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Superconducting nanowire singe-photon detectors (SNSPD) are promising detectors in the field of applications, where single-photon resolution is required like in quantum optics, spectroscopy or astronomy. These cryogenic detectors gain from a broad spectrum in the optical and infrared range and deliver low dark count rates and low jitter times. This thesis improves the understanding of the detection mechanism of SNSPDs and intodruces new and promising multi-pixel readout concepts.

superconductivity --- Multi-pixelsingle-photon detector --- Echtzeitauslese --- SNSPD --- Supraleitung --- real-time readout --- multi-pixel --- Einzelphotonendetektor

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Electronic engineering and design innovation are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation via electronic engineering includes electrical circuits and devices, computer science and engineering, communications and information processing, and electrical engineering communications. The Special Issue selected excellent papers presented at the International Conference on Knowledge Innovation and Invention 2018 (IEEE ICKII 2018) on the topic of electronics and their applications. This conference was held on Jeju Island, South Korea, 23–27 July 2018, and it provided a unified communication platform for researchers from all over the world. The main goal of this Special Issue titled “Selected papers from IEEE ICKII 2018” is to discover new scientific knowledge relevant to the topic of electronics and their applications.

n/a --- bandpass filter --- total harmonic distortion (THD) --- long short term memory (LSTM) --- integrated passive device --- intertwined spiral inductor --- global navigation satellite system (GNSS) --- hardware in the loop (HIL) --- interdigital capacitor --- inertial navigation system (INS) --- finite-time convergence control (FTCC) --- digital speckle correlation measurement method --- discrete grey prediction model (DGPM) --- interior permanent magnet synchronous motor --- fuzzy logic --- full pixel search algorithm --- maximum torque per voltage (MTPV) --- spiral capacitor --- gated recurrent unit (GRU) --- chattering --- microelectronics system (MEMS) --- field weakening --- maximum torque per ampere (MTPA) --- hardware implementation --- AC power supply

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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

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Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.

very high-resolution Pléiades imagery --- surface convergence --- data augmentation --- acquisition geometry --- SVM classification --- urban water mapping --- beaver dam analogue --- agriculture parcel segmentation --- morphological building index --- airborne hypespectral imagery --- sunglint correction --- water index --- over-segmentation index (OSI) --- High-resolution satellite imagery --- multi-resolution segmentation (MRS) --- GaoFen-2 (GF-2) --- benthic mapping --- scene classification --- greenhouse extraction --- edge constraint --- Deformable CNN --- built-up areas extraction --- ultra-dense connection --- seagrass --- beaver mimicry --- forested mountain --- natural hazards --- remote sensing --- dimensionality reduction techniques --- road extraction --- landslide monitoring --- Slumgullion landslide --- synthetic aperture radar --- building detection --- Worldview-2 --- saliency index --- under-segmentation index (USI) --- texture analysis --- fast marching method --- video satellite --- CNN --- capsule --- super-resolution --- feature distillation --- shadow detection --- PrimaryCaps --- semiautomatic --- compensation unit --- superpixels --- riparian --- QuickBird --- submesoscale --- linear unmixing --- accuracy assessment --- composite error index (CEI) --- cyanobacteria --- local feature points --- Faster R-CNN --- occluded object detection --- error index of total area (ETA) --- large displacements --- threshold stability --- remote sensing imagery --- water column correction --- canopy height model --- spiral eddy --- sub-pixel offset tracking --- consensus --- stream restoration --- western Baltic Sea --- Worldview --- very high-resolution image --- CapsNet --- atmospheric correction

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In the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, NIR spectroscopy is essential in various other fields, e.g. NIR imaging techniques in biophotonics, medical applications or used for characterization of food products. Its contribution in basic science and physical chemistry should be noted as well, e.g. in exploration of the nature of molecular vibrations or intermolecular interactions. One of the current development trends involves the miniaturization and simplification of instrumentation, creating prospects for the spread of NIR spectrometers at a consumer level in the form of smartphone attachments—a breakthrough not yet accomplished by any other analytical technique. A growing diversity in the related methods and applications has led to a dispersion of these contributions among disparate scientific communities. The aim of this Special Issue was to bring together the communities that may perceive NIR spectroscopy from different perspectives. It resulted in 30 contributions presenting the latest advances in the methodologies essential in near-infrared spectroscopy in a variety of applications.

