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Remote Sensing Technology Applications in Forestry and REDD+

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ISBN: 9783039284702 9783039284719 Year: Pages: 244 DOI: 10.3390/books978-3-03928-471-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2020-04-07 23:07:09
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Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion.

Keywords

sentinel imagery --- above-ground biomass --- predictive mapping --- machine learning --- geographically weighted regression --- canopy cover (CC) --- spectral --- texture --- digital hemispherical photograph (DHP) --- random forest (RF) --- gray level co-occurrence matrix (GLCM) --- forest inventory --- LiDAR --- tall trees --- overstory trees --- tree mapping --- crown delineation --- aboveground biomass --- Landsat --- random forest --- topography --- human activity --- aboveground biomass estimation --- remote sensing --- crown density --- low-accuracy estimation --- model comparison --- old-growth forest --- multispectral satellite imagery --- random forest --- forest classification --- remote sensing --- forestry --- phenology --- silviculture --- forest growing stock volume (GSV) --- full polarimetric SAR --- subtropical forest --- topographic effects --- environment effects --- geographic information system --- support vector machine --- random forest --- ensemble model --- hazard mapping --- 3D tree modelling --- aboveground biomass estimation --- destructive sampling --- Guyana --- LiDAR --- local tree allometry --- model evaluation --- quantitative structural model --- Pinus massoniana --- specific leaf area --- leaf area --- terrestrial laser scanning --- voxelization --- forest canopy --- REDD+ --- Cameroon --- reference level --- deforestation --- agriculture --- forest baseline --- airborne laser scanning --- terrestrial laser scanning --- remote sensing --- REDD+ --- forestry

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Authors: ---
ISBN: 9783039212156 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

Google Earth Engine Applications

Authors: ---
ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2019-04-25 16:37:17
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In a rapidly changing world, there is an ever-increasing need to monitor the Earth&rsquo;s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth&rsquo;s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Keywords

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems

New Approaches for the Discovery of Pharmacologically-Active Natural Compounds

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ISBN: 9783039211043 9783039211050 Year: Pages: 158 DOI: 10.3390/books978-3-03921-105-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General) --- Therapeutics
Added to DOAB on : 2019-08-28 11:21:27
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A major source of active compounds, natural products from different sources supply a large variety of molecules that have been approved for clinical use or used as the starting points of optimization programs. This book features nine papers (eight full articles and one review paper) written by more than 45 scientists from around the world. These papers illustrate the development and application of a broad range of computational and experimental techniques applied to natural product research. On behalf of the contributors to the book, our hope is that the research presented here contributes to advancements in the field, and encourages multidisciplinary teams, young scientists, and students to further advance in the discovery of pharmacologically-active natural compounds

Sentiment Analysis for Social Media

Authors: ---
ISBN: 9783039285723 / 9783039285730 Year: Pages: 152 DOI: 10.3390/books978-3-03928-573-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-06-09 16:38:56
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Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.

Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

Authors: --- ---
ISBN: 9783039212392 9783039212408 Year: Pages: 334 DOI: 10.3390/books978-3-03921-240-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-12-09 11:49:15
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Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

Keywords

conifer forest --- leaf area index --- smartphone-based method --- canopy gap fraction --- terrestrial laser scanning --- forest inventory --- density-based clustering --- forest aboveground biomass --- root biomass --- tree heights --- GLAS --- artificial neural network --- allometric scaling and resource limitation --- structure from motion (SfM) --- 3D point cloud --- remote sensing --- local maxima --- fixed tree window size --- managed temperate coniferous forests --- point cloud --- spectral information --- structure from motion (SfM) --- unmanned aerial vehicle (UAV) --- chlorophyll fluorescence (ChlF) --- drought --- Mediterranean --- photochemical reflectance index (PRI) --- photosynthesis --- R690/R630 --- recovery --- BAAPA --- remote sensing --- household survey --- forest --- farm types --- automated classification --- sampling design --- adaptive threshold --- over and understory cover --- LAI --- leaf area index --- EPIC --- simulation --- satellite --- MODIS --- biomass --- evaluation --- southern U.S. forests --- VIIRS --- leaf area index (LAI) --- Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) --- MODIS --- consistency --- uncertainty --- evaluation --- downscaling --- Pléiades imagery --- unmanned aerial vehicle --- stem volume estimation --- remote sensing --- clumping index --- leaf area index --- trunk --- terrestrial LiDAR --- HemiView --- forest above ground biomass (AGB) --- polarization coherence tomography (PCT) --- P-band PolInSAR --- tomographic profiles --- canopy closure --- global positioning system --- hemispherical sky-oriented photo --- signal attenuation --- geographic information system --- digital aerial photograph --- aboveground biomass --- leaf area index --- photogrammetric point cloud --- recursive feature elimination --- machine-learning --- forest degradation --- multisource remote sensing --- modelling aboveground biomass --- random forest --- Brazilian Amazon --- validation --- phenology --- NDVI --- LAI --- spectral analyses --- European beech --- altitude --- forests biomass --- remote sensing --- REDD+ --- random forest --- Tanzania --- RapidEye

