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Advanced Signal Processing in Wearable Sensors for Health Monitoring
| Advanced Signal Processing in Wearable Sensors for Health Monitoring |
| Autore | Abbod Maysam |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (206 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
Anomaly Detection
arterial blood pressure artificial neural network automated dietary monitoring behavioral signals biomedical signal processing blind source separation Cardiovascular Disease central venous pressure continuous arterial blood pressure deep convolutional autoencoder diastolic blood pressure drowsiness detection eating detection eating timing error analysis ECG EEG electrocardiography extreme learning machine frequency-domain features genetic algorithm heart rate measurement hemodynamics Hill muscle model independent component analysis intracranial pressure joint moment prediction long short-term memory machine learning Machine Learning motion artifact multi-wavelength multicriteria optimization multilayer perceptron myocardial infarction n/a non-invasive system online input variables pain detection photoplethysmography physiological signals pulmonary arterial pressure remote BCG remote HR remote PPG Review Signal Processing smart eyeglasses spline stress detection systolic blood pressure vectorcardiography wearable health monitoring wearable sensor |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566462503321 |
Abbod Maysam
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent Biosignal Analysis Methods
| Intelligent Biosignal Analysis Methods |
| Autore | Jović Alan |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (256 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
accelerometer
accuracy Alzheimer's disease autonomic nervous system brain functional connectivity classifiers CNN convolution neural network (CNN) covariate shift cross-participant DEAP deep learning disgust drowsiness classification drowsiness detection EEG EEG features electrocardiogram electrocardiography electroencephalogram (EEG) electroencephalography (EEG) emotion emotion recognition emotional state event-centered data segmentation eye blinks rate fall detection fatigue detection feature extraction feature selection frequency band fusion galvanic skin response heart rate individual differences inter-participant inter-subject variability k-fold validation machine learning mental workload Mish myocardial infarction n/a neural network-based refinement non-local attention mechanism non-stationarity olfactory training optimal shrinkage phase-locked value (PLV) photoplethysmography (PPG) psychophysics residual attention residual network sensitivity signal quality index skin conductance level sleep stage scoring sleep staging smell spatial transformer networks stress surgery image T-end annotation tSQI wearable device wearable sensors window duration wine sensory analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557354803321 |
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Jović Alan
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) / John Ball, Bo Tang
| Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) / John Ball, Bo Tang |
| Autore | Ball John |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (344 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
FPGA
recurrence plot (RP) residual learning neural networks driver monitoring navigation depthwise separable convolution optimization dynamic path-planning algorithms object tracking sub-region cooperative systems convolutional neural networks DSRC VANET joystick road scene convolutional neural network (CNN) multi-sensor p-norm occlusion crash injury severity prediction deep leaning squeeze-and-excitation electric vehicles perception in challenging conditions T-S fuzzy neural network total vehicle mass of the front vehicle electrocardiogram (ECG) communications generative adversarial nets camera adaptive classifier updating Vehicle-to-X communications convolutional neural network predictive Geobroadcast infinity norm urban object detector machine learning automated-manual transition red light-running behaviors photoplethysmogram (PPG) panoramic image dataset parallel architectures visual tracking autopilot ADAS kinematic control GPU road lane detection obstacle detection and classification Gabor convolution kernel autonomous vehicle Intelligent Transport Systems driving decision-making model Gaussian kernel autonomous vehicles enhanced learning ethical and legal factors kernel based MIL algorithm image inpainting fusion terrestrial vehicle driverless drowsiness detection map generation object detection interface machine vision driving assistance blind spot detection deep learning relative speed autonomous driving assistance system discriminative correlation filter bank recurrent neural network emergency decisions LiDAR real-time object detection vehicle dynamics path planning actuation systems maneuver algorithm autonomous driving smart band the emergency situations two-wheeled support vector machine model global region biological vision automated driving |
| ISBN |
9783039213764
3039213768 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367757403321 |
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Ball John
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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