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Spectrometric and optical chemical analysis --- fysicochemie --- X-ray spectroscopy --- Fluorescence spectroscopy --- Spectroscopie de rayons X --- Spectroscopie de fluorescence --- analytische chemie --- X-stralen --- Chemistry, Analytic. --- Fluorescence spectroscopy. --- X-ray spectroscopy. --- Chemical analysis --- X-ray fluorescence spectroscopy --- X-ray fluorescence spectroscopy.
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Spectrometric and optical chemical analysis --- fysicochemie --- Spectroscopie par rayons X --- X-ray spectroscopie --- X-ray spectroscopy --- Spectrometry, X-Ray Emission. --- Fluorescence spectroscopy --- #WSCH:AAS3 --- Fluorescence spectrometry --- Spectrometry, Fluorescence --- Spectroscopy, Fluorescence --- Luminescence spectroscopy --- Fluorescent probes --- Emission spectroscopy, X-ray --- Energy dispersive x-ray spectroscopy --- Excitation analysis, Fluorescent --- Fluorescence analysis, X-ray --- Fluorescent excitation analysis --- Fluorescent x-ray spectroscopy --- X-ray emission spectroscopy --- X-ray fluorescence analysis --- Spectrum analysis --- Spectrometry, X-Ray Emission --- Energy Dispersive X-Ray Fluorescence Spectrometry --- Energy Dispersive X-Ray Fluorescence Spectroscopy --- Energy Dispersive X-Ray Spectrometry --- Energy Dispersive X-Ray Spectroscopy --- Particle Induced X Ray Emission Spectrometry --- Proton Induced X Ray Emission Spectrometry --- Spectrometry, Particle Induced X Ray Emission --- Spectrometry, Proton Induced X Ray Emission --- Spectrometry, Xray Emission --- Wavelength Dispersive X-Ray Fluorescence Spectrometry --- Wavelength Dispersive X-Ray Fluorescence Spectroscopy --- Wavelength Dispersive X-Ray Spectrometry --- Wavelength Dispersive X-Ray Spectroscopy --- X-Ray Fluorescence Spectroscopy --- Xray Emission Spectroscopy --- Particle-Induced X-Ray Emission Spectrometry --- Proton-Induced X-Ray Emission Spectrometry --- Spectrometry, Particle-Induced X-Ray Emission --- Spectrometry, Proton-Induced X-Ray Emission --- Spectrometry, X-Ray Fluorescence --- X-Ray Emission Spectrometry --- X-Ray Emission Spectroscopy --- X-Ray Fluorescence Spectrometry --- Emission Spectrometry, X-Ray --- Emission Spectrometry, Xray --- Emission Spectroscopy, X-Ray --- Emission Spectroscopy, Xray --- Energy Dispersive X Ray Fluorescence Spectrometry --- Energy Dispersive X Ray Fluorescence Spectroscopy --- Energy Dispersive X Ray Spectrometry --- Energy Dispersive X Ray Spectroscopy --- Fluorescence Spectrometry, X-Ray --- Fluorescence Spectroscopy, X-Ray --- Spectrometry, X Ray Emission --- Spectrometry, X Ray Fluorescence --- Spectroscopy, X-Ray Emission --- Spectroscopy, X-Ray Fluorescence --- Spectroscopy, Xray Emission --- Wavelength Dispersive X Ray Fluorescence Spectrometry --- Wavelength Dispersive X Ray Fluorescence Spectroscopy --- Wavelength Dispersive X Ray Spectrometry --- Wavelength Dispersive X Ray Spectroscopy --- X Ray Emission Spectrometry --- X Ray Emission Spectroscopy --- X Ray Fluorescence Spectrometry --- X Ray Fluorescence Spectroscopy --- X-Ray Fluorescence Spectroscopies --- Xray Emission Spectrometry --- XES (X-ray emission spectroscopy)
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The cryosphere is very sensitive to climate change, and glaciers represent one of the most important archives of atmospheric composition and its variability. From the Himalaya to the European Alps, the longest mid-latitude mountain chain in the world, lie thousands of glaciers that have collected atmospheric compounds over the last millennia. China and Italy are located at the opposite terminals of this long mountain chain, comprising strategic positions for understanding climate evolution and providing important information for the modeling of future climates. The results presented are highlights of some of the most recent advances in cryospheric studies, especially on the topic of mineral dust and aerosols in the atmosphere. They evidence the complexity of the chemical–physical processes involving solid compounds occurring in glacier, snow, and permafrost environments, covering different aspects such as spatial and temporal trends, as well as the impact of mineral and nonmineral particles. Results also show that recent advances in measurement techniques and source apportionment may be powerful and sophisticated tools to provide novel, high-quality scientific information.
