Cloud service and remote monitoring system testing2.5.1 Internet of Vehicles system2.5.2 BYD cloud service and Dmnk intelligent network system2.5.3 On-board terminal2.5.4 Fault diagnosis of on-board terminal failureLearning scenario3 Vehicle control system inspection and maintenanceTask 1 Communication system fault diagnosis and maintenance3.1.1 CAN bus technology3.1.2 On-board network function3.1.3 BYD E5 vehicle network topology3.1.4 CAN bus typical fault judgment3.1.5 LIN bus technology3.1.6 Measuring CAN terminal resistance and bus voltageTask 2 Input circuit fault diagnosis and maintenance3.2.1 Vehicle control system input signal and circuit3.2.2 Vehicle control system main input signal and circuit3.2.3 Diagnosis method of vehicle control input signal abnormality3.2.4 3.2.5 Diagnosis and repair of faults of low charging power 3.2.6 Diagnosis and repair of faults of gear shift failure Task 3 Diagnosis and repair of output signal faults 3.3.1 Output signal and circuit of vehicle control system 3.3.2 Main input signal and circuit of vehicle control system 3.3.3 Case analysis References
  
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16. LabVIEW Programming and Application (Ji Shujiao, Shang Weiwei, Lei Yanmin)
  
  This book is divided into two parts. The first part, the basic part (Chapters 1 to 6), introduces the basic programming methods of LabVIEW, including the introduction of the development environment, programming and debugging of VI, data expression, program structure, waveform control, network and communication, etc. The second part, the application part (Chapters 7 to 13), mainly combines the professional courses of electronic information majors. First, the main theoretical knowledge of the courses \"Signal Analysis and Processing\", \"Communication Principles\" and \"Automatic Control Principles\" are simulated and designed; then the basics of data acquisition are introduced, and the temperature acquisition system is designed in combination with the experimental platform Nextboard for engineering teaching, and the remote system acquisition design is realized. The last chapter introduces the design of the data acquisition system based on the sound card. The editor has carefully designed the examples in the book and tested them repeatedly based on many years of teaching experience and students\' learning points. This book can be used as a textbook for electronic information, instrumentation and related professional courses in colleges and universities, and can also be used as a study and reference for engineering and technical personnel in related fields. Contents Part 1 Basics Chapter 1 Overview of LabVIEW 1.1 Introduction to LabVIEW 1.2 Installing and Starting LabVIEW 1.2.1 Installing LabVIEW 1.2.2 Starting LabVIEW 1.3 LabVIEW Programming Environment 1.3.1 Control Palette 1.3.2 Tool Palette 1.3.3 Function Palette 1.3.4 Menu Bar and Toolbar 1.4 Project Explorer 1.5 LabVIEW Help Information 1.5.1 Help Documents 1.5.2 Finding Examples 1.5.3 Network Resources Chapter 2 Creating, Editing, and Debugging VIs 2.1 Creating and Editing VIs 2.2 Running and Debugging Programs 2.2.1 Running and Stopping VIs 2.2.2 Correcting VI Errors 2.2.3 Highlighting Program Execution 2.2.4 Running VIs Step by Step 2.3 Creating and Calling SubVIs 2.3.1 Creating SubVIs Using Icon Editing 2.3.2 Creating SubVIs Using Commands 2.3.3 Calling SubVIs Chapter 3 Data Expression in LabVIEW 3.1 Numeric Controls 3.1.1 Numeric Controls and Display Formats 3.1.2 Common Functions for Numerical Operations 3.2 Boolean Controls 3.2.1 Boolean Controls and Display Formats 3.2.2 Common Functions for Boolean Operations 3.3 String and Path Controls 3.3.1 String Controls and Display Formats 3.3.2 Common Functions for String Controls 3.4 Drop-Down Lists and Enumeration Controls 3.5 Array Controls 3.5.1 Creating an Array 3.5.2 Array Functions 3.6 Clusters 3.6.1 Creating a Cluster 3.6.2 Cluster Functions 3.6.3 Error Clusters 3.7 Waveform Data 3.7.1 Waveform Controls 3.7.2 Waveform Functions Chapter 4 Program Flow and Structure 4.1 Loop Structure 4.1.1 For Loop 4.1.2 Shift Register 4.1 .3while loop 4.2 conditional structure 4.3 flat sequence structure 4.4 event structure 4.5 formula node 4.6 local variables and global variables 4.6.1 local variables 4.6.2 global variables Chapter 5 Waveform Control 5.1 Waveform Chart 5.1.1 Right-click shortcut menu of waveform chart 5.1.2 Waveform Chart Application Example 5.2 Waveform Chart 5.3 XY Chart 5.4 Intensity Chart 5.5 Digital Waveform Chart 5.6 Three-dimensional Graphics Chapter 6 Network and Communication 6.1 Data Communication 6.2 Queue Operation Function Programming 6.2.1 Queue Operation Function 6.2.2 Queue Function Application 6.3 DataSocket Programming 6.3.1 DataSocket Communication 6.3.2 DataSocket Function 6.3.3 DataSocket Server Manager 6.3.4 DataSocket Server 6.3.5 DataSocket Communication Implementation 6.4 Protocol 6.4.1 TCP Function 6.4.2 TCP/IP Communication Implementation Part 2 Application Chapter 7 Signal Analysis and Processing 7.1 Waveform Generation 7.2 Signal Generation 7.3 Waveform Conditioning 7.4 Signal Operation 7.5 Waveform Measurement 7.6 Express VI 7.6.1 Input Function 7.6.2 Signal Analysis 7.6.3 Signal Operation Module 7.6.4 Output Function 7.7 Comprehensive Example: Production of signal generator Chapter 8 Application of LabVIEW in communication system 8.1 Analog modulation 8.1.1 AM modulation 8.1.2 FM modulation 8.2 Digital modulation 8.2.1 Binary amplitude shift keying 8.2.2 Binary frequency shift keying 8.2.3 Binary phase shift keying 8.3 Comprehensive design Chapter 9 Application of LabVIEW in automatic control system 9.1 Main functions of LabVIEW control and simulation 9.2 Establishment of LabVIEW control