Digital signal processing (DSP) Books

Book SynopsisTable of Contents 1. Signals and Systems. 2. Linear Time-Invariant Systems. 3. Fourier Series Representation of Periodic Signals. 4. The Continuous-Time Fourier Transform. 5. The Discrete-Time Fourier Transform. 6. Time- and Frequency Characterization of Signals and Systems. 7. Sampling. 8. Communication Systems. 9. The Laplace Transform. 10. The Z-Transform. 11. Linear Feedback Systems. Bibliography. Answers. Index.

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Book SynopsisA guide on the fundamental concepts, applications, algorithms, protocols, new trends and challenges, and research results. It offers information on the core and specialized issues in the field, making it suitable for both the new and experienced researcher.Table of ContentsCognitive Strategies for Green Two-Tier Cellular Networks: A Critical OverviewA Survey of Contemporary Technologies for Smart Home Energy ManagementEmbedded Computing in the Emerging Smart GridIEEE 802.15.4 Based Wireless Sensor Network Design for Smart Grid CommunicationsSmart Grid Communications NetworksWireless Technologies, Protocols, Issues and StandardsIntercell Interference Coordination: Towards A Greener Cellular Network; Energy-efficient Radio Resource Management for Green Radio SystemsGreen computing and Communication ArchitectureGreen Computing Platforms for Biomedical SystemsGreen Datacenter Infrastructures in the Cloud Computing EraEnergy Efficient Cloud Computing: A Green Migration of the Traditional ITGreen Data Centers; Energy-Efficient Sensor NetworksEnergy Efficient Next Generation Wireless CommunicationsEnergy Efficient MIMO-OFDM SystemsConstrained Green Base Station Deployment with Resource Allocation in Wireless NetworksGreen Broadband Access NetworksOverview of Energy Saving Techniques for Mobile and Wireless Access NetworksTowards Energy-Oriented Telecommunication NetworksEnergy-Efficient Peer-to-Peer Networking and OverlaysPower Management for 4G Broadband Wireless Access NetworksGreen Optical Core NetworksAnalysis and Development of Green-Aware Security Mechanisms for Modern Internet ApplicationsUsing Ant Colony Agents for Designing Energy-Efficient Routing Protocols for Wireless Ad Hoc and Sensor NetworksSmart Grid Communications: Opportunities and ChallengesA Survey on Smart Grid Communications: From an Architecture Overview to Standardization ActivitiesTowards Energy Efficiency in Next Generation Green Mobile Networks: a Queueing Theory Perspective

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Book SynopsisThis concise single-semester textbook demonstrates cutting-edge concepts at the intersection of machine learning (ML) and wireless communications. Requiring no previous knowledge of ML, it includes over 20 examples addressing real-world challenges, and over 100 end-of-chapter exercises, including hands-on exercises using Python.

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Book SynopsisFully revised and updated, the second edition of this classic text is the definitive guide to the mathematical models underlying imaging from sensed data. Building on fundamental principles derived from the two- and three-dimensional Fourier transform, and other key mathematical concepts, it introduces a broad range of imaging modalities within a unified framework, emphasising universal theoretical concepts over specific physical aspects. This expanded edition presents new coverage of optical-coherence microscopy, electron-beam microscopy, near-field microscopy, and medical imaging modalities including MRI, CAT, ultrasound, and the imaging of viruses, and introduces additional end-of-chapter problems to support reader understanding. Encapsulating the author''s fifty years of experience in the field, this is the ideal introduction for senior undergraduate and graduate students, academic researchers, and professional engineers across engineering and the physical sciences.

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Book SynopsisTable of Contents1. Feature engineering 2. Heart rate variability 3. Understanding the suitabillity of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers 4. Patient-specific ECG beat classification using EMD and deep learning-based technique 5. Empirical wavelet transform and deep learning-based technique for ECG beat classification 6. Development of an Internet-of-Things (IoT)-based pill monitoring device for geriatric patients 7. Biomedical robotics 8. Combating COVID-19 by implying machine learning predictions and projections 9. Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network 10. Improved extraction of the extreme thermal regions of breast IR images 11. New metrics to asses the subtle changes of the heart's electromagnetic field 12. The role of optimal and modified lead systems in electrocardiogram 13. Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy 14. Multimodal microscopy: A novel low-cost microscope designed for food and biological applications

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Book SynopsisRapid development in different fields of Digital Audio Effects (DAFX) has led to new algorithms. The Second Edition of DAFX - Digital Audio Effects investigates digital signal processing, its application to sound, and how its effects on sound can be used within music.Table of ContentsPreface. List of Contributors. 1 Introduction (V. Verfaille, M. Holters, U. Zölzer). 1.1 Digital Audio Effects DAFX with MATLAB. 1.2 Classifications of DAFX. 1.3 Fundamentals of Digital Signal Processing. 1.4 Conclusion. Bibliography. 2 Filters and Delays (P. Dutilleux, M. Holters, S. Disch, U. Zölzer). 2.1 Introduction. 2.2 Basic Filters. 2.3 Equalizers. 2.4 Time-varying Filters. 2.5 Basic Delay Structures. 2.6 Delay-based Audio Effects. 2.7 Conclusion. Sound and Music. Bibliography. 3 Modulators and Demodulators (P. Dutilleux, M. Holters, S. Disch, U. Zölzer). 3.1 Introduction. 3.2 Modulators. 3.3 Demodulators. 3.4 Applications. 3.5 Conclusion. Sound and Music. Bibliography. 4 Nonlinear Processing (P. Dutilleux, K. Dempwolf, M. Holters, U. Zölzer). 4.1 Introduction. 4.2 Dynamic Range Control. 4.3 Musical Distortion and Saturation Effects. 4.4 Exciters and Enhancers. 4.5 Conclusion. Sound and Music. Bibliography. 5 Spatial Effects (V. Pulkki, T. Lokki, D. Rocchesso). 5.1 Introduction. 5.2 Concepts of spatial hearing. 5.3 Basic spatial effects for stereophonic loudspeaker and headphone playback. 5.4 Binaural techniques in spatial audio. 5.5 Spatial audio effects for multichannel loudspeaker layouts. 5.6 Reverberation. 5.7 Modeling of room acoustics. 5.8 Other spatial effects. 5.9 Conclusion. 5.10 Acknowledgements. References. 6 Time-Segment Processing (P. Dutilleux, G. De Poli, A. von dem Knesebeck, U. Zölzer). 6.1 Introduction. 6.2 Variable Speed Replay. 6.3 Time Stretching. 6.4 Pitch Shifting. 6.5 Time Shuffling and Granulation. 6.6 Conclusion. Sound and Music. References. 7 Time-Frequency Processing (D. Arfib, F. Keiler, U. Zölzer, V. Verfaille, J. Bonada). 7.1 Introduction. 7.2 Phase Vocoder Basics. 7.3 Phase Vocoder Implementations. 7.4 Phase Vocoder Effects. 7.5 Conclusion. References. 8 Source-Filter Processing (D. Arfib, F. Keiler, U. Zölzer, V. Verfaille). 8.1 Introduction. 8.2 Source-Filter Separation. 8.3 Source-Filter Transformations. 8.4 Conclusion. References. 9 Adaptive Digital Audio Effects (V. Verfaille, D. Arfib, F. Keiler, A. von dem Knesebeck, U. Zölzer). 9.1 Introduction. 9.2 Sound-Feature Extraction. 9.3 Mapping Sound Features to Control Parameters. 9.4 Examples of Adaptive DAFX. 9.5 Conclusions. References. 10 Spectral Processing (J. Bonada, X. Serra, X. Amatriain, A. Loscos). 10.1 Introduction. 10.2 Spectral Models. 10.3 Techniques. 10.4 Effects. 10.5 Conclusions. References. 11 Time and Frequency Warping-Musical Signals (G. Evangelista). 11.1 Introduction. 11.2 Warping. 11.3 Musical Uses of Warping. 11.4 Conclusion. References. 12 Virtual Analog Effects (V. Välimäki, S. Bilbao, J. O. Smith, J. S. Abel, J. Pakarinen, D. Berners). 12.1 Introduction. 12.2 Virtual Analog Filters. 12.3 Circuit-Based Valve Emulation. 12.4 Electromechanical Effects. 12.5 Tape-Based Echo Simulation. 12.6 Antiquing of Audio Files. 12.7 Conclusion. References. 13 Automatic Mixing (E. Perez-Gonzalez, J. D. Reiss). 13.1 Introduction. 13.2 AM-DAFX. 13.3 Cross-adaptive AM-DAFX. 13.4 AM-DAFX Implementations. 13.5 Conclusion. References. 14 Sound Source Separation (G. Evangelista, S. Marchand, M. D. Plumbley, E. Vincent). 14.1 Introduction. 14.2 Binaural Source Separation. 14.3 Source Separation from Single-Channel Signals. 14.4 Applications. 14.5 Conclusions. Acknowledgments. References. Glossary. Index.

