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Ultrasound medical imaging stands out among the other diagnostic imaging modalities for its patient-friendliness, high temporal resolution, low cost, and absence of ionizing radiation. On the other hand, it may still suffer from limited detail level, low signal-to-noise ratio, and narrow field-of-view. In the last decade, new beamforming and image reconstruction techniques have emerged which aim at improving resolution, contrast, and clutter suppression, especially in difficult-to-image patients. Nevertheless, achieving a higher image quality is of the utmost importance in diagnostic ultrasound medical imaging, and further developments are still indispensable. From this point of view, a crucial role can be played by novel beamforming techniques as well as by non-conventional image formation techniques (e.g., advanced transmission strategies, and compounding, coded, and harmonic imaging). This Special Issue includes novel contributions on both ultrasound beamforming and image formation techniques, particularly addressed at improving B-mode image quality and related diagnostic content. This indeed represents a hot topic in the ultrasound imaging community, and further active research in this field is expected, where many challenges still persist.

n/a --- signal-to-noise ratio (SNR) --- multi-perspective ultrasound imaging --- dictionary learning --- common carotid artery --- spatial resolution --- contrast enhancement --- sparse representation --- PMUT linear array --- K-singular value decomposition --- time resolution --- cardiac imaging --- coded excitation --- plane wave --- beam pattern --- grating lobe suppression --- spatial coherence --- subcutaneous fat layer --- cylindrical scanning --- parallel beam forming --- microbubble --- MR-visible fiducial marker --- ultrasonic imaging --- speckle reduction --- multi-line transmission --- MRI --- adaptive beamforming --- super-resolution --- filtered-delay multiply and sum beamforming --- B-mode imaging --- medical ultrasound --- intima-media complex longitudinal motion --- synthetic aperture --- quantitative parametrization --- arterial wall motion --- pth root --- beam forming --- medical image processing --- crosstalk artifacts --- ultrasound imaging --- diverging wave --- 1-3 piezocomposite material --- dynamic focusing --- multi-line acquisition --- image reconstruction --- plane wave imaging --- ultrasound --- multi-line transmit --- reconstruction --- thyroid imaging --- beamforming

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In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and to learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves the mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topics range from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspired filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensor signal processing.

History of engineering & technology --- geometric calibration --- long- and short-period errors --- equivalent bias angles --- sparse recovery --- linear array push-broom sensor --- deep learning --- signal detection --- modulation classification --- the single shot multibox detector networks --- the multi-inputs convolutional neural networks --- medical image registration --- similarity measure --- non-rigid transformation --- computational efficiency --- registration accuracy --- signal denoising --- singular value decomposition --- Akaike information criterion --- reaction wheel --- micro-vibration --- permutation entropy (PE) --- weighted-permutation entropy (W-PE) --- reverse permutation entropy (RPE) --- reverse dispersion entropy (RDE) --- time series analysis --- complexity --- sensor signal --- tensor principal component pursuit --- stable recovery --- tensor SVD --- ADMM --- kalman filter --- nonlinear autoregressive --- neural network --- noise filtering --- multiple-input multiple-output (MIMO) --- frequency-hopping code --- dual-function radar-communications --- information embedding --- mutual information (mi) --- waveform optimization --- spectroscopy --- compressed sensing --- inverse problems --- dictionary learning --- image registration --- large deformation --- weakly supervised --- high-order cumulant --- cyclic spectrum --- decision tree–support vector machine --- wind turbine --- gearbox fault --- cosine loss --- long short-term memory network --- indoor localization --- CSI --- fingerprinting --- Bayesian tracking --- image reconstruction --- computed tomography --- nonlocal total variation --- sparse-view CT --- low-dose CT --- proximal splitting --- row-action --- brain CT image --- audio signal processing --- sound event classification --- nonnegative matric factorization --- blind signal separation --- support vector machines --- brain-computer interface --- motor imagery --- machine learning --- internet of things --- pianists --- surface inspection --- aluminum ingot --- mask gradient response --- Difference of Gaussian --- inception-v3 --- EEG --- sleep stage --- wavelet packet --- state space model --- image captioning --- three-dimensional (3D) vision --- human-robot interaction --- Laplacian scores --- data reduction --- sensors --- Internet of Things (IoT) --- LoRaWAN --- n/a --- decision tree-support vector machine

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In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and to learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves the mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topics range from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspired filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensor signal processing.

History of engineering & technology --- geometric calibration --- long- and short-period errors --- equivalent bias angles --- sparse recovery --- linear array push-broom sensor --- deep learning --- signal detection --- modulation classification --- the single shot multibox detector networks --- the multi-inputs convolutional neural networks --- medical image registration --- similarity measure --- non-rigid transformation --- computational efficiency --- registration accuracy --- signal denoising --- singular value decomposition --- Akaike information criterion --- reaction wheel --- micro-vibration --- permutation entropy (PE) --- weighted-permutation entropy (W-PE) --- reverse permutation entropy (RPE) --- reverse dispersion entropy (RDE) --- time series analysis --- complexity --- sensor signal --- tensor principal component pursuit --- stable recovery --- tensor SVD --- ADMM --- kalman filter --- nonlinear autoregressive --- neural network --- noise filtering --- multiple-input multiple-output (MIMO) --- frequency-hopping code --- dual-function radar-communications --- information embedding --- mutual information (mi) --- waveform optimization --- spectroscopy --- compressed sensing --- inverse problems --- dictionary learning --- image registration --- large deformation --- weakly supervised --- high-order cumulant --- cyclic spectrum --- decision tree–support vector machine --- wind turbine --- gearbox fault --- cosine loss --- long short-term memory network --- indoor localization --- CSI --- fingerprinting --- Bayesian tracking --- image reconstruction --- computed tomography --- nonlocal total variation --- sparse-view CT --- low-dose CT --- proximal splitting --- row-action --- brain CT image --- audio signal processing --- sound event classification --- nonnegative matric factorization --- blind signal separation --- support vector machines --- brain-computer interface --- motor imagery --- machine learning --- internet of things --- pianists --- surface inspection --- aluminum ingot --- mask gradient response --- Difference of Gaussian --- inception-v3 --- EEG --- sleep stage --- wavelet packet --- state space model --- image captioning --- three-dimensional (3D) vision --- human-robot interaction --- Laplacian scores --- data reduction --- sensors --- Internet of Things (IoT) --- LoRaWAN --- n/a --- decision tree-support vector machine

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This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries.

virtual power plant (VPP) --- power quality (PQ) --- global index --- distributed energy resources (DER) --- energy storage systems (ESS) --- power systems --- long-term assessment --- battery energy storage systems (BESS) --- smart grids --- conducted disturbances --- power quality --- supraharmonics --- 2–150 kHz --- Power Line Communications (PLC) --- intentional emission --- non-intentional emission --- mains signalling --- virtual power plant --- data mining --- clustering --- distributed energy resources --- energy storage systems --- short term conditions --- cluster analysis (CA) --- nonlinear loads --- harmonics, cancellation, and attenuation of harmonics --- waveform distortion --- THDi --- low-voltage networks --- optimization techniques --- different batteries --- off-grid microgrid --- integrated renewable energy system --- cluster analysis --- K-means --- agglomerative --- ANFIS --- fuzzy logic --- induction generator --- MPPT --- neural network --- renewable energy --- variable speed WECS --- wind energy conversion system --- wind energy --- frequency estimation --- spectrum interpolation --- power network disturbances --- COVID-19 --- time-varying reproduction number --- social distancing --- load profile --- demographic characteristic --- household energy consumption --- demand-side management --- energy management --- time series --- Hidden Markov Model --- short-term forecast --- sparse signal decomposition --- supervised dictionary learning --- dictionary impulsion --- singular value decomposition --- discrete cosine transform --- discrete Haar transform --- discrete wavelet transform --- transient stability assessment --- home energy management --- binary-coded genetic algorithms --- optimal power scheduling --- demand response --- Data Injection Attack --- machine learning --- critical infrastructure --- smart grid --- water treatment plant --- power system --- n/a --- 2-150 kHz

