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Side Channel Attacks

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ISBN: 9783039210008 / 9783039210015 Year: Pages: 258 DOI: 10.3390/books978-3-03921-001-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

This Special Issue provides an opportunity for researchers in the area of side-channel attacks (SCAs) to highlight the most recent exciting technologies. The research papers published in this Special Issue represent recent progress in the field, including research on power analysis attacks, cache-based timing attacks, system-level countermeasures, and so on.

Keywords

cache attack --- cache side-channel attack --- constant-time cryptographic algorithm --- rsa cryptosystem --- scatter-gather implementation --- modular exponentiation --- post-quantum cryptography --- lattice-based cryptography --- Gaussian sampling --- CDT sampling --- side-channel attack --- single trace analysis --- mobile ads --- software development kit (SDK), android package (APK), ad lib --- ad libraries --- ad networks --- graph --- graph similarity --- side-channel authentication --- leakage model --- AES --- FPGA --- unified point addition --- binary Huff curve --- recovery of secret exponent by triangular trace analysis --- horizontal collision correlation analysis --- side channel analysis --- single trace analysis --- post quantum cryptography --- NTRU --- side-channel analysis --- elliptic curve cryptography --- single-trace attack --- key bit-dependent attack --- countermeasure --- side channel analysis --- financial IC card --- first-order analysis --- second-order analysis --- data outsourcing --- integrity --- online authentication --- Merkle (hash) tree --- data loss --- information leakage --- reliability --- side-channel analysis --- power-analysis attack --- embedded system security --- machine-learning classification --- side-channel cache attacks --- cache misses --- AES --- cloud computing --- physically unclonable function --- chaos theory --- chaotic circuit --- FPGA --- CPLD --- challenge-response authentication --- hardware security --- side-channel attacks --- cryptographic keys --- side channel attack --- re-keying --- tweakable block cipher --- provable security --- n/a

Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods

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ISBN: 9783039211838 / 9783039211845 Year: Pages: 382 DOI: 10.3390/books978-3-03921-184-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General)
Added to DOAB on : 2019-08-28 11:21:27
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Environmental health researchers have long used concepts like the neighborhood effect to assessing people’s exposure to environmental influences and the associated health impact. However, these are static notions that ignore people’s daily mobility at various spatial and temporal scales (e.g., daily travel, migratory movements, and movements over the life course) and the influence of neighborhood contexts outside their residential neighborhoods. Recent studies have started to incorporate human mobility, non-residential neighborhoods, and the temporality of exposures through collecting and using data from GPS, accelerometers, mobile phones, various types of sensors, and social media. Innovative approaches and methods have been developed. This Special Issue aims to showcase studies that use new approaches, methods, and data to examine the role of human mobility and non-residential contexts on human health behaviors and outcomes. It includes 21 articles that cover a wide range of topics, including individual exposure to air pollution, exposure and access to green spaces, spatial access to healthcare services, environmental influences on physical activity, food environmental and diet behavior, exposure to noise and its impact on mental health, and broader methodological issues such as the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP). This collection will be a valuable reference for scholars and students interested in recent advances in the concepts and methods in environmental health and health geography.

Keywords

obesity --- built environment --- activity space --- regression analysis --- UGCoP --- foodscape exposure --- activity space --- commuting route --- space-time kernel density estimation --- time-weighted exposure --- Beijing --- cycling for transportation --- bike paths --- train stations --- subway stations --- adults --- Brazil --- fuel consumption --- emissions estimation --- GPS trace --- big data --- air pollution exposure --- human mobility --- mobile phone data --- dynamic assessment --- GIS --- GPS --- activity space --- environmental exposure --- the uncertain geographic context problem --- noise pollution --- mental disorders --- built environment --- multilevel model --- China --- PM concentrations --- crop residue burning --- correlation analysis --- interannual and seasonal variations --- China --- the neighborhood effect averaging problem (NEAP) --- human mobility --- environmental exposure --- the uncertain geographic context problem --- UGCoP --- car ownership --- car use --- built environment --- spatial autocorrelation --- multilevel Bayesian model --- geographical accessibility --- Healthcare services --- GIS --- E2SFCA --- CHAS --- Singapore --- environmental health --- food environment --- environmental context cube --- environmental context exposure index --- the uncertain geographic context problem (UGCoP) --- GPS --- GIS --- healthcare accessibility --- catchment areas --- access probability --- taxi GPS trajectories --- E2SFCA --- greenspace exposure --- health --- human mobility --- physical activity --- structural equation modeling --- Guangzhou --- healthcare accessibility --- population demand --- geographic impedance --- the elderly --- urban planning --- 3SFCA --- real-time traffic --- crowdedness --- well-being experience --- long-distance walking --- collective leisure activity --- walking event --- urban leisure --- missing data --- spatial data --- imputation --- geographic imputation --- activity space --- ecological momentary assessment --- EMA --- walking --- active travel --- ageing --- physical environment --- personal projects --- activity space --- Public Participatory GIS (PPGIS) --- spatial accessibility --- multimodal network --- primary healthcare --- China --- 2009 influenza A(H1N1) pandemic --- transport modes --- rail travel --- spatial spread --- quantile regression --- green space --- road traffic accidents --- cognitive aging --- activity space --- life-course perspectives --- environmental exposures