XAS spectroscopy --- bacteria --- XANES --- mineral elements --- X-ray fluorescence spectroscopy --- iron geochemistry --- ice --- X-ray absorption fine structure spectroscopy --- mineral dust --- compositional data analysis --- synchrotron radiation --- dust --- global warming hiatus --- simultaneous measurements --- TXRF --- low concentration elemental analysis --- global warming slowdown --- paleoclimatology --- water --- X-ray fluorescence --- snow --- long-range transport --- southern hemisphere --- Antarctica --- ice core --- cryoconite --- evaporation --- contaminants --- POPs --- paleoclimate --- XANES and LCF --- ultra-dilution --- particulate matter --- trace elements --- atmospheric mineral dust --- cryospheric sciences --- ice cores --- X-ray absorption near edge spectroscopy --- droplets --- Arctic rapid warming --- microbiology --- cryosphere --- polycapillary optics --- environment --- Laohugou glacier --- iron speciation --- X-ray absorption spectroscopy --- Arctic --- insoluble dust
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The book presents a collection of original scientific studies aimed at identifying the pigments and dyes in several archaeological or historical objects and artworks. The objects under investigation span from ancient Greek vases to modern paintings, and show the importance of scientific analysis not only to reveal the artists’ materials in different contexts, but also to support conservation and curatorial strategies for the preservation and display of cultural heritage.
Research & information: general --- Late Cycladic I --- brominated indigoids --- Muricidae --- murex --- purpurissum --- true purple --- microRaman --- FTIR --- HPLC–DAD --- weld lake pigments --- yellow lakes --- luteolin --- 19th century --- Winsor & --- Newton --- conservation --- Liu Kang --- SEM-EDS --- MA-XRF --- IRFC --- X-RAY --- RTI --- hidden paintings --- pigments --- synthetic organic pigments --- royal talens --- handheld raman spectroscopy --- microraman spectroscopy --- modern artist oil paint --- urolithin C --- brazilein --- brazilwood marker component --- historical textile --- alteration --- cadmium orange --- chlorine --- selenium --- terracotta --- maize stem --- New Spain --- computerized tomography --- Raman --- SEM-EDX --- Mexican sculpture --- polychrome sculpture --- Maria Sibylla Merian --- colored prints --- hyperspectral imaging --- X-ray fluorescence spectroscopy --- photometric stereo --- Prussian blue --- non-invasive pigment characterization --- textiles --- dyes --- Peru --- ambient ionization mass spectrometry --- DART-MS --- paper spray MS --- HPLC --- non-invasive techniques --- FORS --- XRF --- illuminated manuscripts --- brazilwood --- colourants --- Antoine de Lonhy --- Torino --- reflectance spectroscopy --- SERS --- synthetic dyes --- Tholu Bommalata --- puppets --- pigment identification --- Raman spectroscopy --- scanning electron microscopy-energy dispersive spectroscopy --- microstratigraphic analysis --- Viking Age --- dyestuff --- HPLC-DAD-MS --- aragonite --- Brunswick green --- clinochlore --- mortars --- ultramarine yellow --- vermilion --- wall paintings --- XRD --- Henri Matisse --- cut-outs --- gouache --- Pigment identification --- light sensitivity --- X-ray fluorescence spectroscopy (XRF) --- Fourier transform infrared spectroscopy (FTIR) --- microfaedometry (MFT) --- n/a --- HPLC-DAD
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The book presents a collection of original scientific studies aimed at identifying the pigments and dyes in several archaeological or historical objects and artworks. The objects under investigation span from ancient Greek vases to modern paintings, and show the importance of scientific analysis not only to reveal the artists’ materials in different contexts, but also to support conservation and curatorial strategies for the preservation and display of cultural heritage.
