Smart Sensors and Devices in Artificial Intelligence
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 fiel...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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042 | |a dc | ||
072 | 7 | |a TBX |2 bicssc | |
100 | 1 | |a Zhang, Dan |4 edt | |
700 | 1 | |a Duan, Xuechao |4 edt | |
700 | 1 | |a Zhang, Dan |4 oth | |
700 | 1 | |a Duan, Xuechao |4 oth | |
245 | 1 | 0 | |a Smart Sensors and Devices in Artificial Intelligence |
260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
300 | |a 1 electronic resource (336 p.) | ||
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338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a 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 | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a microelectromechanical systems | ||
653 | |a inertial measurement unit | ||
653 | |a long short term memory recurrent neural networks | ||
653 | |a artificial intelligence | ||
653 | |a deep learning | ||
653 | |a CNN | ||
653 | |a LSTM | ||
653 | |a CO2 welding | ||
653 | |a molten pool | ||
653 | |a online monitoring | ||
653 | |a mechanical sensor | ||
653 | |a self-adaptiveness | ||
653 | |a ankle-foot exoskeleton | ||
653 | |a walking assistance | ||
653 | |a visual tracking | ||
653 | |a correlation filter | ||
653 | |a color histogram | ||
653 | |a adaptive hedge algorithm | ||
653 | |a scenario generation | ||
653 | |a autonomous vehicle | ||
653 | |a smart sensor and device | ||
653 | |a wireless sensor networks | ||
653 | |a task assignment | ||
653 | |a distributed | ||
653 | |a reliable | ||
653 | |a energy-efficient | ||
653 | |a audification | ||
653 | |a sensor | ||
653 | |a visualization | ||
653 | |a speech to text | ||
653 | |a text to speech | ||
653 | |a HF-OTH radar | ||
653 | |a AIS | ||
653 | |a radar tracking | ||
653 | |a data fusion | ||
653 | |a fuzzy functional dependencies | ||
653 | |a maritime surveillance | ||
653 | |a surgical robot end-effector | ||
653 | |a clamping force estimation | ||
653 | |a joint torque disturbance observer | ||
653 | |a PSO-BPNN | ||
653 | |a cable tension measurement | ||
653 | |a queue length | ||
653 | |a roadside sensor | ||
653 | |a vehicle detection | ||
653 | |a adverse weather | ||
653 | |a roadside LiDAR | ||
653 | |a data processing | ||
653 | |a air pollution | ||
653 | |a atmospheric data | ||
653 | |a IoT | ||
653 | |a machine learning | ||
653 | |a RNN | ||
653 | |a Sensors | ||
653 | |a smart cities | ||
653 | |a traffic flow | ||
653 | |a traffic forecasting | ||
653 | |a wireless sensor network | ||
653 | |a fruit condition monitoring | ||
653 | |a artificial neural network | ||
653 | |a ethylene gas | ||
653 | |a banana ripening | ||
653 | |a unidimensional ACGAN | ||
653 | |a signal recognition | ||
653 | |a data augmentation | ||
653 | |a link establishment behaviors | ||
653 | |a DenseNet | ||
653 | |a short-wave radio station | ||
653 | |a landing gear | ||
653 | |a adaptive landing | ||
653 | |a vehicle classification | ||
653 | |a FBG | ||
653 | |a smart sensors | ||
653 | |a outlier detection | ||
653 | |a local outlier factor | ||
653 | |a data streams | ||
653 | |a air quality monitoring | ||
653 | |a n/a | ||
653 | |a evacuation path | ||
653 | |a multi-story multi-exit building | ||
653 | |a temperature sensors | ||
653 | |a multi-time-slots planning | ||
653 | |a optimization | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/3573 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/68552 |7 0 |z DOAB: description of the publication |