n/a --- pocket-sized spectrometer --- standard germination tests --- total hydroxycinnamic derivatives --- hyperspectral image --- quantitative analysis modeling --- tissue --- chemotherapy --- FTIR spectroscopy --- cheese --- biomeasurements --- chemometrics --- affine invariance --- rapid identification --- biodiagnosis --- bioanalytical applications --- fat --- NIRS --- pixel-wise --- paraffin-embedded --- late preterm --- maize kernel --- photonics --- hyperspectral image processing --- image processing --- colorectal cancer --- test set validation --- deep convolutional neural network --- near-infrared fluorescence --- classification --- variety discrimination --- near-infrared hyperspectral imaging --- ensemble learning --- light --- origin traceability --- Paris polyphylla var. yunnanensis --- Fourier transform mid-infrared spectroscopy --- dry matter --- Fourier transform infrared spectroscopy --- hyperspectral imaging --- FT-NIR spectroscopy --- proximal sensing --- perfusion measurements --- near-infrared spectroscopy --- stained --- carotenoids --- cellular imaging --- perturbation --- direct model transferability --- clinical classifications --- counterfeit and substandard pharmaceuticals --- hyperspectral imaging technology --- spectral imaging --- SVM --- nutritional parameters --- extra virgin olive oil --- ethanol --- osteopathy --- living cells --- object-wise --- water-mirror approach --- Chrysanthemum --- bootstrapping soft shrinkage --- FTIR --- PLS-R --- multivariate data analysis --- combination bands --- binary dragonfly algorithm --- geographical origin --- Vitis vinifera L. --- glucose --- detection --- di-(2-picolyl)amine --- non-destructive sensor --- splanchnic --- adulteration --- animal origin --- melamine --- artemether --- MicroNIR™ --- brain --- fluorescent probes --- Folin–Ciocalteu --- SCiO --- support vector machine --- anharmonic quantum mechanical calculations --- PLSR --- Zn(II) --- RMSEP --- overtones --- blackberries --- pasta/sauce blends --- FT-IR --- partial least squares calibration --- partial least squares (PLS) --- auxiliary diagnosis --- handheld near-infrared spectroscopy --- precision viticulture --- partial least squares --- seeds vitality --- freeze-damaged --- near infrared --- discriminant analysis --- corn seed --- quantum chemical calculation --- anharmonic calculation --- Trichosanthis Fructus --- moisture --- analytical spectroscopy --- Raman spectroscopy --- NIR spectroscopy --- calibration transfer --- imaging --- water --- lumefantrine --- BRAF V600E mutation --- wavelength selection --- bone cancer --- imaging visualization --- near infrared spectroscopy --- raisins --- chemometric techniques --- data fusion --- prepared slices --- Ewing sarcoma --- biomonitoring --- Rubus fructicosus --- VIS/NIR hyperspectral imaging --- combinations bands --- quantitative analysis model --- partial least square regression --- DFT calculations --- TreeBagger --- antimalarial tablets --- accelerated aging --- agriculture --- crude drugs --- spectroscopy --- rice seeds --- PLS --- isotopic substitution --- multivariate calibration --- phytoextraction --- Fourier-transform near-infrared spectroscopy --- phenolics --- deparaffinized --- near-infrared (NIR) spectroscopy --- SIMCA --- counter propagation artificial neural network --- fructose --- PLS-DA --- ultra-high performance liquid chromatography --- aquaphotomics --- support vector machine-discriminant analysis --- hier-SVM --- DNA --- NIR --- support vector machine model --- API --- principal component analysis --- Folin-Ciocalteu