model 9.3 Time domain analysis of automatic control system 9.4 Frequency domain analysis of automatic control system 9.5 Dynamic performance analysis of automatic control system Chapter 10 Data acquisition 10.1 Introduction to basic knowledge of data acquisition 10.2 Installation and application of configuration management software MAX 10.2.1 Introduction to configuration management software MAX 10.2.2 Installation of NI DAQmx 10.2.3 Application of configuration management software MAX 10.3 DAQmx API function 10.4 DAQ Assistant Express VI Chapter 11 Design of data acquisition system based on Nextboard 11.1 Introduction to Nextboard 11.1.1 Introduction to Nextboard hardware 11.1.2 Introduction to Nextpad soft panel 11.2 Design of temperature acquisition system based on Nextboard 11.2.1 Working principle of thermocouple 11.2.2 Introduction to thermocouple experimental panel 11.2.3 Thermocouple temperature acquisition based on Nextpad software 11.2.4 Thermocouple temperature acquisition based on LabVIEW program design 11.3 Design of digital signal generation and acquisition based on Nextboard 11.3.1 Design of front panel 11.3.2 Design of program flowchart 11.3.3 Program debugging Chapter 12 Design of data acquisition system based on TCP/IP 12.1 Nextkit signal multimeter 12.1.1 Introduction to nextkit hardware 12.1.2 Nextkit tSoft Panel Introduction 12.1.3 Use of NextKit Oscilloscope and Signal Generator 12.2 Implementation of Stand-alone Data Acquisition System Based on LabVIEW 12.2.1 LabVIEW Program Design Based on API Function 12.2.2 Hardware Connection and Program Debugging 12.3 Implementation of TCP/IP Data Acquisition System Based on LabVIEW 12.3.1 Program Design of Signal Transmitter 12.3.2 Program Design of Signal Receiver 12.3.3 Program Debugging Chapter 13 Design of Data Acquisition System Based on Sound Card 13.1 Introduction to Sound Card 13.2 Working Principle of Sound Card 13.3 Main Technical Parameters of Sound Card 13.4 Design of Data Acquisition System Based on Sound Card 13.4.1 Introduction to Sound Card Functions in LabVIEW 13.4.2 LabVIEW Program Design of Sound Acquisition Module 13.4.3 LabVIEW Program Design of Playing Sound According to File 13.4.4 Playing sound according to waveform LabVIEW program design reference [Preface] Preface LabVIEW, an innovative software product of National Instruments (NI), uses a graphical programming language to make computer programming simple. Combined with efficient data acquisition equipment, it can quickly build a virtual measurement and control system. With the development of LabVIEW, new versions are released almost every one or two years. Its application scope covers many fields such as industrial automatic control, measurement and testing, computer simulation, communication and remote measurement and control. LabVIEW has entered the laboratories of many universities at home and abroad, and most engineering majors in universities at home and abroad have also opened related courses. Learning LabVIEW programming design well is also very helpful for learning professional theoretical courses. The author of this book has been engaged in virtual instrument teaching for many years and has accumulated a lot of practical experience in program development, which has been presented in a large number of examples in this book. For LabVIEW beginners, mastering efficient learning methods is an important factor in learning LabVIEW well. It is crucial to use LabVIEW to do more hands-on programming and think about why the program has such a running result. The author of this book has carefully written a large number of examples. Beginners should repeatedly write and test the program as much as possible to achieve the purpose of knowing why it is so. The basic part of this book was mainly written by Ji Shujiao and Lei Yanmin, the application part was written by Ji Shujiao and Shang Weiwei, and the whole book was edited by Ji Shujiao. A large number of literatures were referenced in the process of writing this book. I would like to express my sincere gratitude to the authors of these literatures. At the same time, I would like to thank Engineer Liu Yang, the manager of NI\'s cooperation with Chinese universities, and the technical staff of Panhua Company for their great help. The extensive communication between the editor-in-charge of this book and the author was also very helpful to the publication of this book. I would like to express my gratitude here. At the same time, I would like to thank the students who participated in the program writing and testing of this book, and thank Changchun University for its strong support for the publication of this book. The author wrote this book with the attitude of communication and learning. Due to his limited level, there are inevitably some shortcomings in the book. Readers are welcome to provide valuable suggestions. If you have any questions, please contact the author (shujiaoji@163.com). The publication of this book hopes to make some contributions to the promotion and use of LabVIEW in China, especially in colleges and universities, and to be helpful to the majority of LabVIEW learning enthusiasts. Author\'s courseware and program download in June 2018 [Excellent book excerpt] Chapter 5 Waveform Control Learning objectives of this chapter ? Master the use of waveform charts and waveform graphs ? Understand the use of XY graphs, intensity graphs and digital waveform graphs ? Master the use of three-dimensional graphics The waveform display control is one of the commonly used objects on the front panel in program design. Its sub-selection panel is located in \"Controls\" → \"New\" → \"Graphics\", as shown in Figure 5.1. This chapter first introduces the relevant knowledge of waveform charts and waveform graphs, then introduces the use of XY graphs, intensity graphs and digital waveform graphs, and finally introduces the representation of three-dimensional graphics. Figure 5.1 \"Graphics\" sub-selection panel 5.1 Waveform charts Waveform charts are used as display controls, mainly consisting of waveform display areas, horizontal and vertical coordinates, and legends. Waveform charts can save old data, and the length of the saved data can be specified by yourself. The new data transmitted to the waveform chart is connected to the old data, so that the new data can be displayed while saving part of the old data. You can also imagine the working mode of the waveform chart as a first-in, first-out queue. When new data arrives, the old data of the same length will be squeezed out of the queue. The default length is 1024. Users can also right-click the chart and select \"Icon History Length\" from the pop-up shortcut menu to set the size. 5.1.1 Right-click shortcut menu of the waveform chart 1. Display items Display items are used to set the appearance of the waveform chart. They are used to indicate which elements in the object are visible. As shown in Figure 5.2, it provides a method for selecting display labels, X scroll bars, graphic tool palettes, and ruler legends. Figure 5.2 Waveform chart display item menu Figure 5.3 shows the display style of the ruler legend and graphic tool palette in addition to the waveform chart under default conditions. Figure 5.3 Waveform chart showing the ruler legend and graphic tool palette (X ruler, Y ruler) In the legend, you can expand multiple lines by dragging the mouse. Right-click the line to pop up the dialog box shown in Figure 5.4. You can set the line style, color, width and other properties to distinguish each line. 2. Advanced In the submenu of the \"Advanced\" option, select Refresh Mode to switch the waveform chart to three refresh modes in the interactive data display: Oscilloscope Chart, Strip Chart, and Scan Chart, as shown in Figure 5.5. Strip Chart: Continuously scrolls from left to right to display running data, similar to a paper strip chart recorder. Oscilloscope Chart: When the line reaches the right edge of the drawing area, LabVIEW erases the entire line and starts drawing a new line from the left edge, similar to an oscilloscope. Figure 5.4 Legend Dialog Box Figure 5.5 Waveform Chart Refresh Mode Scan Chart: There is a vertical line in the scan chart that separates the old data on the right from the new data on the left, similar to an electrocardiograph. 3. Properties The Properties dialog box is shown in Figure 5.6, which contains property settings such as appearance, display format, line, and ruler. Users can click the options they need to set as needed. 4. Grid display and cascade display settings In the waveform chart, when multiple lines are displayed, you can select the \"cascade\" overlap mode, that is, grid display lines or cascade display lines, as shown in Figure 5.7. Figure 5.6 Waveform Chart Properties Dialog Box Figure 5.7 Overlay Display Line 5.1.2 Waveform Chart Application Example Example 5.1 Use the waveform chart to output the result of multiplying a random number by 5 and 3. The front panel and flowchart of the program are shown in Figures 5.8 and 5.9. The bundle function is used in the flowchart, and the front panel is displayed in the form of overlay and grid. Figure 5.8 Example 5.1 Front Panel Figure 5.9 Example 5.1 Flowchart Example 5.2 Use the waveform chart to display the sine and cosine waveforms at the same time. The flowchart uses 2π, which is located in the \"Function\" → \"Number\" → \"Mathematics and Scientific Constants\" sub-palette; the \"Sine\" and \"Cosine\" functions used are in the \"Function\" → \"Mathematics\" → \"Elementary and Special Functions\" sub-palette. The front panel and flowchart are shown in Figures 5.10 and 5.11. On the waveform chart on the front panel, right-click and select the \"Properties\" command. In the \"Automatically adjust ruler\" in the ruler properties of the property setting interface, set the value to 359 and the minimum value to 0, as shown in Figure 5.12. The data length of each run is 360. After one cycle, run the program again, and the value of the horizontal axis will change to 360 at the start and 719 at the end. Run it again and increase it in sequence. Figure 5.10 Example 5.2 Front panel Figure 5.11 Example 5.2 Program flowchart Figure 5.12 Ruler setting dialog box 5.2 Waveform chart The waveform chart and the waveform chart have most of the same functions and display styles, and can also receive multiple data types, thereby greatly reducing the workload of type conversion before displaying the data as a graphic. The waveform chart displays the waveform in a batch data refresh mode. The basic form of data input is array, cluster or waveform data. The display contains the main network and auxiliary network in the default state. Example 5.3 Use the create waveform function to create a waveform and display it in the waveform chart. The specific steps are as follows: (1) Create a new VI. In the flowchart, click \"Function\" → \"Programming\" → \"Waveform\" sub-palette, find \"Create Waveform Function\", and display the elements Y, t0, and dt it contains. (2) Add a for loop, connect the output to the Y input terminal of the created waveform, and connect dt to the numerical constant 10. (3) Click \"Function\" → \"Programming\" → \"Numerical\" → \"Conversion\" sub-palette, select the \"Convert to Time Stamp\" function, and its connection is shown in Figure 5.13. (4) Add a waveform graph to the front panel, connect the flowchart to the output port of the created waveform, run the program, and