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Signal Processing and Machine Learning with Applications

presents different kinds of signals humans use and applies them for human machine interaction to communicate. Signal Processing and Machine;

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Digital Signal Processing with Kernel Methods

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to;

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Signal Processing and Machine Learning for Biomedical Big Data

alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the;

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Advances in Machine Learning and Signal Processing

algorithms for machine learning and signal processing for engineering problems.;

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Machine Intelligence and Signal Processing

in the fields of machine learning, big data analytics, signal processing, computational learning theory, and their real-time applications. The;

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Intelligent Sensor Networks

intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration;

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Intelligent Sensor Networks

intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration;

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine;

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Cooperative and Graph Signal Processing

, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the;

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Signal Processing in Medicine and Biology

statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their;

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Financial Signal Processing and Machine Learning

market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and;

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Pattern Recognition and Machine Intelligence

reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning;

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Pattern Recognition and Machine Intelligence

reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning;

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Machine Learning and Information Processing

, deep learning, image processing and pattern recognition, and signal and speech processing.;

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Machine Learning and Information Processing

, artificial intelligence, human-computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.;

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Signal Processing for Cognitive Radios

This book examines signal processing techniques for cognitive radios. The book is divided into three parts: Part I, is an introduction to;

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Learning Approaches in Signal Processing

Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security;

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Brain and Behavior Computing

condition of the brain. Emphasizes real challenges in brain signal processing for a variety of applications for analysis;

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Soft Computing and Signal Processing

This book presents selected research papers on current developments in the fields of soft computing and signal processing from the Third;

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Soft Computing and Signal Processing

This book presents selected research papers on current developments in the fields of soft computing and signal processing from the Third;

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Applied Speech Processing

of the intelligent speech signal processing systems in different domains. The book is proposed for professionals, scientists, and engineers who;

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Pattern Recognition and Image Analysis

. They were organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing;

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Soft Computing and Signal Processing

The book presents selected research papers on current developments in the field of soft computing and signal processing from the;

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Machine Learning and Intelligent Communications

This two volume set constitutes the refereed post-conference proceedings of the Second International Conference on Machine Learning and;

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Digital Signal Processing 101

>Field Orientated Motor Control Matrix Inversion algorithms GPUs for computing Machine Learning Entropy;

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Deep Learning through Sparse and Low-Rank Modeling

variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for;

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Biomedical Signal Processing

This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection;

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