Machine Learning In Signal Processing
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Machine Learning In Signal Processing
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Author : Sudeep Tanwar
language : en
Publisher: CRC Press
Release Date : 2021-12-09
Machine Learning In Signal Processing written by Sudeep Tanwar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-09 with Technology & Engineering categories.
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 Learning For Signal Processing 2010
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Author : International Workshop on Machine Learning for Signal Processing
language : en
Publisher:
Release Date : 2012
Machine Learning For Signal Processing 2010 written by International Workshop on Machine Learning for Signal Processing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Machine Learning For Signal Processing
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Author : Max A. Little
language : en
Publisher: Oxford University Press
Release Date : 2019-08-13
Machine Learning For Signal Processing written by Max A. Little and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-13 with Computers categories.
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.
Special Issue Machine Learning For Signal Processing 2010
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Author : International Workshop on Machine Learning for Signal Processing. 20, 2010, Kittilä
language : en
Publisher:
Release Date : 2012
Special Issue Machine Learning For Signal Processing 2010 written by International Workshop on Machine Learning for Signal Processing. 20, 2010, Kittilä and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Machine Learning Methods For Signal Image And Speech Processing
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Author : M.A. Jabbar
language : en
Publisher: CRC Press
Release Date : 2022-09-01
Machine Learning Methods For Signal Image And Speech Processing written by M.A. Jabbar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-01 with Science categories.
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 yirtual reality domains.
Hyperspectral Image Analysis
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Author : Saurabh Prasad
language : en
Publisher: Springer Nature
Release Date : 2020-04-27
Hyperspectral Image Analysis written by Saurabh Prasad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-27 with Computers categories.
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Signal Processing In Medicine And Biology
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Author : Ammar Ahmed
language : en
Publisher: Springer Nature
Release Date : 2025-09-19
Signal Processing In Medicine And Biology written by Ammar Ahmed and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-19 with Technology & Engineering categories.
Signal Processing in Medicine and Biology: Applications of Deep Learning to the Health Sciences presents expanded versions of selected papers from the 2023 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) at Temple University. The symposium presents multidisciplinary research across a wide range of topics in the life sciences. The Neural Engineering Data Consortium hosts the symposium to promote machine learning and big data applications in bioengineering. Topics covered include: !-- [if !supportLists]--· !--[endif]--Signal and image analysis (e.g., EEG, ECG, MRI); !-- [if !supportLists]--· !--[endif]--Machine learning, data mining, and classification; !-- [if !supportLists]--· !--[endif]--Big data resources and applications; !-- [if !supportLists]--· !--[endif]--Applications of quantum computing; !-- [if !supportLists]--· !--[endif]--Digital pathology; !-- [if !supportLists]--· !--[endif]--Computational biology; !-- [if !supportLists]--· !--[endif]--Genomics, genetics, proteomics. Applications of particular interest at the 2023 symposium included digital pathology, computational biology, genomics, genetics, and proteomics. The book features tutorials and examples of successful applications that will appeal to many professionals and researchers in signal processing, medicine, and biology. For students and professionals new to the field, the book offers an easy-to-understand introduction to various bioengineering topics. For professionals active in the field, it provides essential algorithmic details on valuable benchmarks for the technology.
Signal Processing And Machine Learning With Applications
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Author : Michael M. Richter
language : en
Publisher: Springer
Release Date : 2022-10-01
Signal Processing And Machine Learning With Applications written by Michael M. Richter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-01 with Computers categories.
Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate. Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing, machine learning and the applications.
Signal Processing And Machine Learning For Biomedical Big Data
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Author : Ervin Sejdic
language : en
Publisher: CRC Press
Release Date : 2018-07-04
Signal Processing And Machine Learning For Biomedical Big Data written by Ervin Sejdic and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-04 with Medical categories.
Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.
Digital Signal Processing With Kernel Methods
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Author : Jose Luis Rojo-Alvarez
language : en
Publisher: Wiley
Release Date : 2018-01-05
Digital Signal Processing With Kernel Methods written by Jose Luis Rojo-Alvarez and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-05 with Technology & Engineering categories.