Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Si...

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Main Authors: Maysam Abbod, Jiann-Shing Shieh
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/21/4727
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author Maysam Abbod
Jiann-Shing Shieh
author_facet Maysam Abbod
Jiann-Shing Shieh
author_sort Maysam Abbod
collection DOAJ
description Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.
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spelling doaj.art-728d7cd6fe194e829b31e11a9da8017e2022-12-22T04:19:46ZengMDPI AGSensors1424-82202019-10-011921472710.3390/s19214727s19214727Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”Maysam Abbod0Jiann-Shing Shieh1Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UKDepartment of Mechanical engineering, Yuan Ze University, Taoyuan 32003, TaiwanRecently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.https://www.mdpi.com/1424-8220/19/21/4727health monitoringartificial intelligenceintelligent systemmachine learningbig databiomedical systemsmechanical systems
spellingShingle Maysam Abbod
Jiann-Shing Shieh
Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”
Sensors
health monitoring
artificial intelligence
intelligent system
machine learning
big data
biomedical systems
mechanical systems
title Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”
title_full Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”
title_fullStr Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”
title_full_unstemmed Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”
title_short Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”
title_sort special issue advanced signal processing in intelligent systems for health monitoring
topic health monitoring
artificial intelligence
intelligent system
machine learning
big data
biomedical systems
mechanical systems
url https://www.mdpi.com/1424-8220/19/21/4727
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