PirateBay Download | candy j signal processing applied decomposition approach 2024

candy j signal processing applied decomposition approach 2024

Type:: E-books
Files:: 14
Size:: 522.8 MB
Uploaded On:: Nov. 19, 2024, 10:26 a.m.
Added By:: andryold1
Seeders:: 8
Leechers:: 6
Info Hash:: 79BB0E652E5410BA9EAE9F4C57400688CCEC1A58

Textbook in PDF format Separate signals from noise with this valuable introduction to signal processing by applied decomposition The decomposition of complex signals into the sub-signals, or individual components, is a crucial tool in signal processing. It allows each component of a signal to be analyzed individually, enables the signal to be isolated from noise, and processed in full. Decomposition processes have not always been widely adopted due to the difficult underlying mathematics and complex applications. This text simplifies these obstacles. Signal Processing: An Applied Decomposition Approach demystifies these tools from a model-based perspective. This offers a mathematically informed, “step-by-step” analysis of the process by breaking down a composite signal/system into its constituent parts, while introducing both fundamental concepts and advanced applications. This comprehensive approach addresses each of the major decomposition techniques, making it an indispensable addition to any library specializing in signal processing. Signal Processing readers will find: Signal decomposition techniques developed from the data-based, spectral-based and model-based perspectives incorporate: statistical approaches (PCA, ICA, Singular Spectrum); spectral approaches (MTM, PHD, MUSIC); and model-based approaches (EXP, LATTICE, SSP)In depth discussion of topics includes signal/system estimation and decomposition, time domain and frequency domain techniques, systems theory, modal decompositions, applications and many more Numerous figures, examples, and tables illustrating key concepts and algorithms are developed throughout the text Includes problem sets, case studies, real-world applications as well as MatLAB notes highlighting applicable commands Signal Processing is ideal for engineering and scientific professionals, as well as graduate students seeking a focused text on signal/system decomposition with performance metrics and real-world applications. About the Author Preface Acknowledgments Glossary About the Companion Website Introduction Background Spectral Decomposition Data Decomposition Model-based Decomposition Notation and Terminology Summary MatLAB Notes References Problems Random Signals and Systems Introduction Discrete Random Signals Spectral Representation of Random Signals Discrete Systems with Random Inputs Classical Spectral Estimation Correlation Method (Blackman–Tukey) Average Periodogram Method (Welch) Minimum Variance Distortionless Response (MVDR) Coherence Function Case Study: Sinusoids in Noise Summary MatLAB Notes References Problems Signal Models Data-Based Models Data-Based Response Matrices Data-Based Toeplitz Matrices Data-Based Hankel Matrices Parametric-Based Models ARMAX (AR, ARX, MA, ARMA) Models Lattice Models Transfer Function/Frequency Response Function Models Harmonic Models State-space Models Continuous-time State-space Models Sampled-data State-space Models Discrete-time State-space Models Gauss–Markov State-space Models Innovations Model State-space Equivalence Models Summary MatLAB Notes References Problems Signal Estimation Classical Estimation Estimator Properties Estimator Performance Minimum Variance (MV) Estimation Maximum A-Posteriori (MAP) Estimation Maximum Likelihood (ML) Estimation Least-squares (LS) Estimation Batch Least Squares Recursive Least-squares Optimal Signal Estimation Projection Theory Orthogonal Projections: A Geometric Decomposition Perspective Orthogonal Projections: Singular Value Decomposition Summary MatLAB Notes References Problems Signal Decomposition Introduction Data-Based Decompositions Data Decomposition: Principal Component Analysis (PCA) Data Decomposition: Independent Component Analysis (ICA) Higher Order Statistics Information Theory: Negentropy Information Theory: Mutual Information Estimation Theory: Maximum Likelihood Data Decomposition: Singular Spectral Analysis (SSA) Spectral-Based Decompositions Spectral Decomposition: Multitaper Method (MTM) Spectral Decomposition: Subspace Method Spectral Decomposition: Pisarenko Harmonic Decomposition (PHD) Method Spectral Decomposition: Multiple Signal Classification (MUSIC) Method Model-Based Decomposition Model-Based Decomposition: Damped Exponential Method Model-Based Decomposition: Lattice Method Model-Based Decomposition: State-Space Method Case Study: Harmonics in Noise Summary MatLAB Notes References Problems Model-based Decomposition: Time Domain Background: State-space Systems Discrete Systems Theory Stable Linear Systems Equivalent Linear Systems Modal Systems Realization Problem Realization Theory Balanced Realizations Systems Theory Summary Realization Decomposition Ho–Kalman Realization SVD Realization Subspace Decomposition: Orthogonal Projections Subspace Realization: Orthogonal Projections Multivariable Output Error State-space (MOESP) Algorithm Subspace Decomposition: Oblique Projections Subspace Realization: Oblique Projections Numerical Algorithms for Subspace State-space System Identification (NSID) System Order Estimation and Validation Order Estimation: SVD Approach Model Validation Case Study: Multichannel Mechanical Systems Mechanical Systems Case Study: -mass Mechanical System Summary MatLAB Notes References Problems Model-Based Decomposition: Frequency Domain Introduction Background Frequency Response Functions (FRF) FRF Estimation: Impulse Response Method FRF Spectral Estimation: Polynomial Models FRF-Spectral Estimation: Power Spectra FRF-Spectral Estimation: Frequency Domain Decomposition (FDD) Method Power Spectral Density Decomposition Complex Mode Indicator Function (CMIF) Stabilization Diagram (SDIAG) Least-squares Complex Frequency (LSCF) Method PolyReference Least-Squares Complex Frequency (pLSCF) Method Maximum Likelihood PolyReference Frequency Domain Estimation (ML-pLSCF) Case Study: -DOF Structure Summary MatLAB Notes References Problems Performance Analysis Statistical Performance Methods Zero-Mean Test Whiteness Test Weighted Sum-Squared Residual Test Standard Error Test Correlation Coefficient Function Test Coherence Function Test Ensemble Tests Statistical Order Estimation Signal (Model) Validation MAD Signal Validation Physical Performance Metrics Spectral Peaks: Picking/Histogram Modal Assurance Criterion (MAC) Hankel/SVD Criteria Modal Observability Correlation (MOC) Criterion Modal Singular Value (MSV) Criterion Stabilization Diagram (SDIAG) Modal Frequency Tracker Case Study: Resonant Modal MCK System Summary MatLAB Notes References Applications Modal Decomposition: Sounding Rocket Flight Experimental Test Unit Design and Analysis Sounding Rocket Flight Testing Summary Vibrational Response of a Cylindrical Structure: Identification and Modal Tracking Summary Resonant Ultrasound Spectroscopy RUS Methodology Modal Analysis: FRF and Frequency Histogram Model-Based Decomposition Approach Application: Parallel Piped Structure Synthesized Data: RPP Structure Experimental Data: RPP Structure Model-Based Decomposition Processor Elastic Coefficient Estimation Summary Model-Based Subsystem Decomposition of an -Story (-Mass) Structure Subspace Structural Identification Shaping Filters Subsystem Modal Extraction Summary Data-Based Decomposition: Time-Reversal Processing Iterative Time-Reversal Decomposition Eigen-decomposition Time-reversal Extraction Summary References Probability and Statistics Overview Probability Theory Gaussian Random Vectors Uncorrelated Transformation: Gaussian Random Vectors Toeplitz Correlation Matrices Important Processes References Projection Theory Projections: Deterministic Spaces Projections: Random Spaces Projection: Operators Orthogonal (Perpendicular) Projections Oblique (Parallel) Projections References Matrix Decompositions Singular Value Decomposition QR Decomposition LQ Decomposition References Index