the display result is shown in Figure 5.14. Here, the legend of the waveform graph display is set to highlight the numerical value. Example 5.4 This example shows all the data formats that the waveform can receive. The waveform data comes from two double-precision arrays, and the data of these two arrays comes from the \"output channel\" on the \"Open Index Function\" border. In the for loop, 100 points evenly distributed from 0 to 2π are connected to the sine and cosine functions, and the data is displayed. Seven data formats that the waveform can receive are given. Figure 5.15 and Figure 5.16 are the flowchart and front panel of Example 5.4 respectively. Figure 5.13 Flowchart of Example 5.3 Figure 5.14 Front panel of Example 5.3 Figure 5.15 Flowchart of Example 5.4 Figure 5.16 Example 5.4 The front panel uses Graph to draw one or more lines. In these two cases, the data organization format is different. When drawing a single line, the waveform Graph can receive the following two data formats: (1) One-dimensional array, corresponding to the single-line waveform graph in Figure 5.15 and Figure 5.16. At this time, the time defaults to starting from 0, and the time interval between data points is 1s, that is, time 0 corresponds to the 0th element in the array, time 1 corresponds to the 1st element in the array, and so on. (2) Cluster data type. Corresponding to the single line (Xo=10, dx=2, Y) in Figure 5.15 and Figure 5.16. The cluster should include three elements: the time start point, the time interval, and the value array. When drawing multiple lines, the waveform Graph can receive the following data formats: (1) Two-dimensional array, corresponding to the multi-line waveform graph 1 in Figure 5.15 and Figure 5.16. Each line can be interpreted as a line of data, with time starting from 0 and the interval between each data point being 1s. Because the two-dimensional array itself requires that each line has the same length, this data format requires that the data length of each line is the same. (2) Pack the array into clusters, and then use the clusters as elements to form an array, which corresponds to Multi Plot 2 in Figures 5.15 and 5.16. Each array contained in each cluster is a line. This data organization method can be used when the number of data points of multiple lines is different. Time starts at 0, and the interval between each data point is 1s. (3) The cluster composed of numeric type elements t0, dt and numeric type two-dimensional array Y corresponds to the (Xo=10, dx=0.5, Y) multi-line waveform chart in Figures 5.15 and 5.16, where t0 is the starting time, dt is the time interval between data points, and each row of Y is a line of data. (4) The one-dimensional array with clusters as elements corresponds to (Xo=10, dx=2, Y) Multi Plot 1 in Figures 5.15 and 5.16. Each cluster element consists of three elements: numeric type elements t0, dt and numeric type array. t0 is the starting time, dt is the time interval between data points, and the numerical array represents the data points of a line. This is a common multi-line data format because each line is allowed to have a different starting time, data point time interval, and data point length. (5) In the cluster consisting of three elements, t0, dt, and an array with cluster elements, each cluster element is packed with an array, and each array is a line, corresponding to the (Xo=10, dx=5, Y) multi-line waveform graphs in Figures 5.15 and 5.16. All lines share the starting time t0 and time interval dt parameters provided by the outer cluster. 5.3 XY Graph \"Waveform Chart\" and \"Waveform Graph\" can only be used to display data in a one-dimensional array or a series of single-point data. They are powerless for data that needs to display a horizontal axis and a vertical axis pair. To depict the functional relationship between X and Y, you need to use \"XY Graph\". Example 5.5 Use XY Graph to Describe Concentric Circles. The design steps are as follows: (1) Create a new VI and place an XY graph on the front panel so that the line graph displays two line labels. (2) Place a for loop structure in the program flowchart window and assign 360 to the counting port. Follow the path \"Numerical\" → \"Mathematics\" → \"Elementary and Trigonometric Functions\" → \"Trigonometric Functions\" sub-palette \"Sine and Cosine\" function to calculate the sine and cosine values ??of a period of 0~2π data respectively. Select the \"Bundle\" function to group the pair of sine and cosine values ??generated in each loop into a cluster. After the loop ends, group these 360 ??clusters into a cluster array. (3) Because the display mechanism of the XY graph determines that its input must be a cluster, add two Create Cluster Array functions, and then use the \"Create Array\" function to form a cluster array. Select \"Connect Inputs\" for the Create Array function. Complete the wiring and run the program. The front panel and program flowchart are shown in Figures 5.17 and 5.18. Figure 5.17 Front panel of Example 5.5 Figure 5.18 Flowchart of Example 5.5 5.4 Intensity Graph The \"Intensity Graph\" control provides a method for displaying three-dimensional data on a two-dimensional plane. For example, the brightness of the screen color can be used to reflect the size of a two-dimensional array element. Intensity graphs can be divided into \"Intensity Trend Graph\" and \"Intensity Waveform Graph\". Most of their components and functions are the same. Example 5.6 uses an example to illustrate the use of an intensity waveform graph to display the size of array elements. The design steps are as follows: (1) Create a new VI, place an \"Intensity Graph\" on the front panel, and change the \"Scale\" labels of its X-axis and Y-axis to \"Row\" and \"Column\" respectively. (2) Also place a numeric \"Two-Dimensional Array\" control on the front panel, right-click any array member, change the data type to I8 in the \"Display Type\" command item on the shortcut menu, use the operating tool to enter 4 rows and 3 columns of data into the two-dimensional array, switch to the flowchart editing window, and connect the \"Two-Dimensional Array\" to the \"Intensity Graph\". Run the program, and the front panel and flowchart are shown in Figure 5.19. When the value of an element in a two-dimensional array is changed, the color value in the corresponding intensity waveform graph will also change accordingly. Figure 5.19 Front panel and flowchart of Example 5.6 5.5 Digital Waveform Graph LabVIEW provides a \"digital waveform graph\" to display digital signals represented by 0 and 1. To display digital signals, you must first use the \"bundle function\" to bundle the digital signals. The order of digital bundling is Xo, Delta X, input data and number of sampling points. The number of sampling points here reflects the number of bits or word length of the binary system. When it is equal to 1, it is 8 bits, when it is equal to 2, it is 16 bits, and so on. Example 5.7 Use a digital waveform graph to display a binary array, 1 is a high level, and 0 is a low level. Design steps: (1) Create a new VI and place a \"digital waveform graph\" on the front panel. (2) Place a numeric \"one-dimensional array\" on the front panel, set the data type to I8, select \"binary display\" in the \"format and settings\" dialog box, select an array element, and select \"right alignment\" in the \"font\" setting drop-down menu on the toolbar to set the binary number to right alignment display. Figure 5.20 Flowchart of Example 5.7 (3) Switch to the flow chart, select the \"Package\" function in the \"Cluster Function\" sub-template, add Xo=0, Delta X=1, input array and number of sampling points=1 to the input port of the \"Package\" function, and send the output cluster to the digital waveform display. The flow chart is shown in Figure 5.20. Run the program and the running result is shown in Figure 5.21. Figure 5.21 Front panel of Example 5.7 As can be seen from the program running result chart, the horizontal axis X axis represents the sequence number of the data, which ranges from 0 to 5, and the vertical axis Y axis represents the level change of the digital signal from bit to bit from top to bottom. For example, for the binary number 101 (decimal 5) with sequence number 3, it is represented by a digital waveform chart as 00000101, row 7 represents the bit, and row 0 represents the bit. 5.6 Three-dimensional graphics In actual engineering applications, \"three-dimensional graphics\" is usually an intuitive data display method. It can clearly draw spatial trajectories and give the dependence relationship in the three directions of X, Y and Z. For example, in the analysis of non-stationary random signals, time domain analysis methods are usually used. In this case, three-dimensional graphics can be used to describe it. The X axis represents time, the Y axis represents frequency, and the Z axis represents the time domain spectrum. The three-dimensional graphics included in LabVIEW are shown in Figure 5.22. Figure 5.22 The main modules of the three-dimensional graphics sub-palette are introduced as follows: 1. Three-dimensional surface graphics When a three-dimensional surface graphic is placed on the front panel, the program flowchart will display two icons at the same time, as shown in Figure 5.23, respectively, creat_plot_surface.vi and 3D Graph. The former is used for three-dimensional drawing, and the latter is used to display the graph. The ports and definitions of creat_plot_surface.vi are shown in Figure 5.24. This port draws the surface based on the x, y and z points. This VI has two one-dimensional arrays (x, y) and a two-dimensional array (z) to specify the points on the graph. Figure 5.23 Three-dimensional surface graphics Figure 5.24 Ports of creat_plot_surface.vi 2. Three-dimensional parameter graphics When a three-dimensional parameter graphic is placed on the front panel, the program flowchart will display two icons at the same time, as shown in Figure 5.25, similar to the three-dimensional surface graphics. Figure 5.25 The ports and definitions of creat_plot_parametric.vi are shown in Figure 5.26. The surface is drawn based on the x, y and z points. This VI has three two-dimensional arrays that specify the points on the surface. Example 5.8 Three-dimensional surface graphics routine. This example displays the surface graph of z=sin(x)cos(y). The front panel and program flowchart are shown in Figure 5.27 and Figure 5.28. You can use the mouse to drag the three-dimensional graphics on the front panel to observe the graphics from multiple angles. Figure 5.26Ports of creat_plot_parametric.vi Figure 5.27 Front panel of Example 5.8 Figure 5.28 Flowchart of Example 5.8 Here, the z variable is a two-dimensional array, but x and y are one-dimensional arrays, so the output of the for loop should select \"end value\" instead of \"index\". Example 5.9 Use three-dimensional parametric graphics to draw a hollow sphere. The parametric equation of the hollow sphere is: x=(2 cosα)cosβ y=(2 cosα)sinβ z=sinα(5?1) The flow chart and front panel are shown in Figure 5.29 and Figure 5.30 respectively. Figure 5.29 Flowchart of Example 5.9 There is a three-dimensional graphics property setting on the front panel when you right-click. The dialog box is shown in Figure 5.31, and you can make further settings for the three-dimensional graphics. Figure 5.30 Front panel of Example 5.9 Figure 5.31 \"Three-dimensional Graphics Properties\" dialog box
  
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17. Research on new luminescent materials and optoelectronic technology applications (by Li Xiaokui and Wang Shimin)
  
  Research on new luminescent materials and optoelectronic technology applications (by Li Xiaokui and Wang Shimin)
  
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18. Convex Optimization in Signal Processing and Communications: From Foundations to Applications (Qi Zhongyong, Li Weichang, Lin Jiaxiang)
  
  This book is designed to help graduate students, scholars, and engineers who need to learn \"convex optimization\" or \"nonlinear optimization\" methods to solve optimization problems in the field of signal processing and communication. This book builds a bridge from basic mathematical theory to practical application, and emphasizes the balance between the two. It consists of 10 chapters and 1 appendix. [1] Chapter 1 introduces some commonly used mathematical foundations and definitions, Chapter 2 introduces convex sets, Chapter 3 introduces convex functions, and Chapter 4 introduces convex optimization problems and problem reformulation. The above 4 chapters constitute the mathematical foundation required for basic convex optimization problems. Next, some typical convex optimization problems are introduced, including geometric programming in Chapter 5, linear programming, quadratic programming, and quadratically constrained quadratic programming in Chapter 6, second-order cone programming in Chapter 7, semidefinite programming in Chapter 8, and the \"duality\" principle in Chapter 9. In these chapters, readers can see how the basic knowledge introduced in Chapters 2 to 4 can be correctly and effectively applied to practical problems in communication and/or signal processing. Finally, Chapter 10 introduces the interior point method, which is widely used to solve specific convex optimization problems, in an attempt to provide more efficient computational performance for solving linear programming or nonlinear convex optimization problems numerically. Chapter 1 Mathematical Background 1.1 Mathematical Foundations 1.1.1 Vector Norm 1.1.2 Matrix Norm 1.1.3 Inner Product 1.1.4 Norm Ball 1.1.5 Interior Point 1.1.6 Complement, Extension and Sum 1.1.7 Closure and Bounds 1.1.8 Supremum and Infimum 1.1.9 Function 1.1.10 Continuity 1.1.11 Derivative and Gradient 1.1.12 Hessian Matrix 1.1.13 Taylor Series 1.2 Linear Algebra Review 1.2.1 Vector Subspace 1.2.2 Span Space, Null Space and Orthogonal Projection Operator 1.2.3 Matrix Determinant and Inverse 1.2.4 Positive Definiteness and Semi-Positive Definiteness 1.2.5 Eigenvalue Decomposition 1.2.6 Square Root Decomposition of Semi-Positive Definite Matrix 1.2.7 Singular Value Decomposition 1.2.8 Least Squares Approximation 1.3 Summary and Discussion References Chapter 2 Convex Sets 2.1 Affine Sets and Convex Sets 2.1.1 Lines and Line Segments 2.1.2 Affine Sets and Affine Hulls 2.1.3 Relative Interiors and Relative Boundaries 2.1.4 Convex Sets and Convex Hulls 2.1.5 Cones and Cone Hulls 2.2 Important Examples of Convex Sets 2.2.1 Hyperplanes and Half-Spaces 2.2.2 Euclidean Spheres and Ellipsoids 2.2.3 Polyhedrons 2.2.3 Polyhedrons 2.2.5 Norm Cones 2.2.6 Positive Semidefinite Cones 2.3 Convexity Preserving Operations 2.3.1 Intersection 2.3.2 Affine Functions 2.3.3 Perspective Functions and Linear Fraction Functions 2.4 Generalized Inequalities 2.4.1 Proper Cones and Generalized Inequalities 2.4.2 Properties of Generalized Inequalities 2.4.3 Minimum and Minimal Element 2.5 Dual Norms and Dual Cones 2.5.1 3.1.1 Definition and basic properties 3.1.2 First-order conditions 3.1.3 Second-order conditions 3.1.4 Examples 3.1.5 Upper bounds 3.1.6 Jensen’s inequality 3.2 Convexity-preserving operations 3.2.1 Nonnegative weighted sums 3.2.2 Composition of affine maps 3.2.3 Composition functions 3.2.4 Pointwise maximum and supremum 3.2.5 Pointwise minimum and infimum 3.2.6 Perspective functions 3.3 Quasiconvex functions 3.3.1 Definition and examples 3.3.2 Modified Jensen’s inequality 3.3.3 First-order conditions 3.3.4 Second-order conditions 3.4 On the Monotonicity of Generalized Inequalities 3.5 On the Convexity of Generalized Inequalities 3.6 Summary and Discussion References [1] Chapter 4 Convex Optimization Problems 4.1 Standard Form of Optimization Problems 4.1.1 Some Professional Terms 4.1.2 Optimal Value and Optimal Solution 4.1.3 Equivalence Problem and Feasible Problem 4.2 Convex Optimization Problems 4.2.1 Global Optimality 4.2.2 Optimality Criterion 4.3 Equivalent Representation and Transformation 4.3.1 Equivalence Problem: Upper Bound Graph Form 4.3.2 Equivalence Problem: Eliminating Equality Constraints 4.3.3 Equivalence Problem: Function Transformation 4.3.4 Equivalence Problem: Variable Transformation 4.3.5 Reconstruction of Complex Variable Problems 4.4 Convex Optimization Problems in the Sense of Generalized Inequalities 4.4.1 Convex Optimization Problems in the Sense of Generalized Inequalities 4.4.2 Vector Optimization 4.5 Quasi-convex Optimization 4.6 Block Continuous Upper Bound Minimization 4.6.1 Stable Points 4.6.2 4.7 Continuous Convex Approximation 4.8 Summary and Discussion References Chapter 5 Geometric Programming 5.1 Some Basics 5.2 Geometric Programming 5.3 Convex Geometric Programming 5.4 Contraction Method 5.4.1 Continuous GP Approximation 5.4.2 Physical Layer Secret Communication 5.5 Summary and Discussion References Chapter 6 Linear Programming and Quadratic Programming 6.1 Linear Programming (LP) 6.2 LP Application Examples 6.2.1 Recipe Problem 6.2.2 Chebyshev Center 6.2.3 -Norm Approximation Problem 6.2.4 -Norm Approximation Problem 6.2.5 Determinant Maximization 6.3 Application of Linear