Keynote: Contactless Sensing with Ubiquitous RF Signals: Theory and Applications

Abstract: While ubiquitous RF signals such as WiFi/4G/5G/Bluetooth/Zigbee signals are usually used for wireless communication, they could also be used to sense human motion and recognize activities of various scales in a device-free manner. In this talk, I will give an overview of the existing device-free wireless sensing techniques and show why contactless sensing with ubiquitous RF signals is appealing in many smart city applications. Through analysis of the related work in contactless RF sensing field, I list some fundamental research issues, and introduce the Fresnel zone based sensing theory and a series of properties to answer these open issues. Building on the Fresnel zone reflection and diffraction model and the properties, we could not only develop a series of techniques to enable various applications and reveal the sensing limit of ubiquitous RF signals, we could also understand why most of the previous wireless activity recognition systems don’t work in practical settings. I will use human respiration sensing, intrusion detection, indoor human tracking and in-air gesture recognition as application examples to demonstrate the power of the proposed theory and techniques.

BIO: 张大庆教授是北京大学博雅讲席教授，欧洲科学院院士，IEEE Fellow，中国计算机学会（CCF）普适计算专委会主任。1996年获得意大利罗马大学博士学位。主要研究方向包括普适计算、情境感知计算、城市计算、大数据分析和智能物联网等。在相关国际期刊、会议发表学术论文 300余篇，文章总引用20000余次，H因子71 (根据 Google Scholar), 国际国内专利30余项。所创的情境感知、无线感知模型被国际普适计算、移动计算和服务计算学术界广泛采用, 近期工作先后获得中国计算机学会推荐的全部4个普适计算国际会议的最佳论文或提名奖，包括CCF A类会议ACM UbiComp 2015、2016 的最佳论文提名奖，以及IEEE UIC 2019和 IEEE PerCom 2013 授予的“十年最具影响力论文奖”。张大庆教授是普适计算顶级期刊IEEE Pervasive Computing、ACM会刊IMWUT、 ACM Transactions on Intelligent Systems and Technology的编委, 担任过10多个国际会议的大会或程序委员会主席, 应邀在20多个国际会议做大会特邀报告。

Keynote: Personal Health Navigation

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Keynote: Swarm intelligence computing for human-machine-things fusion

Abstract: 近来，在智能物联网、边缘智能、群体智能等兴起背景下，跨空间人、机、物异构智能体协作增强将成为新一代群智感知计算最重要的演进方向——即“人机物融合群智计算”（CrowdHMI）。人机物融合群智计算涉及物联网、人工智能、生物学、群落生态学等多学科交叉，探索生物集群交互协同与异构群智能体间高效协作的隐含关联和映射机理，将通过异构群智能体的有机交互、协作、竞争与对抗，构建具有自组织、自学习、自适应、持续演化等能力的智能感知计算空间。本报告将探讨CrowdHMI的基础理论、特质与科学挑战，并介绍我们在这方面的研究进展。

BIO: 郭斌，工学博士，西北工业大学计算机学院教授/博导，国家杰出青年科学基金获得者，工信部智能感知与计算重点实验室副主任，西北工业大学计算与艺术交叉研究中心主任。2009年在日本庆应大学获博士学位，2009-2011年在法国国立电信学院进行博士后研究。入选教育部“新世纪优秀人才”（2012）和国家“万人计划”青年拔尖人才（2017）。
主要从事普适计算、移动群智感知、大数据智能方面研究。在IEEE/ACM汇刊等国内外重要期刊和会议上发表论文150余篇，国际上率先研发了开源可定制的移动群智感知平台CrowdOS (http://www.crowdos.cn/)，且面向智慧城市、公共安全、智能制造等国家重大需求开展领域应用和技术推广。曾获得教育部自然科学一等奖、陕西省自然科学一等奖以及IEEE UIC’17、ISI’19等国际会议“最佳论文奖”。担任《IEEE Transactions on Human-Machine Systems》、《IEEE Communications Magazine》、《ACM IMWUT》等国际权威期刊编委。IEEE高级会员，CCF杰出会员，CCF西安分部副主席。

Keynote: 6G: Connecting Everything in Intelligence Era

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BIO: Dr. SHEN Jia graduated from Department of Electronic Engineering, Tsinghua University, China, and then received his Ph.D in Electronics degree from University of York, UK. Dr. SHEN joined in 4G standardization from 2005, and was a key contributor to TD-LTE standards. Since 2009, he has been the chairman of Testing and Specification Group of IMT-Advanced Promotion Group, MIIT of China, and responsible for the specifications and test result analysis in China’s 4G large-scale trial. Dr. Shen has been working with OPPO as a principal researcher of telecommunication standard, and joined in 5G NR standardization and 6G research since 2016. He served as 3GPP Feature Leads on 5G NR-PUCCH and URLLC (Ultra Reliable and Low-Latency Communications) enhancements. Dr. Shen has been the rapporteur of “AI/ML Model Transfer (AMMT) in 5GS” requirements Study/Work Item in 3GPP SA1 since 2019. He is the first author of books ”3GPP Long-Term Evolution (LTE): Technical Principle and System Design ” and “5G NR and Enhancements: From R15 to R16”, and is the inventor of over 100 patents.

