Compressive Sensing in Healthcare Books

Click Get Book Button To Download or read online Compressive Sensing in Healthcare books, Available in PDF, ePub, Tuebl and Kindle. This site is like a library, Use search box in the widget to get ebook that you want.

Compressive Sensing in Healthcare


Compressive Sensing in Healthcare
  • Author : Mahdi Khosravy
  • Publisher : Academic Press
  • Release : 2020-05-18
  • ISBN : 9780128212486
  • Language : En, Es, Fr & De
GET BOOK

Compressive Sensing in Healthcare, part of the Advances in Ubiquitous Sensing Applications for Healthcare series gives a review on compressive sensing techniques in a practical way, also presenting deterministic compressive sensing techniques that can be used in the field. The focus of the book is on healthcare applications for this technology. It is intended for both the creators of this technology and the end users of these products. The content includes the use of EEG and ECG, plus hardware and software requirements for building projects. Body area networks and body sensor networks are explored. Provides a toolbox for compressive sensing in health, presenting both mathematical and coding information Presents an intuitive introduction to compressive sensing, including MATLAB tutorials Covers applications of compressive sensing in health care

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms


Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
  • Author : Bhabesh Deka
  • Publisher : Springer
  • Release : 2018-12-29
  • ISBN : 9789811335976
  • Language : En, Es, Fr & De
GET BOOK

This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.

Compressed Sensing Supplement


Compressed Sensing Supplement
  • Author :
  • Publisher :
  • Release : 2016
  • ISBN : OCLC:1075833321
  • Language : En, Es, Fr & De
GET BOOK

Wireless Mobile Communication and Healthcare


Wireless Mobile Communication and Healthcare
  • Author : Balwant Godara
  • Publisher : Springer
  • Release : 2013-04-03
  • ISBN : 9783642378935
  • Language : En, Es, Fr & De
GET BOOK

This book constitutes the refereed proceedings of the Third International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2012, and of the two workshops: Workshop on Advances in Personalized Healthcare Services, Wearable Mobile Monitoring, and Social Media Pervasive Technologies (APHS 2012), and Workshop on Advances in Wireless Physical Layer Communications for Emerging Healthcare Applications (IWAWPLC 2012), all held in Paris, France, in November 2012. The 39 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections covering wearable, outdoor and home-based applications; remote diagnosis and patient management; data processing; sensor devices and systems; biomedical monitoring in relation to society and the environment; body area networks; telemedicine systems for disease-specific applications; data collection and management; papers from the invited session "Implants"; papers from the IWAWPLC and APHS workshops.

Sensors for Health Monitoring


Sensors for Health Monitoring
  • Author : Nilanjan Dey
  • Publisher : Academic Press
  • Release : 2019-09-09
  • ISBN : 9780128193624
  • Language : En, Es, Fr & De
GET BOOK

Sensors for Health Monitoring discusses the characteristics of U-Healthcare systems in different domains, providing a foundation for working professionals and undergraduate and postgraduate students. The book provides information and advice on how to choose the best sensors for a U-Healthcare system, advises and guides readers on how to overcome challenges relating to data acquisition and signal processing, and presents comprehensive coverage of up-to-date requirements in hardware, communication and calculation for next-generation uHealth systems. It then compares new technological and technical trends and discusses how they address expected u-Health requirements. In addition, detailed information on system operations is presented and challenges in ubiquitous computing are highlighted. The book not only helps beginners with a holistic approach toward understanding u-Health systems, but also presents researchers with the technological trends and design challenges they may face when designing such systems. Presents an outstanding update on the use of U-Health data analysis and management tools in different applications, highlighting sensor systems Highlights Internet of Things enabled U-Healthcare Covers different data transmission techniques, applications and challenges with extensive case studies for U-Healthcare systems