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Sensors are the eyes or/and ears of an intelligent system, such as UAV, AGV and robots. With the development of material, signal processing, and multidisciplinary interactions, more and more smart sensors are proposed and fabricated under increasing demands for homes, the industry, and military fields. Networks of sensors will be able to enhance the ability to obtain huge amounts of information (big data) and improve precision, which also mirrors the developmental tendency of modern sensors. Moreover, artificial intelligence is a novel impetus for sensors and networks, which gets sensors to learn and think and feed more efficient results back. This book includes new research results from academia and industry, on the subject of “Smart Sensors and Networks”, especially sensing technologies utilizing Artificial Intelligence. The topics include: smart sensors biosensors sensor network sensor data fusion artificial intelligence deep learning mechatronics devices for sensors applications of sensors for robotics and mechatronics devices

History of engineering & technology --- microelectromechanical systems --- inertial measurement unit --- long short term memory recurrent neural networks --- artificial intelligence --- deep learning --- CNN --- LSTM --- CO2 welding --- molten pool --- online monitoring --- mechanical sensor --- self-adaptiveness --- ankle-foot exoskeleton --- walking assistance --- visual tracking --- correlation filter --- color histogram --- adaptive hedge algorithm --- scenario generation --- autonomous vehicle --- smart sensor and device --- wireless sensor networks --- task assignment --- distributed --- reliable --- energy-efficient --- audification --- sensor --- visualization --- speech to text --- text to speech --- HF-OTH radar --- AIS --- radar tracking --- data fusion --- fuzzy functional dependencies --- maritime surveillance --- surgical robot end-effector --- clamping force estimation --- joint torque disturbance observer --- PSO-BPNN --- cable tension measurement --- queue length --- roadside sensor --- vehicle detection --- adverse weather --- roadside LiDAR --- data processing --- air pollution --- atmospheric data --- IoT --- machine learning --- RNN --- Sensors --- smart cities --- traffic flow --- traffic forecasting --- wireless sensor network --- fruit condition monitoring --- artificial neural network --- ethylene gas --- banana ripening --- unidimensional ACGAN --- signal recognition --- data augmentation --- link establishment behaviors --- DenseNet --- short-wave radio station --- landing gear --- adaptive landing --- vehicle classification --- FBG --- smart sensors --- outlier detection --- local outlier factor --- data streams --- air quality monitoring --- n/a --- evacuation path --- multi-story multi-exit building --- temperature sensors --- multi-time-slots planning --- optimization

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Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue “Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments” present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland –

Traffic sign detection and tracking (TSDR) --- advanced driver assistance system (ADAS) --- computer vision --- 3D convolutional neural networks --- machine learning --- CT brain --- brain hemorrhage --- visual inspection --- one-class classifier --- grow-when-required neural network --- evolving connectionist systems --- automatic design --- bio-inspired techniques --- artificial bee colony --- image analysis --- feature extraction --- ship classification --- marine systems --- citrus --- pests and diseases identification --- convolutional neural network --- parameter efficiency --- vehicle detection --- YOLOv2 --- focal loss --- anchor box --- multi-scale --- deep learning --- neural network --- generative adversarial network --- synthetic images --- tool wear monitoring --- superalloy tool --- image recognition --- object detection --- UAV imagery --- vehicular traffic flow detection --- vehicular traffic flow classification --- vehicular traffic congestion --- video classification --- benchmark --- semantic segmentation --- atrous convolution --- spatial pooling --- ship radiated noise --- underwater acoustics --- surface electromyography (sEMG) --- convolution neural networks (CNNs) --- hand gesture recognition --- fabric defect --- mixed kernels --- cross-scale --- cascaded center-ness --- deformable localization --- continuous casting --- surface defects --- 3D imaging --- defect detection --- object detector --- object tracking --- activity measure --- Yolo --- deep sort --- Hungarian algorithm --- optical flows --- spatiotemporal interest points --- sports scene --- CT images --- convolutional neural networks --- hepatic cancer --- visual question answering --- three-dimensional (3D) vision --- reinforcement learning --- human–robot interaction --- few shot learning --- SVM --- CNN --- cascade classifier --- video surveillance --- RFI --- artefacts --- InSAR --- image processing --- pixel convolution --- thresholding --- nearest neighbor filtering --- data acquisition --- augmented reality --- pose estimation --- industrial environments --- information retriever sensor --- multi-hop reasoning --- evidence chains --- complex search request --- high-speed trains --- hunting --- non-stationary --- feature fusion --- multi-sensor fusion --- unmanned aerial vehicles --- drone detection --- UAV detection --- visual detection --- n/a --- human-robot interaction

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• Reference Manager
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Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue “Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments” present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland –

Information technology industries --- Traffic sign detection and tracking (TSDR) --- advanced driver assistance system (ADAS) --- computer vision --- 3D convolutional neural networks --- machine learning --- CT brain --- brain hemorrhage --- visual inspection --- one-class classifier --- grow-when-required neural network --- evolving connectionist systems --- automatic design --- bio-inspired techniques --- artificial bee colony --- image analysis --- feature extraction --- ship classification --- marine systems --- citrus --- pests and diseases identification --- convolutional neural network --- parameter efficiency --- vehicle detection --- YOLOv2 --- focal loss --- anchor box --- multi-scale --- deep learning --- neural network --- generative adversarial network --- synthetic images --- tool wear monitoring --- superalloy tool --- image recognition --- object detection --- UAV imagery --- vehicular traffic flow detection --- vehicular traffic flow classification --- vehicular traffic congestion --- video classification --- benchmark --- semantic segmentation --- atrous convolution --- spatial pooling --- ship radiated noise --- underwater acoustics --- surface electromyography (sEMG) --- convolution neural networks (CNNs) --- hand gesture recognition --- fabric defect --- mixed kernels --- cross-scale --- cascaded center-ness --- deformable localization --- continuous casting --- surface defects --- 3D imaging --- defect detection --- object detector --- object tracking --- activity measure --- Yolo --- deep sort --- Hungarian algorithm --- optical flows --- spatiotemporal interest points --- sports scene --- CT images --- convolutional neural networks --- hepatic cancer --- visual question answering --- three-dimensional (3D) vision --- reinforcement learning --- human-robot interaction --- few shot learning --- SVM --- CNN --- cascade classifier --- video surveillance --- RFI --- artefacts --- InSAR --- image processing --- pixel convolution --- thresholding --- nearest neighbor filtering --- data acquisition --- augmented reality --- pose estimation --- industrial environments --- information retriever sensor --- multi-hop reasoning --- evidence chains --- complex search request --- high-speed trains --- hunting --- non-stationary --- feature fusion --- multi-sensor fusion --- unmanned aerial vehicles --- drone detection --- UAV detection --- visual detection