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The sensing, adapting, responding, multifunctionality, low energy, small size and weight, ease of forming, and low-cost attributes of smart textiles and their multidisciplinary scope offer numerous end uses in medical, sports and fitness, military, fashion, automotive, aerospace, the built environment, and energy industries. The research and development on these new and high-value materials cross scientific boundaries, redefine material science design and engineering, and enhance quality of life and our environment. “Novel Smart Textiles” is a focused Special Issue that reports the latest research of this field and facilitates dissemination, networking, discussion, and debate.
power spectral density --- encapsulation --- spectral analysis --- visual brain --- quasi-static and cyclic mechanical loading --- transmission line --- PEDOT:PSS --- in-line monitoring --- equivalent circuit --- conductivity --- optimization --- Peltier element --- half-textile --- stretchable electronics --- e-textiles --- carbon nanotextiles --- wearable --- motion sensor --- electric properties --- metal flake --- durability --- stitch-based sensors --- energy harvesting --- metamaterials --- black-box --- smart textiles --- conductive thread --- stiffness --- life-time expectancy --- woven textiles --- visual response --- carbon nanomaterials --- skin-electrode impedance --- biofunctional textiles --- motion tracking --- fabric --- microencapsulation --- thermal textile pixels --- hybrid electrodes --- textile-based stretch sensors --- SMART pattern-changing fabric --- conductive textiles --- smart fabrics --- targeted delivery --- event-related potential (ERP) --- wearables --- analytical model --- split ring resonator --- textile electrode --- composites --- pattern effect --- nanofiber mat --- polymeric composites --- reduced graphene oxide --- EMS textiles --- carbon nanotubes --- electrospinning --- electrically conductive textiles --- psychotextiles --- polymers --- art and design --- parameter identification --- surface area evaluation --- thermal textile pixel --- mechanical and electrical properties --- textile sensors --- multifunctional --- coating --- wearable stretch sensor --- thermal communication --- non-auditory and nonvisual communication --- ECG --- grey-box --- dye-sensitized solar cell --- embroidered e-textiles --- electromagnetic shielding effectiveness --- textile/polymer composite --- stitch structure --- drug-delivery --- biofunctional --- polyacrylonitrile (PAN) --- thermal conductivity --- dye-sensitized solar cell (DSSC) --- smart textile --- electrospun solar cells --- e-textile
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The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue.
Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- feature selection --- spectral angle mapper --- support vector machine --- support vector regression --- hyperspectral imaging --- UAV --- cross-scale --- yellow rust --- spatial resolution --- winter wheat --- MODIS --- northern Mongolia --- remote sensing indices --- spring wheat --- yield estimation --- UAV-based LiDAR --- biomass --- crop height --- field phenotyping --- oasis crop type mapping --- Sentinel-1 and 2 integration --- statistically homogeneous pixels (SHPs) --- red-edge spectral bands and indices --- recursive feature increment (RFI) --- random forest (RF) --- unmanned aerial vehicles (UAVs) --- remote sensing (RS) --- thermal UAV RS --- thermal infrared (TIR) --- precision agriculture (PA) --- crop water stress monitoring --- plant disease detection --- vegetation status monitoring --- Landsat --- data blending --- crop yield prediction --- gap-filling --- volumetric soil moisture --- synthetic aperture radar (SAR) --- Sentinel-1 --- soil moisture semi-empirical model --- soil moisture Karnataka India --- reflectance --- digital number (DN) --- vegetation index (VI) --- Parrot Sequoia (Sequoia) --- DJI Phantom 4 Multispectral (P4M) --- Synthetic Aperture Radar --- SAR --- lodging --- Hidden Markov Random Field --- HMRF --- CDL --- corn --- soybean --- crop Monitoring --- crop management --- apple orchard damage --- polarimetric decomposition --- entropy --- anisotropy --- alpha angle --- storm damage mapping --- economic loss --- insurance support
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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.
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 --- n/a
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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.
SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill–Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching–learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- n/a --- Hill-Tsai failure criterion --- teaching-learning
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