Keynote: Innovations in Smart Health: A 10-Year Retrospective Review

Abstract: Smart technologies interact with people by tightly integrating sensing, communication, and computation with human objects. Through digitalizing the human body, living behavior environment, and medication, smart health technologies can advance the management of healthcare, augment human performance, illuminate the emerging socio-technological landscape, and eventually enhance the quality of life. In this talk, I will report our research practice on smart health in the past decade, from childhood health (e.g., Autism screening) to elderly care (e.g., Parkinson’s Disease and Post-stroke care) and something in-between. Also, I will provide an overview of our ongoing effort to address the challenges at the convergence of technologies, systems, data, and applications in the field of smart health.

BIO: Wenyao is an Associate Professor of the Computer Science and Engineering Department, University at Buffalo, the State University of New York (SUNY). Currently, he also serves as the Associate Department Chair. Wenyao received the Ph.D. degree from the University of California at Los Angeles, Los Angeles, USA, and both the Master and Bachelor degrees from Zhejiang University, China. His group has focused on exploring novel sensing and computing technologies to build up novel mobile and Internet-of-Things (IoT) systems for high-impact human-technology applications in the fields of Smart Health, Biometrics, Cyber-Security Applications. Results have been published in peer-reviewed top research venues across multiple disciplines, including Computer Science conferences (e.g., ACM MobiCom, SenSys, MobiSys, UbiComp, CCS, Oakland, NDSS, ASPLOS, ISCA, and HPCA), Biomedical Engineering journals (e.g., IEEE TBME, TBioCAS, and JBHI), and Medicine journals (e.g., LANCET, JMIR). To date, his group has published over 200 papers, won 14 best paper awards/runner-up and six international best design awards. His inventions have been filed within U.S. and internationally as patents. His research has been widely reported in high-impact media outlets, including the Discovery Channel, CNN, NPR, and the Wall Street Journal. Wenyao is an Associate Editor of IEEE Internet-of-Things Journal, serves as organizer and committee member of numerous conferences in the field of Smart Health and the Internet of Things.

Keynote: Robust Autonomous Vehicle Perception beyond the Visible Spectrum: Super-Resolution RF Sensing for Autonomous Vehicles

Abstract: Self-driving vehicles are anticipated to become a common sight on the roads in the near future. To achieve higher automation levels, self-driving vehicles rely heavily on on-board sensors, such as camera, lidar and radar, to perceive the surrounding environment. In spite of the remarkable advancements of computer vision technologies, visual sensors including camera and lidar still fall short of robustness due to occlusion, low lighting, and inclement weather conditions. On the other hand, radio frequency (RF) sensors such as radar are intrinsically resilient against such impediments, yet their resolution is orders of magnitude coarser. In this talk, I will present new RF sensing technologies that can potentially overcome the fundamental limitations. First, I will introduce ways of smartening the roadside infrastructure through passive chipless tags, which can directly convey environmental information to radar sensors. Second, I will introduce ways of enabling robust lidar sensing under adverse weather, by fusing radar signals. I will also discuss the potential of modern broadband communication technologies in bolstering autonomous vehicle perception.

BIO: Xinyu Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California San Diego. Prior to joining UC San Diego in 2017, he was an Assistant Professor at the University of Wisconsin-Madison. He received his Ph.D. degree from the University of Michigan in 2012. His research interest lies in wireless systems and ubiquitous computing, and more specifically in (i) designing next-generation wireless architectures and physical-layer informed protocols; (ii) designing ubiquitous systems that leverage wireless signals to sense micro-locations and micro-activities at near-vision precision. His research work regularly appears in top conferences in these areas, especially ACM MobiCom, MobiSys, SIGCOMM, USENIX NSDI, and IEEE INFOCOM. He is the recipient of two ACM MobiCom Best Paper Awards (2011 and 2020), Communications of the ACM Research Highlight (2018), ACM SIGMOBILE Research Highlight (2018), NSF CAREER Award (2014), Google Research Award (2017, 2018, 2020), and Sony Research Award (2018, 2020). He served as the TPC chair for ACM MobiCom 2019, IEEE SECON 2017, co-chair of NSF millimeter-wave research coordination network, and Associate Editor for IEEE Transactions on Mobile Computing from 2017 to 2020.