Best Paper Competition

Best Paper Competition

I-Hsi Kao (University of California, Berkeley, USA); Xiao Zhou (Wuhan University of Technology, China); I-Ming Chen, Pin Wang and Ching-Yao Chan (University of California, Berkeley, USA)

Accurately predicting the trajectory of pedestrians helps autonomous vehicles to drive safely. In this paper, a work of predicting the trajectory of pedestrians by considering their posture is described. Two seconds of historical data are used to predict the pedestrian's actions in the next second based on a long short-term memory approach. The purpose of this experiment is to estimate whether pedestrians will cross the road in a mid-block setting without crosswalks and what paths they will take. The scene of this experiment is located on a street near the campus of the University of California at Berkeley.
Toshiro Nunome and Takuro Ito (Nagoya Institute of Technology, Japan)

This paper considers multi-view video and audio transmission from multiple servers. When the servers have a time lag because of the failure of time synchronization, the lag can be an influencing factor of QoE. If two servers have different clocks, the skew causes forward and back when the viewpoint change occurs. We evaluate the effect of lag by a subjective experiment. We then investigate the acceptable time lag for the users.
Hsi-Tseng Chou (National Taiwan University, Taiwan); Rong-Chung Liu (Yuan Ze University & WavePro Technology Inc., Taiwan); Yao-Chiang Kan (Yuan Ze University, Taiwan); Chih-Te Huang (National Chung-Shan Institute of Science and Technology, Taiwan); Hsien-Kwei Ho (WavePro Technology Inc., Taiwan)

This paper presents a study of electromagnetic (EM) backscattering from a metal corner reflector (MCR) in an imperfect radome scenario. The "defocus" behavior of scattering fields by the imperfect radome results in the degradation of radar cross-section (RCS) from the MCR. It causes incorrect justification of antenna behaviors in RCS estimation for the target under detection. In this study, three simplified radome models are simulated by EM full-wave analysis to verify the impact of defocusing behaviors on RCS estimations. The computed RCS curves show the explicit degradation of RCS performance, especially on the horizontal plane, the most often scanned plane to estimate RCSs.
Ssu-Chi Kuai and Wen-Hwa Liao (National Taipei University of Business, Taiwan); Chih-Yung Chang (Tamkang University, Taiwan); Gwo-Jong Yu (Aletheia University, Taiwan)

In the age of Internet, people usually use search engines by giving keywords to find the information they need. For content providers, the reference performance of a given content is majorly determined by the set of keywords. In literature, there have been many studies proposed algorithms for finding keywords. However, most of them cannot reflect important properties, such as the expert's knowledge and trends. This work proposes a novel keyword extraction algorithm, called FB-KEA, which extracts the keywords based on the features including semantic, expert experience as well as trend of hot search. Experimental results show that the proposed FB-KEA has significant improvements in terms of hit ratio, as compared with traditional methods.
Yi-Wen Hung, Yao-Tse Chang and Shuenn-Yuh Lee (National Cheng Kung University, Taiwan); Chou-Ching K. Lin (National Cheng Kung University Hospital, Taiwan); Gia-Shing Shieh (Ministry of Health and Welfare Tainan Hospital, Taiwan)

This paper has proposed an energy-efficient epilepsy detection framework for embedded systems. The epilepsy detection framework is implemented in 11 layers Convolutional Neural Network (CNN) with a 2-stage RISC-V core and a coprocessor to accelerate CNN inferences. The CNN algorithm provides 97.8% and 93.5% accuracy on floating-point and fixed-point operations respectively. The proposed CNN coprocessor is designed to offload CNN inference from RISC-V core to hardware with 51 nJ data transfer energy and 0.9 µJ inference energy for each 500 points input data frame. The coprocessor reduces the runtime of CNN inferences over 10^6x to perform only 0.012 s latency for each classification. According to the energy-efficient coprocessor, an AI-based solution is practical for real-time epilepsy detection on wearable devices for consumer electronics.
Shyang-Yuh Wang (Chinese Culture University, Taiwan); Sheng-Tsung Tu (Ming Chuan University, Taiwan); Chian-Shin You and Yue-Nuo Yan (Chinese Culture University, Taiwan)); Chien-Hsing Chou (Tamkang University, Taiwan)

With the rapid development of networks and the popularization of mobile devices, the consumer behavior of viewers has changed. Online viewing of audio and video has become a major entertainment activity. The OTT industry was first developed in the United States, and when it matured, they transferred it to overseas markets to expand. It entered Taiwan in 2016, enabling the domestic online audio-visual streaming industry to develop rapidly. Beside the convenience of mobile audio and video, it also effects on traditional cable television. The number of subscribers fell from 5.26 million subscribers to 4.86 million. So far, the total number of subscribers is still on a downward trend. The shows that the usage habits of Taiwanese people have shifted from TV to OTT platforms. Therefore, this study will collect samples of the two platforms with a higher usage rate and combine the KEYPO big data to analyze the network volume, network popularity, and community activity to analyze the development of the industry.