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This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms.

Technology: general issues --- History of engineering & technology --- resistive switching memory --- in-memory computing --- crosspoint array --- artificial intelligence --- deep learning --- dielectric --- RTN --- TAT --- Wiener–Khinchin --- transient analysis --- phonon --- surface roughness --- spectral index --- power spectrum --- program suspend --- 3D NAND Flash --- Solid State Drives --- MOSFET --- low-frequency noise --- random telegraph noise --- evaluation method --- array test pattern --- STT-MRAM --- spintronics --- CoFeB --- composite free layer --- low power electronics --- NAND Flash memory --- endurance --- reliability --- oxide trapped charge --- artificial neural networks --- neuromorphic computing --- NOR Flash memory arrays --- program noise --- pulse-width modulation --- 3D NAND --- floating gate cell --- charge-trap cell --- CMOS under array --- bumpless --- TSV --- WOW --- COW --- BBCube --- bandwidth --- yield --- power consumption --- thermal management --- n/a --- Wiener-Khinchin

Choose an application

• Reference Manager
• EndNote
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This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms.

Technology: general issues --- History of engineering & technology --- resistive switching memory --- in-memory computing --- crosspoint array --- artificial intelligence --- deep learning --- dielectric --- RTN --- TAT --- Wiener-Khinchin --- transient analysis --- phonon --- surface roughness --- spectral index --- power spectrum --- program suspend --- 3D NAND Flash --- Solid State Drives --- MOSFET --- low-frequency noise --- random telegraph noise --- evaluation method --- array test pattern --- STT-MRAM --- spintronics --- CoFeB --- composite free layer --- low power electronics --- NAND Flash memory --- endurance --- reliability --- oxide trapped charge --- artificial neural networks --- neuromorphic computing --- NOR Flash memory arrays --- program noise --- pulse-width modulation --- 3D NAND --- floating gate cell --- charge-trap cell --- CMOS under array --- bumpless --- TSV --- WOW --- COW --- BBCube --- bandwidth --- yield --- power consumption --- thermal management

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This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- UAS --- multiple sensors --- vegetation index --- leaf nitrogen accumulation --- plant nitrogen accumulation --- pasture quality --- airborne hyperspectral imaging --- random forest regression --- sun-induced chlorophyll fluorescence (SIF) --- SIF yield indices --- upward --- downward --- leaf nitrogen concentration (LNC) --- wheat (Triticum aestivum L.) --- laser-induced fluorescence --- leaf nitrogen concentration --- back-propagation neural network --- principal component analysis --- fluorescence characteristics --- canopy nitrogen density --- radiative transfer model --- hyperspectral --- winter wheat --- flooded rice --- pig slurry --- aerial remote sensing --- vegetation indices --- N recommendation approach --- Mediterranean conditions --- nitrogen --- vertical distribution --- plant geometry --- remote sensing --- maize --- UAV --- multispectral imagery --- LNC --- non-parametric regression --- red-edge --- NDRE --- dynamic change model --- sigmoid curve --- grain yield prediction --- leaf chlorophyll content --- red-edge reflectance --- spectral index --- precision N fertilization --- chlorophyll meter --- NDVI --- NNI --- canopy reflectance sensing --- N mineralization --- farmyard manures --- Triticum aestivum --- discrete wavelet transform --- partial least squares --- hyper-spectra --- rice --- nitrogen management --- reflectance index --- multiple variable linear regression --- Lasso model --- Multiplex®3 sensor --- nitrogen balance index --- nitrogen nutrition index --- nitrogen status diagnosis --- precision nitrogen management --- terrestrial laser scanning --- spectrometer --- plant height --- biomass --- nitrogen concentration --- precision agriculture --- unmanned aerial vehicle (UAV) --- digital camera --- leaf chlorophyll concentration --- portable chlorophyll meter --- crop --- PROSPECT-D --- sensitivity analysis --- UAV multispectral imagery --- spectral vegetation indices --- machine learning --- plant nutrition --- canopy spectrum --- non-destructive nitrogen status diagnosis --- drone --- multispectral camera --- SPAD --- smartphone photography --- fixed-wing UAV remote sensing --- random forest --- canopy reflectance --- crop N status --- Capsicum annuum --- proximal optical sensors --- Dualex sensor --- leaf position --- proximal sensing --- cross-validation --- feature selection --- hyperparameter tuning --- image processing --- image segmentation --- nitrogen fertilizer recommendation --- supervised regression --- RapidSCAN sensor --- nitrogen recommendation algorithm --- in-season nitrogen management --- nitrogen use efficiency --- yield potential --- yield responsiveness --- standard normal variate (SNV) --- continuous wavelet transform (CWT) --- wavelet features optimization --- competitive adaptive reweighted sampling (CARS) --- partial least square (PLS) --- grapevine --- hyperparameter optimization --- multispectral imaging --- precision viticulture --- RGB --- multispectral --- coverage adjusted spectral index --- vegetation coverage --- random frog algorithm --- active canopy sensing --- integrated sensing system --- discrete NIR spectral band data --- soil total nitrogen concentration --- moisture absorption correction index --- particle size correction index --- coupled elimination