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This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms.Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to thestatistical approach to the analysis of complex networks.In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition “à la carte”. Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7. As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding.

Networking standards & protocols --- Network analysis, statistical analysis, network modeling, community detection, graph-based semi-supervised learning, sampling in networks

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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

Technology: general issues --- History of engineering & technology --- star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance --- star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance

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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

Technology: general issues --- History of engineering & technology --- star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance

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Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

Technology: general issues --- History of engineering & technology --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging

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• Reference Manager
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Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging