scholarly journals China’s Robotic Spy Birds Take Surveilance to New Heights

2020 ◽  
pp. 1-5
Author(s):  
Robert Skopec ◽  
Keyword(s):  
High School ◽  
Science Policy ◽  
Research Institutes ◽  
Experimental Programme ◽  
Institute Of Technology ◽  
Beijing Institute ◽  
Weapons Systems

China takes surveillance to new heights with flock of robotic Doves, but do they come in peace? Hi-tech drones that look and move like real birds have already flown over restive Xinjiang region. Beijing Institute of Technology recruits 31 ‘patriotic’ youngsters for new AI weapons development programe. Expert in international science policy describes course as ‘extremely powerful and troubling.’ Also group of some of China’s smartest students have been recruited straight from high school to begin training as the world’s youngest AI weapons scientists. The 27 boys and four girls, all aged 18 and under, were selected for the four-year “experimental programme for intelligent weapons systems” at the Beijing Institute of Technology (BIT) from more than 5,000 candidates, the school said on its website. The BIT is one of the country’s top weapons research institutes, and the launch of the new programme is evidence of the weight it places on the development of AI technology for military use.

scholarly journals “Space Robotics”

2021 ◽  
Keyword(s):  
System Engineering ◽  
Space Science ◽  
Theory And Practice ◽  
Robotic Systems ◽  
Space Robotics ◽  
Series Space ◽  
Chinese Academy Of Sciences ◽  
Institute Of Technology ◽  
Beijing Institute ◽  
Academy Of Sciences

“Space Robotics” by Yaobing Wang belongs to the series Space Science and Technologies co-published by Beijing Institute of Technology Press, China, and Springer Nature Pte Ltd, Singapore. The Editor-in-Chief of the series, Peijian Ye, is Academician of the Chinese Academy of Sciences in Beijing and has published a collection of 10 volumes. This volume’s author, Yaobin Wang, is a research professor of Beijing Institute of Spacecraft System Engineering and Director of Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications. The book’s 363 pages provide a condensed combination of theory and practice as engineering guidance.

scholarly journals Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Preetha Jagannathan ◽  
Sujatha Rajkumar ◽  
Jaroslav Frnda ◽  
Parameshachari Bidare Divakarachari ◽  
Prabu Subramani
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Vehicle Classification ◽  
Traffic Surveillance ◽  
Local Descriptor ◽  
Learning Technique ◽  
Institute Of Technology ◽  
Beijing Institute

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

scholarly journals JACIII Distinguished Editor & Outstanding Reviewer Awards 2021

2021 ◽  
Vol 25 (6) ◽  
pp. 929-930
Author(s):  
Keyword(s):  
Significant Contribution ◽  
Scientific Research ◽  
Professional Work ◽  
Chinese Academy Of Sciences ◽  
Institute Of Technology ◽  
Beijing Institute ◽  
Academy Of Sciences

The JACIII Distinguished Editor and Outstanding Reviewer Awards were established for the purpose to honor and editors who have made a significant contribution to the growth of the JACIII in 2018 and to acknowledge reviewers who have made a significant contribution to reviewing in 2019. We express our deepest gratitude for their professional work, which we believe conductive to development of not only the JACIII but also scientific research. JACIII DISTINGUISHED EDITOR AWARD 2021 Tomomi Hashimoto (Saitama Institute of Technology, Japan) Zhen-Tao Liu (China University of Geosciences, China) Bin Xin (Beijing Institute of Technology, China) Jianqiang Yi (Institute of Automation, Chinese Academy of Sciences, China) Junzo Watada (Waseda University, Japan) Yaping Dai (Beijing Institute of Technology, China) Zhiyang Jia (Beijing Institute of Technology, China) Wentao Gu (Zhejiang Gongshang University, China)

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