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richter michael m.; paul sheuli; këpuska veton; silaghi marius - signal processing and machine learning with applications

Signal Processing and Machine Learning with Applications

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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 10/2022
Edizione: 1st ed. 2022





Trama

Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate. 

Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engagedwith signal processing, machine learning and the applications. 






Sommario

Part I Realms of Signal Processing.- 1 Digital Signal Representation.- 1.1 Introduction.- 1.2 Numbers.- 1.2.1 Numbers and Numerals.- 1.2.2 Types of Numbers.- 1.2.3 Positional Number Systems.- 1.3 Sampling and Reconstruction of Signals.- 1.3.1 Scalar Quantization.- 1.3.2 Quantization Noise.- 1.3.3 Signal-To-Noise Ratio.- 1.3.4 Transmission Rate.- 1.3.5 Nonuniform Quantizer.- 1.3.6 Companding.- 1.4 Data Representations.- 1.4.1 Fixed-Point Number Representations.- 1.4.2 Sign-Magnitude Format.- 1.4.3 One’s-Complement Format.- 1.4.4 Two’s-Complement Format.- 1.5 Fix-Point DSP’s.- 1.6 Fixed-Point Representations Based on Radix-Point.- 1.7 Dynamic Range.- 1.8 Precision.- 1.9 Background Information.- 1.10 Exercises.- 2 Signal Processing Background.- 2.1 Basic Concepts.- 2.2 Signals and Information.- 2.3 Signal Processing.- ix.- x Contents.- 2.4 Discrete Signal Representations.- 2.5 Delta and Impulse Function.- 2.6 Parseval’s Theorem.- 2.7 Gibbs Phenomenon.- 2.8 Wold Decomposition.- 2.9 State Space Signal Processing.- 2.10 Common Measurements.- 2.10.1 Convolution.- 2.10.2 Correlation.- 2.10.3 Auto Covariance.- 2.10.4 Coherence.- 2.10.5 Power Spectral Density (PSD).- 2.10.6 Estimation and Detection.- 2.10.7 Central Limit Theorem.- 2.10.8 Signal Information Processing Types.- 2.10.9 Machine Learning.- 2.10.10Exercises.- 3 Fundamentals of Signal Transformations.- 3.1 Transformation Methods.- 3.1.1 Laplace Transform.- 3.1.2 Z-Transform .- 3.1.3 Fourier Series.- 3.1.4 Fourier Transform.- 3.1.5 Discrete Fourier Transform and Fast Fourier Transform .- 3.1.6 Zero Padding.- 3.1.7 Overlap-Add and Overlap-Save Convolution.- Algorithms.- 3.1.8 Short Time Fourier Transform (STFT).- 3.1.9 Wavelet Transform.- 3.1.10 Windowing Signal and the DCT Transforms.- 3.2 Analysis and Comparison of Transformations.- 3.3 Background Information.- 3.4 Exercises.- 3.5 References.- 4 Digital Filters.- 4.1 Introduction.- 4.1.1 FIR and IIR Filters.- 4.1.2 Bilinear Transform.-4.2 Windowing for Filtering.- 4.3 Allpass Filters.- 4.4 Lattice Filters.- 4.5 All-Zero Lattice Filter.- 4.6 Lattice Ladder Filters.- Contents xi.- 4.7 Comb Filter.- 4.8 Notch Filter.- 4.9 Background Information.- 4.10 Exercises.- 5 Estimation and Detection.- 5.1 Introduction.- 5.2 Hypothesis Testing.- 5.2.1 Bayesian Hypothesis Testing.- 5.2.2 MAP Hypothesis Testing.- 5.3 Maximum Likelihood (ML) Hypothesis Testing.- 5.4 Standard Analysis Techniques.- 5.4.1 Best Linear Unbiased Estimator (BLUE).- 5.4.2 Maximum Likelihood Estimator (MLE).- 5.4.3 Least Squares Estimator (LSE).- 5.4.4 Linear Minimum Mean Square Error Estimator.- (LMMSE).- 5.5 Exercises.- 6 Adaptive Signal Processing.- 6.1 Introduction.- 6.2 Parametric Signal Modeling.- 6.2.1 Parametric Estimation.- 6.3 Wiener Filtering.- 6.4 Kalman Filter.- 6.4.1 Smoothing.- 6.5 Particle Filter.- 6.6 Fundamentals of Monte Carl.- 6.6.1 Importance Sampling (IS).- 6.7 Non-Parametric Signal Modeling.- 6.8 Non-Parametric Estimation.- 6.8.1 Correlogram.- 6.8.2 Periodogram.- 6.9 Filter Bank Method.- 6.10 Quadrature Mirror Filter Bank (QMF).- 6.11 Background Information.- 6.12 Exercises.- 7 Spectral Analysis.- 7.1 Introduction.- 7.2 Adaptive Spectral Analysis.- 7.3 Multivariate Signal Processing.- 7.3.1 Sub-band Coding and Subspace Analysis.- 7.4 Wavelet Analysis.- 7.5 Adaptive Beam Forming.- xii Contents.- 7.6 Independent Component Analysis (ICA).- 7.7 Principal Component Analysis (PCA).- 7.8 Best Basis Algorithms.- 7.9 Background Information.- 7.10 Exercises.- Part II Machine Learning and Recognition.- 8 General Learning.- 8.1 Introduction to Learning.- 8.2 The Learning Phases.- 8.2.1 Search and Utility.- 8.3 Search.