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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Civil engineering, surveying & building --- biomass variability --- conversion-ready feedstocks --- preprocessing --- blending --- fractionation --- sorting --- resource diversity --- integrated biorefineries --- biomass variability --- conversion-ready feedstocks --- preprocessing --- blending --- fractionation --- sorting --- resource diversity --- integrated biorefineries

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Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following:  Fuzziness in smart grids computing  Emerging techniques for renewable energy forecasting  Robust and proactive solution of optimal smart grids operation  Fuzzy-based smart grids monitoring and control frameworks  Granular computing for uncertainty management in smart grids  Self-organizing and decentralized paradigms for information processing

Information technology industries --- voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models --- voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models

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Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following:  Fuzziness in smart grids computing  Emerging techniques for renewable energy forecasting  Robust and proactive solution of optimal smart grids operation  Fuzzy-based smart grids monitoring and control frameworks  Granular computing for uncertainty management in smart grids  Self-organizing and decentralized paradigms for information processing

voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models

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Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas.

image binarization --- optical character recognition --- local entropy filter --- thresholding --- image preprocessing --- image entropy --- image encryption --- medical color images --- RGB --- chaotic system --- crowd behavior analysis --- salient crowd motion detection --- repulsive force --- direction entropy --- node strength --- Pompe disease --- children --- quantitative muscle ultrasound --- texture-feature parametric imaging --- compound chaotic system --- S-box --- image information entropy --- image chaotic encryption --- cryptography --- Latin cube --- bit cube --- chosen plaintext attack --- atmosphere background --- engine flame --- infrared radiation --- detectability --- image quality evaluation --- image retrieval --- pooling method --- convolutional neural network --- feature distribution entropy --- lossless compression --- pattern classification --- machine learning --- malaria infection --- entropy --- Golomb–Rice codes --- image processing --- image segmentation --- weld segmentation --- weld evaluation --- convolution neural network --- Python --- Keras --- RSNNS --- MXNet --- brain-computer interface (BCI) --- electroencephalography (EEG) --- motor imagery (MI) --- continuous wavelet transform (CWT) --- convolutional neural network (CNN) --- hyperchaotic system --- filtering --- DNA computing --- diffusion --- deep neural network --- data expansion --- blind image quality assessment --- saliency and distortion --- human visual system --- declining quality --- data hiding --- AMBTC --- steganography --- stego image --- dictionary-based coding --- pixel value adjusting --- neuroaesthetics --- symmetry --- balance --- complexity --- chiaroscuro --- normalized entropy --- renaissance --- portrait paintings --- art history --- art statistics --- chaotic systems --- DNA coding --- security analysis --- magnetic resonance images --- non-maximum suppression --- object detection --- key-point detection --- IoU --- feature fusion --- quasi-resonant Rossby/drift wave triads --- Mordell elliptic curve --- pseudo-random numbers --- substitution box --- nuclear spin generator --- medical image --- peak signal-to-noise ratio --- key space calculation --- Duchenne muscular dystrophy --- ultrasound --- backscattered signals --- medical imaging --- neural engineering --- computer vision --- crowd motion detection --- security

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Radar-related technology is mainly processed within the time and frequency domains but, at the same time, is a multi-dimensional integrated system including a spatial domain for transmitting and receiving electromagnetic waves. As a result of the enormous technological advancements of the pioneers actively discussed in this book, research and development in multi-dimensional undeveloped areas is expected to continue. This book contains state-of-the-art work that should guide your research.

History of engineering & technology --- inverse synthetic aperture ladar (ISAL) --- maneuvering target --- integral cubic phase function (ICPF) --- fractional Fourier transform (FRFT) --- non-uniform fast Fourier transform (NUFFT) --- CLEAN technique --- simultaneous polarimetric radar --- constant modulus sequences --- correlation properties --- doppler tolerance --- saliency preprocessing LLC --- saliency detection --- image processing --- scene classification --- antenna array --- automatic guided vehicle --- DoA/DoD estimation --- MIMO radar --- direct position determination --- Doppler --- Doppler rate --- maximum likelihood estimator --- coherent pulse trains --- single moving sensor --- Cramer–Rao lower bound --- bistatic MIMO radar --- DOD/DOA estimation --- mutual coupling --- off-grid sparse problem --- unmanned aerial vehicle --- clustering methods --- man-made targets --- synthetic aperture radar (SAR) --- inverse synthetic aperture radar (ISAR) --- polarimetric decomposition --- Synthetic Aperture Radar (SAR) --- microwave imaging --- constitutive parameters --- conductivity --- permittivity --- tomography --- RF MEMS --- switch --- analytical approach --- low control voltage --- high switching speed --- high reliability --- radar echo cancellation --- frequency shifting modulation --- interrupted sampling --- radar jamming --- deception jamming --- remote sensing --- SAR --- radon transform --- speckle noise filtering --- maritime traffic monitoring --- wake detection and analysis --- synthetic aperture radar --- differential SAR tomography --- squinted SAR --- 3-D deformation --- 2-D PPS --- maneuvering target detection --- coherent integration --- motion parameter estimation --- second-order phase difference (SoPD) --- time-frequency analysis --- image fusion --- sparse representation --- hyperbolic tangent function --- guided filter --- narrowband interference separation --- block sparse Bayesian learning --- sensing matrix optimization --- block coherence measure --- bistatic inverse synthetic aperture radar --- linear geometry distortion --- prior information --- least square error --- lunar penetrating radar --- local correlation --- SNR --- K-L transform --- seislet transform --- generative adversarial nets --- through-wall radar imaging --- multipath ghost suppression --- generator and discriminator --- ultrahigh resolution --- spaceborne --- curved orbit --- series reversion --- singular value decomposition (SVD) --- deramping-based approach --- crosshole ground penetrating radar (GPR) --- Bayesian inversion --- Markov chain Monte Carlo (MCMC) --- forward model --- modeling error --- discrete cosine transform (DCT) --- through-wall imaging --- contrast target detection --- clutter reduction --- entropy thresholding --- low-rank approximation --- S-transformation --- ISAR --- micro-Doppler --- synchrosqueezing --- PBR (passive bistatic radar) --- clutter suppression --- non-uniform grid --- dilation morphology --- passive bistatic radar --- phased array radar --- parameter estimation --- aircraft surveillance --- GPR --- seasonal permafrost --- electromagnetic wave attribute --- relative water content --- marine radar --- wind direction retrieval --- small wind streak --- local gradient method --- adaptive reduced method --- energy spectrum method --- metamaterial absorber --- double negative --- dual-band --- FMCW radio altimeter --- methodological error --- critical height --- altitude measurement accuracy --- height pulses --- ultra-wide frequency deviation --- sparse recovery --- wideband noise interference --- dechirping --- subspace extraction --- denoising detection --- orthogonal matching pursuit --- pulse radar --- rotating target --- micro-motion feature extraction --- interrupted transmitting and receiving (ITR) --- dual-polarized radar --- DOA estimation --- atomic norm --- comprehensive SAR --- multiparametric SAR observation --- discrete scatterer model --- n/a --- Cramer-Rao lower bound