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Book SynopsisThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this teTrade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsPreface; Notation; 27. Mean-Square-Error inference; 28. Bayesian inference; 29. Linear regression; 30. Kalman filter; 31. Maximum likelihood; 32. Expectation maximization; 33. Predictive modeling; 34. Expectation propagation; 35. Particle filters; 36. Variational inference; 37. Latent Dirichlet allocation; 38. Hidden Markov models; 39. Decoding HMMs; 40. Independent component analysis; 41. Bayesian networks; 42. Inference over graphs; 43. Undirected graphs; 44. Markov decision processes; 45. Value and policy iterations; 46. Temporal difference learning; 47. Q-learning; 48. Value function approximation; 49. Policy gradient methods; Author index; Subject index.

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Book SynopsisWith its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for gradTrade Review'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory'With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.' politcommerce.com'This is a comprehensive textbook on the fundamental concepts of machine learning. In the second edition, the authors provide a very accessible introduction to the main ideas behind machine learning models.' Helena Mihaljević, zbMATHTable of Contents1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.

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Book SynopsisThink DSP: Digital Signal Processing in Python is an introduction to signal processing and system analysis using a computational approach. The premise of this book (like the others in the Think X series) is that if you know how to program, you can use that skill to learn other things.

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Book SynopsisAn overview of the state in design and implementation of advanced tracking for single and multiple sensor systems. The text provides evaluations of sensor management, kinematic and attribute data processing, data association, situation assessment, and modern tracking and data fusion methods as applied in both military and non-military arenas. The book offers full coverage of tracking topics such as passive ranging and interactive multiple model (IMM) filtering; multiple hypothesis tracking (MHT) data association; Bayesian and Dempster-Shafer attribute fusion; multiple sensor tracking methods for distributed systems such as space-based surveillance systems; use of tracking data for situation assessment and sensor management; track fusion and track-before-detect (TBD) methods; and efficient allocation of agile beam radar resources. It also covers the interpretation and optimisation of tracker data, and solves the problems associated with algorithm choice and design.Table of ContentsThe Basics of Target Tracking. Sensor and Source Characteristics. Kinematic State Estimation: Filtering and Prediction. Modelling and Tracking Dynamic Targets. Passive Sensor Tracking. Basic Methods for Data Association. Advanced Methods for MTT Data Association. Attribute Data Fusion. Multiple Sensor Tracking -- Issues and Methods. Multiple Sensor Tracking -- System Implementation and Applications. Reasoning Schemes for Situation Assessment and Sensor Management. Situation Assessment. Tracking System Performance Prediction, and Evaluation. Multi Target Tracking with an Agile Beam Radar. Sensor Management. Multiple Hypothesis Tracking System Design and Application. Detection and Tracking of Dim Targets in Clutter.

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Book SynopsisThis book develops the basic concepts in understanding Analog Communications. Beginning with coverage of amplitude modulation, including the time and frequency domain representations of double sideband, single sideband, and vestigial sideband modulation, and introduces the student to the fundamental ideas of quadrature amplitude modulation, frequency division multiplexing, and digital communications using on-off keying. The author continues with additional discussion and coverage of the time and frequency domain representations of frequency and phase modulation, including bandwidth calculations, and the use of frequency shift keying, phase shift keying, and differential phase shift keying for the transmission of digital information. Contents include applications and further analyses of the effects of channel noise on amplitude, phase, and frequency modulation performance based on input versus output signal to noise ratios and some system comparisons are discussed.Table of ContentsPreface.- Amplitude Modulation.- Phase and Frequency Modulation.- Noise in Analog Modulation.

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This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.Table of ContentsIntroduction.- Scene Understanding Datasets.- Indoor/Outdoor classification with Multiple Experts.- Outdoor Scene Classification Using Labeled Segments.- Global-Attributes Assisted Outdoor Scene Geometric Labeling.- Conclusion and Future Work.

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Book SynopsisThis book is devoted to the application of advanced signal processing on event-related potentials (ERPs) in the context of electroencephalography (EEG) for the cognitive neuroscience. ERPs are usually produced through averaging single-trials of preprocessed EEG, and then, the interpretation of underlying brain activities is based on the ordinarily averaged EEG. We find that randomly fluctuating activities and artifacts can still present in the averaged EEG data, and that constant brain activities over single trials can overlap with each other in time, frequency and spatial domains. Therefore, before interpretation, it will be beneficial to further separate the averaged EEG into individual brain activities. The book proposes systematic approaches pre-process wavelet transform (WT), independent component analysis (ICA), and nonnegative tensor factorization (NTF) to filter averaged EEG in time, frequency and space domains to sequentially and simultaneously obtain the pure ERP of interest. Software of the proposed approaches will be open-accessed.Table of ContentsIllustrations of Digital Filter, Fourier Transform and Wavelet Transform by the Definition of Correlation; Wavelet Filter Design Based on Frequency Responses for Filtering ERP Data with Duration of One Epoch; Individual-Level Independent Component Analysis to Extract ERPs' Components from Averaged EEG Data Over Single Trials; Multi-Domain Feature of an ERP Extracted by Nonnegative Tensor Factorization: New Approach for Group-Level Analysis of EEPs; Analysis of Ongoing EEG During Real-World Music Experiences by Nonnegative Tensor Factorization;

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Book SynopsisThis book provides an overview on the current progresses in artificial intelligence and neural nets in data science. The book is reporting on intelligent algorithms and applications modeling, prediction, and recognition tasks and many other application areas supporting complex multimodal systems to enhance and improve human–machine or human–human interactions. This field is broadly addressed by the scientific communities and has a strong commercial impact since investigates on the theoretical frameworks supporting the implementation of sophisticated computational intelligence tools. Such tools will support multidisciplinary aspects of data mining and data processing characterizing appropriate system reactions to human-machine interactional exchanges in interactive scenarios. The emotional issue has recently gained increasing attention for such complex systems due to its relevance in helping in the most common human tasks (like cognitive processes, perception, learning, communication, and even "rational" decision-making) and therefore improving the quality of life of the end users.Table of Contents1. Michele Scarpiniti, Edoardo Massaro, Danilo Comminiello and Aurelio Uncini: Generating New Sounds by Vector Arithmetic in the Latent Space of the MelGAN Architecture2. Kamyar Zeinalipour and Marco Gori: Graph Neural Networks for Topological Feature Extraction in ECG Classification3. Francesco Camastra, Angelo Casolaro and Gennaro Iannuzzo: Manifold Learning by a Deep Gaussian Process Variational Autoencoder4. Marcos Faundez-Zanuy, Anna Faura-Pujol, Hector Montalvo-Ruiz, Alexia Losada-Fors, Pablo Genovese and Pilar Sanz-Cartagena: Analysis of Sensors for Movement Analysis5. Giansalvo Cirrincione, Vincenzo Randazzo, Pietro Barbiero, Gabriele Ciravegna and Eros Pasero: Dual Seep Clustering6. Vincenzo Randazzo, Gaia Marchetti, Carla Giustetto, Erica Gugliermina, Rahul Kumar, Giansalvo Cirrincione, Fiorenzo Gaita and Eros Pasero: Learning-Based Approach to Predict Fatal Events in Brugada Syndrome7. Francesco Prinzi, Marco Insalaco, Salvatore Gaglio and Salvatore Vitabile: Breast Cancer Localization and Classification in Mammograms Using YoloV58. Jonathan Melchiorre, Marco Martino Rosso, Raffaele Cucuzza, Emanuela D'Alto, Amedeo Manuello Bertetto and Giuseppe Carlo Marano: Deep Acoustic Emission Detection Trained on Seismic Signals9. Elena Sibilano, Michael Lassi, Alberto Mazzoni, Vitoantonio Bevilacqua and Antonio Brunetti: A Deep Learning Framework for the Classification of Pre-Prodromal and Prodromal Alzheimer’s Disease Using Resting-State EEG signals10. Giovanni Di Gennaro, Amedeo Buonanno, Francesco Verolla, Giovanni Fioretti, Francesco A. N. Palmieri, and Krishna R. Pattipati: Imitation Learning Through Prior Injection in Markov Decision Processes 11. Giovanni Di Gennaro, Amedeo Buonanno, Marilena Baldi, Enzo Capoluongo, and Francesco A.N. Palmieri: Vision-Based Human Activity Recognition Methods Using Pose Estimation12. Stefano Fiscale, Laura Inno, Angelo Ciaramella, Alessio Ferone, Alessandra Rotundi, Pasquale De Luca, Ardelio Galletti, Livia Marcellino and Giovanni Covone: Identifying Exoplanets in TESS Data by Deep Learning13. Vincenzo Bevilacqua, Antonio Di Marino, Angelo Ciaramella, Anastasia Angela Biancardi, Giorgio Budillon, Paola de Ruggiero, Emanuele Della Volpe, Luigi Gifuni, Danilo Mascolo, Stefano Pierini and Enrico Zambianchi: Computational Intelligence for Marine Litter Recovery14. Alessio Ferone, Marco Lazzaro, Vincenzo Mariano Scarrica, Angelo Ciaramella and Antonino Staiano: A Synthetic Dataset for Learning Optical Flow in Underwater Environment15. Giorgia Ghione, Marta Lovino, Elisa Ficarra and Giansalvo Cirrincione: An Interpretable BERT-Based Architecture for SARS-CoV-2 Variant Identification16. Michele Fedrizzi and Silvio Giove: Competence-Based Coalition Choice, a Non-Additive Approach17. Michele La Rocca, Cira Perna, Marilena Sibillo and Antonio Vignes: Forecasting Mortality with Autoencoders: An Application to Italian Mortality Data.18. Michele Scarpiniti, Edoardo Bini, Marco Ferraro, Alessandro Giannetti, Danilo Comminiello, Yong-Cheol Lee and Aurelio Uncini: Leaky Echo State Network for Audio Classification in Construction Sites19. Sidhant Kumar, Vijayeskar Kumar, Krishnil Ram, Michael Wood, Giansalvo Cirrincione and Rahul Kumar: ECG Signal Classification Using Long-Short-Term-Memory Neural Networks20. Michele Lo Giudice, Nadia Mammone, Cosimo Ieracitano, Umberto Aguglia, Danilo Mandic and Francesco Carlo Morabito: A Convolutional Neural Network Approach for the Classification of Subjects with Epileptic Seizures vs Psychogenic Non-Epileptic Seizures and Control Based on Automatic Feature Extraction from Empirical Mode Decomposition of Interictal EEG Recordings21. Nicola Camatti, Andrea Ellero and Paola Ferretti: Commerce Districts: Conditions for Customer Overall Satisfaction in a Multi-Attribute Framework22. Andrea Ellero, Paola Ferretti, and Elena Zocchia: Problematic Merging and Cartels: A Collusion Risk Factors Analysis

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Book SynopsisRichard G. Lyons is a consulting Systems Engineer and lecturer with Besser Associates in Mountain View, California. He is author of the book Understanding Digital Signal Processing, editor and contributor to the book Streamlining Digital Signal Processing, and has authored numerous articles on DSP. Lyons has taught DSP at the University of California Santa Cruz Extension and recently received the IEEE Signal Processing Society's  2012 Educator of the Year award.Table of ContentsPreface xv About the Author xxiii Chapter 1: Discrete Sequences and Systems 1 1.1 Discrete Sequences and their Notation 2 1.2 Signal Amplitude, Magnitude, Power 8 1.3 Signal Processing Operational Symbols 10 1.4 Introduction to Discrete Linear Time-Invariant Systems 12 1.5 Discrete Linear Systems 12 1.6 Time-Invariant Systems 17 1.7 The Commutative Property of Linear Time-Invariant Systems 18 1.8 Analyzing Linear Time-Invariant Systems 19 References 21 Chapter 1 Problems 23 Chapter 2: Periodic Sampling 33 2.1 Aliasing: Signal Ambiguity in the Frequency Domain 33 2.2 Sampling Lowpass Signals 38 2.3 Sampling Bandpass Signals 42 2.4 Practical Aspects of Bandpass Sampling 45 References 49 Chapter 2 Problems 50 Chapter 3: The Discrete Fourier Transform 59 3.1 Understanding the DFT Equation 60 3.2 DFT Symmetry 73 3.3 DFT Linearity 75 3.4 DFT Magnitudes 75 3.5 DFT Frequency Axis 77 3.6 DFT Shifting Theorem 77 3.7 Inverse DFT 80 3.8 DFT Leakage 81 3.9 Windows 89 3.10 DFT Scalloping Loss 96 3.11 DFT Resolution, Zero Padding, and Frequency-Domain Sampling 98 3.12 DFT Processing Gain 102 3.13 The DFT of Rectangular Functions 105 3.14 Interpreting the DFT Using the Discrete-Time Fourier Transform 120 References 124 Chapter 3 Problems 125 Chapter 4: The Fast Fourier Transform 135 4.1 Relationship of the FFT to the DFT 136 4.2 Hints on Using FFTs in Practice 137 4.3 Derivation of the Radix-2 FFT Algorithm 141 4.4 FFT Input/Output Data Index Bit Reversal 149 4.5 Radix-2 FFT Butterfly Structures 151 4.6 Alternate Single-Butterfly Structures 154 References 158 Chapter 4 Problems 160 Chapter 5: Finite Impulse Response Filters 169 5.1 An Introduction to Finite Impulse Response (FIR) Filters 170 5.2 Convolution in FIR Filters 175 5.3 Lowpass FIR Filter Design 186 5.4 Bandpass FIR Filter Design 201 5.5 Highpass FIR Filter Design 203 5.6 Parks-McClellan Exchange FIR Filter Design Method 204 5.7 Half-band FIR Filters 207 5.8 Phase Response of FIR Filters 209 5.9 A Generic Description of Discrete Convolution 214 5.10 Analyzing FIR Filters 226 References 235 Chapter 5 Problems 238 Chapter 6: Infinite Impulse Response Filters 253 6.1 An Introduction to Infinite Impulse Response Filters 254 6.2 The Laplace Transform 257 6.3 The z-Transform 270 6.4 Using the z-Transform to Analyze IIR Filters 274 6.5 Using Poles and Zeros to Analyze IIR Filters 282 6.6 Alternate IIR Filter Structures 289 6.7 Pitfalls in Building IIR Filters 292 6.8 Improving IIR Filters with Cascaded Structures 295 6.9 Scaling the Gain of IIR Filters 300 6.10 Impulse Invariance IIR Filter Design Method 303 6.11 Bilinear Transform IIR Filter Design Method 319 6.12 Optimized IIR Filter Design Method 330 6.13 A Brief Comparison of IIR and FIR Filters 332 References 333 Chapter 6 Problems 336 Chapter 7: Specialized Digital Networks and Filters 361 7.1 Differentiators 361 7.2 Integrators 370 7.3 Matched Filters 376 7.4 Interpolated Lowpass FIR Filters 381 7.5 Frequency Sampling Filters: The Lost Art 392 References 426 Chapter 7 Problems 429 Chapter 8: Quadrature Signals 439 8.1 Why Care about Quadrature Signals? 440 8.2 The Notation of Complex Numbers 440 8.3 Representing Real Signals Using Complex Phasors 446 8.4 A Few Thoughts on Negative Frequency 450 8.5 Quadrature Signals in the Frequency Domain 451 8.6 Bandpass Quadrature Signals in the Frequency Domain 454 8.7 Complex Down-Conversion 456 8.8 A Complex Down-Conversion Example 458 8.9 An Alternate Down-Conversion Method 462 References 464 Chapter 8 Problems 465 Chapter 9: The Discrete Hilbert Transform 479 9.1 Hilbert Transform Definition 480 9.2 Why Care about the Hilbert Transform? 482 9.3 Impulse Response of a Hilbert Transformer 487 9.4 Designing a Discrete Hilbert Transformer 489 9.5 Time-Domain Analytic Signal Generation 495 9.6 Comparing Analytical Signal Generation Methods 497 References 498 Chapter 9 Problems 499 Chapter 10: Sample Rate Conversion 507 10.1 Decimation 508 10.2 Two-Stage Decimation 510 10.3 Properties of Downsampling 514 10.4 Interpolation 516 10.5 Properties of Interpolation 518 10.6 Combining Decimation and Interpolation 521 10.7 Polyphase Filters 522 10.8 Two-Stage Interpolation 528 10.9 z-Transform Analysis of Multirate Systems 533 10.10 Polyphase Filter Implementations 535 10.11 Sample Rate Conversion by Rational Factors 540 10.12 Sample Rate Conversion with Half-band Filters 543 10.13 Sample Rate Conversion with IFIR Filters 548 10.14 Cascaded Integrator-Comb Filters 550 References 566 Chapter 10 Problems 568 Chapter 11: Signal Averaging 589 11.1 Coherent Averaging 590 11.2 Incoherent Averaging 597 11.3 Averaging Multiple Fast Fourier Transforms 600 11.4 Averaging Phase Angles 603 11.5 Filtering Aspects of Time-Domain Averaging 604 11.6 Exponential Averaging 608 References 615 Chapter 11 Problems 617 Chapter 12: Digital Data Formats and their Effects 623 12.1 Fixed-Point Binary Formats 623 12.2 Binary Number Precision and Dynamic Range 632 12.3 Effects of Finite Fixed-Point Binary Word Length 634 12.4 Floating-Point Binary Formats 652 12.5 Block Floating-Point Binary Format 658 References 658 Chapter 12 Problems 661 Chapter 13: Digital Signal Processing Tricks 671 13.1 Frequency Translation without Multiplication 671 13.2 High-Speed Vector Magnitude Approximation 679 13.3 Frequency-Domain Windowing 683 13.4 Fast Multiplication of Complex Numbers 686 13.5 Efficiently Performing the FFT of Real Sequences 687 13.6 Computing the Inverse FFT Using the Forward FFT 699 13.7 Simplified FIR Filter Structure 702 13.8 Reducing A/D Converter Quantization Noise 704 13.9 A/D Converter Testing Techniques 709 13.10 Fast FIR Filtering Using the FFT 716 13.11 Generating Normally Distributed Random Data 722 13.12 Zero-Phase Filtering 725 13.13 Sharpened FIR Filters 726 13.14 Interpolating a Bandpass Signal 728 13.15 Spectral Peak Location Algorithm 730 13.16 Computing FFT Twiddle Factors 734 13.17 Single Tone Detection 737 13.18 The Sliding DFT 741 13.19 The Zoom FFT 749 13.20 A Practical Spectrum Analyzer 753 13.21 An Efficient Arctangent Approximation 756 13.22 Frequency Demodulation Algorithms 758 13.23 DC Removal 761 13.24 Improving Traditional CIC Filters 765 13.25 Smoothing Impulsive Noise 770 13.26 Efficient Polynomial Evaluation 772 13.27 Designing Very High-Order FIR Filters 775 13.28 Time-Domain Interpolation Using the FFT 778 13.29 Frequency Translation Using Decimation 781 13.30 Automatic Gain Control (AGC) 783 13.31 Approximate Envelope Detection 784 13.32 AQuadrature Oscillator 786 13.33 Specialized Exponential Averaging 789 13.34 Filtering Narrowband Noise Using Filter Nulls 792 13.35 Efficient Computation of Signal Variance 797 13.36 Real-time Computation of Signal Averages and Variances 799 13.37 Building Hilbert Transformers from Half-band Filters 802 13.38 Complex Vector Rotation with Arctangents 805 13.39 An Efficient Differentiating Network 810 13.40 Linear-Phase DC-Removal Filter 812 13.41 Avoiding Overflow in Magnitude Computations 815 13.42 Efficient Linear Interpolation 815 13.43 Alternate Complex Down-conversion Schemes 816 13.44 Signal Transition Detection 820 13.45 Spectral Flipping around Signal Center Frequency 821 13.46 Computing Missing Signal Samples 823 13.47 Computing Large DFTs Using Small FFTs 826 13.48 Computing Filter Group Delay without Arctangents 830 13.49 Computing a Forward and Inverse FFT Using a Single FFT 831 13.50 Improved Narrowband Lowpass IIR Filters 833 13.51 A Stable Goertzel Algorithm 838 References 840 Appendix A: The Arithmetic of Complex Numbers 847 A.1 Graphical Representation of Real and Complex Numbers 847 A.2 Arithmetic Representation of Complex Numbers 848 A.3 Arithmetic Operations of Complex Numbers 850 A.4 Some Practical Implications of Using Complex Numbers 856 Appendix B: Closed Form of a Geometric Series 859 Appendix C: Time Reversal and the DFT 863 Appendix D: Mean, Variance, and Standard Deviation 867 D.1 Statistical Measures 867 D.2 Statistics of Short Sequences 870 D.3 Statistics of Summed Sequences 872 D.4 Standard Deviation (RMS) of a Continuous Sinewave 874 D.5 Estimating Signal-to-Noise Ratios 875 D.6 The Mean and Variance of Random Functions 879 D.7 The Normal Probability Density Function 882 Appendix E: Decibels (DB and DBM) 885 E.1 Using Logarithms to Determine Relative Signal Power 885 E.2 Some Useful Decibel Numbers 889 E.3 Absolute Power Using Decibels 891 Appendix F: Digital Filter Terminology 893 Appendix G: Frequency Sampling Filter Derivations 903 G.1 Frequency Response of a Comb Filter 903 G.2 Single Complex FSF Frequency Response 904 G.3 Multisection Complex FSF Phase 905 G.4 Multisection Complex FSF Frequency Response 906 G.5 Real FSF Transfer Function 908 G.6 Type-IV FSF Frequency Response 910 Appendix H: Frequency Sampling Filter Design Tables 913 Appendix I: Computing Chebyshev Window Sequences 927 I.1 Chebyshev Windows for FIR Filter Design 927 I.2 Chebyshev Windows for Spectrum Analysis 929 Index 931

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Book SynopsisThis is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications. The regression basis makes it straightforward to handle non-normal data, like in generalized linear models. The authors demonstrate optimal smoothing, using mixed model technology and Bayesian estimation, in addition to classical tools like cross-validation and AIC, covering theory and applications with code in R. Going far beyond simple smoothing, they also show how to use P-splines for regression on signals, varying-coefficient models, quantile and expectile smoothing, and composite links for grouped data. Penalties are the crucial elements of P-splines; with proper modifications they can handle periodic and circular data as well as shape constraints. Combining penalties with tensor products of B-splines extends theseTrade Review'The title says it all. This is a practical book which shows how P-splines are used in an astonishingly wide range of settings. If you use P-splines already the book is indispensable; if you don't, then reading it will convince you it's time to start. Every example comes with an R-program available on the book's web-site, an important feature for the experienced user and novice alike.' Iain Currie, Heriot-Watt University'This book is an enlightening and at the same time extremely enjoyable read. It will serve the applied statistician who is looking for practical solutions but also the connoisseur in search of elegant concepts. The accompanying website offers reproducible code and invites to promptly enter the fascinating universe of P-splines.' Jutta Gampe, Max Planck Institute for Demographic Research'Everything you always wanted to know about P-splines, from the inventors themselves. Paul H.C. Eilers and Brian D. Marx make a compelling case for their claim that P-splines are the best practical smoother out there, providing intuition, methodology, applications, and R code that clearly demonstrate the power, flexibility, and wide applicability of this approach to smoothing.' Jeffrey Simonoff, New York University'This is the book that everyone working on smoothing models should keep handy. At last we have a manuscript that shows the real power of P-splines, their versatility, and the different perspectives you can take to use them. Chapters 1 to 3 will certainly appeal to those who want to start working in this field, and to researchers that need to deepen their knowledge of this technique. Scientists and practitioners from other areas will find chapters 4 to 8 very useful for the wide range of examples and applications. The companion package and the fact that all results (even figures) are reproducible is a real bonus. Thank you Paul and Brian for being truthful to your motto: 'show, don't tell'.' Maria Durbán, University Carlos III de MadridTable of Contents1. Introduction; 2. Bases, penalties, and likelihoods; 3. Optimal smoothing in action; 4. Multidimensional smoothing; 5. Smoothing of scale and shape; 6. Complex counts and composite links; 7. Signal regression; 8. Special subjects; A. P-splines for the impatient; B. P-splines and competitors; C. Computational details; D. Array algorithms; E. Mixed model equations; F. Standard errors in detail; G. The website.

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Book Synopsis Emmanuel Ifeachor is Professor of Intelligent Electronic Systems and Director of the Centre for Communications, Networks and Information Systems at the University of Plymouth, UK. Barrie Jervis is Professor of Electronic Engineering at Sheffield Hallam University, UK. This book evolved from the authors' extensive experience in teaching practically oriented courses in DSP to both undergraduates and engineers in industry. Their own research in applied DSP has influenced the contents of the book and provided many of the examples and case studies. Table of Contents 1. Introduction. 2. Analog I/O interface for real-time DSP systems. 3. Discrete transforms. 4. The z-transform and its applications in signal processing. 5. Correlation and convolution. 6. A framework for digital filter design. 7. Finite impulse response (FIR) filter design. 8. Design of infinite impulse response (IIR) digital filters. 9. Multirate digital signal processing. 10. Adaptive digital filters. 11. Spectrum estimation and analysis. 12. General- and special-purpose digital signal processors. 13. Analysis of finite wordlength effects in fixed-point DSP systems. 14. Applications and design studies.

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Book SynopsisSteganography is the art of communicating a secret message, hiding the very existence of a secret message. This book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context.Table of ContentsPreface xi PART I OVERVIEW 1 Introduction 3 1.1 Real Threat or Hype? 3 1.2 Artificial Intelligence and Learning 4 1.3 How to Read this Book 5 2 Steganography and Steganalysis 7 2.1 Cryptography versus Steganography 7 2.2 Steganography 8 2.3 Steganalysis 17 2.4 Summary and Notes 23 3 Getting Started with a Classifier 25 3.1 Classification 25 3.2 Estimation and Confidence 28 3.3 Using libSVM 30 3.4 Using Python 33 3.5 Images for Testing 38 3.6 Further Reading 39 PART II FEATURES 4 Histogram Analysis 43 4.1 Early Histogram Analysis 43 4.2 Notation 44 4.3 Additive Independent Noise 44 4.4 Multi-dimensional Histograms 54 4.5 Experiment and Comparison 63 5 Bit-plane Analysis 65 5.1 Visual Steganalysis 65 5.2 Autocorrelation Features 67 5.3 Binary Similarity Measures 69 5.4 Evaluation and Comparison 72 6 More Spatial Domain Features 75 6.1 The Difference Matrix 75 6.2 Image Quality Measures 82 6.3 Colour Images 86 6.4 Experiment and Comparison 86 7 The Wavelets Domain 89 7.1 A Visual View 89 7.2 The Wavelet Domain 90 7.3 Farid’s Features 96 7.4 HCF in the Wavelet Domain 98 7.5 Denoising and the WAM Features 101 7.6 Experiment and Comparison 106 8 Steganalysis in the JPEG Domain 107 8.1 JPEG Compression 107 8.2 Histogram Analysis 114 8.3 Blockiness 122 8.4 Markov Model-based Features 124 8.5 Conditional Probabilities 126 8.6 Experiment and Comparison 128 9 Calibration Techniques 131 9.1 Calibrated Features 131 9.2 JPEG Calibration 133 9.3 Calibration by Downsampling 137 9.4 Calibration in General 146 9.5 Progressive Randomisation 148 PART III CLASSIFIERS 10 Simulation and Evaluation 153 10.1 Estimation and Simulation 153 10.2 Scalar Measures 158 10.3 The Receiver Operating Curve 161 10.4 Experimental Methodology 170 10.5 Comparison and Hypothesis Testing 173 10.6 Summary 176 11 Support Vector Machines 179 11.1 Linear Classifiers 179 11.2 The Kernel Function 186 11.3 ν-SVM 189 11.4 Multi-class Methods 191 11.5 One-class Methods 192 11.6 Summary 196 12 Other Classification Algorithms 197 12.1 Bayesian Classifiers 198 12.2 Estimating Probability Distributions 203 12.3 Multivariate Regression Analysis 209 12.4 Unsupervised Learning 212 12.5 Summary 215 13 Feature Selection and Evaluation 217 13.1 Overfitting and Underfitting 217 13.2 Scalar Feature Selection 220 13.3 Feature Subset Selection 222 13.4 Selection Using Information Theory 225 13.5 Boosting Feature Selection 238 13.6 Applications in Steganalysis 239 14 The Steganalysis Problem 245 14.1 Different Use Cases 245 14.2 Images and Training Sets 250 14.3 Composite Classifier Systems 258 14.4 Summary 262 15 Future of the Field 263 15.1 Image Forensics 263 15.2 Conclusions and Notes 265 Bibliography 267 Index 279