Late Cycladic I --- brominated indigoids --- Muricidae --- murex --- purpurissum --- true purple --- microRaman --- FTIR --- HPLC–DAD --- weld lake pigments --- yellow lakes --- luteolin --- 19th century --- Winsor & --- Newton --- conservation --- Liu Kang --- SEM-EDS --- MA-XRF --- IRFC --- X-RAY --- RTI --- hidden paintings --- pigments --- synthetic organic pigments --- royal talens --- handheld raman spectroscopy --- microraman spectroscopy --- modern artist oil paint --- urolithin C --- brazilein --- brazilwood marker component --- historical textile --- alteration --- cadmium orange --- chlorine --- selenium --- terracotta --- maize stem --- New Spain --- computerized tomography --- Raman --- SEM-EDX --- Mexican sculpture --- polychrome sculpture --- Maria Sibylla Merian --- colored prints --- hyperspectral imaging --- X-ray fluorescence spectroscopy --- photometric stereo --- Prussian blue --- non-invasive pigment characterization --- textiles --- dyes --- Peru --- ambient ionization mass spectrometry --- DART-MS --- paper spray MS --- HPLC --- non-invasive techniques --- FORS --- XRF --- illuminated manuscripts --- brazilwood --- colourants --- Antoine de Lonhy --- Torino --- reflectance spectroscopy --- SERS --- synthetic dyes --- Tholu Bommalata --- puppets --- pigment identification --- Raman spectroscopy --- scanning electron microscopy-energy dispersive spectroscopy --- microstratigraphic analysis --- Viking Age --- dyestuff --- HPLC-DAD-MS --- aragonite --- Brunswick green --- clinochlore --- mortars --- ultramarine yellow --- vermilion --- wall paintings --- XRD --- Henri Matisse --- cut-outs --- gouache --- Pigment identification --- light sensitivity --- X-ray fluorescence spectroscopy (XRF) --- Fourier transform infrared spectroscopy (FTIR) --- microfaedometry (MFT) --- n/a --- HPLC-DAD
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Fluorescence spectroscopy --- X-ray spectroscopy --- 548.73 --- 543.42 --- 548.73 X-ray analysis (Roentgen radiography) of crystals. X-ray investigation of properties. X-ray structure --- X-ray analysis (Roentgen radiography) of crystals. X-ray investigation of properties. X-ray structure --- Emission spectroscopy, X-ray --- Energy dispersive x-ray spectroscopy --- Excitation analysis, Fluorescent --- Fluorescence analysis, X-ray --- Fluorescent excitation analysis --- Fluorescent x-ray spectroscopy --- X-ray emission spectroscopy --- X-ray fluorescence analysis --- Spectrum analysis --- Fluorescence spectrometry --- Spectrometry, Fluorescence --- Spectroscopy, Fluorescence --- Luminescence spectroscopy --- Fluorescent probes --- 543.42 Spectrum analysis. Spectroscopy. Spectrography. Spectrometry. Spectrophotometry. Fluorescence analysis --- Spectrum analysis. Spectroscopy. Spectrography. Spectrometry. Spectrophotometry. Fluorescence analysis --- Fluorescentie-spectraalanalyse --- Spectrometry [Fluorescence ] --- Spectroscopie par fluorescence --- Spectroscopie par rayons X --- Spectroscopy [Fluorescence ] --- X-ray spectroscopie --- X-ray spectroscopy. --- Fluorescence spectroscopy. --- XES (X-ray emission spectroscopy) --- Analysis --- Total reflexion x-ray fluorescence spectroscopy --- X-ray fluorescence
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Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.
optical sensor --- spectral analysis --- response surface sampling --- sensor evaluation --- electromagnetic induction --- multivariate water quality parameters --- mandarin orange --- crop inspection platform --- SPA-MLR --- object tracking --- feature selection --- simultaneous measurement --- diseases --- genetic algorithms --- processing of sensed data --- electrochemical sensors --- thermal image --- ECa-directed soil sampling --- handheld --- recognition patterns --- salt concentration --- clover-grass --- bovine embedded hardware --- weed control --- soil --- field crops --- vineyard --- connected dominating set --- water depth sensors --- SS-OCT --- wheat --- striped stem-borer --- silage --- geostatistics --- detection --- NIR hyperspectral imaging --- electronic nose --- machine learning --- virtual organizations of agents --- packing density --- data validation and calibration --- dataset --- Wi-SUN --- temperature sensors --- geoinformatics --- gas sensor --- X-ray fluorescence spectroscopy --- vegetable oil --- photograph-grid method --- Vitis vinifera --- WSN distribution algorithms --- laser-induced breakdown spectroscopy --- irrigation --- quality assessment --- energy efficiency --- wireless sensor network (WSN) --- geo-information --- Fusarium --- texture features --- weeds --- discrimination --- big data --- soil moisture sensors --- meat spoilage --- land cover --- stereo imaging --- near infrared sensors --- biological sensing --- compound sensor --- pest management --- moisture --- plant localization --- heavy metal contamination --- artificial neural networks --- spectral pre-processing --- moisture content --- apparent soil electrical conductivity --- data fusion --- semi-arid regions --- smart irrigation --- back propagation model --- wireless sensor network --- energy balance --- light-beam --- fluorescent measurement --- agriculture --- precision agriculture --- deep learning --- spectroscopy --- hulled barely --- dielectric probe --- RPAS --- water supply network --- rice leaves --- mobile app --- gradient boosted machines --- hyperspectral camera --- one-class --- nitrogen --- LiDAR --- total carbon --- chemometrics analysis --- rice --- agricultural land --- on-line vis-NIR measurement --- CARS --- obstacle detection --- stratification --- neural networks --- regression estimator --- Kinect --- proximity sensing --- distributed systems --- pest --- noninvasive detection --- texture feature --- soil mapping --- classification --- soil salinity --- visible and near-infrared reflectance spectroscopy --- germination --- computer vision --- hyperspectral imaging --- diffusion --- dielectric dispersion --- UAS --- random forests --- case studies --- total nitrogen --- thermal imaging --- cameras --- dry matter composition --- near-infrared --- salt tolerance --- deep convolutional neural networks --- soil type classification --- water management --- preprocessing methods --- wireless sensor networks (WSN) --- remote sensing image classification --- precision plant protection --- radar --- spatial variability --- GF-1 satellite --- plant disease --- naked barley --- leaf area index --- CIE-Lab --- change of support --- radiative transfer model --- 3D reconstruction --- plant phenotyping --- vine --- near infrared --- vegetation indices --- remote sensing --- greenhouse --- time-series data --- scattering --- sensor --- crop area --- speckle --- spatial data --- grapevine breeding --- wide field view --- partial least squares-discriminant analysis --- spiking --- area frame sampling --- chromium content --- machine-learning --- RGB-D sensor --- pest scouting --- PLS --- Capsicum annuum --- spatial-temporal model --- drying temperature --- boron tolerance --- ambient intelligence --- laser wavelength --- fuzzy logic --- dynamic weight --- landslide --- management zones --- real-time processing --- event detection --- crop monitoring --- apple shelf-life --- rice field monitoring --- wireless sensor --- birth sensor --- proximal sensor
Choose an application
Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.
