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