Choose an application

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

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected.

History of engineering & technology --- anomaly detection --- recurrent neural networks --- neural networks compression --- LHC --- WirelessHART network --- delay analysis --- real-time systems --- multi-channel processing --- simulation modeling --- transmission scheduling scheme --- industrial internet of things --- wireless networks --- industrial control systems --- wireless networked control systems --- industrial IoT --- security --- legacy production machinery --- real-time condition monitoring --- wireless local area network --- IEEE 802.11ah --- medium access control --- timeliness --- Wireless Sensor and Actuator Networks (WSANs) --- multipath retransmission --- resource scheduling --- realtime wireless communication --- monitoring and control system --- virtualization --- controller area network --- fieldbus --- real-time --- container --- Industrial Internet of Things (IIoT) --- LoRa --- WiFi HaLow --- Time Slotted Channel Hopping (TSCH) --- Narrowband IoT (NB-IoT) --- Bluetooth Low Energy (BLE) --- BLE Long Range --- WirelessHART --- ISA100.11a --- deep sparse coding --- convolutional neural networks --- signal analysis --- respiratory diseases --- medication adherence --- hardware design --- trust --- cryptography --- anomaly detection --- recurrent neural networks --- neural networks compression --- LHC --- WirelessHART network --- delay analysis --- real-time systems --- multi-channel processing --- simulation modeling --- transmission scheduling scheme --- industrial internet of things --- wireless networks --- industrial control systems --- wireless networked control systems --- industrial IoT --- security --- legacy production machinery --- real-time condition monitoring --- wireless local area network --- IEEE 802.11ah --- medium access control --- timeliness --- Wireless Sensor and Actuator Networks (WSANs) --- multipath retransmission --- resource scheduling --- realtime wireless communication --- monitoring and control system --- virtualization --- controller area network --- fieldbus --- real-time --- container --- Industrial Internet of Things (IIoT) --- LoRa --- WiFi HaLow --- Time Slotted Channel Hopping (TSCH) --- Narrowband IoT (NB-IoT) --- Bluetooth Low Energy (BLE) --- BLE Long Range --- WirelessHART --- ISA100.11a --- deep sparse coding --- convolutional neural networks --- signal analysis --- respiratory diseases --- medication adherence --- hardware design --- trust --- cryptography

Choose an application

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

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected.

anomaly detection --- recurrent neural networks --- neural networks compression --- LHC --- WirelessHART network --- delay analysis --- real-time systems --- multi-channel processing --- simulation modeling --- transmission scheduling scheme --- industrial internet of things --- wireless networks --- industrial control systems --- wireless networked control systems --- industrial IoT --- security --- legacy production machinery --- real-time condition monitoring --- wireless local area network --- IEEE 802.11ah --- medium access control --- timeliness --- Wireless Sensor and Actuator Networks (WSANs) --- multipath retransmission --- resource scheduling --- realtime wireless communication --- monitoring and control system --- virtualization --- controller area network --- fieldbus --- real-time --- container --- Industrial Internet of Things (IIoT) --- LoRa --- WiFi HaLow --- Time Slotted Channel Hopping (TSCH) --- Narrowband IoT (NB-IoT) --- Bluetooth Low Energy (BLE) --- BLE Long Range --- WirelessHART --- ISA100.11a --- deep sparse coding --- convolutional neural networks --- signal analysis --- respiratory diseases --- medication adherence --- hardware design --- trust --- cryptography --- n/a

Choose an application

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

Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

Technology: general issues --- History of engineering & technology --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging

Choose an application

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

This book includes 23 published papers on Special issues of "Image and Video Processing and Recognition Based on Artificial Intelligence" in the journal Sensors. The purpose of this Special Issue was to invite high-quality and state-of-the-art academic papers on challenging issues in the field of AI-based image and video processing and recognition.

Technology: general issues --- emotion recognition --- brain computer interface --- bag of deep features --- continuous wavelet transform --- face image analysis --- deep learning --- face parsing --- facial attributes classification --- building extraction --- convolutional neural networks --- mask R-CNN --- high-resolution remote sensing image --- autoencoders --- semi-supervised learning --- computer vision --- pathology --- epidermis --- skin --- image processing --- generative models --- generative adversarial net --- depth map --- super-resolution --- guidance --- residual network --- channel interaction --- pose estimation --- body orientation --- multi-person --- multi-task --- surface defect detection --- active learning --- generative adversarial network --- presentation attack detection --- artificial image generation --- presentation attack face images --- ultrasound image --- malignant thyroid nodule --- artificial intelligence --- weighted binary cross-entropy loss --- infrared circumferential scanning system --- target recognition --- deep convolutional neural networks --- data augmentation --- transfer learning --- bounding box regression --- loss function --- medical image fusion --- convolutional neural network --- image pyramid --- multi-scale decomposition --- armature --- surface inspection --- action recognition --- social robotics --- common spatial patterns --- vehicle recognition --- multi resolution network --- optimization --- semantic segmentation --- global context --- local context --- fully convolutional networks --- image-to-image conversion --- image de-raining --- label to photos --- edges to photos --- generative adversarial network (GAN) --- remote sensing --- helicopter footage --- crowd counting --- multitask learning --- normalized cross-correlation --- Marr wavelets --- entropy and response --- graph matching --- RANSAC --- GC–LSTM model --- typhoon --- satellite image --- prediction system --- monocular depth estimation --- feature distillation --- joint attention --- finger-vein recognition --- camera position --- finger position --- lighting --- unobserved database --- heterogeneous database --- domain adaptation --- cycle-consistent adversarial networks --- SDUMLA-HMT-DB --- HKPolyU-DB --- biometrics --- face recognition --- single-sample face recognition --- binarized statistical image features --- K-nearest neighbors --- sparse coding --- fast approximation --- homotopy iterative hard thresholding --- object recognition --- character recognition --- orthogonal polynomials --- orthogonal moments --- Krawtchouk polynomials --- Tchebichef polynomials --- support vector machine

Choose an application

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

Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging