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Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data.

cardiopulmonary resuscitation (CPR) --- electroencephalogram (EEG) --- hemodynamic data --- carotid blood flow (CBF) --- cerebral circulation --- frequency-shift keying radar --- cross-correlation --- envelope detection --- continuous-wave radar --- frequency discrimination --- vital-signs monitoring --- heartbeat accuracy improvement --- heartbeat detection --- absolute distance measurement --- radar signal processing --- 3D+t modeling --- coronary artery --- non-rigid registration --- cage deformation --- 4D CT --- passenger detection --- CW radar --- radar feature vector --- radar machine learning --- wearable sensors --- physiology --- medical monitoring --- vital signs --- compensatory reserve --- ultra-high resolution --- cone-beam computed tomography --- low-contrast object --- optimal filter --- modulation transfer function --- noise power spectrum --- doppler cardiogram --- wavelet transform --- denoising --- mother wavelet function --- decomposition level --- signal decomposition --- signal-to-noise-ratio

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This Special Issue was launched to promote a subject that is deserving of more attention: the study of new metrics, indicators or evaluation methods for noise exposure, and the relationship of noise with annoyance or other health effects, thus not relying only on an average noise exposure measure. This Special Issue on the theme of the New Indicators for the Assessment and Prevention of Noise Nuisance has attracted the interest of authors from all over the world, with the publication of two reviews and two communications, as well as original research papers. Progress has been made in the investigated topic; however, it is still necessary to increase the awareness of the population, both in geographical terms and for workers in specific sectors, such as the marine industry. It emerged that it is essential to carry out future studies that distinguish better between different sound sources with respect to their sound quality in terms of frequency, time pattern (fluctuation, emergence), and psychoacoustic indices, because a differential human reaction to sound sources is increasingly evident. More longitudinal studies are required. However, cross-sectional studies employing a more detailed soundscape description (including background) by competing sound indices are also useful to further the required knowledge to understand the human response in terms of the broad spectrum of potential adverse effects on health and quality of life.

Technology: general issues --- History of engineering & technology --- sound emergence --- legislation --- annoyance --- measurement --- prediction --- uncertainty --- audibility --- signal-to-noise ratio --- sound pressure level --- field measurements --- spectrum analysis --- interior noise and vibration of vehicle --- COVID-19 --- noise --- soundscape --- metrics --- indicators --- descriptors --- sound --- lockdown --- Twitter --- geolocation --- noise classification --- seafarers --- acoustic pollution --- noise onboard ship --- health impact --- environmental pollution --- noise survey --- hypertension --- environmental noise --- railway noise --- recreational noise --- airport noise --- road traffic noise --- blood pressure --- noise annoyance --- diastolic blood pressure --- helicopter cabin --- noise levels --- noise reduction --- acoustic evaluation --- IAR Puma 330 --- ultrasound --- active noise control --- adjustable PAL --- quiet areas --- macro-temporal pattern --- noise indicator --- cognitive performance --- Stroop task --- listening experiment --- port noise --- noise sources --- noise mapping --- noise mitigations --- noise modeling --- ship noise --- sustainable management --- noise exposure prevention --- noise measurements --- research projects --- noise indicators --- noise metrics --- psychoacoustic --- nuisance --- sleep disturbance --- peak noise --- impulsive events --- health related quality of life --- health effects

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Optical microelectromechanical systems (MEMS), microoptoelectromechanical systems (MOEMS), or optical microsystems are devices or systems that interact with light through actuation or sensing at a micro- or millimeter scale. Optical MEMS have had enormous commercial success in projectors, displays, and fiberoptic communications. The best-known example is Texas Instruments’ digital micromirror devices (DMDs). The development of optical MEMS was impeded seriously by the Telecom Bubble in 2000. Fortunately, DMDs grew their market size even in that economy downturn. Meanwhile, in the last one and half decade, the optical MEMS market has been slowly but steadily recovering. During this time, the major technological change was the shift of thin-film polysilicon microstructures to single-crystal–silicon microsructures. Especially in the last few years, cloud data centers are demanding large-port optical cross connects (OXCs) and autonomous driving looks for miniature LiDAR, and virtual reality/augmented reality (VR/AR) demands tiny optical scanners. This is a new wave of opportunities for optical MEMS. Furthermore, several research institutes around the world have been developing MOEMS devices for extreme applications (very fine tailoring of light beam in terms of phase, intensity, or wavelength) and/or extreme environments (vacuum, cryogenic temperatures) for many years. Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles that focus on (1) novel design, fabrication, control, and modeling of optical MEMS devices based on all kinds of actuation/sensing mechanisms; and (2) new developments of applying optical MEMS devices of any kind in consumer electronics, optical communications, industry, biology, medicine, agriculture, physics, astronomy, space, or defense.

stray light --- input shaping --- n/a --- wavefront sensing --- signal-to-noise ratio (SNR) --- LC micro-lenses controlled electrically --- infrared --- intraoperative microscope --- MEMS mirror --- MLSSP --- ocular aberrations --- MEMS scanning micromirror --- electrothermal actuation --- electrothermal bimorph --- open-loop control --- wavelength dependent loss (WDL) --- NIR fluorescence --- infrared Fabry–Perot (FP) filtering --- two-photon --- resonant MEMS scanner --- residual oscillation --- 3D measurement --- parametric resonance --- digital micromirror device --- quality map --- metalens --- flame retardant 4 (FR4) --- angle sensor --- optical switch --- metasurface --- vibration noise --- optical coherence tomography --- spectrometer --- reliability --- quasistatic actuation --- Huygens’ metalens --- confocal --- large reflection variations --- electrostatic --- dual-mode liquid-crystal (LC) device --- field of view (FOV) --- scanning micromirror --- fluorescence confocal --- variable optical attenuator (VOA) --- micro-electro-mechanical systems (MEMS) --- microscanner --- laser stripe width --- polarization dependent loss (PDL) --- fringe projection --- 2D Lissajous --- usable scan range --- laser stripe scanning --- bio-optical imaging --- MEMS scanning mirror --- digital micromirror device (DMD) --- Cu/W bimorph --- echelle grating --- achromatic --- DMD chip --- tunable fiber laser --- programmable spectral filter --- higher-order modes --- electromagnetic actuator --- infrared Fabry-Perot (FP) filtering --- Huygens' metalens

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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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The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

water resources management --- landslide --- dammed lake --- flood risk --- time-varying parameter --- GR4J model --- changing environments --- temporal transferability --- western China --- cascade hydropower reservoirs --- multi-objective optimization --- TOPSIS --- gravitational search algorithm --- opposition learning --- partial mutation --- elastic-ball modification --- Snowmelt Runoff Model --- parameter uncertainty --- data-scarce deglaciating river basin --- climate change impacts --- generalized likelihood uncertainty estimation --- Yangtze River --- cascade reservoirs --- impoundment operation --- GloFAS-Seasonal --- forecast evaluation --- small and medium-scale rivers --- highly urbanized area --- flood control --- whole region perspective --- coupled models --- flood-risk map --- hydrodynamic modelling --- Sequential Gaussian Simulation --- urban stormwater --- probabilistic forecast --- Unscented Kalman Filter --- artificial neural networks --- Three Gorges Reservoir --- Mahalanobis-Taguchi System --- grey entropy method --- signal-to-noise ratio --- degree of balance and approach --- interval number --- multi-objective optimal operation model --- feasible search space --- Pareto-front optimal solution set --- loss–benefit ratio of ecology and power generation --- elasticity coefficient --- empirical mode decomposition --- Hushan reservoir --- data synthesis --- urban hydrological model --- Generalized Likelihood Uncertainty Estimation (GLUE) --- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) --- uncertainty analysis --- NDVI --- Yarlung Zangbo River --- machine learning model --- random forest --- Internet of Things (IoT) --- regional flood inundation depth --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- artificial intelligence --- machine learning --- multi-objective reservoir operation --- hydrologic forecasting --- uncertainty --- risk