Get This Torrent

Readme-!!!_Eng.txt 1.3 KB
Proakis J. Digital Signal Processing. Solutions 4ed 2007.pdf 2.5 MB
McClellan J., Schafer R., Yoder M. DSP First 2ed 2015.pdf 11.6 MB
Sundararajan D. Digital Signal Processing. An Introduction 2ed 2024.pdf 12.7 MB
Proakis J. Fundamentals Of Communication Systems 2ed 2014.pdf 14.2 MB
Proakis J. Digital Communications 5ed 2007.pdf 15.8 MB
Delmas J. Elliptically Symmetric Distributions in Signal Processing and ML 2024.pdf 19.1 MB
Candy J. Signal Processing. An Applied Decomposition Approach 2025.pdf 19.4 MB
Marven C., Ewers G. A Simple Approach to Digital Signal Processing 1996.pdf 32.1 MB
Anand R. Digital Signal Processing. An Introduction 2022.pdf 41.1 MB
Robinson E. MIT and the Birth of Digital Signal Processing 2014.pdf 56.9 MB
McClellan J., Schafer R., Yoder M. Signal Processing First 2003.pdf 85.3 MB
Mitra S. Digital Signal Processing. A Computer-Based Approach 2001.pdf 98.8 MB
Proakis J. Digital Signal Processing Principles, Algorithms & Apps 4ed 2006.pdf 113.2 MB

Details for: Candy J. Signal Processing. An Applied Decomposition Approach 2024

candy j signal processing applied decomposition approach 2024

Similar Posts: