Machine Learning in Signal Processing Book

Machine Learning in Signal Processing


  • Author : Sudeep Tanwar
  • Publisher : CRC Press
  • Release Date : 2021-12-10
  • Genre: Technology & Engineering
  • Pages : 388
  • ISBN 10 : 9781000487817

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Machine Learning in Signal Processing Excerpt :

Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML). ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML. The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML. FEATURES Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readers Oriented toward material and flow with regards to general introduction and technical aspects Comprehensively elaborates on the material with examples and diagrams This book is a complete resource designed exclusively for advanced undergraduate students, post-graduate students, research scholars, faculties, and academicians of computer science and engineering, computer science and applications, and electronics and telecommunication engineering.

Machine Intelligence and Signal Processing Book

Machine Intelligence and Signal Processing


  • Author : Sonali Agarwal
  • Publisher : Springer Nature
  • Release Date : 2020-02-25
  • Genre: Technology & Engineering
  • Pages : 466
  • ISBN 10 : 9789811513664

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Machine Intelligence and Signal Processing Excerpt :

This book features selected high-quality research papers presented at the International Conference on Machine Intelligence and Signal Processing (MISP 2019), held at the Indian Institute of Technology, Allahabad, India, on September 7–10, 2019. The book covers the latest advances in the fields of machine learning, big data analytics, signal processing, computational learning theory, and their real-time applications. The topics covered include support vector machines (SVM) and variants like least-squares SVM (LS-SVM) and twin SVM (TWSVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. Further, it discusses the real-time challenges involved in processing big data and adapting the algorithms dynamically to improve the computational efficiency. Lastly, it describes recent developments in processing signals, for instance, signals generated from IoT devices, smart systems, speech, and videos and addresses biomedical signal processing: electrocardiogram (ECG) and electroencephalogram (EEG).

Machine Intelligence and Signal Analysis Book

Machine Intelligence and Signal Analysis


  • Author : M. Tanveer
  • Publisher : Springer
  • Release Date : 2018-08-07
  • Genre: Technology & Engineering
  • Pages : 767
  • ISBN 10 : 9789811309236

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Machine Intelligence and Signal Analysis Excerpt :

The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.

Machine Learning for Signal Processing Book

Machine Learning for Signal Processing


  • Author : Max A. Little
  • Publisher : Oxford University Press, USA
  • Release Date : 2019-08-13
  • Genre: Machine learning
  • Pages : 384
  • ISBN 10 : 9780198714934

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Machine Learning for Signal Processing Excerpt :

This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications. Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic.

Signal Processing and Machine Learning with Applications Book

Signal Processing and Machine Learning with Applications


  • Author : Michael M. Richter
  • Publisher : Springer
  • Release Date : 2017-05-10
  • Genre: Computers
  • Pages : null
  • ISBN 10 : 3319453718

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Signal Processing and Machine Learning with Applications Excerpt :

The authors offer a comprehensive guide to machine learning applied to signal processing and recognition problems, and then discuss real applications in domains such as speech processing and biomedical signal processing, with a focus on handling noise. This textbook is intended for advanced undergraduate and graduate students of computer science and engineering.

Financial Signal Processing and Machine Learning Book

Financial Signal Processing and Machine Learning


  • Author : Ali N. Akansu
  • Publisher : John Wiley & Sons
  • Release Date : 2016-04-20
  • Genre: Technology & Engineering
  • Pages : 312
  • ISBN 10 : 9781118745649

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Financial Signal Processing and Machine Learning Excerpt :

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Artificial Intelligence and Signal Processing Book

Artificial Intelligence and Signal Processing


  • Author : Ali Movaghar
  • Publisher : Springer
  • Release Date : 2014-10-13
  • Genre: Computers
  • Pages : 346
  • ISBN 10 : 3319108484

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Artificial Intelligence and Signal Processing Excerpt :

This book constitutes the refereed proceedings of the International Symposium, on Artificial Intelligence and Signal Processing, AISP 2013, held in Tehran, Iran, in December 2013. The 35 full papers presented were carefully reviewed and selected from 106 submissions. They are organized in topical sections such as image processing, machine vision, medical image processing, signal processing, speech processing, natural language processing, systems and AI applications, robotics.

Biomedical Signal Processing and Artificial Intelligence in Healthcare Book

Biomedical Signal Processing and Artificial Intelligence in Healthcare


  • Author : Walid A. Zgallai
  • Publisher : Academic Press
  • Release Date : 2020-07-29
  • Genre: Technology & Engineering
  • Pages : 268
  • ISBN 10 : 9780128189474

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Biomedical Signal Processing and Artificial Intelligence in Healthcare Excerpt :

Biomedical Signal Processing and Artificial Intelligence in Healthcare is a new volume in the Developments in Biomedical Engineering and Bioelectronics series. This volume covers the basics of biomedical signal processing and artificial intelligence. It explains the role of machine learning in relation to processing biomedical signals and the applications in medicine and healthcare. The book provides background to statistical analysis in biomedical systems. Several types of biomedical signals are introduced and analyzed, including ECG and EEG signals. The role of Deep Learning, Neural Networks, and the implications of the expansion of artificial intelligence is covered. Biomedical Images are also introduced and processed, including segmentation, classification, and detection. This book covers different aspects of signals, from the use of hardware and software, and making use of artificial intelligence in problem solving. Dr Zgallai’s book has up to date coverage where readers can find the latest information, easily explained, with clear examples and illustrations. The book includes examples on the application of signal and image processing employing artificial intelligence to Alzheimer, Parkinson, ADHD, autism, and sleep disorders, as well as ECG and EEG signals. Developments in Biomedical Engineering and Bioelectronics is a 10-volume series which covers recent developments, trends and advances in this field. Edited by leading academics in the field, and taking a multidisciplinary approach, this series is a forum for cutting-edge, contemporary review articles and contributions from key ‘up-and-coming’ academics across the full subject area. The series serves a wide audience of university faculty, researchers and students, as well as industry practitioners. Coverage of the subject area and the latest advances and applications in biomedical signal processing and Artificial Intelligence. Contributions by recognized researchers and field leaders. On-line presentat

Signal Processing and Machine Learning for Biomedical Big Data Book

Signal Processing and Machine Learning for Biomedical Big Data


  • Author : Ervin Sejdic
  • Publisher : CRC Press
  • Release Date : 2018-07-04
  • Genre: Medical
  • Pages : 606
  • ISBN 10 : 9781351061216

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Signal Processing and Machine Learning for Biomedical Big Data Excerpt :

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.

Digital Signal Processing with Kernel Methods Book

Digital Signal Processing with Kernel Methods


  • Author : José Luis Rojo-Ã?lvarez
  • Publisher : John Wiley & Sons
  • Release Date : 2018-01-23
  • Genre: Technology & Engineering
  • Pages : 672
  • ISBN 10 : 9781118611791

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Digital Signal Processing with Kernel Methods Excerpt :

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. Presents the necessary basic ideas from both digital signal processing and machine learning concepts Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

Machine Intelligence and Signal Processing Book

Machine Intelligence and Signal Processing


  • Author : Richa Singh
  • Publisher : Springer
  • Release Date : 2015-10-01
  • Genre: Technology & Engineering
  • Pages : 163
  • ISBN 10 : 9788132226253

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Machine Intelligence and Signal Processing Excerpt :

This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence and Signal Processing was one of the few unique events that are focused on the convergence of the two fields. The book is comprised of chapters based on the top presentations at the workshop. This book has three chapters on various topics of biometrics – two are on face detection and one on iris recognition; all from top researchers in their field. There are four chapters on different biomedical signal / image processing problems. Two of these are on retinal vessel classification and extraction; one on biomedical signal acquisition and the fourth one on region detection. There are three chapters on data analysis – a topic gaining immense popularity in industry and academia. One of these shows a novel use of compressed sensing in missing sales data interpolation. Another chapter is on spam detection and the third one is on simple one-shot movie rating prediction. Four other chapters cover various cutting edge miscellaneous topics on character recognit

Machine Learning Methods for Signal  Image and Speech Processing Book

Machine Learning Methods for Signal Image and Speech Processing


  • Author : Meerja Akhil Jabbar
  • Publisher : Unknown
  • Release Date : 2021-11-30
  • Genre: Uncategoriezed
  • Pages : 250
  • ISBN 10 : 8770223696

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Machine Learning Methods for Signal Image and Speech Processing Excerpt :

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and image analysis as well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests, etc. This book focuses on AI utilization in the speech, image, communications and virtual reality domains.

Machine Learning in Bio Signal Analysis and Diagnostic Imaging Book

Machine Learning in Bio Signal Analysis and Diagnostic Imaging


  • Author : Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2018-11-30
  • Genre: Science
  • Pages : 345
  • ISBN 10 : 9780128160879

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Machine Learning in Bio Signal Analysis and Diagnostic Imaging Excerpt :

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Learning Approaches in Signal Processing Book

Learning Approaches in Signal Processing


  • Author : Francis Ring
  • Publisher : CRC Press
  • Release Date : 2018-12-07
  • Genre: Computers
  • Pages : 654
  • ISBN 10 : 9780429590320

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Learning Approaches in Signal Processing Excerpt :

Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.

Intelligent Speech Signal Processing Book

Intelligent Speech Signal Processing


  • Author : Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2019-03-27
  • Genre: Technology & Engineering
  • Pages : 209
  • ISBN 10 : 9780128181317

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Intelligent Speech Signal Processing Excerpt :

Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multidisciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, development and management of intelligent systems, neural networks and related machine learning techniques for speech signal processing. Highlights different data analytics techniques in speech signal processing, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and neural networks techniques for speech signal processing Includes coverage of biomodal speech recognition, voice activity detection, spoken language and speech disorder identification, automatic speech to speech summarization, and convolutional neural networks