报告题目（一）：Network Resource Management in Wireless Networked Control Systems
报告人： 美国圣母大学 胡晓波 教授 IEEE Fellow
报告时间：2019年6月27日 周四 下午 15:00
报告摘要：Wireless networked control systems (WNCSs) are fundamental to many Internet-of-Things (IoT) applications that must work under real-time constraints in order to ensure timely collection of environmental data and proper delivery of control decisions. The Quality of Service (QoS) offered by a WNCS is thus often measured by how well it satisfies the end-to-end deadlines of the real-time tasks executed in the WNCS. Network resource management in WNCSs plays a critical role in achieving the desired QoS. Unexpected internal and external disturbances that may appear in WNCSs concurrently make resource management inherently challenging. The explosive growth of IoT applications especially in terms of their scale and complexity further exacerbate the level of difficulty in network resource management.
In this talk, I first give a general introduction of WNCSs and the challenges that they present to network resource management. In particular, I will discuss the complications due to external disturbances and the need for dynamic data-link layer scheduling. I then highlight our recent work that aims at tackling this challenge. Our work balances the scheduling effort between a gateway (or access points) and the rest of the nodes in a network. It paves the way towards decentralized network resource management in order to achieve scalability. Experimental implementation on a wireless test bed further validates the applicability of our proposed research. I will end the talk outlining our on-going effort in this exciting and growing area of research.
报告人简介： 胡晓波，美国圣母大学计算机科学与工程系教授，IEEE Fellow，ACM SIGDA主席。主要学术方向为低功耗系统设计、基于新兴技术的电路和架构设计、软硬件协同设计及嵌入式系统等，已发表论文300余篇，并获得Design Automation Conference，ACM/IEEE International Symposium on Low Power Electronics and Design 最佳论文奖，NSF杰出成就奖（NSF CAREER Award）。她参与多项政府-企业联合资助的研究中心级别项目，包括担任NSF/SRC E2CDA项目的负责人。她还担任2018年度ACM/IEEE Design Automation Conference大会主席，2015年度DAC TPC主席；IEEE Transactions on VLSI、ACM Transaction on Design Automation of Electronic Systems、ACM Transactions on Embedded Computing Systems、ACM Transactions on Cyber-Physical Systems 等学术期刊的副主编。
报告题目（二）：Intelligent Computing, Big Data, and Modern Medicine and Healthcare
报告人： 美国圣母大学 陈子仪 教授 IEEE Fellow
报告时间：2019年6月27日 周四 下午 16:00
报告摘要：Computer technology plays a crucial role in modern medicine, healthcare, and life sciences, especially in medical imaging, human genome study, clinical diagnosis and prognosis, treatment planning and optimization, treatment response evaluation and monitoring, and medical data management and analysis. As computer technology rapidly evolves, computer science solutions will inevitably become an integral part of modern medicine and healthcare. Computational research and applications on modeling, formulating, solving, and analyzing core problems in medicine and healthcare are not only critical, but are actually indispensable!
Recently emerging deep learning (DL) techniques have achieved remarkably high quality results for many computer vision tasks, such as image classification, object detection, and semantic segmentation, largely outperforming traditional image processing methods. In this talk, we first discuss some development trends in the area of intelligent medicine and healthcare. We then present new approaches based on DL techniques for solving a set of medical imaging problems, such as segmentation and analysis of glial cells, analysis of the relations between glial cells and brain tumors, segmentation of neuron cells, and new training strategies for deep learning using sparsely annotated medical image data. We develop new deep learning models, based on fully convolutional networks (FCN), recurrent neural networks (RNN), and active learning, to effectively tackle the target medical imaging problems. For example, we combine FCN and RNN for 3D biomedical image segmentation; we propose a new complete bipartite network model for neuron cell segmentation. Further, we show that simply applying DL techniques alone is often insufficient to solve medical imaging problems. Hence, we construct other new methods to complement and work with DL techniques. For example, we devise a new cell cutting method based on k-terminal cut in geometric graphs, which complements the voxel-level segmentation of FCN to produce object-level segmentation of 3D glial cells. We show how to combine a set of FCNs with an approximation algorithm for the maximum k-set cover problem to form a new training strategy that takes significantly less annotation data. A key point we make is that DL is often used as one main step in our approaches, which is complemented by other main steps. We also show experimental data and results to illustrate the practical applications of our new DL approaches.
报告人简介：陈子仪博士1985年获得美国旧金山大学计算机科学和数学学士学位，并分别于1988年和1992年获得美国普渡大学西拉法叶分校的计算机科学硕士和博士学位，他自1992年以来一直在美国圣母大学计算机科学与工程系任教，现任教授。陈教授的主要研究兴趣是计算生物医学，生物医学成像，计算几何，算法和数据结构，机器学习，数据挖掘和VLSI。他在这些领域发表了130多篇期刊论文和210多篇经过同行评审的会议论文，并拥有5项美国计算机科学与工程和生物医学应用技术开发专利。他于1996年获得NSF CAREER奖，2011年获得计算机世界荣誉计划的荣誉奖，用于开发“弧度调制放射治疗”（一种新的放射性癌症治疗方法）及2017年获美国国家科学院的PNAS Cozzarelli奖。他是IEEE Fellow和ACM杰出科学家。