Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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 electronic resource (206 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566462503321 |
Abbod Maysam
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 electronic resource (256 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
sleep stage scoring
neural network-based refinement residual attention T-end annotation signal quality index tSQI optimal shrinkage emotion EEG DEAP CNN surgery image disgust autonomic nervous system electrocardiogram galvanic skin response olfactory training psychophysics smell wearable sensors wine sensory analysis accuracy convolution neural network (CNN) classifiers electrocardiography k-fold validation myocardial infarction sensitivity sleep staging electroencephalography (EEG) brain functional connectivity frequency band fusion phase-locked value (PLV) wearable device emotional state mental workload stress heart rate eye blinks rate skin conductance level emotion recognition electroencephalogram (EEG) photoplethysmography (PPG) machine learning feature extraction feature selection deep learning non-stationarity individual differences inter-subject variability covariate shift cross-participant inter-participant drowsiness detection EEG features drowsiness classification fatigue detection residual network Mish spatial transformer networks non-local attention mechanism Alzheimer's disease fall detection event-centered data segmentation accelerometer window duration |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557354803321 |
|
Jović Alan
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)
| Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) |
| Autore | Tang Bo |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (344 p.) |
| 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 | 3-03921-376-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Machine Learning and Embedded Computing in Advanced Driver Assistance Systems |
| Record Nr. | UNINA-9910367757403321 |
|
Tang Bo
|
||
| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||