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Book SynopsisThoroughly revised and updated, this second edition of Adaptive Control covers new developments in the field, including multi-model adaptive control with switching, direct and indirect adaptive regulation, and adaptive feedforward disturbance compensation.Trade ReviewFrom the book reviews:“This book is intended as a textbook for graduate students, and a basic reference for control researchers, applied mathematicians and practicing engineers. It has a clear and coherent exposition, showing the themes addressed and providing solutions to these, highlighting its relevance and possible applications.” (Guillermo Fernández-Anaya, Mathematical Reviews, February, 2015)“The aim of this book is to provide a coherent and comprehensive treatment of the field of adaptive control. Throughout the book, the mathematical aspects of the synthesis and analysis of various algorithms are emphasized. The book contains various applications of control techniques. The book is intended as a textbook for graduate students as well as basic reference for practicing engineers facing the problem of designing adaptive control systems.” (Vjatscheslav Vasiliev, Zentralblatt MATH, Vol. 1234, 2012)Table of ContentsIntroduction to Adaptive Control.- Discrete-time System Models for Control.- Parameter Adaptation Algorithms: Deterministic Environment.- Parameter Adaptation Algorithms: Stochastic Environment.- Recursive Plant Model Identification in Open Loop.- Adaptive Prediction.- Digital Control Strategies.- Robust Digital Control Design.- Recursive Plant Model Identification in Closed Loop.- Robust Parameter Estimation.- Direct Adaptive Control.- Indirect Adaptive Control.- Practical Aspects and Applications.- Multimodel Adaptive Control with Switching.- Adaptive Regulation: Rejection of Unknown Disturbances.- Adaptive Feedforward Compensation of Disturbances.- Appendices: Stochastic Processes; Stability; Passive (Hyperstable) Systems; Martingales.

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Book SynopsisWritten in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to core topics in inference and learning. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning, and engineering.Trade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsContents; Preface; Notation; 1. Matrix theory; 2. Vector differentiation; 3. Random variables; 4. Gaussian distribution; 5. Exponential distributions; 6. Entropy and divergence; 7. Random processes; 8. Convex functions; 9. Convex optimization; 10. Lipschitz conditions; 11. Proximal operator; 12. Gradient descent method; 13. Conjugate gradient method; 14. Subgradient method; 15. Proximal and mirror descent methods; 16. Stochastic optimization; 17. Adaptive gradient methods; 18. Gradient noise; 19. Convergence analysis I: Stochastic gradient algorithms; 20. Convergence analysis II: Stochasic subgradient algorithms; 21: Convergence analysis III: Stochastic proximal algorithms; 22. Variance-reduced methods I: Uniform sampling; 23. Variance-reduced methods II: Random reshuffling; 24. Nonconvex optimization; 25. Decentralized optimization I: Primal methods; 26: Decentralized optimization II: Primal-dual methods; Author index; Subject index.

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Book SynopsisThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbookTrade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsPreface; Notation; 50. Least-squares problems; 51. Regularization; 52. Nearest-neighbor rule; 53. Self-organizing maps; 54. Decision trees; 55. Naive Bayes classifier; 56. Linear discriminant analysis; 57. Principal component analysis; 58. Dictionary learning; 59. Logistic regression; 60. Perceptron; 61. Support vector machines; 62. Bagging and boosting; 63. Kernel methods; 64. Generalization theory; 65. Feedforward neural networks; 66. Deep belief networks; 67. Convolutional networks; 68. Generative networks; 69. Recurrent networks; 70. Explainable learning; 71. Adversarial attacks; 72. Meta learning; Author index; Subject index.

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Book SynopsisMaster the fundamentals of digital communications systems with this accessible and hands-on introductory textbook, carefully interweaving theory and practice. The just-in-time approach introduces essential background as needed, keeping academic theory firmly linked to practical applications. The example-led teaching frames key concepts in the context of real-world systems, such as 5G, WiFi, and GPS. Stark provides foundational material on the trade-offs between energy and bandwidth efficiency, giving students a solid grounding in the fundamental challenges of designing digital communications systems. Features include over 300 illustrative figures, 80 examples, and 130 end-of-chapter problems to reinforce student understanding, with solutions for instructors. Accompanied online by lecture slides, computational MATLAB and Python resources, and supporting data sets, this is the ideal introduction to digital communications for senior undergraduate and graduate students in electrical engineering.Trade Review'This book emphasizes the fundamentals of digital communication as well as its practice. It provides examples to enhance the understanding, and the many illustrations explain the basic concepts very well. Several concepts from actual engineering practice are discussed in detail.' Ender Ayanoglu, University of California, Irvine'Wayne Stark is a widely respected researcher in digital communications, as well as a dedicated and talented teacher. This book reflects his years of experience teaching a challenging and rapidly changing subject to senior undergraduate and first-year graduate students. His choice of topics and careful balance between theory and practice ensure that this book will be a valuable resource in electrical engineering curricula for years to come.' Tom Fuja, University of Notre Dame'This self-contained book is excellent for a first course in digital communications. It strikes a perfect balance in theory, practice, and insights, so that a beginner can get a good understanding without getting lost in advanced mathematical concepts.' Sudharman K. Jayaweera, University of New Mexico'This is an extraordinary textbook on digital communication theory and practices. Key results are derived step by step, and it provides many examples and figures that help students grasp key concepts. I wish it had been available when I was a student.' Sang Wu Kim, Iowa State University'Not only is this textbook comprehensive and well written, it is mathematically rigorous. The specific numerical examples and practical applications enhance the theoretical derivations. The author does an excellent job of communicating the importance of each result, making it an appropriate textbook for senior undergraduates taking a solid course in the theory of digital communications.' Laurence B. Milstein, University of California, San Diego'I enjoyed this book's clarity and logical presentation. It is easy to read, balancing mathematical fundamentals with practical applications, problem sets, and examples. I'd be delighted to use it when teaching my undergraduate course on Communication Systems and Principles. This concise resource provides a thorough foundation on digital communication concepts, systems, and techniques, explaining communication systems in general and digital communications specifically.' Lina Mohjazi, University of Glasgow'The real jewel of the book is the introduction chapter. It lays out the most important design considerations and trade-offs at a high (but not superficial) level straightaway, serving as a roadmap to the material in the rest of the book. It is the best and most useful introduction chapter that no one should skip!' Tan F. Wong, University of Florida'This is an excellent textbook for students, communications engineers, and researchers alike. Based on many years' teaching experience, it includes detailed and illustrative examples that help students understand the fundamentals of digital communications. Professor Stark explains the trade-offs of different key parameters in digital communications, and covers state-of-the-art technologies such as LDPC codes. Each chapter contains clear goals, summaries, and useful exercises.' Xiang-Gen Xia, University of DelawareTable of ContentsContents; Preface; Acknowledgement; List of abbreviations; 1. Fundamentals of digital communications; 2. Modulation and demodulation; 3. Probability, random variables, random processes, signal bandwidth; 4. Error probability for binary signals; 5. Optimal receivers for M-ary communication; 6. Modulation techniques; 7. Wireless channels and transmission techniques; 8. Block codes; 9. Convolutional codes; Appendix A. Pseudorandom sequences; Appendix B. Trigonometric and fourier transform iIdentities; Appendix C. Finite fields and BCH codes; Appendix D. Simulation of signals and noise; References; Index.

