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New Insights into Microbial Ecology through Subtle Nucleotide Variation

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889199884 Year: Pages: 133 DOI: 10.3389/978-2-88919-988-4 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Microbiology
Added to DOAB on : 2016-01-19 14:05:46
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The 16S ribosomal RNA gene commonly serves as a molecular marker for investigating microbial community composition and structure. Vast amounts of 16S rRNA amplicon data generated from environmental samples thanks to the recent advances in sequencing technologies allowed microbial ecologists to explore microbial community dynamics over temporal and spatial scales deeper than ever before. However, widely used methods for the analysis of bacterial communities generally ignore subtle nucleotide variations among high-throughput sequencing reads and often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial datasets. Lack of proper partitioning of the sequencing data into relevant units often masks important ecological patterns. Our research topic contains articles that use oligotyping to demonstrate the importantance of high-resolution analyses of marker gene data, and providides further evidence why microbial ecologists should open the "black box" of OTUs identified through arbitrary sequence similarity thresholds.

Sea Surface Temperature Retrievals from Remote Sensing

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ISBN: 9783038974796 9783038974802 Year: Pages: 340 DOI: 10.3390/books978-3-03897-480-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Chemistry (General)
Added to DOAB on : 2019-02-14 10:39:15
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This book covers topics ranging from a detailed error analysis of SSTs to new applications employed, for example, in the study of the El Niño–La Niña Southern Oscillation, lake temperatures, and coral bleaching. New techniques for interpolation and algorithm development are presented, including improvements for cloud detection. Analysis of the pixel-to-pixel uncertainties provides insight to applications for high spatial resolutions. New approaches for the estimation and evaluation of SSTs are presented. In addition, an overview of the Climate Change Initiative, with specific applications to SST, is presented. The book provides an excellent overview of the current technology, while also highlighting new technologies and their applications to new missions.

Laserbasierte hochauflösende Pixellichtsysteme

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Book Series: Spektrum der Lichttechnik / Karlsruher Institut für Technologie (KIT), Lichttechnisches Institut ISSN: 21951152 ISBN: 9783731507109 Year: Volume: 15 Pages: VI, 240 p. DOI: 10.5445/KSP/1000073122 Language: GERMAN
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-28 18:37:01
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This work describes how human vision can be influenced at night time. In the first part, front lighting systems, that are already available on the market, are described and analyzed in terms of their positive effects on traffic safety. Furthermore this work is discussing, how traffic safety at night time has been increased since the introduction of high resolution headlamps.

Imaging and monitoring astrocytes in health and disease

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889193936 Year: Pages: 189 DOI: 10.3389/978-2-88919-393-6 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Neurology
Added to DOAB on : 2015-12-03 13:02:24
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Astrocytes are key cellular partners to neurons in the brain. They play an important role in multiple processes such as neurotransmitter recycling, trophic support, antioxidant defense, ionic homeostasis, inflammatory modulation, neurovascular and neurometabolic coupling, neurogenesis, synapse formation and synaptic plasticity. In addition to their crucial involvement in normal brain physiology, it is well known that astrocytes adopt a reactive phenotype under most acute and chronic pathological conditions such as ischemia, trauma, brain cancer, epilepsy, demyelinating and neurodegenerative diseases. However, the functional impact of astrocyte reactivity is still unclear. During the last decades, the development of innovative approaches to study astrocytes has significantly improved our understanding of their prominent role in brain function and their contribution to disease states. In particular, new genetic tools, molecular probes, and imaging techniques that achieve high spatial and temporal resolution have revealed new insight into astrocyte functions in situ. This Research Topic provides a collection of cutting-edge techniques, approaches and models to study astrocytes in health and disease. It also suggests new directions to achieve discoveries on these fascinating cells.

Remote Sensing based Building Extraction

Authors: --- --- ---
ISBN: 9783039283828 9783039283835 Year: Pages: 442 DOI: 10.3390/books978-3-03928-383-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Construction
Added to DOAB on : 2020-04-07 23:07:09
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Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D

Keywords

roof segmentation --- outline extraction --- convolutional neural network --- boundary regulated network --- very high resolution imagery --- building boundary extraction --- convolutional neural network --- active contour model --- high resolution optical images --- LiDAR --- richer convolution features --- building edges detection --- high spatial resolution remote sensing imagery --- building --- modelling --- reconstruction --- change detection --- LiDAR --- point cloud --- 3-D --- building extraction --- deep learning --- attention mechanism --- very high resolution --- imagery --- building detection --- aerial images --- feature-level-fusion --- straight-line segment matching --- occlusion --- building regularization technique --- point clouds --- boundary extraction --- regularization --- building reconstruction --- digital building height --- 3D urban expansion --- land-use --- DTM extraction --- open data --- developing city --- accuracy analysis --- building detection --- building index --- feature extraction --- mathematical morphology --- morphological attribute filter --- morphological profile --- building extraction --- deep learning --- semantic segmentation --- data fusion --- high-resolution satellite images --- GIS data --- high-resolution aerial images --- deep learning --- generative adversarial network --- semantic segmentation --- Inria aerial image labeling dataset --- Massachusetts buildings dataset --- building extraction --- simple linear iterative clustering (SLIC) --- multiscale Siamese convolutional networks (MSCNs) --- binary decision network --- unmanned aerial vehicle (UAV) --- image fusion --- high spatial resolution remotely sensed imagery --- object recognition --- deep learning --- method comparison --- LiDAR point cloud --- building extraction --- elevation map --- Gabor filter --- feature fusion --- semantic segmentation --- urban building extraction --- deep convolutional neural network --- VHR remote sensing imagery --- U-Net --- remote sensing --- deep learning --- building extraction --- web-net --- ultra-hierarchical sampling --- 3D reconstruction --- indoor modelling --- mobile laser scanning --- point clouds --- 5G signal simulation --- building extraction --- high-resolution aerial imagery --- fully convolutional network --- semantic segmentation --- n/a

Wine Traceability

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ISBN: 9783039217687 9783039217694 Year: Pages: 138 DOI: 10.3390/books978-3-03921-769-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Biochemistry
Added to DOAB on : 2019-12-09 11:49:16
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Wine traceability is a central theme in the current world market where consumers are increasingly demanding the quality and origin of food and drink. The wine production chain and wine composition are generally controlled by different laws (International Organization of Vine and Wine (OIV), European Union (EU), and national governments) and need specific documentation. Nevertheless, wine production is subject to fraud. Consequently, the improvement of the methods applied to verify the origin and quality of wines is very important to protect wine consumers and producers. In this book, eight different papers—six research papers and two reviews—address the topic from different points of view.

Annually Laminated Lake Sediments

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ISBN: 9783039287864 / 9783039287871 Year: Pages: 130 DOI: 10.3390/books978-3-03928-787-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Environmental Sciences
Added to DOAB on : 2020-06-09 16:38:57
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The collection of papers presented in this book illustrates the recent progress made in varved sediment research and highlights the large variety of methodological approaches and research directions applied. The contributions cover monitoring of modern sediment fluxes using sediment traps; geochronological and sedimentological analyses of annually laminated lacustrine sediments or varves; and multiproxy investigations, including geochemical and biological proxies as well as spatiotemporal analyses based on multicore studies supported by satellite images and X-ray computed tomography (CT). The scientific issues discuss the influences of hydrological and climatological phenomena on short-term changes in sediment flux, the relationships between biogeochemical (limnological) processes in the water column and the formation of varves, the preservation of environmental signals in varved sediments, and possibilities of synchronizing varved records with other high-resolution environmental archives such as tree rings.

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

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ISBN: 9783039217564 9783039217571 Year: Pages: 262 DOI: 10.3390/books978-3-03921-757-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.

Keywords

road extraction --- very high-resolution image --- fast marching method --- semiautomatic --- edge constraint --- beaver mimicry --- beaver dam analogue --- QuickBird --- riparian --- stream restoration --- Worldview --- benthic mapping --- seagrass --- airborne hypespectral imagery --- Worldview-2 --- atmospheric correction --- sunglint correction --- water column correction --- dimensionality reduction techniques --- SVM classification --- linear unmixing --- building detection --- built-up areas extraction --- local feature points --- saliency index --- morphological building index --- Deformable CNN --- Faster R-CNN --- data augmentation --- occluded object detection --- very high-resolution Pléiades imagery --- canopy height model --- acquisition geometry --- forested mountain --- accuracy assessment --- remote sensing imagery --- super-resolution --- ultra-dense connection --- feature distillation --- video satellite --- compensation unit --- urban water mapping --- water index --- shadow detection --- threshold stability --- agriculture parcel segmentation --- superpixels --- consensus --- texture analysis --- multi-resolution segmentation (MRS) --- greenhouse extraction --- over-segmentation index (OSI) --- under-segmentation index (USI) --- error index of total area (ETA) --- composite error index (CEI) --- GaoFen-2 (GF-2) --- synthetic aperture radar --- landslide monitoring --- sub-pixel offset tracking --- Slumgullion landslide --- natural hazards --- large displacements --- remote sensing --- scene classification --- CNN --- capsule --- PrimaryCaps --- CapsNet --- High-resolution satellite imagery --- submesoscale --- spiral eddy --- cyanobacteria --- surface convergence --- western Baltic Sea

Nanoscale Photonic Imaging

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Book Series: Topics in Applied Physics ISBN: 9783030344139 Year: Pages: 634 DOI: 10.1007/978-3-030-34413-9 Language: English
Publisher: Springer Nature
Subject: Mathematics --- Biology --- Computer Science --- Agriculture (General)
Added to DOAB on : 2020-06-16 23:59:44
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This open access book, edited and authored by a team of world-leading researchers, provides a broad overview of advanced photonic methods for nanoscale visualization, as well as describing a range of fascinating in-depth studies. Introductory chapters cover the most relevant physics and basic methods that young researchers need to master in order to work effectively in the field of nanoscale photonic imaging, from physical first principles, to instrumentation, to mathematical foundations of imaging and data analysis. Subsequent chapters demonstrate how these cutting edge methods are applied to a variety of systems, including complex fluids and biomolecular systems, for visualizing their structure and dynamics, in space and on timescales extending over many orders of magnitude down to the femtosecond range. Progress in nanoscale photonic imaging in Göttingen has been the sum total of more than a decade of work by a wide range of scientists and mathematicians across disciplines, working together in a vibrant collaboration of a kind rarely matched. This volume presents the highlights of their research achievements and serves as a record of the unique and remarkable constellation of contributors, as well as looking ahead at the future prospects in this field. It will serve not only as a useful reference for experienced researchers but also as a valuable point of entry for newcomers.

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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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.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

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