optical sensor --- spectral analysis --- response surface sampling --- sensor evaluation --- electromagnetic induction --- multivariate water quality parameters --- mandarin orange --- crop inspection platform --- SPA-MLR --- object tracking --- feature selection --- simultaneous measurement --- diseases --- genetic algorithms --- processing of sensed data --- electrochemical sensors --- thermal image --- ECa-directed soil sampling --- handheld --- recognition patterns --- salt concentration --- clover-grass --- bovine embedded hardware --- weed control --- soil --- field crops --- vineyard --- connected dominating set --- water depth sensors --- SS-OCT --- wheat --- striped stem-borer --- silage --- geostatistics --- detection --- NIR hyperspectral imaging --- electronic nose --- machine learning --- virtual organizations of agents --- packing density --- data validation and calibration --- dataset --- Wi-SUN --- temperature sensors --- geoinformatics --- gas sensor --- X-ray fluorescence spectroscopy --- vegetable oil --- photograph-grid method --- Vitis vinifera --- WSN distribution algorithms --- laser-induced breakdown spectroscopy --- irrigation --- quality assessment --- energy efficiency --- wireless sensor network (WSN) --- geo-information --- Fusarium --- texture features --- weeds --- discrimination --- big data --- soil moisture sensors --- meat spoilage --- land cover --- stereo imaging --- near infrared sensors --- biological sensing --- compound sensor --- pest management --- moisture --- plant localization --- heavy metal contamination --- artificial neural networks --- spectral pre-processing --- moisture content --- apparent soil electrical conductivity --- data fusion --- semi-arid regions --- smart irrigation --- back propagation model --- wireless sensor network --- energy balance --- light-beam --- fluorescent measurement --- agriculture --- precision agriculture --- deep learning --- spectroscopy --- hulled barely --- dielectric probe --- RPAS --- water supply network --- rice leaves --- mobile app --- gradient boosted machines --- hyperspectral camera --- one-class --- nitrogen --- LiDAR --- total carbon --- chemometrics analysis --- rice --- agricultural land --- on-line vis-NIR measurement --- CARS --- obstacle detection --- stratification --- neural networks --- regression estimator --- Kinect --- proximity sensing --- distributed systems --- pest --- noninvasive detection --- texture feature --- soil mapping --- classification --- soil salinity --- visible and near-infrared reflectance spectroscopy --- germination --- computer vision --- hyperspectral imaging --- diffusion --- dielectric dispersion --- UAS --- random forests --- case studies --- total nitrogen --- thermal imaging --- cameras --- dry matter composition --- near-infrared --- salt tolerance --- deep convolutional neural networks --- soil type classification --- water management --- preprocessing methods --- wireless sensor networks (WSN) --- remote sensing image classification --- precision plant protection --- radar --- spatial variability --- GF-1 satellite --- plant disease --- naked barley --- leaf area index --- CIE-Lab --- change of support --- radiative transfer model --- 3D reconstruction --- plant phenotyping --- vine --- near infrared --- vegetation indices --- remote sensing --- greenhouse --- time-series data --- scattering --- sensor --- crop area --- speckle --- spatial data --- grapevine breeding --- wide field view --- partial least squares-discriminant analysis --- spiking --- area frame sampling --- chromium content --- machine-learning --- RGB-D sensor --- pest scouting --- PLS --- Capsicum annuum --- spatial-temporal model --- drying temperature --- boron tolerance --- ambient intelligence --- laser wavelength --- fuzzy logic --- dynamic weight --- landslide --- management zones --- real-time processing --- event detection --- crop monitoring --- apple shelf-life --- rice field monitoring --- wireless sensor --- birth sensor --- proximal sensor
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