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Book SynopsisAccurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging including compressed sensing, wavelets and optimization in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the piTable of Contents1. Introduction; Part I. The Essentials of Compressive Imaging: 2. Images, transforms and sampling; 3. A short guide to compressive imaging; 4. Techniques for enhancing performance; Part II. Compressed Sensing, Optimization and Wavelets: 5. An introduction to conventional compressed sensing; 6. The LASSO and its cousins; 7. Optimization for compressed sensing; 8. Analysis of optimization algorithms; 9. Wavelets; 10. A taste of wavelet approximation theory; Part III. Compressed Sensing with Local Structure: 11. From global to local; 12. Local structure and nonuniform recovery; 13. Local structure and uniform recovery; 14. Infinite-dimensional compressed sensing; Part IV. Compressed Sensing for Imaging: 15. Sampling strategies for compressive imaging; 16. Recovery guarantees for wavelet-based compressive imaging; 17. Total variation minimization; Part V. From Compressed Sensing to Deep Learning: 18. Neural networks and deep learning; 19. Deep learning for compressive imaging; 20. Accuracy and stability of deep learning for compressive imaging; 21. Stable and accurate neural networks for compressive imaging; 22. Epilogue; Appendices: A. Linear Algebra; B. Functional analysis; C. Probability; D. Convex analysis and convex optimization; E. Fourier transforms and series; F. Properties of Walsh functions and the Walsh transform; Notation; Abbreviations; References; Index.

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Book SynopsisTable of Contents 1. Signal and System Modeling Concepts. 2 . System Modeling and Analysis in the Time Domain. 3. The Fourier Series. 4. The Fourier Transform and Its Applications. 5. The Laplace Transformation. 6. Applications of the Laplace Transform. 7. State-Variable Techniques. 8. Discrete-Time Signals and Systems. 9. Analysis and Design of Digital Filters. 10. The Discrete Fourier Transform and Fast Fourier Transform Algorithms. Appendix A: Comments and Hints on Using MATLAB. Appendix B: Functions of a Complex Variable—Summary of Important Definitions and Theorems. Appendix C: Matrix Algebra. Appendix D: Analog Filters. Appendix E: Mathematical Tables. Appendix F: Answers to Selected Problems. Appendix G: Index of MATLAB Functions Used. Index.

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Book SynopsisThis book focuses on broadband power amplifier design for wireless communication. Nonlinear model embedding is described as a powerful tool for designing broadband continuous Class-J and continuous class F power amplifiers. The authors also discuss various techniques for extending bandwidth of load modulation based power amplifiers, such as Doherty power amplifier and Chireix outphasing amplifiers. The book also covers recent trends on digital as well as analog techniques to enhance bandwidth and linearity in wireless transmitters. Presents latest trends in designing broadband power amplifiers; Covers latest techniques for using nonlinear model embedding in designing power amplifiers based on waveform engineering; Describes the latest techniques for extending bandwidth of load modulation based power amplifiers such as Doherty power amplifier and Chireix outphasing amplifiers; Includes coverage of hybrid analog/digital predistortion as wideband solution for wireless transmitters; Discusses recent trends on on-chip power amplifier design with GaN /GaAs MMICs for high frequency applications. Table of ContentsIntroduction to RF Power Amplifier Design and Architecture.- Non-linear Device Characterization and Modeling for Power Amplifier Design.- Power Amplifier Design using nonlinear Model Embedding.- Broadband Techniques in Power Amplifiers.- Digital Techniques for Broadband and Linearized Transmitters.- Advance Material for Power Amplifiers Design and Packaging.

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Book SynopsisThis book provides a true A to Z of recorded sound, from its inception to the present day, outlining how technologies, techniques, and social attitudes have changed things, noting what is good and what is less good. The author starts by discussing the physics of sound generation and propagation. He then moves on to outline the history of recorded sound and early techniques and technologies, such as the rise of multi-channel tape recorders and their impact on recorded sound. He goes on to debate live sound versus recorded sound and why there is a difference, particularly with classical music. Other topics covered are the sound of real instruments and how that sound is produced and how to record it; microphone techniques and true stereo sound; digital workstations, sampling, and digital media; and music reproduction in the home and how it has changed. The author wraps up the book by discussing where we should be headed for both popular and classical music recording and reproduction, the role of the Audio Engineer in the 21st century, and a brief look at technology today and where it is headed. This book is ideal for anyone interested in recorded sound. “[Julian Ashbourn] strives for perfection and reaches it through his recordings… His deep knowledge of both technology and music is extensive and it is with great pleasure that I see he is passing this on for the benefit of others. I have no doubt that this book will be highly valued by many in the music industry, as it will be by me.” -- Claudio Di Meo, Composer, Pianist and Principal Conductor of The Kensington Philharmonic Orchestra, The Hemel Symphony Orchestra and The Lumina ChoirTable of ContentsIntroduction.- How the war changed audio.- The V record label for US troops.- Stereo sound, multi-channel sound, film sound and more.- The physics of sound.- The advent of tape and moving coil microphones.- The development of microphone techniques.- Multi-channel Tape recorders.- The advent of the Big Studios.- The record business.- The Maverick producers and freelance engineers.- The big time with 24 track everywhere and heaps of signal processing.- How the technology changed the music.- Classical music recording is effectively broken by the technology.- Digital arrives, but something is not right.- A to D and D to A convertors and compressors in the digital domain.- High resolution Digital recording and re-sampling.- Lossless compression.- The revolution in playback technology.- The social revolution in consumed music.- The change in musicians.- How to do things properly.- The use of Digital Audio Workstations and the impact on music.- Why recordings sound worse now than they did in the 50s and 60s.- Music and Civilisation and why it is important.- Where is the future archive for serious music being produced now.- Are advances in technology always good.- Teaching Audio Engineers.- The future.- Conclusion.

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Book SynopsisRemote Sensing Digital Image Analysis provides a comprehensive treatment of the methods used for the processing and interpretation of remotely sensed image data. Over the past decade there have been continuing and significant developments in the algorithms used for the analysis of remote sensing imagery, even though many of the fundamentals have substantially remained the same. As with its predecessors this new edition again presents material that has retained value but also includes newer techniques, covered from the perspective of operational remote sensing. The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image analysis in remote sensing. The presentation level is for the mathematical non-specialist. Since the very great number of operational users of remote sensing come from the earth sciences communities, the text is pitched at a level commensurate with their background. The chapters progress logically through means for the acquisition of remote sensing images, techniques by which they can be corrected, and methods for their interpretation. The prime focus is on applications of the methods, so that worked examples are included and a set of problems conclude each chapter.Table of ContentsSources and characteristics of remote sensing image data.- correcting and registering images.- interpreting images.- radiometric enhancement of images.- geometric processing and enhancement: image domain techniques.- spectral domain image transforms.- spatial domain image transforms.- supervised classification techniques.- clustering and unsupervised classification.- Feature Reduction.- Image Classification in Practice.- Multisource Image Analysis.

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Book SynopsisNear-infrared astronomy has become one of the most rapidly developing branches in modern astrophysics. Innovative observing techniques, near-infrared detectors with quantum efficiencies in excess of 90%, highly specialised instruments as well as advanced data reduction techniques have allowed major breakthroughs in various areas like exoplanets, star-forming regions, the supermassive black hole in the Galactic center, and the high-redshift Universe. In this book, the reader will be introduced to the basic concepts of how to prepare near-infrared observations with maximized scientific return. Equal weight is given to all aspects of the data reduction for both - imaging and spectroscopy. Information is also provided on the state of the art instrumentation available and planned, on detector technology or the physics of the atmosphere, all of which influence the preparation and execution of observations and data reduction techniques. The beginner but also the expert will find a lot of information in compact form which is otherwise widely dispersed across the internet or other sources.Table of ContentsIntroduction.- Setting the stage - NIR surveys and their calibration.- What we have to deal with - the NIR sky.- What can be built to deal with that - detectors, instrumentation & AO.- Signal-to-noise considerations.- Observing & calibration strategies.- Data reduction recipes.- Taking data above the atmosphere - changes in observing and data reduction principles.- Concluding remarks.

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Book SynopsisFundamentals of Inertial Navigation, Satellite-based Positioning and their Integration is an introduction to the field of Integrated Navigation Systems. It serves as an excellent reference for working engineers as well as textbook for beginners and students new to the area. The book is easy to read and understand with minimum background knowledge. The authors explain the derivations in great detail. The intermediate steps are thoroughly explained so that a beginner can easily follow the material. The book shows a step-by-step implementation of navigation algorithms and provides all the necessary details. It provides detailed illustrations for an easy comprehension. The book also demonstrates real field experiments and in-vehicle road test results with professional discussions and analysis. This work is unique in discussing the different INS/GPS integration schemes in an easy to understand and straightforward way. Those schemes include loosely vs tightly coupled, open loop vs closed loop, and many more. Table of ContentsReference Frames and Earth Geometry.- Global Positioning System.- Inertial Navigation System.- Inertial Navigation System Modeling.- Modeling INS Errors by Linear State Equations.- Kalman Filter.- INS/GPS integration.- Three Dimensional Reduced Inertial Sensor System / GPS Integration for Land-Based Vehicles.- Two Case Studies- full IMU/GPS and 3D RISS/GPS Integration.

