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Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology.

Research & information: general --- Physics --- optical communication --- frame transmission --- synchronization word --- blind recognition --- Hough transform --- machine learning --- optical communications --- patent analysis --- network analysis --- technical analysis --- fiber nonlinearities --- deep learning (DL) --- artificial intelligence (AI) --- BPSK-VSB --- BPSK-SSB --- fiber transmission --- optical neural networks --- time lens --- fiber --- dispersion Fourier transform --- high-flux imaging --- classification --- cancer cell recognition --- photon time stretching (PTS) --- passive optical networks --- laser tuning time --- TWDM --- Dynamic Bandwidth Allocation --- optical access networks --- scheduling --- FSOC system --- omnidirectional communication --- miniaturization --- ASE noise --- ICC --- DWDM-FSO/PON optical fiber network --- hybrid OOK/M-ary DPPM-M-PAPM --- hybrid fiber FSO (HFFSO) link --- n/a

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Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology.

Research & information: general --- Physics --- optical communication --- frame transmission --- synchronization word --- blind recognition --- Hough transform --- machine learning --- optical communications --- patent analysis --- network analysis --- technical analysis --- fiber nonlinearities --- deep learning (DL) --- artificial intelligence (AI) --- BPSK-VSB --- BPSK-SSB --- fiber transmission --- optical neural networks --- time lens --- fiber --- dispersion Fourier transform --- high-flux imaging --- classification --- cancer cell recognition --- photon time stretching (PTS) --- passive optical networks --- laser tuning time --- TWDM --- Dynamic Bandwidth Allocation --- optical access networks --- scheduling --- FSOC system --- omnidirectional communication --- miniaturization --- ASE noise --- ICC --- DWDM-FSO/PON optical fiber network --- hybrid OOK/M-ary DPPM-M-PAPM --- hybrid fiber FSO (HFFSO) link

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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.

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

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This book presents the results of the successful Sensors Special Issue on Intelligent Vehicles that received submissions between March 2019 and May 2020. The Guest Editors of this Special Issue are Dr. David Fernández-Llorca, Dr. Ignacio Parra-Alonso, Dr. Iván García-Daza and Dr. Noelia Parra-Alonso, all from the Computer Engineering Department at the University of Alcalá (Madrid, Spain). A total of 32 manuscripts were finally accepted between 2019 and 2020, presented by top researchers from all over the world. The reader will find a well-representative set of current research and developments related to sensors and sensing for intelligent vehicles. The topics of the published manuscripts can be grouped into seven main categories: (1) assistance systems and automatic vehicle operation, (2) vehicle positioning and localization, (3) fault diagnosis and fail-x systems, (4) perception and scene understanding, (5) smart regenerative braking systems for electric vehicles, (6) driver behavior modeling and (7) intelligent sensing. We, the Guest Editors, hope that the readers will find this book to contain interesting papers for their research, papers that they will enjoy reading as much as we have enjoyed organizing this Special Issue

History of engineering & technology --- tracking-by-detection --- multi-vehicle tracking --- Siamese network --- data association --- Markov decision process --- driving behavior --- real-time monitoring --- driver distraction --- mobile application --- portable system --- simulation test --- dynamic driving behavior --- traffic scene augmentation --- corridor model --- IMU --- vision --- classification networks --- Hough transform --- lane markings detection --- semantic segmentation --- transfer learning --- autonomous --- off-road driving --- tire-road forces estimation --- slip angle estimation --- gauge sensors --- fuzzy logic system --- load transfer estimation --- simulation results --- normalization --- lateral force empirical model --- driver monitor --- lane departure --- statistical process control --- fault detection --- sensor fault --- signal restoration --- intelligent vehicle --- autonomous vehicle --- kinematic model --- visual SLAM --- sparse direct method --- photometric calibration --- corner detection and filtering --- loop closure detection --- road friction coefficient --- tire model --- nonlinear observer --- self-aligning torque --- lateral displacement --- Lyapunov method --- automatic parking system (APS) --- end-to-end parking --- reinforcement learning --- parking slot tracking --- deceleration planning --- multi-layer perceptron --- smart regenerative braking --- electric vehicles --- vehicle speed prediction --- driver behavior modeling --- electric vehicle control --- driver characteristics online learning --- objects’ edge detection --- stixel histograms accumulate --- point cloud segmentation --- autonomous vehicles --- scene understanding --- occlusion reasoning --- road detection --- advanced driver assistance system --- trajectory prediction --- risk assessment --- collision warning --- connected vehicles --- vehicular communications --- vulnerable road users --- fail-operational systems --- fall-back strategy --- automated driving --- advanced driving assistance systems --- illumination --- shadow detection --- shadow edge --- image processing --- traffic light detection --- intelligent transportation system --- lane-changing --- merging maneuvers --- game theory --- decision-making --- intelligent vehicles --- model predictive controller --- automatic train operation --- softness factor --- fusion velocity --- online obtaining --- hardware-in-the-loop simulation --- driving assistant --- driving diagnosis --- accident risk maps --- driving safety --- intelligent driving --- virtual test environment --- millimeter wave radar --- lane-change decision --- risk perception --- mixed traffic --- minimum safe deceleration --- automated driving system (ADS) --- sensor fusion --- multi-lane detection --- particle filter --- self-driving car --- unscented Kalman filter --- vehicle model --- Monte Carlo localization --- millimeter-wave radar --- square-root cubature Kalman filter --- Sage-Husa algorithm --- target tracking --- stationary and moving object classification --- localization --- LiDAR --- GNSS --- Global Positioning System (GPS) --- monte carlo --- autonomous driving --- robot motion --- path planning --- piecewise linear approximation --- multiple-target path planning --- autonomous mobile robot --- homotopy based path planning --- LiDAR signal processing --- sensor and information fusion --- advanced driver assistance systems --- autonomous racing --- high-speed camera --- real-time systems --- LiDAR odometry --- fail-aware --- sensors --- sensing --- percepction --- object detection and tracking --- scene segmentation --- vehicle positioning --- fail-x systems --- driver behavior modelling --- automatic operation

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book presents the results of the successful Sensors Special Issue on Intelligent Vehicles that received submissions between March 2019 and May 2020. The Guest Editors of this Special Issue are Dr. David Fernández-Llorca, Dr. Ignacio Parra-Alonso, Dr. Iván García-Daza and Dr. Noelia Parra-Alonso, all from the Computer Engineering Department at the University of Alcalá (Madrid, Spain). A total of 32 manuscripts were finally accepted between 2019 and 2020, presented by top researchers from all over the world. The reader will find a well-representative set of current research and developments related to sensors and sensing for intelligent vehicles. The topics of the published manuscripts can be grouped into seven main categories: (1) assistance systems and automatic vehicle operation, (2) vehicle positioning and localization, (3) fault diagnosis and fail-x systems, (4) perception and scene understanding, (5) smart regenerative braking systems for electric vehicles, (6) driver behavior modeling and (7) intelligent sensing. We, the Guest Editors, hope that the readers will find this book to contain interesting papers for their research, papers that they will enjoy reading as much as we have enjoyed organizing this Special Issue

History of engineering & technology --- tracking-by-detection --- multi-vehicle tracking --- Siamese network --- data association --- Markov decision process --- driving behavior --- real-time monitoring --- driver distraction --- mobile application --- portable system --- simulation test --- dynamic driving behavior --- traffic scene augmentation --- corridor model --- IMU --- vision --- classification networks --- Hough transform --- lane markings detection --- semantic segmentation --- transfer learning --- autonomous --- off-road driving --- tire-road forces estimation --- slip angle estimation --- gauge sensors --- fuzzy logic system --- load transfer estimation --- simulation results --- normalization --- lateral force empirical model --- driver monitor --- lane departure --- statistical process control --- fault detection --- sensor fault --- signal restoration --- intelligent vehicle --- autonomous vehicle --- kinematic model --- visual SLAM --- sparse direct method --- photometric calibration --- corner detection and filtering --- loop closure detection --- road friction coefficient --- tire model --- nonlinear observer --- self-aligning torque --- lateral displacement --- Lyapunov method --- automatic parking system (APS) --- end-to-end parking --- reinforcement learning --- parking slot tracking --- deceleration planning --- multi-layer perceptron --- smart regenerative braking --- electric vehicles --- vehicle speed prediction --- driver behavior modeling --- electric vehicle control --- driver characteristics online learning --- objects’ edge detection --- stixel histograms accumulate --- point cloud segmentation --- autonomous vehicles --- scene understanding --- occlusion reasoning --- road detection --- advanced driver assistance system --- trajectory prediction --- risk assessment --- collision warning --- connected vehicles --- vehicular communications --- vulnerable road users --- fail-operational systems --- fall-back strategy --- automated driving --- advanced driving assistance systems --- illumination --- shadow detection --- shadow edge --- image processing --- traffic light detection --- intelligent transportation system --- lane-changing --- merging maneuvers --- game theory --- decision-making --- intelligent vehicles --- model predictive controller --- automatic train operation --- softness factor --- fusion velocity --- online obtaining --- hardware-in-the-loop simulation --- driving assistant --- driving diagnosis --- accident risk maps --- driving safety --- intelligent driving --- virtual test environment --- millimeter wave radar --- lane-change decision --- risk perception --- mixed traffic --- minimum safe deceleration --- automated driving system (ADS) --- sensor fusion --- multi-lane detection --- particle filter --- self-driving car --- unscented Kalman filter --- vehicle model --- Monte Carlo localization --- millimeter-wave radar --- square-root cubature Kalman filter --- Sage-Husa algorithm --- target tracking --- stationary and moving object classification --- localization --- LiDAR --- GNSS --- Global Positioning System (GPS) --- monte carlo --- autonomous driving --- robot motion --- path planning --- piecewise linear approximation --- multiple-target path planning --- autonomous mobile robot --- homotopy based path planning --- LiDAR signal processing --- sensor and information fusion --- advanced driver assistance systems --- autonomous racing --- high-speed camera --- real-time systems --- LiDAR odometry --- fail-aware --- sensors --- sensing --- percepction --- object detection and tracking --- scene segmentation --- vehicle positioning --- fail-x systems --- driver behavior modelling --- automatic operation