This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology.

This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. Bringing together expanded versions of selected papers presented at the 2020 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB), it examines the vital role signal processing plays in enabling a new generation of technology based on big data and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. Biomedical Sensing and Analysis: Signal Processing in Medicine and Biology presents tutorials and examples of successful applications, and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology. Presents an interdisciplinary look at research trends in signal processing and biomedicine; Promotes collaboration between healthcare practitioners and signal processing researchers; Includes tutorials and examples of successful applications.

This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. It examines the vital role signal processing plays in enabling a new generation of technology based on big data, and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. Biomedical Signal Processing: Innovation and Applications presents tutorials and examples of successful applications, and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology.

Signal Processing in Medicine and Biology: Innovations in Big Data Processing provides an interdisciplinary look at state-of-the-art innovations in biomedical signal processing, especially as it applies to large data sets and machine learning. Chapters are presented with detailed mathematics and complete implementation specifics so that readers can completely master these techniques. The book presents tutorials and examples of successful applications and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology at the intersection between healthcare, engineering, and computer science.

This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. Bringing together expanded versions of selected papers presented at the 2020 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB), it examines the vital role signal processing plays in enabling a new generation of technology based on big data and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. Biomedical Sensing and Analysis: Signal Processing in Medicine and Biology presents tutorials and examples of successful applications, and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology. Presents an interdisciplinary look at research trends in signal processing and biomedicine; Promotes collaboration between healthcare practitioners and signal processing researchers; Includes tutorials and examples of successful applications.

This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. It examines the vital role signal processing plays in enabling a new generation of technology based on big data, and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. Biomedical Signal Processing: Innovation and Applications presents tutorials and examples of successful applications, and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology.

This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson's; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Cutting-edge transmitter and receiver waveform design techniquesOptimum design can improve signal direction, interference, and noise suppression across various disciplines that utilize waveforms, including radar, sonar, and communications. Waveform Diversity explains the role of transmitter and receiver waveform design to boost overall performance. Written by experts in the field, this monograph covers joint transmitter receiver design, optimum design methods, constant envelope transmit signals, and sparsity-based receivers. Proven methods for mitigating noise and clutter and maximizing output signal power are included in this practical guide. Waveform Diversity covers: Waveform design and matched filtering New methods for optimum transmitter and receiver design Transmitter threshold energy and energy-bandwidth tradeoff Increasing transmit power efficiency with constant envelope transmit signals Optimum waveform design to reduce noise and clutter Discrete-time waveform design Sparsity-based receiver design methods