- 8.3.1 General Search Model.- 8.3.2 Preference relations.- 8.3.3 Different learning methods.- 8.3.4 Similarities .- 8.3.5 Learning to Recognize.- 8.3.6 Learning again.- 8.4 Background Information.- 8.5 Exercises.- 9 Signal Processes, Learning, and Recognition.- 9.1 Learning.- 9.2Bayesian Formalism.- 9.2.1 Dynamic Bayesian Theory.- 9.2.2 Recognition and Search.- 9.2.3 Influences.- 9.3 Subjectivity.- 9.4 Background Information.- 9.5 Exercises.- 10 Stochastic Processes.- 10.1 Preliminaries on Probabilities.- 10.2 Basic Concepts of Stochastic Processes.- 10.2.1 Markov Processes.- 10.2.2 Hidden Stochastic Models (HSM).- 10.2.3 HSM Topology.- 10.2.4 Learning Probabilities.- 10.2.5 Re-estimation.- 10.2.6 Redundancy.- 10.2.7 Data Preparation.- 10.2.8 Proper Redundancy Removal.- 10.3 Envelope Detection.- 10.3.1 Silence Threshold Selection.- 10.3.2 Pre-emphasis.- Contents xiii.- 10.4 Several Processes.- 10.4.1 Similarity.- 10.4.2 The Local-Global Principle.- 10.4.3 HSM Similarities.- 10.5 Conflict and Support.- 10.6 Examples and Applications.- 10.7 Predictions.- 10.8 Background Information.- 10.9 Exercises.- 11 Feature Extraction.- 11.1 Feature Extractions.- 11.2 Basic Techniques.- 11.2.1 Spectral Shaping.- 11.3 Spectral Analysis and Feature Transformation.- 11.3.1 Parametric Feature Transformations and Cepstrum.- 11.3.2 Standard Feature Extraction Techniques.- 11.3.3 Frame Energy.- 11.4 Linear Prediction Coe_cients (LPC).- 11.5 Linear Prediction Cepstral Coe_cients (LPCC).- 11.6 Adaptive Perceptual Local Trigonometric Transformation.- (APLTT).- 11.7 Search.- 11.7.1 General Search Model.- 11.8 Predictions.- 11.8.1 Purpose.- 11.8.2 Linear Prediction.- 11.8.3 Mean Squared Error Minimization.- 11.8.4 Computation of Probability of an Observation Sequence.- 11.8.5 Forward and Backward Prediction.- 11.8.6 Forward-Backward Prediction.- 11.9 Background Information.- 11.10Exercises.- 12 Unsupervised Learning.- 12.1 Generalities.- 12.2 Clustering Principles.- 12.3 Cluster Analysis Methods.- 12.4 Special Methods.- 12.4.1 K-means.- 12.4.2 Vector Quantization (VQ).- 12.4.3 Expectation Maximization (EM).- 12.4.4 GMM Clustering.- 12.5 Background Information.- 12.6 Exercises.- xiv Contents.- 13 Markov Model and Hidden Stochastic Model.-13.1 Markov Process.- 13.2 Gaussian Mixture Model (GMM).- 13.3 Advantages of using GMM.- 13.4 Linear Prediction Analysis.- 13.4.1 Autocorrelation Method.- 13.4.2 Yule-Walker Approach.- 13.4.3 Covariance Method.- 13.4.4 Comparison of Correlation and Covariance methods.- 13.5 The ULS Approach.- 13.6 Comparison of ULS and Covariance Methods.- 13.7 Forward Prediction.- 13.8 Backward Prediction.- 13.9 Forward-Backward Prediction.- 13.10Baum-Welch Algorithm.- 13.11Viterbi Algorithm.- 13.12Background Information.- 13.13Exercises.- 14 Fuzzy Logic and Rough Sets.- 14.1 Rough Sets.- 14.2 Fuzzy Sets.- 14.2.1 Basis Elements.- 14.2.2 Possibility and Necessity.- 14.3 Fuzzy Clustering.- 14.4 Fuzzy Probabilities.- 14.5 Background Information.- 14.6 Exercises.- 15 Neural Networks.- 15.1 Neural Network Types.- 15.1.1 Neural Network Training.- 15.1.2 Neural Network Topology.- 15.2 Parallel Distributed Processing.- 15.2.1 Forward and Backward Uses.- 15.2.2 Learning.- 15.3 Applications to Signal Processing.- 15.4 Background Information.- 15.5 Exercises.- Part III Real Aspects and Applications.- Contents xv.- 16 Noisy Signals.- 16.1 Introduction.- 16.2 Noise Questions.- 16.3 Sources of Noise.- 16.4 Noise Measurement.- 16.5 Weights and A-Weights.- 16.6 Signal to Noise Ratio (SNR).- 16.7 Noise Measuring Filters and Evaluation.- 16.8 Types of noise.- 16.9 Origin of noises.- 16.10Box Plot Evaluation.- 16.11Individual noise types.- 16.11.1Residual.- 16.11.2Mild.- 16.11.3Steady-unsteady Time varying Noise.- 16.11.4Strong Noise.- 16.12Solution to Strong Noise: Matched Filter.- 16.13Background Information.- 16.14Exercises.- 17 Reasoning Methods and Noise Removal.- 17.1 Generalities.- 17.2 Special Noise Removal Methods.- 17.2.1 Residual Noise.- 17.2.2 Mild Noise.- 17.2.3 Steady-Unsteady Noise.- 17.2.4 Strong Noise.- 17.3 Poisson Distribution.- 17.3.1 Outliers and Shots.- 17.3.2 Underlying probability of Shots.- 17.4 Kalman Filter.- 17.4.1 Prediction Estimates.-17.4





Autore

Professor Michael M. Richter taught at the University of Texas at Austin and at RWTH Aachen, in addition to numerous visiting professorships.  He is one of the founding scientific director of the DFKI (German Research Center for Artificial Intelligence). He taught, researched, and published extensively in the areas of mathematical logic and artificial intelligence. Professor Richter was one of the pioneers of case-based reasoning: he founded the leading European event on the subject, he led many of the key academic research projects, and demonstrated the real-world viability of the approach with successful commercial products. Michael Richter passed away during the final publishing phase of this book.

Dr. Sheuli Paul is a scientist in Defence Research and Development Canada, engaged in  applied research in the areas of signal processing, machine learning, artificial intelligence and  human-robot interaction.   Trying to solve complex problems in interdisciplinary areas is her passion.

Dr. Veton  Këpuska  is an  inventor of  Wake-Up-Word Speech Recognition, a method of communication with machines for which he was granted two patents. He joined Florida Institute of Technology (FIT) in 2003 and engaged in numerous research activities in speech and image processing, digital processes, and machine learning. Dr. Këpuska won the First Annual Digital Signal Processing Design competition by applying his Wake-up-Word on embedded Analog Devices Platform. Dr. Këpuska won numerous awards including “the Kerry Bruce Clark” award for teaching excellence and received numerous best paper awards.

Prof. Marius Silaghi has taught, researched, and published in the areas of artificial intelligence and networking. Professor Silaghi is involved in human-machine interaction research and proposed techniques for motion capture, speech recognition, and robotics. He founded the conference on Distributed Constraint Optimization and gave multiple tutorials on the topic at the main artificial intelligence conferences. He received numerous best paper awards.

 












Altre Informazioni

ISBN:

9783319453712

Condizione: Nuovo
Dimensioni: 235 x 155 mm
Formato: Copertina rigida
Illustration Notes:XLI, 607 p. 300 illus., 237 illus. in color.
Pagine Arabe: 607
Pagine Romane: xli


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