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Book Synopsis"Pattern Recognition, Machine Intelligence and Biometrics" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics.The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering.Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.Trade ReviewFrom the reviews:“This book is a collection of 31 scientific papers organized in four main sections: ‘Pattern recognition and Machine Intelligence’, ‘Computer Vision and Image Processing’, ‘Face Recognition and Forensics’ and ‘Biometrics Authentication’. These chapters cover a broad domain making the book appealing to a large group of specialists.” (Leon Todoran, IAPR Newsletter, Vol. 34 (4), October, 2012)Table of ContentsIntroductions and Editorial.- Fingerprint Analysis and Recognition.- Handwriting Analysis and Extraction.- Symbolic Factorial Discriminant Analysis for Face Recognition.- Noniterative 3D Face Reconstruction Based on Photometric Stereo.- Multimodal Biometrics by Face and Hand Images.- Signature Verification Technique Using Data Glove.- Performance Comparisons of Facial Expression Recognition in JAFFE Database.- Inverse Biometrics for Mouse Dynamics.- User Friendly Identification with Stepping.- Colored Faces and Facial Features Extractions.- Comparison of ROC and Likelihood Decision Methods in Fingerprint Analysis and Recognition.- Facial Metamorphosis for Biometric Applications.- Genetic Algorithm Using Fingerprint and Iris Biometrics.- Fingerprint Asymmetry Measures.- Application Iris Recognition Using Classifier Combination.- Development of Handwriting Recognition of Whiteboard Notes.- Recent Development of Speech Analysis and Recognition in Biometrics.- Case Studies of Ambiguities of Biometrics.- Conclusions, Open Problems, and Future Research.- Other Related Subtopics by Prominent Professionals.

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Book SynopsisThis book includes a novel motif discovery for time series, KITE (ill-Known motIf discovery in Time sEries data), to identify ill-known motifs transformed by affine mappings such as translation, uniform scaling, reflection, stretch, and squeeze mappings. Additionally, such motifs may be covered with noise or have variable lengths. Besides KITE’s contribution to motif discovery, new avenues for the signal and image processing domains are explored and created. The core of KITE is an invariant representation method called Analytic Complex Quad Tree Wavelet Packet transform (ACQTWP). This wavelet transform applies to motif discovery as well as to several signal and image processing tasks. The efficiency of KITE is demonstrated with data sets from various domains and compared with state-of-the-art algorithms, where KITE yields the best outcomes.Trade Review“The book under review provides one such vantage point, and anyone whose work involves finding patterns in large amounts of data should take heed. … For those well versed in the mathematics of harmonics and waves, the book should prove very useful in showing how these theories can be applied to data series. But even those who are not specialists in this area, such as myself, can still gain many ideas from this useful tome.” (Eugene Callahan, Computing Reviews, October 11, 2022)Table of ContentsIntroduction.- Preliminaries.- General Principles of Time Series Motif Discovery.- State of the Art in Time Series Motif Discovery.- Distortion-Invariant Motif Discovery.- Evaluation.- Conclusion and Outlook.- Appendices A-D.

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Book SynopsisThis book explores novel aspects of social robotics, spoken dialogue systems, human-robot interaction, spoken language understanding, multimodal communication, and system evaluation. It offers a variety of perspectives on and solutions to the most important questions about advanced techniques for social robots and chat systems. Chapters by leading researchers address key research and development topics in the field of spoken dialogue systems, focusing in particular on three special themes: dialogue state tracking, evaluation of human-robot dialogue in social robotics, and socio-cognitive language processing. The book offers a valuable resource for researchers and practitioners in both academia and industry whose work involves advanced interaction technology and who are seeking an up-to-date overview of the key topics. It also provides supplementary educational material for courses on state-of-the-art dialogue system technologies, social robotics, and related research fields.Table of ContentsDigiSami and Digital Natives: Interaction Technology for the NorthSami Language.- A Comparative Study of Text Preprocessing Techniques for Natural Language Call Routing.- Compact and Interpretable Dialogue State Representation with Genetic Sparse Distributed Memory.- Incremental Human-machine Dialogue Simulation.- Active Learning for Example-based Dialog Systems.- Question Selection based on Expected Utility to Acquire Informationthrough Dialogue.- Separating Representation, Reasoning, and Implementation forInteraction Management: Lessons from Automated Planning.- SimpleDS: A Simple Deep Reinforcement Learning Dialogue System.- Breakdown Detector for Chat-oriented Dialogue.- User Involvement in Collaborative Decision-Making Dialog Systems.- Natural Language Dialog System Considering Speaker’s EmotionCalculated from Acoustic Features.- Salient Cross-lingual Acoustic and Prosodic Features for English and German Emotion Recognition.- Entropy-Driven Dialog for Topic Classification: Detecting and Tackling Uncertainty.- Evaluation of Question-Answering System about Conversational Agent’s Personality.- Fisher Kernels on Phase-based Features for Speech Emotion Recognition.- Internationalisation and Localisation of Spoken Dialogue Systems

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Book SynopsisBayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applicTable of ContentsNotation; 1. Introduction; 2. Gaussian processes; 3. Modeling with Gaussian processes; 4. Model assessment, selection, and averaging; 5. Decision theory for optimization; 6. Utility functions for optimization; 7. Common Bayesian optimization policies; 8. Computing policies with Gaussian processes; 9. Implementation; 10. Theoretical analysis; 11. Extensions and related settings; 12. A brief history of Bayesian optimization; A. The Gaussian distribution; B. Methods for approximate Bayesian inference; C. Gradients; D. Annotated bibliography of applications; References; Index.

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Book SynopsisHarry Van Trees s Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. This new edition has been thoroughly revised and expanded, making it again the most comprehensive and up-to-date treatment of the subject.Table of ContentsPreface xv Preface to the First Edition xix 1 Introduction 1 1.1 Introduction 1 1.2 Topical Outline 1 1.3 Possible Approaches 11 1.4 Organization 14 2 Classical Detection Theory 17 2.1 Introduction 17 2.2 Simple Binary Hypothesis Tests 20 2.3 m Hypotheses 51 2.4 Performance Bounds and Approximations 63 2.5 Monte Carlo Simulation 80 2.6 Summary 109 2.7 Problems 110 3 General Gaussian Detection 125 3.1 Detection of Gaussian Random Vectors 126 3.2 Equal Covariance Matrices 138 3.3 Equal Mean Vectors 174 3.4 General Gaussian 197 3.5 m Hypotheses 209 3.6 Summary 213 3.7 Problems 215 4 Classical Parameter Estimation 230 4.1 Introduction 230 4.2 Scalar Parameter Estimation 232 4.3 Multiple Parameter Estimation 293 4.4 Global Bayesian Bounds 332 4.5 Composite Hypotheses 348 4.6 Summary 375 4.7 Problems 377 5 General Gaussian Estimation 400 5.1 Introduction 400 5.2 Nonrandom Parameters 401 5.3 Random Parameters 483 5.4 Sequential Estimation 495 5.5 Summary 507 5.6 Problems 510 6 Representation of Random Processes 519 6.1 Introduction 519 6.2 Orthonormal Expansions: Deterministic Signals 520 6.3 Random Process Characterization 528 6.4 Homogeous Integral Equations and Eigenfunctions 540 6.5 Vector Random Processes 564 6.6 Summary 568 6.7 Problems 569 7 Detection of Signals–Estimation of Signal Parameters 584 7.1 Introduction 584 7.2 Detection and Estimation in White Gaussian Noise 591 7.3 Detection and Estimation in Nonwhite Gaussian Noise 629 7.4 Signals with Unwanted Parameters: The Composite Hypothesis Problem 675 7.5 Multiple Channels 712 7.6 Multiple Parameter Estimation 716 7.7 Summary 721 7.8 Problems 722 8 Estimation of Continuous-Time Random Processes 771 8.1 Optimum Linear Processors 771 8.2 Realizable Linear Filters: Stationary Processes, Infinite Past: Wiener Filters 787 8.3 Gaussian–Markov Processes: Kalman Filter 807 8.4 Bayesian Estimation of Non-Gaussian Models 842 8.5 Summary 852 8.6 Problems 855 9 Estimation of Discrete–Time Random Processes 880 9.1 Introduction 880 9.2 Discrete-Time Wiener Filtering 882 9.3 Discrete-Time Kalman Filter 919 9.4 Summary 1016 9.5 Problems 1016 10 Detection of Gaussian Signals 1030 10.1 Introduction 1030 10.2 Detection of Continuous-Time Gaussian Processes 1030 10.3 Detection of Discrete-Time Gaussian Processes 1067 10.4 Summary 1076 10.5 Problems 1077 11 Epilogue 1084 11.1 Classical Detection and Estimation Theory 1084 11.2 Representation of Random Processes 1093 11.3 Detection of Signals and Estimation of Signal Parameters 1095 11.4 Linear Estimation of Random Processes 1098 11.5 Observations 1105 11.6 Conclusion 1106 Appendix A: Probability Distributions and Mathematical Functions 1107 Appendix B: Example Index 1119 References 1125 Index 1145

