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Slavistische Linguistik 1993

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Book Series: Slavistische Beitraege ISBN: 9783876905907 Year: Pages: 331 DOI: 10.3726/b12428 Language: Russian|German
Publisher: Peter Lang International Academic Publishing Group
Subject: Linguistics
Added to DOAB on : 2019-01-15 13:31:43
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In der Reihe Slavistische Beiträge werden vor allem slavistische Dissertationen des deutschsprachigen Raums sowie vereinzelt auch amerikanische, englische und russische publiziert. Darüber hinaus stellt die Reihe ein Forum für Sammelbände und Monographien etablierter Wissenschafter/innen dar.

Religious Experience in the Hindu Tradition

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ISBN: 9783039210503 / 9783039210510 Year: Pages: 196 DOI: 10.3390/books978-3-03921-051-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Social Sciences --- Sociology
Added to DOAB on : 2019-08-28 11:21:27
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This Special Issue of Religions brings together a talented group of international scholars who have studied and written on the Hindu tradition. The topic of religious experience is much debated in the field of Religious Studies, and here, we present studies of the Hindu religious experience explored from a variety of regions and perspectives. Our intention is to show that the religious experience has long been an important part of Hinduism, and should not be dismissed or considered as irrelevant. As a body of scholarship, these articles refine our understanding of the range and variety of religious experience in Hinduism. In addition to their substantive contributions, the authors also show important new directions in the study of the third-largest religion in the world, with over one billion followers.

Keywords

Hinduism --- renunciation --- Nirguna Bhakti --- devotion --- performance --- Sadhus --- India --- Indian Religions --- religious experience --- Ethnography --- tantra --- yantra --- Srividya --- Bengali home --- sacred domesticity --- shankh --- conch --- Lakshmi --- Manasa --- shongshar --- Indian music --- sacred sound --- Hinduism --- K?rtan --- Bhajan --- N?da-Brahman --- Dhrupad --- Khayal --- Bhakti --- Rasa --- Sang?ta --- R?ga --- T?la --- religious experience --- Erfahrung --- Erlebnis --- seeing --- being seen --- I --- not I --- S??khya --- Yoga --- puru?a --- prak?ti --- puru??rtha --- religious experience --- bhakti --- Tukaram --- Mahipati --- Mariyamman --- ammai --- poxes --- affliction --- Tamil --- religious experience --- Hinduism --- rain --- agricultural field --- goddess --- Hinduism --- religious experience --- Ramakrishna --- Vedanta --- pluralism --- Santmat --- Sants --- religious experience --- divine light and sound --- medieval Sant tradition --- moral conduct --- modern gurus --- ??nti --- dhy?n --- guru --- William James --- Yoga --- G?yatr? mantra --- Bhagavad G?t? --- Patañjali --- S??khya philosophy --- Gandhi --- Bhakti --- Ved?nta --- Yoga S?tra --- religious experience --- creativity --- modern Hinduism --- ??kta tantra --- tantric s?dhan? --- yoga --- ku??alin? --- ?akti --- arts & religions --- Tantric Studies --- ?r? Vidy? --- India --- Hinduism --- yoga --- tantra --- devotion --- meditation --- prayer --- saints --- brahman --- bh?va --- mah?bh?va --- bhakti --- trance --- sam?dhi --- mok?a --- dar?an --- bhakti --- anta?kara?a --- Yogas?tras --- sa?yama --- possession --- Balaji --- Ganges --- pilgrimage

Plant Development and Organogenesis: From Basic Principles to Applied Research

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ISBN: 9783039281268 / 9783039281275 Year: Pages: 246 DOI: 10.3390/books978-3-03928-127-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Plant Sciences --- Biology --- Science (General)
Added to DOAB on : 2020-01-30 16:39:46
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The way plants grow and develop organs significantly impacts the overall performance and yield of crop plants. The basic knowledge now available in plant development has the potential to help breeders in generating plants with defined architectural features to improve productivity. Plant translational research effort has steadily increased over the last decade due to the huge increase in the availability of crop genomic resources and Arabidopsis-based sequence annotation systems. However, a consistent gap between fundamental and applied science has yet to be filled. One critical point often brought up is the unreadiness of developmental biologists on one side to foresee agricultural applications for their discoveries, and of the breeders to exploit gene function studies to apply to candidate gene approaches when advantageous on the other. In this book, both developmental biologists and breeders make a special effort to reconcile research on the basic principles of plant development and organogenesis with its applications to crop production and genetic improvement. Fundamental and applied science contributions intertwine and chase each other, giving the reader different but complementary perspectives from only apparently distant corners of the same world.

Keywords

wheat-rye hybrids --- genes of reproductive isolation --- stem apical meristem --- molecular marker --- Rht18 --- reduced height --- wheat --- semi-dwarf --- linkage map --- CLE --- CLV --- WUS --- stem cells --- meristem --- SAM --- signaling --- locule --- Arabidopsis --- auxin --- HD-Zip transcription factors --- light environment --- photoreceptors --- wounding --- root plasticity --- hydrogen peroxide --- protoxylem --- plant development and organogenesis --- proline biosynthesis --- RolD --- rol genes --- Vasculature --- Organogenesis --- Development --- Brassicaceae --- Asteraceae --- flowering time --- photoperiod --- vernalization --- ambient temperature --- gibberellins --- age --- plant breeding --- grass --- ligule --- organogenesis --- boundaries --- shoot meristem --- morphogenesis --- molecular regulation --- cell wall --- cytoskeleton --- Arabidopsis --- root --- stem cells --- root development --- differentiation --- ground tissue --- radial patterning --- proximodistal patterning --- Plant in vitro cultures --- somatic cell selection --- hairy roots --- rol genes --- Agrobacterium rhizogenes --- genetic transformation --- recalcitrant species --- KNOX transcription factors --- plant development --- tree phase change --- transformation --- morphogenic --- embryogenesis --- meristem formation --- organogenesis --- GRETCHEN HAGEN 3 (GH3) IAA-amido synthase group II --- root apical meristem --- auxin --- cytokinin --- lateral root cap --- auxin minimum --- auxin conjugation --- plant development and organogenesis --- translational research --- crop productivity --- genetic improvement --- Arabidopsis thaliana --- regulatory networks --- phytohormones --- rol genes --- plant cell and tissue culture

Machining—Recent Advances, Applications and Challenges

Authors: ---
ISBN: 9783039213771 / 9783039213788 Year: Pages: 554 DOI: 10.3390/books978-3-03921-378-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 16:10:12
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The Special Issue Machining—Recent Advances, Applications and Challenges is intended as a humble collection of some of the hottest topics in machining. The manufacturing industry is a varying and challenging environment where new advances emerge from one day to another. In recent years, new manufacturing procedures have retained increasing attention from the industrial and scientific community. However, machining still remains the key operation to achieve high productivity and precision for high-added value parts. Continuous research is performed, and new ideas are constantly considered. This Special Issue summarizes selected high-quality papers which were submitted, peer-reviewed, and recommended by experts. It covers some (but not only) of the following topics: High performance operations for difficult-to-cut alloys, wrought and cast materials, light alloys, ceramics, etc.; Cutting tools, grades, substrates and coatings. Wear damage; Advanced cooling in machining: Minimum quantity of lubricant, dry or cryogenics; Modelling, focused on the reduction of risks, the process outcome, and to maintain surface integrity; Vibration problems in machines: Active and passive/predictive methods, sources, diagnosis and avoidance; Influence of machining in new concepts of machine–tool, and machine static and dynamic behaviors; Machinability of new composites, brittle and emerging materials; Assisted machining processes by high-pressure, laser, US, and others; Introduction of new analytics and decision making into machining programming. We wish to thank the reviewers and staff from Materials for their comments, advice, suggestions and invaluable support during the development of this Special Issue.

Keywords

dry-cutting --- concrete --- segmented diamond blade --- topography --- diameter variation --- weight loss --- in situ estimation --- SACE-drilled hole depth --- spark-assisted chemical engraving --- glass machining --- computer vision --- electrochemical discharge machining --- butt weld joint --- fatigue --- crack growth rate --- weld reinforcement --- cooling rate --- artificial neutral network --- cutting parameters --- magnesium alloys --- optimization --- prime machining costs --- surface roughness --- electropulsing --- machinability --- chip compression ratio --- current density --- specific cutting energy --- cutting edge microgeometry --- residual stress --- finite element model --- cutting edge preparation --- Inconel 718 --- stiffness properties --- parameter identification --- connections --- machine tool --- response surface methodology --- design of experiments --- modal testing --- plastic zone --- fracture mechanism --- steel sheet --- cutting process --- Huber–Mises stress --- finite element method --- microscopic analysis --- PVD Ti0.41Al0.59N/Ti0.55Al0.45N coating --- cutting temperature --- Inconel 718 --- cutting tool wear --- secondary adhesion wear --- turning --- machining --- aluminium --- flank super abrasive machining (SAM) --- flank milling --- Inconel® 718 --- roughness --- residual stress --- machine vision --- on-machine monitoring --- tool insert condition --- computer numerical control --- turning machine tools --- multi-beam laser --- heat transfer analysis --- fast simulation --- GPU --- analytic solution --- minimum quantity lubrication --- surface grinding --- multi-objective optimization --- grey relational analysis --- surface topography --- sustainable machining --- Gamma-TiAl --- superalloys --- slight materials --- drilling --- titanium aluminides --- additive manufacturing --- single point incremental sheet forming --- residual stresses --- X-ray diffraction --- ultra-precision machining --- slow tool servo --- surface topography --- simulation --- microlens array --- sinusoidal grid --- Milling stability --- variable pitch --- chatter --- self-excitation --- milling --- ceramics --- ductile machining --- PCD --- corner radius --- material removal rate --- adhesive --- machining --- modelling --- dry --- CFRP/UNS A92024 --- induction assisted milling --- tool wear --- taguchi method --- cutting tool --- machining temperatures at two deformation zones --- force–temperature correlation through analytical modeling --- high computational efficiency --- real-time prediction --- hybrid stacks drilling --- minimum quantity lubrication --- hole quality --- tool wear --- WEDM --- EN 31 steel --- surface roughness --- fractal dimension --- ANN --- GA --- cryogenic machining --- cutting tool --- cutting geometry --- titanium --- power consumption --- material-removal rate --- specific energy consumption --- grain density --- modeling --- shape memory alloy --- superelastic nitinol --- WEDM --- heat transfer search algorithm --- DSC test --- shape memory effect --- dish angle --- trochoidal step --- response surface methodology --- surface roughness --- desirability approach --- tool wear --- trochoidal milling --- titanium alloy --- chip morphology --- thin-wall machining --- chatter --- vibration --- deflection --- damping --- prediction --- workholding --- fixture --- dynamic --- stability --- titanium alloys --- machining --- turning --- machinability --- tool wear --- laser-assisted machining --- Taguchi method --- optimal machining conditions --- machining characteristic --- milling --- the cutting force components --- vibrations --- magnesium alloys --- artificial neural networks

Learning to Understand Remote Sensing Images

Author:
ISBN: 9783038976844 / 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: eng
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

Learning to Understand Remote Sensing Images

Author:
ISBN: 9783038976981 / 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

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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