First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base.
To this end, we develop a calibration method for a RS-camera-IMU system with continuous-time B-splines by using a calibration target.
Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system.
The performance of those integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite.
In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.
Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data.
However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i. e., localization without training data that have known location labels).
no code implementations • 7 Apr 2020 • You Li, Yuan Zhuang, Xin Hu, Zhouzheng Gao, Jia Hu, Long Chen, Zhe He, Ling Pei, Kejie Chen, Maosong Wang, Xiaoji Niu, Ruizhi Chen, John Thompson, Fadhel Ghannouchi, Naser El-Sheimy
Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges.
Networking and Internet Architecture Signal Processing