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This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions.

Technology: general issues --- Energy industries & utilities --- automotive --- current --- electric power train --- electric vehicle --- embedded systems --- delay --- detection --- distributed systems --- measurements --- power train --- sensor --- signals --- time delay estimation --- unmanned aerial vehicles --- wireless sensor networks --- intelligent data processing --- trajectory planning --- relevant data extraction --- data consensus --- Internet of Things --- precision agriculture --- system identification --- smart building --- artificial neural network --- energy efficiency --- black box modeling --- educational robotics --- data acquisition --- sensors --- ROS --- STEM --- CNN (Convolutional neural networks) --- deep learning --- pavement defects --- residual connection --- attention gate --- atrous spatial pyramid pooling --- intelligent charging --- demand response --- linear programming --- optimization --- smart parking --- smart grid --- ODE Solver --- OpenCL --- Parareal --- parallel/multi-core computing --- sensing systems --- heterogenous embedded systems --- deep sparse auto-encoders --- medical diagnosis --- linear model --- data classification --- PSO algorithm --- safety-related system --- component --- FPGA-designing --- logical and power-oriented checkability --- hidden faults --- clock signal --- consumed and dissipated power --- temperature and current consumption sensors --- n/a

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This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions.

Technology: general issues --- Energy industries & utilities --- automotive --- current --- electric power train --- electric vehicle --- embedded systems --- delay --- detection --- distributed systems --- measurements --- power train --- sensor --- signals --- time delay estimation --- unmanned aerial vehicles --- wireless sensor networks --- intelligent data processing --- trajectory planning --- relevant data extraction --- data consensus --- Internet of Things --- precision agriculture --- system identification --- smart building --- artificial neural network --- energy efficiency --- black box modeling --- educational robotics --- data acquisition --- sensors --- ROS --- STEM --- CNN (Convolutional neural networks) --- deep learning --- pavement defects --- residual connection --- attention gate --- atrous spatial pyramid pooling --- intelligent charging --- demand response --- linear programming --- optimization --- smart parking --- smart grid --- ODE Solver --- OpenCL --- Parareal --- parallel/multi-core computing --- sensing systems --- heterogenous embedded systems --- deep sparse auto-encoders --- medical diagnosis --- linear model --- data classification --- PSO algorithm --- safety-related system --- component --- FPGA-designing --- logical and power-oriented checkability --- hidden faults --- clock signal --- consumed and dissipated power --- temperature and current consumption sensors --- n/a

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The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT.

Internet of Things (IoT) --- ReRoute --- Multicast Repair (M-REP) --- internet of things (IoT) --- Fast Reroute --- bit repair (B-REP) --- failure repair --- WSN --- MANET --- DRONET --- multilayered network model --- 5G --- IoT --- smart sensors --- smart sensor --- IoT system --- Velostat --- pressure sensor --- convolutional neural network --- data classification --- position detection --- magnetometer --- traffic --- vehicle --- classification --- measurement --- detection --- Internet of Things --- Bluetooth --- indoor tracking --- mobile localization --- optical sensors --- vibration sensing --- quality of service differentiation --- wireless optical networks --- free space optics --- multiwavelength laser --- optical code division multiple access (OCDMA) --- underwater wireless sensor network --- energy-efficient --- clustering --- depth-based routing --- mm-wave radars --- GNSS-RTK positioning --- wireless technology --- electromagnetic scanning --- point cloud --- localization --- IMU --- Wi-Fi --- positioning --- dead reckoning --- particle filter --- fingerprinting --- Wi-Fi sensing --- human activity recognition --- location-independent --- meta learning --- metric learning --- few-shot learning --- ACR --- H.264/AVC --- H.265/HEVC --- QoE --- subjective assessment --- n/a

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The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- pine wilt disease dataset --- GIS application visualization --- test-time augmentation --- object detection --- hard negative mining --- video synthetic aperture radar (SAR) --- moving target --- shadow detection --- deep learning --- false alarms --- missed detections --- synthetic aperture radar (SAR) --- on-board --- ship detection --- YOLOv5 --- lightweight detector --- remote sensing image --- spectral domain translation --- generative adversarial network --- paired translation --- synthetic aperture radar --- ship instance segmentation --- global context modeling --- boundary-aware box prediction --- land-use and land-cover --- built-up expansion --- probability modelling --- landscape fragmentation --- machine learning --- support vector machine --- frequency ratio --- fuzzy logic --- artificial intelligence --- remote sensing --- interferometric phase filtering --- sparse regularization (SR) --- deep learning (DL) --- neural convolutional network (CNN) --- semantic segmentation --- open data --- building extraction --- unet --- deeplab --- classifying-inversion method --- AIS --- atmospheric duct --- ship detection and classification --- rotated bounding box --- attention --- feature alignment --- weather nowcasting --- ResNeXt --- radar data --- spectral-spatial interaction network --- spectral-spatial attention --- pansharpening --- UAV visual navigation --- Siamese network --- multi-order feature --- MIoU --- imbalanced data classification --- data over-sampling --- graph convolutional network --- semi-supervised learning --- troposcatter --- tropospheric turbulence --- intercity co-channel interference --- concrete bridge --- visual inspection --- defect --- deep convolutional neural network --- transfer learning --- interpretation techniques --- weakly supervised semantic segmentation --- n/a