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Book SynopsisSignals and Systems: Analysis Using Transform Methods and MATLAB has been extensively updated, while retaining the emphasis on fundamental applications and theory. The text includes a wealth of exercises, including drill exercises, and more challenging conceptual problems.McGraw-Hill''s Connect, is also available as an optional, add on item. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, how they need it, so that class time is more effective. Connect allows the professor to assign homework, quizzes, and tests easily and automatically grades and records the scores of the student''s work. Problems are randomized to prevent sharing of answers an may also have a multi-step solution which helps move the students'' learning along if they experience difficulty.Table of Contents1) Introduction2) Mathematical Description of Continuous-Time Signals3) Discrete-Time Signal Description4) Description of Systems5) Time-Domain System Analysis6) Continuous-Time Fourier Methods7) Discrete-Time Fourier Methods8) The Laplace Transform9) The z Transform10) Sampling and Signal Processing11) Frequency Response Analysis12) Laplace System Analysis13) z-Transform System Analysis14) Filter Analysis and DesignAppendix I – Useful Mathematical RelationsAppendix II – Continuous-Time Fourier Series PairsAppendix III – Discrete Fourier Transform PairsAppendix IV – Continuous-Time Fourier Transform PairsAppendix V – Discrete-Time Fourier Transform PairsAppendix VI – Tables of Laplace Transform PairsAppendix VII – z-Transform PairsBibliographyIndex

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Book SynopsisThis cohesive treatment of cognitive radio and networking technology integrates information and decision theory to provide insight into relationships throughout all layers of networks and across all wireless applications. It encompasses conventional considerations of spectrum and waveform selection and covers topology determination, routing policies, content positioning and future hybrid architectures that fully integrate wireless and wired services. Emerging flexibility in spectrum regulation and the imminent adoption of spectrum-sharing policies make this topic of immediate relevance both to the research community and to the commercial wireless community. Features specific examples of decision-making structures and criteria required to extend network density and scaling to unprecedented levels Integrates sensing, control plane and content operations into a single cohesive structure Provides simpler and more powerful models of network operation Presents a unique approach to decisTrade Review'This is an extremely important text that comes at a critical time in the evolution of our understanding of both the characteristics of the spectrum need and the means by which this increasingly urgent need can be satisfied. [Marshall] has been one of the long term leaders in the development of the framework for dynamic spectrum access networks and cognitive radio technology, giving him a historic as well as current perspective on the challenges. The insights in this book should be of enormous value to students, active researchers, wireless systems developers, and regulatory and policy leaders.' Dennis A. Roberson, Illinois Institute of Technology'… highly original and brilliantly insightful. Preston Marshall has examined cognitive technologies under three essential key headings - scalability, density and decision-making. In doing this he unlocks the power of cognitive technologies and builds a realisable and compelling vision for communication networks of the future. Every section … is full of new ideas and insights that could only be written by someone who has been a leader in this field and has a handle on the bigger picture as well as a deep understanding of the technical details. This book is so far removed from the myriad of books that simple relate information to the reader. It is packed full of ideas, opinions and most crucially supporting evidence … a breath of fresh air … essential reading for someone who has any interest in how the challenges for communication systems of the future will be met.' Linda Doyle, University of Dublin'… a refreshing take … I strongly recommend this book to both scholars and experts in the field, and I am convinced that it will be frequently used as reference material for all interested in the future potential of cognitive wireless networks.' Shaunak Joshi, IEEE Communications MagazineTable of ContentsPreface; Part I. Overview: 1. Introduction; 2. Theoretical foundations; 3. Future wireless operation, environments and dynamic spectrum access; 4. Fundamental challenges in cognitive radio and wireless systems; Part II. Generalized Environmental Characterization: 5. The spectrum and channel environment; 6. Propagation modeling, characterization and control; 7. The connectivity environment; 8. The information and content environment; Part III. System Performance of Cognitive Wireless Systems: 9. Network scaling; 10. Network physical density limitations; 11. Network sensing and exchange information effectiveness; 12. Content access information effectiveness; 13. Minimizing nonlinear circuit effects; Part IV. Integrated Analysis and Decision-Making: 14. Awareness structure for cognitive wireless systems; 15. Instantiating and updating beliefs across wireless networks; 16. Decision-making structure for cognitive wireless systems; Part V. Summary: 17. Further research needs in cognitive wireless networks; Appendix A. Terms and acronyms; Appendix B. Symbols; Appendix C. Mathematica and MATLAB routines.

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Book SynopsisCompressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate efficiently the idea of compressive sensing over assorted wireless network scenarios, interweaving concepts from signal processing, optimization, information theory, communications and networking to address the issues in question from an engineering perspective. It enables students, researchers and communications engineers to develop a working knowledge of compressive sensing, including background on the basics of compressive sensing theory, an understanding of its benefits andTable of Contents1. Introduction; 2. Overview of wireless networks; Part I. Compressive Sensing Techniques: 3. Compressive sensing framework; 4. Sparse optimization algorithms; Part II. Compressive Sensing-Based Wireless Communication: 5. Analog to digital converter; 6. Channel estimation; 7. Ultra wide band; 8. Positioning; 9. Multiple access; 10. Cognitive radio networks and sensor networks.

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Book SynopsisThis detailed, up-to-date introduction to heterogeneous cellular networking introduces its characteristic features, the technology underpinning it and the issues surrounding its use. Comprehensive and in-depth coverage of core topics catalogue the most advanced, innovative technologies used in designing and deploying heterogeneous cellular networks, including system-level simulation and evaluation, self-organisation, range expansion, cooperative relaying, network MIMO, network coding and cognitive radio. Practical design considerations and engineering tradeoffs are also discussed in detail, including handover management, energy efficiency and interference management techniques. A range of real-world case studies, provided by industrial partners, illustrate the latest trends in heterogeneous cellular networks development. Written by leading figures from industry and academia, this is an invaluable resource for all researchers and practitioners working in the field of mobile communications.Table of Contents1. Introduction Xiaoli Chu, David López-Pérez, Fredrik Gunnarsson and Yang Yang; 2. Radio propagation modeling Zhihua Lai, Guillaume Villemaud, Meiling Luo and Jie Zhang; 3. System-level simulation and evaluation models David López-Pérez and Mats Folke; 4. Access mechanisms Vikram Chandrasekhar, Tony Ekpenyong and Ralf Bendlin; 5. Interference modeling and spectrum allocation in two-tier networks Tony Q. S. Quek and Marios Kountouris; 6. Self-organization Fredrik Gunnarsson; 7. Dynamic interference management Ismail Güvenç, Fredrik Gunnarsson and David López-Pérez; 8. Uncoordinated femtocell deployments David López-Pérez, Xiaoli Chu and Holger Claussen; 9. Mobility and handover management Huaxia Chen, Shengyao Jin, Honglin Hu, Yang Yang, David López-Pérez, Ismail Guvenc and Xiaoli Chu; 10. Cooperative relaying Jing Xu, Jiang Wang and Ting Zhou; 11. Network MIMO techniques Gan Zheng, Yongming Huang and Kai-Kit Wong; 12. Network coding Haishi Ning and Cong Ling; 13. Cognitive radio Miguel López Benitez; 14. Energy-efficient architectures and techniques Weisi Guo, Min Chen and Athanasios V. Vasilakos.

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Book SynopsisDiscover a fresh approach for designing more efficient and cooperative wireless communications networks with this systematic guide. Covering everything from fundamental theory to current research topics, leading researchers describe a new, network-aware coding strategy that exploits the signal interactions that occur in dense wireless networks directly at the waveform level. Using an easy-to-follow, layered structure, this unique text begins with a gentle introduction for those new to the subject, before moving on to explain key information-theoretic principles and establish a consistent framework for wireless physical layer network coding (WPNC) strategies. It provides a detailed treatment of Network Coded Modulation, covers a range of WPNC techniques such as Noisy Network Coding, Compute and Forward, and Hierarchical Decode and Forward, and explains how WPNC can be applied to parametric fading channels, frequency selective channels, and complex stochastic networks. This is essential Table of ContentsPart I. Motivation and General Introduction: 1. Introduction; 2. Wireless physical layer network coding: a gentle introduction; Part II. Fundamental Principles of WPNC: 3. Fundamental principles and system model; 4. Components of WPNC; 5. WPNC in cloud communications; Part III. Design of Source, Relay and Destination Strategies: 6. NCM and hierarchical decoding design for H-MAC; 7. NCM design and processing for parametric channels; 8. NCM design for partial HSI and asymmetric H-MAC; 9. Joint hierarchical interference processing; 10. WPNC in complex stochastic networks; Appendix: background theory and selected fundamentals.

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Book SynopsisThis unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.Trade Review'A very nice balanced treatment over two large-scale signal processing aspects: mathematical backgrounds versus big data applications, with a strong flavor of distributed optimization and computation.' Shuguang Cui, University of California, DavisTable of ContentsPart I. Overview of Big Data Applications: 1. Introduction; 2. Data parallelism: the supporting architecture; Part II. Methodology and Mathematical Background: 3. First order methods; 4. Sparse optimization; 5. Sublinear algorithms; 6. Tensor for big data; 7. Deep learning and applications; Part III. Big Data Applications: 8. Compressive sensing based big data analysis; 9. Distributed large-scale optimization; 10. Optimization of finite sums; 11. Big data optimization for communication networks; 12. Big data optimization for smart grid systems; 13. Processing large data set in MapReduce; 14. Massive data collection using wireless sensor networks.

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