Programming/Convex Geometry in Blind Source Separation 6.3.1 Independent Source nBSS Based on LP 6.3.2 Hyperspectral Decomposition Based on Linear Programming 6.3.3 Hyperspectral Decomposition Based on Simplex Geometry 6.4 Quadratic Programming 6.5 Application of QP and Convex Geometry Theory in Hyperspectral Image Analysis 6.5.1 GENE-CH Algorithm for Endmember Number Estimation6.5.2 GENE-AH Algorithm for Endmember Number Estimation6.6 Quadratically Constrained Quadratic Programming6.7 Application of QP and QCQP in Beamforming Design6.7.1 Receive Beamforming: Minimization of Average Sidelobe Energy6.7.2 Receive Beamforming: Minimization of Maximum Sidelobe Energy6.7.3 Application of QCQP in Transmit Beamforming Design for Cognitive Radio 6.8 Summary and Discussion References Chapter 7 Second-Order Cone Programming7.1 Second-Order Cone Programming7.2 Robust Linear Programming7.3 Linear Programming with Probability Constraints7.4 Robust Least Squares Approximation7.5 Robust Receive Beamforming Based on Second-Order Cone Programming7.5.1 Minimum Variance Beam Design7.5.2 Robust Beamforming Based on Second-Order Cone Programming7.6 Downlink Beamforming Based on Second-Order Cone Programming7.6.1 Beamforming under Power Minimization Criterion7.6.2 7.6.3 Multi-cell Beamforming 7.6.4 Home Base Station Beamforming 7.7 Summary and Discussion References [1] Chapter 8 Semidefinite Programming 8.1 Semidefinite Programming 8.2 Converting QCQP and SOCP to SDP Using Schur Complement 8.3 S-procedure 8.4 Application of SDP in Combinatorial Optimization 8.4.1 Boolean Quadratic Programming 8.4.2 Example I: MAXCUT 8.4.3 Example II: ML MIMO Detection 8.4.4 BQP Approximation Based on Semidefinite Relaxation 8.4.5 Example III: High-order QAM OSTBC Incoherent LFSDR Method 8.5 Application of SDR in Transmit Beamforming Design 8.5.1 Beamforming for Downlink Broadcast Channel 8.5.2 Transmit Beamforming for Cognitive Radio 8.5.3 Transmit Beamforming Design for Secure Communications: An Artificial Noise-Assisted Approach 8.5.4 Worst-Case Robust Transmit Beamforming: Single-Cell MISO Scenario 8.5.5 Worst-Case Robust Transmit Beamforming: Multi-Cell MISO Scenario 8.5.6 Cooperative Beamforming for MISO Interference Channels with Outage Constraints: A Centralized Algorithm 242 8.5.7 Cooperative Beamforming for MISO Interference Channels with Outage Constraints: An Efficient Algorithm Based on BSUM 8.5.8 Robust Transmit Beamforming with Outage Constraints: Single-Cell MISO Scenario 255 8.5.9 Robust Transmit Beamforming with Outage Constraints: Multi-Cell MISO Scenario 260 8.6 Summary and Discussion References Chapter 9 Duality 9.1 Lagrange Dual Functions and Conjugate Functions 9.1.1 Lagrange Dual Functions.2 Conjugate Functions 9.1.3 Relationship between Lagrange Dual and Conjugate Functions 9.2 Lagrange Dual Problems 9.3 Strong Duality 9.3.1 Slater Condition 9.3.2 S-Lemma 9.4 The Meaning of Strong Duality 9.4.1 Maximal and Minimum Properties of Strong and Weak Duality 9.4.2 Suboptimal Conditions 9.4.3 Complementary Relaxation 9.5 Karush-Kuhn-Tucker (KKT) Optimality Condition 9.6 Lagrange Dual Optimization 9.7 Alternating Direction Method of Multipliers (ADMM) 9.8 Duality of Generalized Inequality Problems 9.8.1 Lagrange Duality and KKT Condition 9.8.2 Lagrange Duality and KKT Condition for Cone Programming 9.8.3 Lagrange Duality and KKT Condition for SDP 9.9 Alternative Theorem 9.9.1 10.6 Summary and Discussion References Appendix A Convex Optimization Solver A.1 SeDuMi A.2 CVX A.3 Design of Finite Impulse Response (FIR) Filters A.3.1 Problem Formulation A.3.2 Using SeDuMi to Solve Convex Optimization Problems Solving Problem A.3.3 Using CVX Solving Problem A.4 Conclusion References
  
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19. Fourier Transforms in Signal Processing - Continuous and Discrete Methods for Practice
  
  The book includes: Discrete Fourier Transform, Fast Convolution and Correlation, Digital Methods of Spectral Analysis, etc. Contents Chapter 1 Introduction Chapter 2 Signal and Spectrum Chapter 3 Discrete Fourier Transform Chapter 4 Numerical Operation of Discrete Fourier Transform Chapter 5 Fast Convolution and Correlation Chapter 6 Fourier Transform and Spline Interpolation in Signal Processing Chapter 7 Digital Methods of Spectral Analysis References
  
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20. Virtual Instrument and LabVIEW Programming (Higher Vocational) (Edited by Chen Dong and Cui Xiuhua)
  
  This book systematically introduces the concept of virtual instruments and the programming technology of the graphical programming language LabVIEW. The book is divided into 9 chapters, covering the basic concepts of virtual instruments, the LabVIEW development platform, the creation of virtual instrument programs, structures, arrays, clusters, waveform graphs, waveform charts, strings and file control, instrument control, etc. The book introduces the basic principles of LabVIEW and virtual instrument programming technology through a large number of examples and exercises, so as to better help students use virtual instrument technology. Chapter 1 Overview of Virtual Instruments 1 1.1 Virtual Instruments 1 1.1.1 The Development of Measuring Instruments 1 1.1.2 Basic Concepts of Virtual Instruments 2 1.1.3 Comparison between Virtual Instruments and Traditional Instruments 3 1.1.4 Basic Functions of Virtual Instruments 3 1.1.5 Basic Components of Virtual Instruments 4 1.1.6 Virtual Instruments Are Everywhere 5 1.2 LabVIEW Development Platform 5 1.2.1 Introduction to LabVIEW 6 1.2.2 Front Panel 7 1.2.3 Block Diagram 9 1.2.4 Tool Palette, Control Palette, and Function Palette 12 1.2.5 Menu Bar 15 1.2.6 LabVIEW Help Options 17 1.3 LabVIEW Project 21 1.3.1 Creating a LabVIEW Project 21 1.3.2 Using a LabVIEW Project 21 1.4 Test System Based on Virtual Instrument Technology 22 1.4.1 Hardware System of Virtual Test Instruments 23 1.4.2 1.5 LabVIEW Learning Tips 1.5.1 Summary 1.5.2 Practice 1.5.3 Chapter 11 11 Chapter 12 12 Chapter 13 13 Chapter 14 14 Chapter 15 15 Chapter 16 16.1 Introduction 16.2 Introduction 16.3 Chapter 16 16.3.1 Introduction 16.3.2 Introduction 16.3.3 Chapter 17 17.4 Chapter 17 17.5.2 Introduction 17.4.3 Chapter 17 17.5.3 Chapter 17 17.5.4 Chapter 17 17.5.5 Chapter 17 3.1.2 Automatic Indexing of For Loops 44 3.2 While Loops 45 3.2.1 Creating a While Loop 45 3.2.2 Automatic Indexing of While Loops 47 3.2.3 Mechanical Action of a Boolean Switch 47 3.3 Loop Timing 48 3.4 Shift Registers 50 3.4.1 Concept of Shift Registers 50 3.4.2 Creating Shift Registers 50 3.4.3 Initializing Shift Registers 51 3.4.4 Creating Cascaded Shift Registers 53 3.5 Feedback Nodes 54 3.5.1 Creating Feedback Nodes 54 3.5.2 Initializing Feedback Nodes 55 3.6 Programming for Factorial Operations 57 3.7 Programming for Arithmetic Mean of Measurement Results 58 3.8 Case Structures 59 3.8.1 Creating Case Structures 60 3.8.2 Setting Case Structures 61 3.9 3.9.1 Creating a Sequence Structure 64 3.9.2 Sequence Local Variables 65 3.10 Event Structure 67 3.10.1 The Concept of Event Driven 67 3.10.2 Creating an Event Structure 68 3.10.3 Configuring an Event Structure 69 3.10.4 User Interface Event Classification and Event Registration Mode 70 3.11 Disable Structure 71 3.11.1 Conditional Disable Structure 71 3.11.2 Block Diagram Disable Structure 72 3.12 Formula Node 72 3.12.1 Creating a Formula Node 73 3.12.2 Formula Node Syntax and Usage Instructions 74 3.12.3 Expression Node 75 3.13 Programming of Limit-Out-of-Limit Alarm 76 Chapter Summary 77 Thinking and Exercises 78 Chapter 4 Arrays, Clusters, and Waveform Display 79 4.1 Arrays 79 4.1.1 The Concept of Arrays 79 4.1.2 Creating an Array 80 4.1.3 Array Functions 82 4.2 Polymorphic Functions 88 4.3 Clusters 88 4.3.1 The Concept of Clusters 88 4.3.2 Creating Clusters 89 4.3.3 Cluster Functions 90 4.3.4 Error Clusters 94 4.4 Waveforms 94 4.4.1 The Concept of Waveforms 94 4.4.2 Creating Waveforms 95 4.5 Waveform Graphs 95 4.5.1 Displaying a Single Plot in a Waveform Graph 96 4.5.2 Displaying Multiple Plots in a Waveform Graph 97 4.6 Waveform Charts 98 4.6.1 Displaying a Single Plot in a Waveform Graph 100 4.6.2 Displaying Multiple Plots in a Waveform Graph 101 4.7 Customizing Waveform Graphs and Charts 102 4.7.1 Customizing the Appearance of Waveform Graphs and Charts 102 4.7.2 Graphics Tool Palette 102 4.7.3 4.9 Other Types of Graphs and Charts 104 4.9.1 XY Graph 105 4.9.2 Digital Waveform Graph 106 4.9.3 Windows 3D Graph 107 4.9.4 Mixed Signal Graph 111 Chapter Summary 115 Exercises 115 Chapter 5 Strings and File Input/Output 116 5.1 Strings 116 5.1.1 Creating String Controls and Indicators 116 5.1.2 String Display Types 116 5.1.3 String Functions 118 5.2 File Input/Output 124 5.2.1 Selecting a File I/O Format 124 5.2.2 File I/O Functions 125 Chapter Summary 134 Exercises 134 Chapter 6 Data Acquisition 135 6.1 6.2 Data Acquisition Equipment 135 6.2.1 Types of Data Acquisition Equipment 136 6.2.2 Main Specifications of Data Acquisition Equipment 136 6.3 Software Structure of Data Acquisition System 139 6.3.1 System Software Structure 139 6.3.2 Driver Software 140 6.3.3 Application Software 142 6.4 Data Acquisition Equipment Setup and Test 143 6.4.1 Test and Automation Explorer 144 6.4.2 Data Acquisition Equipment Setup and Test 144 6.5 Basics of Data Acquisition 147 6.5.1 Sampling 147 6.5.2 Types of Input Signals 149 6.5.3 Connection Methods of Input Signals 150 6.5.4 Signal Conditioning 152 6.6 Signal Generation, Processing and Analysis 152 6.6.1 Signal Generation 153 6.6.2 Time Domain Analysis 155 6.6.3 Frequency Domain Analysis 156 6.6.4 Digital Filter 159 6.7 Data Acquisition Application Based on NI USB-6009 Acquisition Card 161 6.7.1 Analog Input 162 6.7.2 Analog Output 168 6.7.3 Digital Input/Output 168 6.8 Data Acquisition Application Based on Third-Party Acquisition Card 169 6.8.1 How to Use Third-Party Data Acquisition Cards in LabVIEW 170 6.8.2 Application of ADLINK PCI9118DG Multi-Function Data Acquisition Card 170 Summary of This Chapter 174 Thinking and Exercises 175 Chapter 7 Instrument Control 176 7.1 Composition of Instrument Control System 176 7.2 GPIB 177 7.2.1 Overview 177 7.2.2 Composition of GPIB System 177 7.2.3 GPIB Message 178 7.2.4 Bus Composition 178 7.2.5 GPIB Function 179 7.3 Serial Communication 181 7.3.1 Overview 181 7.3.2 Serial Communication Function 181 7.4 VISA 182 7.4.1 Overview 182 7.4.2 Why use VISA 183 7.4.3 VISA Function 183 7.5 Instrument Driver 184 Chapter Summary 186 Chapter 8 Practical Programming Techniques 187 8.1 Local Variables and Global Variables 187 8.1.1 Local Variables 188 8.1.2 Global Variables 190 8.1.3 Tips for Using Local Variables and Global Variables 192 8.2 Property Nodes 192 8.2.1 Creating Property Nodes 193 8.2.2 Using Property Nodes 194 8.2.3 Setting VI Properties 197 8.3 Dynamically Loading and Calling VIs 200 Chapter Summary 203 Chapter 9 Virtual Instrument Application Design 204 9.1 Design of a Dual-Trace Virtual Oscilloscope 204 9.1.1 Design Purpose 204 9.1.2 Design Content 204 9.1.3 Design Report Requirements 204 9.1.4 Oscilloscope Measurement Theory 205 9.1.5 Front Panel of a Virtual Oscilloscope 205 9.2 9.2.1 Design Objectives 206 9.2.2 Design Contents 206 9.2.3 Design Report Requirements 206 9.2.4 Theory of Voltage, Current, and Resistance Testers 207 9.2.5 Front Panel of Voltage, Current, and Resistance (VCR) Characteristic Tester 207 References 208
  
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