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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

metadata --- image classification --- sensitivity analysis --- ROI detection --- residual learning --- image alignment --- adaptive convolutional kernels --- Hough transform --- class imbalance --- land surface temperature --- inundation mapping --- multiscale representation --- object-based --- convolutional neural networks --- scene classification --- morphological profiles --- hyperedge weight estimation --- hyperparameter sparse representation --- semantic segmentation --- vehicle classification --- flood --- Landsat imagery --- target detection --- multi-sensor --- building damage detection --- optimized kernel minimum noise fraction (OKMNF) --- sea-land segmentation --- nonlinear classification --- land use --- SAR imagery --- anti-noise transfer network --- sub-pixel change detection --- Radon transform --- segmentation --- remote sensing image retrieval --- TensorFlow --- convolutional neural network --- particle swarm optimization --- optical sensors --- machine learning --- mixed pixel --- optical remotely sensed images --- object-based image analysis --- very high resolution images --- single stream optimization --- ship detection --- ice concentration --- online learning --- manifold ranking --- dictionary learning --- urban surface water extraction --- saliency detection --- spatial attraction model (SAM) --- quality assessment --- Fuzzy-GA decision making system --- land cover change --- multi-view canonical correlation analysis ensemble --- land cover --- semantic labeling --- sparse representation --- dimensionality expansion --- speckle filters --- hyperspectral imagery --- fully convolutional network --- infrared image --- Siamese neural network --- Random Forests (RF) --- feature matching --- color matching --- geostationary satellite remote sensing image --- change feature analysis --- road detection --- deep learning --- aerial images --- image segmentation --- aerial image --- multi-sensor image matching --- HJ-1A/B CCD --- endmember extraction --- high resolution --- multi-scale clustering --- heterogeneous domain adaptation --- hard classification --- regional land cover --- hypergraph learning --- automatic cluster number determination --- dilated convolution --- MSER --- semi-supervised learning --- gate --- Synthetic Aperture Radar (SAR) --- downscaling --- conditional random fields --- urban heat island --- hyperspectral image --- remote sensing image correction --- skip connection --- ISPRS --- spatial distribution --- geo-referencing --- Support Vector Machine (SVM) --- very high resolution (VHR) satellite image --- classification --- ensemble learning --- synthetic aperture radar --- conservation --- convolutional neural network (CNN) --- THEOS --- visible light and infrared integrated camera --- vehicle localization --- structured sparsity --- texture analysis --- DSFATN --- CNN --- image registration --- UAV --- unsupervised classification --- SVMs --- SAR image --- fuzzy neural network --- dimensionality reduction --- GeoEye-1 --- feature extraction --- sub-pixel --- energy distribution optimizing --- saliency analysis --- deep convolutional neural networks --- sparse and low-rank graph --- hyperspectral remote sensing --- tensor low-rank approximation --- optimal transport --- SELF --- spatiotemporal context learning --- Modest AdaBoost --- topic modelling --- multi-seasonal --- Segment-Tree Filtering --- locality information --- GF-4 PMS --- image fusion --- wavelet transform --- hashing --- machine learning techniques --- satellite images --- climate change --- road segmentation --- remote sensing --- tensor sparse decomposition --- Convolutional Neural Network (CNN) --- multi-task learning --- deep salient feature --- speckle --- canonical correlation weighted voting --- fully convolutional network (FCN) --- despeckling --- multispectral imagery --- ratio images --- linear spectral unmixing --- hyperspectral image classification --- multispectral images --- high resolution image --- multi-objective --- convolution neural network --- transfer learning --- 1-dimensional (1-D) --- threshold stability --- Landsat --- kernel method --- phase congruency --- subpixel mapping (SPM) --- tensor --- MODIS --- GSHHG database --- compressive sensing