關於 journal of electrical engineering technology ，我們在網路上蒐集到這些相關的討論、資訊與評價

The 2nd International Symposium on Engineering and Technology(ISET 2020)

URL: http://www.bme.nchu.edu.tw/iset2020/
Email: bcheng@dragon.nchu.edu.tw
Nantou County, Taiwan, 14~16 November, 2020

Organized by
College of Engineering, National Chung Hsing University (NCHU), Taiwan
Graduate Institute of Biomedical Engineering, NCHU, Taiwan

Call for Papers
ISET 2020 will take place on 14~16 November 2020, in Nantou County, Taiwan, and it will provide an
international platform for scholars to share their research achievements, explore the hot issues, and
exchange new experiences and ideas in the field of engineering and technology. Papers in the following
areas are encouraged to submit.
Engineering: Biomedical engineering, electrical and electronics engineering, civil and environmental
engineering, chemical and material engineering, computer engineering, mechanical engineering, industrial
engineering, and other related topics.
Technology: Assistive technology, information technology, biotechnology and nanotechnology, and other
related topics.

Excellent papers selected from ISET 2020 will be recommended to be
published in Applied Sciences (SCI journal; IF: 2.474).
ISET 2020 has invited academician of the Academia Sinica, Taiwan, and internationally renowned scholars to
give plenary and keynotes speeches.

Important Dates
Submission of Full Paper Due: 18 September 2020
Acceptance of Notification: 30 September 2020
Early Registration (Student: 30 USD; Regular: 60 USD) Due: 09 October 2020
Registration Due: 23 October 2020
Conference Date: 14~16 November 2020

交通大學IBM中心特別邀請到ECE Department at New York University 的 Prof. H. Jonathan Chao 前來為我們演講，歡迎有興趣的老師與同學免費報名參加！

演講標題：CFR-RL: Traffic Engineering with Reinforcement Learning in SDN
演 講 者：Prof. H. Jonathan Chao (ECE Department at New York University)
時 間：2020/01/20(一) 15:00 ~ 17:00
地 點：交大工程四館816室
活動報名網址：https://forms.gle/k5txEfTX6jM7PBR98
聯絡方式：曾紫玲 Tel：03-5712121分機54599 Email：tzuling@nctu.edu.tw

Abstract:
Traffic Engineering (TE) is one of important network features for Software-Defined Networking (SDN) with an aim to help Internet Service Providers (ISPs) optimize network performance and resource utilization by configuring the routing across their backbone networks. Although TE solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using SDN to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is immense. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this talk, we describe a Reinforcement Learning (RL)-based scheme, called CFR-RL, that learns a policy to select critical flows for each given traffic matrix automatically. It then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL outperforms the best heuristic by 7.4% - 12.2% and reroutes only 10% - 21.3% of total traffic.

Biography:
H. Jonathan Chao is Professor of Electrical and Computer Engineering (ECE) at NYU, where he joined in January 1992. He is currently Director of High-Speed Networking Lab. He was Head of ECE Department from 2004-2014. He has been doing research in the areas of software defined networking, network function virtualization, datacenter networks, packet processing and switching, network security, and machine learning for networking. He holds 63 patents and has published more than 265 journal and conference papers. During 2000–2001, he was Co-Founder and CTO of Coree Networks, NJ, where he led a team to implement a multi-terabit router with carrier-class reliability. From 1985 to 1992, he was a Member of Technical Staff at Bellcore, where he was involved in network architecture designs and ASIC implementations, such as the world’s first SONET-like Framer chip, ATM Layer chip, Sequencer chip (the first chip handling packet scheduling), and ATM switch chip. He is a Fellow of National Academy of Inventors (NAI) for “having demonstrated a highly prolific spirit of innovation in creating or facilitating outstanding inventions that have made a tangible impact on quality of life, economic development, and the welfare of society.” He is a Fellow of the IEEE for his contributions to the architecture and application of VLSI circuits in high-speed packet networks. He received Bellcore Excellence Award in 1987. He is a co-recipient of the 2001 Best Paper Award from the IEEE Transaction on Circuits and Systems for Video Technology. He coauthored three networking books. He worked for Telecommunication Lab in Taiwan from 1977 to 1981. He received his B.S. and M.S. degrees in electronics engineering from National Chiao Tung University, Taiwan, in 1977 and 1980, respectively, and his Ph.D. degree in electrical engineering from The Ohio State University in 1985.

【演講】2019/11/19 (二) @工四816 (智易空間)，邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan） 演講「Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management」

IBM中心特別邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan）前來為我們演講，歡迎有興趣的老師與同學報名參加！

演講標題：Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management
演 講 者：Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間：2019/11/19(二) 9:00 ~ 12:00
地 點：交大工程四館816 (智易空間)
活動報名網址：https://forms.gle/vUr3kYBDB2vvKtca6

報名方式：
費用：（費用含講義、午餐及茶水）
1.費用：(1) 校內學生免費，校外學生300元/人 (2) 業界人士與老師1500/人
2.人數：60人，依完成報名順序錄取（完成繳費者始完成報名程序）

※報名及繳費方式：
1.報名：請至報名網址填寫資料
2.繳費：
（1）親至交大工程四館813室完成繳費(前來繳費者請先致電)
（2）匯款資訊如下：
戶名: 曾紫玲（國泰世華銀行 竹科分行013）
帳號: 075506235774 (國泰世華銀行 竹科分行013）
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據

聯絡方式：曾紫玲 Tel：03-5712121分機54599 Email：tzuling@nctu.edu.tw

Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.

2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.

3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.

Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.

Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).