Symmetry in Engineering Sciences

Authors: ---
ISBN: 9783039218745 9783039218752 Year: Pages: 220 DOI: 10.3390/books978-3-03921-875-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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This book presents interesting samples of theoretical and practical advances of symmetry in multidisciplinary engineering applications. It covers several applications, such as accessibility and traffic congestion management, path planning for mobile robots, analysis of shipment service networks, fault diagnosis methods in electrical circuits and electrical machines, geometrical issues in architecture, geometric modeling and virtual reconstruction, design of noise detectors, filters, and segmentation methods for image processing, and cyclic symmetric structures in turbomachinery applications, to name but a few. The contributions included in this book depict the state of the art in this field and lay the foundation for the possibilities that the study of symmetry has in multidisciplinary applications in the field of engineering.

Keywords

express shipment --- service network design --- linearization technique --- railway network --- path planning --- mobile robot --- environmental modeling --- optimization criteria --- path search --- aged --- high order urban hospitals (HOUHs) --- accessibility --- evaluation model --- trip impedance based on public transportation --- urban traffic planning --- 3D slicer --- classification --- extension --- random forest --- segmentation --- sensitivity analysis --- support vector machine --- tumor --- thin-walled gear --- ring damper --- vibration --- energy dissipation --- friction damping --- fault diagnosis --- lifting wavelet --- local preserving projection --- Fisher linear discriminant analysis --- semi-supervised random forest --- adaptive threshold --- clustering --- edge preserving --- noise detector --- random value impulse noise --- weighted mean filter --- anomaly detection --- local data features --- BP neural network --- local monotonicity --- convexity/concavity --- local inflection --- peaks distribution --- inclined plane --- Coalbrookdale (Shropshire) --- Agustín de Betancourt --- geometric modeling --- virtual reconstruction --- industrial heritage --- industrial archaeology --- symmetry --- rampant arch --- geometry --- optimum --- flying buttresses --- cathedral --- rolling bearings --- fault diagnosis --- broad learning model --- variational mode decomposition --- Hilbert transform --- railway transportation --- time-space network --- A* algorithm --- traffic congestion --- traffic forecasting --- traffic control --- railcar flow distribution --- asymmetry --- synchronization --- topology --- electrical circuits --- electronic devices --- mechanical structures --- robots --- graphic modelling --- complex networks --- optimization --- computing applications --- feature selection --- conditional mutual information --- feature interaction --- classification --- computer engineering

Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions

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ISBN: 9783038979920 9783038979937 Year: Pages: 212 DOI: 10.3390/books978-3-03897-993-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General) --- Therapeutics
Added to DOAB on : 2019-06-26 08:44:06
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Oral health is general health. If the oral cavity is kept healthy, the whole body is always healthy. Bacteria in the oral cavity do not stay in the oral cavity, but rather they travel throughout the body and can induce various diseases. Periodontal pathogens are involved in tooth loss. The number of remaining teeth decreases with age. People with more residual teeth can bite food well and live longer with lower incidence of dementia. There are many viruses in the oral cavity that also cause various diseases. Bacteria and viruses induce and aggravate inflammation, and therefore should be removed from the oral cavity. In the natural world, there are are many as yet undiscovered antiviral, antibacterial and anti-inflammatory substances. These natural substances, as well as chemically modified derivatives, help our oral health and lead us to more fulfilling, high quality lives. This Special Issue, entitled “Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions”, was written by specialists from a diverse variety of fields. It serves to provide readers with up-to-date information on incidence rates in each age group, etiology and treatment of stomatitis, and to investigate the application of such treatments as oral care and cosmetic materials.

