Narrow your search

Library

FARO (3)

KU Leuven (3)

LUCA School of Arts (3)

Odisee (3)

Thomas More Kempen (3)

Thomas More Mechelen (3)

UCLL (3)

ULB (3)

ULiège (3)

VIVES (3)

More...

Resource type

book (5)


Language

English (5)


Year
From To Submit

2022 (3)

2019 (2)

Listing 1 - 5 of 5
Sort by

Book
Cosmic Plasmas and Electromagnetic Phenomena
Authors: --- ---
ISBN: 3039214667 3039214659 Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

During the past few decades, plasma science has witnessed a great growth in laboratory studies, in simulations, and in space. Plasma is the most common phase of ordinary matter in the universe. It is a state in which ionized matter (even as low as 1%) becomes highly electrically conductive. As such, long-range electric and magnetic fields dominate its behavior. Cosmic plasmas are mostly associated with stars, supernovae, pulsars and neutron stars, quasars and active galaxies at the vicinities of black holes (i.e., their jets and accretion disks). Cosmic plasma phenomena can be studied with different methods, such as laboratory experiments, astrophysical observations, and theoretical/computational approaches (i.e., MHD, particle-in-cell simulations, etc.). They exhibit a multitude of complex magnetohydrodynamic behaviors, acceleration, radiation, turbulence, and various instability phenomena. This Special Issue addresses the growing need of the plasma science principles in astrophysics and presents our current understanding of the physics of astrophysical plasmas, their electromagnetic behaviors and properties (e.g., shocks, waves, turbulence, instabilities, collimation, acceleration and radiation), both microscopically and macroscopically. This Special Issue provides a series of state-of-the-art reviews from international experts in the field of cosmic plasmas and electromagnetic phenomena using theoretical approaches, astrophysical observations, laboratory experiments, and state-of-the-art simulation studies.


Book
Advanced Signal Processing in Wearable Sensors for Health Monitoring
Authors: ---
Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods.

Keywords

Technology: general issues --- History of engineering & technology --- automated dietary monitoring --- eating detection --- eating timing error analysis --- biomedical signal processing --- smart eyeglasses --- wearable health monitoring --- artificial neural network --- joint moment prediction --- extreme learning machine --- Hill muscle model --- online input variables --- Review --- ECG --- Signal Processing --- Machine Learning --- Cardiovascular Disease --- Anomaly Detection --- photoplethysmography --- motion artifact --- independent component analysis --- multi-wavelength --- continuous arterial blood pressure --- systolic blood pressure --- diastolic blood pressure --- deep convolutional autoencoder --- genetic algorithm --- electrocardiography --- vectorcardiography --- myocardial infarction --- long short-term memory --- spline --- multilayer perceptron --- pain detection --- stress detection --- wearable sensor --- physiological signals --- behavioral signals --- non-invasive system --- hemodynamics --- arterial blood pressure --- central venous pressure --- pulmonary arterial pressure --- intracranial pressure --- heart rate measurement --- remote HR --- remote PPG --- remote BCG --- blind source separation --- drowsiness detection --- EEG --- frequency-domain features --- multicriteria optimization --- machine learning --- automated dietary monitoring --- eating detection --- eating timing error analysis --- biomedical signal processing --- smart eyeglasses --- wearable health monitoring --- artificial neural network --- joint moment prediction --- extreme learning machine --- Hill muscle model --- online input variables --- Review --- ECG --- Signal Processing --- Machine Learning --- Cardiovascular Disease --- Anomaly Detection --- photoplethysmography --- motion artifact --- independent component analysis --- multi-wavelength --- continuous arterial blood pressure --- systolic blood pressure --- diastolic blood pressure --- deep convolutional autoencoder --- genetic algorithm --- electrocardiography --- vectorcardiography --- myocardial infarction --- long short-term memory --- spline --- multilayer perceptron --- pain detection --- stress detection --- wearable sensor --- physiological signals --- behavioral signals --- non-invasive system --- hemodynamics --- arterial blood pressure --- central venous pressure --- pulmonary arterial pressure --- intracranial pressure --- heart rate measurement --- remote HR --- remote PPG --- remote BCG --- blind source separation --- drowsiness detection --- EEG --- frequency-domain features --- multicriteria optimization --- machine learning


Book
Advanced Signal Processing in Wearable Sensors for Health Monitoring
Authors: ---
Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods.


Book
Advanced Signal Processing in Wearable Sensors for Health Monitoring
Authors: ---
Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods.


Book
Asymmetric Planetary Nebulae VII
Authors: ---
ISBN: 3038976415 3038976407 Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

This book contains the best and most up-to-date contributions in the field of late stage stellar evolution, as presented at the APNVII conference in Hong Kong in December 2017. A total of 60 scientists from 20 countries gathered to present, listen, interact and discuss the most current issues and problems in planetary nebulae and related objects research. The emphasis of this influential series of meetings, which was the seventh occasion over the last 20 years, has always been on the hypothesized and observed physical shaping mechanisms of the ejected nebulae that have such wonderful and intriguing forms. This special Galaxies conference issue of fully refereed contributions brings together a representative compilation of the meeting presentations in paper form. It captures the current “snap shot” status of this research field in some real sense. Such proceedings are well received and can be used as a reference material by both participants and all others working in the field for years to come.

Keywords

UIE bands --- stars: binaries --- X-rays --- binary stars --- planetary systems --- abundances --- post-AGB --- normal modes --- theory and observation --- binaries: spectroscopic --- stellar evolution --- binaries: close --- AGB stars --- stars: individual: WD 1751+106 --- displacement vectors --- AGB and post-AGB --- extinction --- circumstellar matter --- stars: individual: WD 2134+25 --- asymptotic giant branch stars --- winds and outflows --- ISM: abundances --- stars: AGB and post-AGB --- late stage stellar evolution --- central stars of planetary nebulae --- ultraviolet radiation --- supernovae --- stellar mass loss --- circumstellar dust --- integral field spectroscopy --- planetary nebulae --- radial velocity --- mass-loss --- pre-PN hydrodynamic models --- infra-red --- planetary nebulae: Common Envelope --- astrochemistry --- dust --- multi-wavelength photometry --- ISM: jets and outflows --- planetary nebulae: individual (OH231+8+04.2) --- radio continuum --- stars: abundances --- shock wave --- stars: individual: WD 0044–121 --- post-AGB stars --- proto-planetary nebulae --- binarity: transients: planetary nebulae --- stars: atmospheres --- stars: variables: general --- AGB and post-AGB stars --- jets --- (sub)millimeter interferometry --- discs --- binarity --- winds --- observations --- mass loss --- X-ray --- stars: winds --- aperture masking --- outflows --- fullerenes --- planetary nebula --- pulsation --- interstellar medium --- planetary nebulae: individual (NGC 6781) --- late-stage stellar evolution --- infrared interferometry --- accretion disks

Listing 1 - 5 of 5
Sort by