Arid Land Systems: Sciences and Societies

Authors: ---
ISBN: 9783039213474 / 9783039213481 Year: Pages: 380 DOI: 10.3390/books978-3-03921-348-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Environmental Sciences
Added to DOAB on : 2019-12-09 16:10:12
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Understanding deserts and drylands is essential, as arid landscapes cover >40% of the Earth and are home to two billion people. Today's problematic environment–human interaction needs contemporary knowledge to address dryland complexity. Physical dimensions in arid zones—land systems, climate and hazards, ecology—are linked with social processes that directly impact drylands, such as land management, livelihoods, and development. The challenges require integrated research that identifies systemic drivers across global arid regions. Measurement and monitoring, field investigation, remote sensing, and data analysis are effective tools to investigate natural dynamics. Equally, inquiry into how policy and practice affect landscape sustainability is key to mitigating detrimental activity in deserts. Relations between socio-economic forces and degradation, agro-pastoral rangeland use, drought and disaster and resource extraction reflect land interactions. Contemporary themes of food security, conflict, and conservation are interlinked in arid environments. This book unifies desert science, arid environments, and dryland development. The chapters identify land dynamics, address system risks and delineate human functions through original research in arid zones. Mixed methodologies highlight the vital links between social and environmental science in global deserts. The book engages with today's topical themes and presents novel analyses of arid land systems and societies.

Keywords

Central Asia --- landscape --- One Belt --- One Road --- Kazakhstan --- Kyrgyzstan --- infrastructure --- environment --- New Silk Road --- drylands --- wind erosion modelling --- drag partition --- aerodynamic roughness --- remote sensing --- computational fluid dynamics --- cellular automata --- remote sensing --- modelling --- coverage --- grass height --- Cuchillas de la Zarca --- Chobe --- forest resources --- ecosystem services --- non-linear change --- protected areas --- disturbance --- drought --- sustainable livelihoods --- ecotone --- dryland --- KAZA --- Southern Africa --- nomadic pastoralism --- spatial migration model --- Afar --- livestock --- fodder demand --- fodder supply --- Asian dust --- human health --- Mongolia --- Japan --- subarctic agriculture --- Greenland --- soil quality index --- farming at its limits --- air temperature increase --- increase of growing season --- dry lake beds --- dust storm emission --- remote sensing --- Gobi Desert region --- communal rangelands --- property rights --- environmental impacts --- policy implementation --- drylands --- arid region --- LUCC --- driving forces --- snow index --- SPOT VGT --- Kashgar Region --- degrading --- tamarind age --- regeneration --- invasive vine --- vegetation survey --- erosion --- rotational grazing --- continuous grazing --- grassland degradation --- case study of nomadic and settlement grazing system --- remote sensing --- Mongolian grassland --- arid area --- land use change --- soil carbon storage --- global carbon balance --- the Shiyang River Basin --- riparian ecosystems --- Sonoran desert --- remote sensing --- land cover/land use --- drip irrigation --- groundwater --- common-pool resource --- water rights --- local farming --- desert reclamation --- desertification --- river basin development --- political ecology --- water --- vegetation response to precipitation --- dust storm outbreak --- cross correlation analysis --- the Hovmoller diagram --- environmental regime shift --- Gobi desert of Mongolia --- climate hazard --- Asia --- drylands --- risk --- drought --- desert --- Central Asia --- Kyrgyzstan --- infrastructure --- environment --- mining --- social movements --- protest --- environmental justice --- subversive clientelism --- China --- Tibetan Plateau --- Sanjiangyuan region --- social–ecological systems --- pastoralism --- partnerships --- co-management --- national parks --- Belt and Road Initiative --- mountains of Central Asia --- pastoralism --- Ethiopia --- South Omo --- Nyangatom --- Jordan River Basin --- water productivity --- Jordan --- Israel --- Palestine --- agriculture --- agricultural water intensity --- decoupling --- water security --- institutional change --- ecosystem services --- economic valuation --- drylands --- absence --- afforestation --- charisma --- China --- conservation --- desertification --- Gobi --- Mongolia

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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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

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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eng (5)


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2019 (5)