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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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In From Grain to Pixel, Giovanna Fossati analyzes the transition from analog to digital film and its profound effects on filmmaking and film archiving. Reflecting on the theoretical conceptualization of the medium itself, Fossati poses significant questions about the status of physical film and the practice of its archival preservation, restoration, and presentation. From Grain to Pixel attempts to bridge the fields of film archiving and academic research by addressing the discourse on film's ontology and analyzing how different interpretations of what film is affect the role and practices of film archives. By proposing a novel theorization of film archival practice, Fossati aims to stimulate a renewed dialogue between film scholars and film archivists. Almost a decade after its first publication, this revised edition covers the latest developments in the field. Besides a new general introduction, a new conclusion, and extensive updates to each chapter, a novel theoretical framework and an additional case study have been included.

Motion pictures. --- Film. --- Pixel. --- Cinema --- Feature films --- Films --- Movies --- Moving-pictures --- Audio-visual materials --- Mass media --- Performing arts --- History and criticism --- Film archives. --- Motion picture film --- Digital preservation. --- Preservation. --- Computer files --- Digital curation --- Digital media --- Electronic preservation --- Preservation of digital information --- Preservation of materials --- Motion pictures --- Archives, Motion picture --- Motion picture archives --- Archives --- Motion picture film collections --- Conservation and restoration --- Preservation --- Preservation and storage --- Motion pictures.. --- Film.. --- Digital Archiving, Pixel. --- Cinemateques --- Preservació digital --- Pel·lícules cinematogràfiques (Material fotogràfic) --- Conservació i restauració --- Digital Archiving, Pixel --- Films cinematogràfics --- Pel·lícules cinematogràfiques --- Cinematografia (Tècnica) --- Fotografia --- Conservació de documents electrònics --- Conservació de recursos electrònics --- Conservacio digital --- Conservació electrònica --- Conservació electrònica de documents --- Digitalització --- Mitjans de comunicació digitals --- Preservació d'informació digital --- Preservació electrònica --- Recursos electrònics --- Materials --- Materials d'arxiu --- Materials de biblioteca --- Arxius cinematogràfics --- Biblioteques cinematogràfiques --- Cinematografia --- Filmoteques --- Pel·lícules cinematogràfiques (Cinematografia) --- Biblioteques especialitzades --- Biblioteques i cinematografia --- Aparells i accessoris --- Pel·lícules --- Preservació --- Conservació --- Arxius --- Biblioteques --- Col·leccions

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Coastal areas are remarkable regions with high spatiotemporal variability. A large population is affected by their physical and biological processes—resulting from effects on tourism to biodiversity and productivity. Coastal ecosystems perform several critical ecosystem services and functions, such as water oxygenation and nutrients provision, seafloor and beach stabilization (as sediment is controlled and trapped within the rhizomes of the seagrass meadows), carbon burial, as areas for nursery, and as refuge for several commercial and endemic species. Knowledge of the spatial distribution of marine habitats is prerequisite information for the conservation and sustainable use of marine resources. Remote sensing from UAVs to spaceborne sensors is offering a unique opportunity to measure, analyze, quantify, map, and explore the processes on the coastal areas at high temporal frequencies. This Special Issue on “Application of Remote Sensing in Coastal Areas” is specifically addresses those successful applications—from local to regional scale—in coastal environments related to ecosystem productivity, biodiversity, sea level rise.

Research & information: general --- Geography --- satellite remote sensing --- Landsat --- coastline --- barrier island --- morphological change --- coastal ocean --- Photon-counting lidar --- MABEL --- land cover --- remote sensing --- signal photons --- ground settlement --- marine reclamation land --- time series InSAR --- Sentinel-1 --- Xiamen New Airport --- Pleiades --- photogrammetry --- LiDAR --- RTK-GPS --- beach topography --- cliff coastlines --- time-series analysis --- terrestrial laser scanner --- southern Baltic Sea --- non-parametric Bayesian network --- satellite-derived bathymetry --- hydrography --- CubeSats --- hypertemporal --- zones of confidence --- PlanetScope --- vegetation mapping --- dunes --- unmanned aerial system --- pixel-based classification --- object-based classification --- dune vegetation classification --- coastal monitoring --- multispectral satellite images --- multi-temporal NDVI --- pixels based supervised classification --- Random Forest --- harmonization --- shoreline mapping --- semi-global subpixel localization --- intensity integral error --- polarimetric SAR --- polarimetric decomposition --- ship detection --- Euclidean distance --- mutual information --- new feature --- Bohai sea ice --- sea ice extent --- OLCI imagery --- sea ice information index --- waterline extraction --- sub-pixel --- surface water mapping --- data cube --- contour extraction --- water extraction --- water indices --- thresholding --- Coastal process --- wind wake --- heat advection --- multi-sensor --- ASAR --- oceanic thermal response --- Hainan Island --- coastal remote sensing --- habitat mapping --- unmanned aerial vehicle (UAV) --- unmanned aircraft system (UAS) --- drone --- object-based image analysis (OBIA) --- UAS data acquisition --- satellite remote sensing --- Landsat --- coastline --- barrier island --- morphological change --- coastal ocean --- Photon-counting lidar --- MABEL --- land cover --- remote sensing --- signal photons --- ground settlement --- marine reclamation land --- time series InSAR --- Sentinel-1 --- Xiamen New Airport --- Pleiades --- photogrammetry --- LiDAR --- RTK-GPS --- beach topography --- cliff coastlines --- time-series analysis --- terrestrial laser scanner --- southern Baltic Sea --- non-parametric Bayesian network --- satellite-derived bathymetry --- hydrography --- CubeSats --- hypertemporal --- zones of confidence --- PlanetScope --- vegetation mapping --- dunes --- unmanned aerial system --- pixel-based classification --- object-based classification --- dune vegetation classification --- coastal monitoring --- multispectral satellite images --- multi-temporal NDVI --- pixels based supervised classification --- Random Forest --- harmonization --- shoreline mapping --- semi-global subpixel localization --- intensity integral error --- polarimetric SAR --- polarimetric decomposition --- ship detection --- Euclidean distance --- mutual information --- new feature --- Bohai sea ice --- sea ice extent --- OLCI imagery --- sea ice information index --- waterline extraction --- sub-pixel --- surface water mapping --- data cube --- contour extraction --- water extraction --- water indices --- thresholding --- Coastal process --- wind wake --- heat advection --- multi-sensor --- ASAR --- oceanic thermal response --- Hainan Island --- coastal remote sensing --- habitat mapping --- unmanned aerial vehicle (UAV) --- unmanned aircraft system (UAS) --- drone --- object-based image analysis (OBIA) --- UAS data acquisition

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Silicon (Si) technologies provide an excellent platform for the design of microsystems where photonic and microelectronic functionalities are monolithically integrated on the same substrate. In recent years, a variety of passive and active Si photonic devices have been developed, and among them, photodetectors have attracted particular interest from the scientific community. Si photodiodes are typically designed to operate at visible wavelengths, but, unfortunately, their employment in the infrared (IR) range is limited due to the neglectable Si absorption over 1100 nm, even though the use of germanium (Ge) grown on Si has historically allowed operations to be extended up to 1550 nm. In recent years, significant progress has been achieved both by improving the performance of Si-based photodetectors in the visible range and by extending their operation to infrared wavelengths. Near-infrared (NIR) SiGe photodetectors have been demonstrated to have a “zero change” CMOS process flow, while the investigation of new effects and structures has shown that an all-Si approach could be a viable option to construct devices comparable with Ge technology. In addition, the capability to integrate new emerging 2D and 3D materials with Si, together with the capability of manufacturing devices at the nanometric scale, has led to the development of new device families with unexpected performance. Accordingly, this Special Issue of Micromachines seeks to showcase research papers, short communications, and review articles that show the most recent advances in the field of silicon photodetectors and their respective applications.

Technology: general issues --- graphene --- polycrystalline silicon --- photodiode --- phototransistor --- pixel --- high dynamic range (HDR) image --- Ni/4H-SiC Schottky barrier diodes (SBDs) --- C/Si ratios --- 1/f noise --- resonant cavity --- photodetectors --- near-infrared --- silicon --- p-Si/i-ZnO/n-AZO --- avalanche photodiode (APD) --- impact ionization coefficients --- GeSn alloys --- silicon photonics --- photonic integrated circuits --- microbolometer --- complementary metal oxide semiconductor (CMOS)-compatible --- uncooled infrared detectors --- thermal detectors --- infrared focal plane array (IRFPA) --- read-out integrated circuit (ROIC) --- photodetector --- semiconductor --- microphotonics --- group IV --- colloidal systems --- single-photon avalanche diode (SPAD) --- gating --- avalanche transients --- 3.3 V/0.35 µm complementary metal-oxide-semiconductor (CMOS) --- graphene --- polycrystalline silicon --- photodiode --- phototransistor --- pixel --- high dynamic range (HDR) image --- Ni/4H-SiC Schottky barrier diodes (SBDs) --- C/Si ratios --- 1/f noise --- resonant cavity --- photodetectors --- near-infrared --- silicon --- p-Si/i-ZnO/n-AZO --- avalanche photodiode (APD) --- impact ionization coefficients --- GeSn alloys --- silicon photonics --- photonic integrated circuits --- microbolometer --- complementary metal oxide semiconductor (CMOS)-compatible --- uncooled infrared detectors --- thermal detectors --- infrared focal plane array (IRFPA) --- read-out integrated circuit (ROIC) --- photodetector --- semiconductor --- microphotonics --- group IV --- colloidal systems --- single-photon avalanche diode (SPAD) --- gating --- avalanche transients --- 3.3 V/0.35 µm complementary metal-oxide-semiconductor (CMOS)

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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