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This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read.
Technology: general issues --- History of engineering & technology --- data hiding --- AMBTC --- BTC --- Hamming code --- LSB --- predicate encryption --- inner product encryption --- constant-size private key --- efficient decryption --- constant pairing computations --- watermarking --- self-embedding --- digital signature --- fragile watermarking --- constrained backtracking matching pursuit --- sparse reconstruction --- compressed sensing --- greedy pursuit algorithm --- image processing --- visual surveillance --- deep learning --- object detection --- latency optimization --- mobile edge cloud --- connected autonomous cars --- smart city --- video surveillance --- physical layer security --- secure transmission --- secrecy capacity --- secrecy capacity optimization artificial noise --- power allocation --- channel estimation error --- neural network --- transfer learning --- scalograms --- MFCC --- Log-mel --- pre-trained models --- seismic patch classification --- CNN-features --- data complexity --- handwritten text recognition --- Residual Network --- Transformer model --- named entity recognition --- n/a
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This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg production
Research & information: general --- Biology, life sciences --- Animals & society --- broiler --- activity index --- time interval --- age --- image processing --- poultry --- cage-free --- preening behavior --- mask R-CNN --- residual network --- broiler chicken --- machine vision --- image restoring --- precision poultry farming --- feeding system --- pecking force --- precision livestock farming --- poultry farming --- information management --- cloud database --- disease detection --- acoustic --- audio --- frequency --- behavior --- image analysis --- animal welfare --- movement analysis --- LED --- comfort index --- manure area --- manure coverage proportion --- environment control --- ammonia emission --- layer house --- laying hen --- daily behavior --- machine learning --- inertia sensor --- walking ability --- animal behavior --- precision livestock --- PLF --- precise feeding --- ideal protein --- animal health --- immune system --- productive parameters --- management
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This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg production
Research & information: general --- Biology, life sciences --- Animals & society --- broiler --- activity index --- time interval --- age --- image processing --- poultry --- cage-free --- preening behavior --- mask R-CNN --- residual network --- broiler chicken --- machine vision --- image restoring --- precision poultry farming --- feeding system --- pecking force --- precision livestock farming --- poultry farming --- information management --- cloud database --- disease detection --- acoustic --- audio --- frequency --- behavior --- image analysis --- animal welfare --- movement analysis --- LED --- comfort index --- manure area --- manure coverage proportion --- environment control --- ammonia emission --- layer house --- laying hen --- daily behavior --- machine learning --- inertia sensor --- walking ability --- animal behavior --- precision livestock --- PLF --- precise feeding --- ideal protein --- animal health --- immune system --- productive parameters --- management
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This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg production
broiler --- activity index --- time interval --- age --- image processing --- poultry --- cage-free --- preening behavior --- mask R-CNN --- residual network --- broiler chicken --- machine vision --- image restoring --- precision poultry farming --- feeding system --- pecking force --- precision livestock farming --- poultry farming --- information management --- cloud database --- disease detection --- acoustic --- audio --- frequency --- behavior --- image analysis --- animal welfare --- movement analysis --- LED --- comfort index --- manure area --- manure coverage proportion --- environment control --- ammonia emission --- layer house --- laying hen --- daily behavior --- machine learning --- inertia sensor --- walking ability --- animal behavior --- precision livestock --- PLF --- precise feeding --- ideal protein --- animal health --- immune system --- productive parameters --- management
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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.
Information technology industries --- 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 --- n/a --- Alzheimer's disease
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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.
Information technology industries --- 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 --- n/a --- Alzheimer's disease
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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.
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 --- n/a --- Alzheimer's disease
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The book focuses on the techniques for UAV-based 3D mapping and its applications in varying fields since the explosive development of UAV-based photogrammetric 3D mapping and their wide applications from traditional surveying and mapping to other related fields have been witnessed in photogrammetry and remote sensing. In the last decade, unmanned aerial vehicle (UAV) images have become one of the most important remote sensing data sources for photogrammetric 3D mapping. Besides, the rapid development of recent techniques, e.g., SfM (Structure from Motion) for off-line image orientation, SLAM (Simultaneous Localization and Mapping) for on-line UAV navigation, and the deep learning (DL) embedded 3D reconstruction pipeline, has promoted UAV-based 3D mapping towards the direction of automation and intelligence. It is really worthy to collecting the cutting-edge techniques and reporting their promising applications.
