Prof. Lin Wang
Xiamen University, China
Personal Web Sites: https://wwcs.xmu.edu.cn/#/tutor?tutor_id=wanglin
Experience: Prof. Lin Wang was the vice dean of the School of Information Science and Technology of Xiamen University, the director of the National Experimental Teaching Demonstration Center of Electronic Information, the first New Century Talents selected by the Ministry of Education in 2004, the senior member of IEEE in 2009, the editorial board of the Electronic Journal from 2011 to 2015, and the Sabendong Chair Professor Award of Xiamen University in 2012. He has chaired or co-chaired the IEEE International Conference and Proceedings Committee for many times. He received his Ph.D. degree in Electrical Circuits and systems from University of Electronic Science and Technology of China in 2001, his Master of Science degree in Applied Mathematics from Kunming University of Science and Technology in 1989 and his Bachelor of Science degree in basic mathematics from Chongqing Normal University in 1984. From 1995 to 1996, he studied for his PhD in Mathematical Physics at the University of New England, Australia. From January to April 2003, he was a visiting researcher at the Department of Electronic Engineering, City University of Hong Kong. From January to July 2013, he was a senior research scholar at the University of California, Davis, USA.
Speech Title: Robust MDCSK Transmission Performances over Non-Stationary Channels
For the future networks, esp. Internet of Things (loT) there is obvious demand about low power, low cost, and low delay for the connectivity chips. Meanwhile the nonstationary transmission properties need to be considered over the harsh environments, such as PLC and underwater acoustic channels. Not only the single chip design methodologies integrating different function chips together but also new signal design technique with strong against interferes over non-standard or nonstationary channels will become popular in the future networks. As one of these cases, joint spreading spectrum and modulations, DCSK has been confirmed to achieve remarkable advantages under the non-standard or nonstationary channels since it owns stronger against interferes and simple implementation complexity. The talk will show you how Mary DCSK (MDCSK) systems and their Coded Modulation Schemes ( BICM modes) are designed and analyzed over nonstationary channels, particularly over underwater acoustic communications and PLC. It has been found that the superiorities (low power, low cost, high robustness, etc) of proposed systems are evident over traditional counterpart over harsh transmission environments.
Prof. Xiaogang Liu
School of Mechanical and Electronic Engineering, Wuhan University of Technology, China
Personal Web Sites: http://smee.whut.edu.cn/rsgz/szdw/201510/t20151014_200317.shtml
Experience: Prof. Xiaogang Liu acquired his Ph.D. degree in mechanical engineering from the University of Queensland, Australia, and he is now the doctoral supervisor of Mechanical engineering and Instrument Science and Technology at the School of Mechanical and Electrical Engineering, Wuhan University of Technology. He has chaired two scientific research projects supported by the National Natural Science Foundation of China, and his academic outputs include academic papers, invention patents and software copyrights in the fields of Intelligent Manufacturing, Contact Mechanics, Mechanical Vibration and Electromechanical Control. As the leader of a provincial teaching and research project about mechanical manufacturing, he summarised these academic outputs into monographs to integrate research and education, and was recognized as Fellow of Higher Education Academy (FHEA). Currently, he is an assessment expert of the national Natural Science Foundation of China, an assessment expert of China Scholarship Council, a senior member of the Chinese Mechanical Engineering Society, an expert of the high-tech industry in Wuhan, and was awarded the “T A Stewart-Dyer Prize/Frederick Harvey Trevithick Prize” by the Institution of Mechanical Engineers in London.
Speech Title: Prediction and Control of Wheel Squeal
Chongqing University of Posts and Telecommunications
Personal Web Sites: https://faculty.cqupt.edu.cn/gaocq/zh_CN/index.htm
Experience: Prof. Chenqiang Gao received the B.S. degree in computer science from China University of Geosciences, Wuhan, China, in 2004 and the Ph.D. degree in Control Science and Engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2009. In August 2009, he joined School of Communications and Information Engineering at Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China. In September 2012, he joined the Informedia Group in School of Computer Science at Carnegie Mellon University (CMU), working on Multimedia Event Detection (MED) and Surveillance Event Detection (SED) as a visiting scholar. In April 2013, he became a PostDoctoral Fellow and continued work on MED and SED until March 2014 when he returned to CQUPT. He is currently a Professor at CQUPT. His research interests include infrared image analysis, video analysis, machine learning. He has published approximately 80 technical articles in refereed journals and proceedings, including TIP, TMM, TGRS, PR, and CVPR, ECCV, AAAi, ACM MM, etc.
Speech Title: SS3D: Sparsely-Supervised 3D Object Detection from Point Cloud
Conventional deep learning based methods for 3D object detection require a large amount of 3D bounding box annotations for training, which is expensive to obtain in practice. Sparsely annotated object detection, which can largely reduce the annotations, is very challenging since the missingannotated instances would be regarded as the background during training. In this talk,I will introduce a sparselysupervised 3D object detection method, named SS3D. Aiming to eliminate the negative supervision caused by the missing annotations, we design a missing-annotated instance mining module with strict filtering strategies to mine positive instances. In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision. The proposed SS3D is a general framework that can be used to learn any modern 3D object detector. Extensive experiments on the KITTI dataset reveal that on different 3D detectors, the proposed SS3D framework with only 20% annotations required can achieve on-par performance comparing to fullysupervised methods. Comparing with the state-of-the-art semi-supervised 3D objection detection on KITTI, our SS3D improves the benchmarks by significant margins under the same annotation workload. Moreover, our SS3D also outperforms the state-of-the-art weakly-supervised method by remarkable margins, highlighting its effectiveness.
Prof. Wenqiang Jin
Hunan University, China
Personal Web Sites: http://csee.hnu.edu.cn/people/jinwenqiang
Experience: Prof. Wenqiang Jin was recognized as a National Overseas High-level Young Talent, Overseas High-level Young Talent of Hunan Province, Expert of Hunan Provincial Network Security and Informatization Expert Advisory Committee, Yuelu Scholar. He was graduated from The University of Texas at Arlington (UTA). He carried out a number of innovative researches in the fields of system security and pervasive computing. The research outcomes have been published in CCF-A top conferences and journals in recent years. Pro. Wenqiang Jin was awarded the Excellent Doctoral Dissertation Award by the UTA when he graduated, and won the Student Travel Grant of NSF multiple times. He is severing as a reviewer for top journals and conferences, including IEEE TDSC, IEEE TIFS, IEEE TVT, IEEE INFOCOM, etc.
Speech Title: Emerging Threats Against Sensors
The development of sensor technology has pushed the society into the era of Internet of Everything. However, we are also facing emerging treats against the IoT system’s sensors, which unavoidably impacts the system integrity and leads to severe privacy leakages. This talk mainly discuss the sensor security of generalized IoT systems. I will briefly introduce the threat models proposed in the recent years and provide the audience with an overview of the key technologies attacking against the sensors.