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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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This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective.

subject-dependent emotion recognition --- subject-independent emotion recognition --- electrodermal activity (EDA) --- deep learning --- convolutional neural networks --- automatic facial emotion recognition --- intensity of emotion recognition --- behavioral biometrical systems --- machine learning --- artificial intelligence --- driving stress --- electrodermal activity --- road traffic --- road types --- Viola-Jones --- facial emotion recognition --- facial expression recognition --- facial detection --- facial landmarks --- infrared thermal imaging --- homography matrix --- socially assistive robot --- EEG --- arousal detection --- valence detection --- data transformation --- normalization --- mental stress detection --- electrocardiogram --- respiration --- in-ear EEG --- emotion classification --- emotion monitoring --- elderly caring --- outpatient caring --- stress detection --- deep neural network --- convolutional neural network --- wearable sensors --- psychophysiology --- sensor data analysis --- time series analysis --- signal analysis --- similarity measures --- correlation statistics --- quantitative analysis --- benchmarking --- boredom --- emotion --- GSR --- classification --- sensor --- face landmark detection --- fully convolutional DenseNets --- skip-connections --- dilated convolutions --- emotion recognition --- physiological sensing --- multimodal sensing --- flight simulation --- activity recognition --- physiological signals --- thoracic electrical bioimpedance --- smart band --- stress recognition --- physiological signal processing --- long short-term memory recurrent neural networks --- information fusion --- pain recognition --- long-term stress --- electroencephalography --- perceived stress scale --- expert evaluation --- affective corpus --- multimodal sensors --- overload --- underload --- interest --- frustration --- cognitive load --- stress research --- affective computing --- human-computer interaction --- deep convolutional neural network --- transfer learning --- auxiliary loss --- weighted loss --- class center --- stress sensing --- smart insoles --- smart shoes --- unobtrusive sensing --- stress --- center of pressure --- regression --- signal processing --- arousal --- aging adults --- musical genres --- emotion elicitation --- dataset --- emotion representation --- feature selection --- feature extraction --- computer science --- virtual reality --- head-mounted display --- n/a

Choose an application

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

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective.

Technology: general issues --- subject-dependent emotion recognition --- subject-independent emotion recognition --- electrodermal activity (EDA) --- deep learning --- convolutional neural networks --- automatic facial emotion recognition --- intensity of emotion recognition --- behavioral biometrical systems --- machine learning --- artificial intelligence --- driving stress --- electrodermal activity --- road traffic --- road types --- Viola-Jones --- facial emotion recognition --- facial expression recognition --- facial detection --- facial landmarks --- infrared thermal imaging --- homography matrix --- socially assistive robot --- EEG --- arousal detection --- valence detection --- data transformation --- normalization --- mental stress detection --- electrocardiogram --- respiration --- in-ear EEG --- emotion classification --- emotion monitoring --- elderly caring --- outpatient caring --- stress detection --- deep neural network --- convolutional neural network --- wearable sensors --- psychophysiology --- sensor data analysis --- time series analysis --- signal analysis --- similarity measures --- correlation statistics --- quantitative analysis --- benchmarking --- boredom --- emotion --- GSR --- classification --- sensor --- face landmark detection --- fully convolutional DenseNets --- skip-connections --- dilated convolutions --- emotion recognition --- physiological sensing --- multimodal sensing --- flight simulation --- activity recognition --- physiological signals --- thoracic electrical bioimpedance --- smart band --- stress recognition --- physiological signal processing --- long short-term memory recurrent neural networks --- information fusion --- pain recognition --- long-term stress --- electroencephalography --- perceived stress scale --- expert evaluation --- affective corpus --- multimodal sensors --- overload --- underload --- interest --- frustration --- cognitive load --- stress research --- affective computing --- human-computer interaction --- deep convolutional neural network --- transfer learning --- auxiliary loss --- weighted loss --- class center --- stress sensing --- smart insoles --- smart shoes --- unobtrusive sensing --- stress --- center of pressure --- regression --- signal processing --- arousal --- aging adults --- musical genres --- emotion elicitation --- dataset --- emotion representation --- feature selection --- feature extraction --- computer science --- virtual reality --- head-mounted display --- subject-dependent emotion recognition --- subject-independent emotion recognition --- electrodermal activity (EDA) --- deep learning --- convolutional neural networks --- automatic facial emotion recognition --- intensity of emotion recognition --- behavioral biometrical systems --- machine learning --- artificial intelligence --- driving stress --- electrodermal activity --- road traffic --- road types --- Viola-Jones --- facial emotion recognition --- facial expression recognition --- facial detection --- facial landmarks --- infrared thermal imaging --- homography matrix --- socially assistive robot --- EEG --- arousal detection --- valence detection --- data transformation --- normalization --- mental stress detection --- electrocardiogram --- respiration --- in-ear EEG --- emotion classification --- emotion monitoring --- elderly caring --- outpatient caring --- stress detection --- deep neural network --- convolutional neural network --- wearable sensors --- psychophysiology --- sensor data analysis --- time series analysis --- signal analysis --- similarity measures --- correlation statistics --- quantitative analysis --- benchmarking --- boredom --- emotion --- GSR --- classification --- sensor --- face landmark detection --- fully convolutional DenseNets --- skip-connections --- dilated convolutions --- emotion recognition --- physiological sensing --- multimodal sensing --- flight simulation --- activity recognition --- physiological signals --- thoracic electrical bioimpedance --- smart band --- stress recognition --- physiological signal processing --- long short-term memory recurrent neural networks --- information fusion --- pain recognition --- long-term stress --- electroencephalography --- perceived stress scale --- expert evaluation --- affective corpus --- multimodal sensors --- overload --- underload --- interest --- frustration --- cognitive load --- stress research --- affective computing --- human-computer interaction --- deep convolutional neural network --- transfer learning --- auxiliary loss --- weighted loss --- class center --- stress sensing --- smart insoles --- smart shoes --- unobtrusive sensing --- stress --- center of pressure --- regression --- signal processing --- arousal --- aging adults --- musical genres --- emotion elicitation --- dataset --- emotion representation --- feature selection --- feature extraction --- computer science --- virtual reality --- head-mounted display