Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities
The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in art...
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 05174naaaa2201225uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_68433 | ||
| 005 | 20210501 | ||
| 020 | |a books978-3-0365-0123-9 | ||
| 020 | |a 9783036501222 | ||
| 020 | |a 9783036501239 | ||
| 024 | 7 | |a 10.3390/books978-3-0365-0123-9 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a TBX |2 bicssc | |
| 100 | 1 | |a Li, Tiancheng |4 edt | |
| 700 | 1 | |a Yan, Junkun |4 edt | |
| 700 | 1 | |a Cao, Yue |4 edt | |
| 700 | 1 | |a Bajo, Javier |4 edt | |
| 700 | 1 | |a Li, Tiancheng |4 oth | |
| 700 | 1 | |a Yan, Junkun |4 oth | |
| 700 | 1 | |a Cao, Yue |4 oth | |
| 700 | 1 | |a Bajo, Javier |4 oth | |
| 245 | 1 | 0 | |a Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (266 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing. | ||
| 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 clustering | ||
| 653 | |a data fusion | ||
| 653 | |a target detection | ||
| 653 | |a Grey Wolf Optimizer | ||
| 653 | |a Fireworks Algorithm | ||
| 653 | |a hybrid algorithm | ||
| 653 | |a exploitation and exploration | ||
| 653 | |a GNSS | ||
| 653 | |a MIMU | ||
| 653 | |a odometer | ||
| 653 | |a state constraints | ||
| 653 | |a simultaneous localization and mapping (SLAM) | ||
| 653 | |a range-only SLAM | ||
| 653 | |a sum of Gaussian (SoG) filter | ||
| 653 | |a cooperative approach | ||
| 653 | |a automatic fare collection system | ||
| 653 | |a passenger flow forecasting | ||
| 653 | |a time series decomposition | ||
| 653 | |a singular spectrum analysis | ||
| 653 | |a ensemble learning | ||
| 653 | |a extreme learning machine | ||
| 653 | |a wheeled mobile robot | ||
| 653 | |a path panning | ||
| 653 | |a laser simulator | ||
| 653 | |a fuzzy logic | ||
| 653 | |a laser range finder | ||
| 653 | |a Wi-Fi camera | ||
| 653 | |a sensor fusion | ||
| 653 | |a local map | ||
| 653 | |a odometry | ||
| 653 | |a deep learning | ||
| 653 | |a softmax | ||
| 653 | |a decision-making | ||
| 653 | |a classification | ||
| 653 | |a sensor data | ||
| 653 | |a Internet of Things | ||
| 653 | |a extended target tracking | ||
| 653 | |a gamma-Gaussian-inverse Wishart | ||
| 653 | |a Poisson multi-Bernoulli mixture | ||
| 653 | |a 5G IoT | ||
| 653 | |a indoor positioning | ||
| 653 | |a tracking | ||
| 653 | |a localization | ||
| 653 | |a navigation | ||
| 653 | |a positioning accuracy | ||
| 653 | |a single access point positioning | ||
| 653 | |a fuzzy inference | ||
| 653 | |a calibration | ||
| 653 | |a car-following | ||
| 653 | |a Takagi-Sugeno | ||
| 653 | |a Kalman filter | ||
| 653 | |a microscopic traffic model | ||
| 653 | |a continuous-time model | ||
| 653 | |a LoRa | ||
| 653 | |a positioning | ||
| 653 | |a LoRaWAN | ||
| 653 | |a TDoA | ||
| 653 | |a map matching | ||
| 653 | |a compass | ||
| 653 | |a automotive LFMCW radar | ||
| 653 | |a radial velocity | ||
| 653 | |a lateral velocity | ||
| 653 | |a Doppler-frequency estimation | ||
| 653 | |a waveform | ||
| 653 | |a signal model | ||
| 653 | |a tensor modeling | ||
| 653 | |a smart community system | ||
| 653 | |a power efficiency | ||
| 653 | |a object-detection coprocessor | ||
| 653 | |a histogram of oriented gradient | ||
| 653 | |a support vector machine | ||
| 653 | |a block-level once sliding detection window | ||
| 653 | |a multi-shape detection-window | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/3450 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/68433 |7 0 |z DOAB: description of the publication |