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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
| Artificial Neural Networks and Evolutionary Computation in Remote Sensing |
| Autore | Kavzoglu Taskin |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (256 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
convolutional neural network
image segmentation multi-scale feature fusion semantic features Gaofen 6 aerial images land-use Tai’an convolutional neural networks (CNNs) feature fusion ship detection optical remote sensing images end-to-end detection transfer learning remote sensing single shot multi-box detector (SSD) You Look Only Once-v3 (YOLO-v3) Faster RCNN statistical features Gaofen-2 imagery winter wheat post-processing spatial distribution Feicheng China light detection and ranging LiDAR deep learning convolutional neural networks CNNs mask regional-convolutional neural networks mask R-CNN digital terrain analysis resource extraction hyperspectral image classification few-shot learning quadruplet loss dense network dilated convolutional network artificial neural networks classification superstructure optimization mixed-inter nonlinear programming hyperspectral images super-resolution SRGAN model generalization image downscaling mixed forest multi-label segmentation semantic segmentation unmanned aerial vehicles classification ensemble machine learning Sentinel-2 geographic information system (GIS) earth observation on-board microsat mission nanosat AI on the edge CNN |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557148403321 |
Kavzoglu Taskin
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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 Methods with Noisy, Incomplete or Small Datasets
| Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| Autore | Solé-Casals Jordi |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (316 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
open contours
similarly shaped fish species Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) Extreme Learning Machines (ELM) feature engineering small data-sets optimization machine learning preprocessing image generation weighted interpolation map binarization single sample per person root canal measurement multifrequency impedance data augmentation neural network functional magnetic resonance imaging independent component analysis deep learning recurrent neural network functional connectivity episodic memory small sample learning feature selection noise elimination space consistency label correlations empirical mode decomposition sparse representations tensor decomposition tensor completion machine translation pairwise evaluation educational data small datasets noisy datasets smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model Artificial Neural Network Discriminant Analysis dengue feature extraction sound event detection non-negative matrix factorization ultrasound images shadow detection shadow estimation auto-encoders semi-supervised learning prediction feature importance feature elimination hierarchical clustering Parkinson’s disease few-shot learning permutation-variable importance topological data analysis persistent entropy support-vector machine data science intelligent decision support social vulnerability gender-gap digital-gap COVID19 policy-making support artificial intelligence imperfect dataset |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557324603321 |
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Solé-Casals Jordi
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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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Smart Sensor Technologies for IoT
| Smart Sensor Technologies for IoT |
| Autore | Brida Peter |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (270 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
Internet of Things (IoT)
ReRoute Multicast Repair (M-REP) internet of things (IoT) Fast Reroute bit repair (B-REP) failure repair WSN MANET DRONET multilayered network model 5G IoT smart sensors smart sensor IoT system Velostat pressure sensor convolutional neural network data classification position detection magnetometer traffic vehicle classification measurement detection Internet of Things Bluetooth indoor tracking mobile localization optical sensors vibration sensing quality of service differentiation wireless optical networks free space optics multiwavelength laser optical code division multiple access (OCDMA) underwater wireless sensor network energy-efficient clustering depth-based routing mm-wave radars GNSS-RTK positioning wireless technology electromagnetic scanning point cloud localization IMU Wi-Fi positioning dead reckoning particle filter fingerprinting Wi-Fi sensing human activity recognition location-independent meta learning metric learning few-shot learning ACR H.264/AVC H.265/HEVC QoE subjective assessment |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557599903321 |
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Brida Peter
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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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