Keywords

metabolomics --- oral cell --- benzaldehyde --- eugenol --- inflammation --- cytotoxicity --- stomatitis --- recurrent aphthous stomatitis --- oral lichen planus --- CCN2 --- glucocorticoids --- alkaloids --- anti-human immunodeficiency virus (HIV) --- antiviral --- natural product --- human virus --- inflammatory disease --- stomatitis --- periodontitis --- anti-osteoclast activity --- cepharanthin --- herbal medicine --- natural product --- arachidonic acid cascade --- allergic rhinitis --- mice --- quercetin --- thioredoxin --- nasal epithelial cell --- production --- increase --- in vitro --- in vivo --- nutritionally variant streptococci --- antimicrobial susceptibilities --- oral microbiota --- infective endocarditis --- kampo formula --- traditional Japanese herbal medicine --- stomatitis --- mucositis --- Hangeshashinto --- polyphenol --- chromone --- lignin-carbohydrate complex --- alkaline extract --- Kampo medicine --- glucosyltransferase --- angiotensin II blocker --- QSAR analysis --- oral diseases --- dental application --- Chinese herbal remedies --- stomatitis --- periodontitis --- Kampo --- traditional medicine --- Jixueteng --- Juzentaihoto --- technical terms --- gargle --- tongue diagnosis --- mastic --- pathogenic factors --- quantitative structure-activity relationship --- machine learning --- random forest --- natural products --- tumour-specificity --- Kampo medicine --- constituent plant extract --- stomatitis --- oral inflammation --- quantitative structure-activity relationship (QSAR) analysis --- metabolomics

Remote Sensing of Above Ground Biomass

Authors: ---
ISBN: 9783039212095 9783039212101 Year: Pages: 264 DOI: 10.3390/books978-3-03921-210-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

Keywords

multi-angle remote sensing --- forest structure information --- vegetation indices --- forest biomass --- Bidirectional Reflectance Distribution Factor --- biomass --- yield --- AquaCrop model --- spectral index --- particle swarm optimization --- winter wheat --- TerraSAR-X --- Landsat --- pasture biomass --- Wambiana grazing trial --- foliage projective cover --- fractional vegetation cover --- ALOS2 --- mixed forest --- biomass --- lidar --- NDVI --- grass biomass --- SPLSR --- vegetation indices --- estimation accuracy --- pasture biomass --- ground-based remote sensing --- ultrasonic sensor --- field spectrometry --- sensor fusion --- short grass --- alpine grassland conservation --- anthropogenic disturbance --- ecological policies --- climate change --- grazing exclusion --- grazing management --- regional sustainability --- rice --- biomass --- dry matter index --- chlorophyll index --- CIRed-edge --- NDLMA --- forest above ground biomass (AGB) --- random forest --- mapping --- alpine meadow grassland --- above-ground biomass --- inversion model --- error analysis --- applicability evaluation --- Land Surface Phenology --- wetlands --- above ground biomass --- NDVI --- MODIS time series --- food security --- Sahel --- Niger --- rangeland productivity --- livestock --- MODIS --- NDVI --- aboveground biomass --- Atriplex nummularia --- carbon mitigation --- carbon inventory --- forage crops --- remote sensing --- vegetation index --- stem volume --- dry biomass --- conifer --- broadleaves --- light detection and ranging (LiDAR) --- regression analysis --- correlation coefficient --- n/a

Visible Light Communication and Positioning

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ISBN: 9783039214358 9783039214365 Year: Pages: 144 DOI: 10.3390/books978-3-03921-436-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2019-12-09 11:49:16
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In recent years, wireless communications have significantly evolved due to the advanced technology of smartphones;, portable devices; and the rapid growth of Internet of Things, e-Health, and intelligent transportation systems . Moreover, there is anare increasing need fors of emerging intelligent services like positioning and sensing in athe future intelligence society. Recent years have witnessed the growing research interests and activities in the communication and intelligencet services in the optical wireless spectrum, as a complementary technology to more

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