compound building reconstruction --- LiDAR --- point clouds --- semantic decomposition --- structure from motion --- match pair --- cycle consistency inference --- repetitive structure --- very short baseline --- high-resolution remote sensing images --- building extraction --- multiscale features --- aggregate semantic information --- feature pyramid --- spatial eight-quadrant kernel convolution --- 3D point cloud --- semantic segmentation --- indoor scene --- wide-baseline stereo image --- deep learning --- convolutional neural network --- affine invariant feature --- image matching --- photogrammetric mesh model --- building façade --- 3D reconstruction --- least square fitting --- single image super-resolution --- lightweight image super-resolution --- U-shaped residual network --- dense shortcut --- effective feature distillation --- high-frequency loss --- power lines --- UAV inspection --- red-black propagation --- depth map fusion --- PatchMatch --- digital photogrammetry --- camera self-calibration --- Brown model --- polynomial model --- aerial triangulation --- GF-7 image --- building footprint --- building height --- multi-view --- point cloud --- multi-view reconstruction --- detail preserving --- depth estimation --- surface meshing --- texture mapping --- coplanar extraction --- deep convolutional neural network --- geometric topology --- homography matrix --- airborne LiDAR --- coal mine --- surface subsidence --- deformation detection --- digital subsidence model --- n/a --- building façade
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This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis.
Technology: general issues --- History of engineering & technology --- process monitoring --- dynamics --- variable time lag --- dynamic autoregressive latent variables model --- sintering process --- hammerstein output-error systems --- auxiliary model --- multi-innovation identification theory --- fractional-order calculus theory --- canonical variate analysis --- disturbance detection --- power transmission system --- k-nearest neighbor analysis --- statistical local analysis --- intelligent fault diagnosis --- stacked pruning sparse denoising autoencoder --- convolutional neural network --- anti-noise --- flywheel fault diagnosis --- belief rule base --- fuzzy fault tree analysis --- Bayesian network --- evidential reasoning --- aluminum reduction process --- alumina concentration --- subspace identification --- distributed predictive control --- spatiotemporal feature fusion --- gated recurrent unit --- attention mechanism --- fault diagnosis --- evidential reasoning rule --- system modelling --- information transformation --- parameter optimization --- event-triggered control --- interval type-2 Takagi–Sugeno fuzzy model --- nonlinear networked systems --- filter --- gearbox fault diagnosis --- convolution fusion --- state identification --- PSO --- wavelet mutation --- LSSVM --- data-driven --- operational optimization --- case-based reasoning --- local outlier factor --- abnormal case removal --- bearing fault detection --- deep residual network --- data augmentation --- canonical correlation analysis --- just-in-time learning --- fault detection --- high-speed trains --- autonomous underwater vehicle --- thruster fault diagnostics --- fault tolerant control --- robust optimization --- ocean currents --- n/a --- interval type-2 Takagi-Sugeno fuzzy model
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Using hyperspectral imaging (HSI) to exploit data has been found in a wide variety of applications. This reprint book only presents a small glimpse of it. Many other important applications using HSI which have emerged in data exploitation are not covered in this reprint book. For example, such applications may include water pollution and toxic waste in environmental monitoring, pesticide residual detection in food safety and inspection, plant and crop disease detection in agriculture, tumor detection and breast cancer detection in medical imaging, drug traffic in law enforcement, etc. Nevertheless, this reprint book provides many techniques which may find their ways in these applications as well.
Technology: general issues --- History of engineering & technology --- hyperspectral image few-shot classification --- deep learning --- meta-learning --- relation network --- convolutional neural network --- constrained-target optimal index factor band selection (CTOIFBS) --- hyperspectral image --- underwater spectral imaging system --- underwater hyperspectral target detection --- band selection (BS) --- constrained energy minimization (CEM) --- lightweight convolutional neural networks --- hyperspectral imagery classification --- transfer learning --- air temperature --- spatial measurement --- FTIR --- MWIR --- carbon dioxide absorption --- target detection --- coffee beans --- insect damage --- hyperspectral imaging --- band selection --- visualization --- color formation models --- multispectral image --- image fusion --- joint tensor decomposition --- anomaly detection --- constrained sparse representation --- hyperspectral imagery --- moving target detection --- spatio-temporal processing --- hyperspectral remote sensing --- image classification --- constraint representation --- superpixel segmentation --- multiscale decision fusion --- plug-and-play --- denoising --- nonlinear unmixing --- spectral reconstruction --- residual augmented attentional u-shape network --- spatial augmented attention --- channel augmented attention --- boundary-aware constraint --- atmospheric transmittance --- temperature --- emissivity --- separation --- midwave infrared --- hyperspectral images --- hyperspectral image super-resolution --- data fusion --- spectral-spatial residual network --- self-supervised training --- hyperspectral --- vegetation --- generative adversarial network --- data augmentation --- classification --- rice leaf blast --- hyperspectral imaging data --- deep convolutional neural networks --- fused features --- evolutionary computation --- heuristic algorithms --- machine learning --- unmanned aerial vehicles (UAVs) --- vegetation mapping --- upland swamps --- mine environment --- rice --- rice leaf folder --- hyperspectral image classification --- change detection --- self-supervised learning --- attention mechanism --- multi-source image fusion --- SFIM --- least square estimation --- spatial filter --- hyperspectral imaging (HSI) --- hyperspectral target detection --- hyperspectral reconstruction --- hyperspectral unmixing
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