SUNMONTUEWEDTHUFRISAT
Events for March 05, 2020
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Xiaonan Hui - ECE-EP Seminar, Thursday, March 5th at 11am in EEB 248
Thu, Mar 05, 2020 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Xiaonan Hui, Cornell University
Talk Title: Harmonic RF sensing from indoor localization to vital signs monitoring
Abstract: When wireless is perfectly applied, the whole earth will be converted into a huge brain, which in fact it is all things being particles of a real and rhythmic whole." For almost a century, electrical engineers are endeavoring to approach what Nikola Tesla predicted in 1926 for a "World Wireless System". However, as of today, many hurdles remain when we think of all things connected rhythmically with interaction and links between the cyber and the physical worlds, because sensing of the "things", especially "living things", is still heavily constrained. The location and shape of objects, as well as the vital signs of people and animals are critical information to the overall systems. In this talk, I will first highlight our solutions of highly reliable and accurate indoor RF ranging, localization and imaging. The demonstrated radio frequency (RF) localization method bypasses the Uncertainty-Principle mathematical model commonly seen in the radar-like system, so that the high temporal (kHz) and spatial (microns) resolutions can be achieved simultaneously with ~915 MHz signals which have deep penetration to many dielectrics of interests such as building materials and living tissues. Vital-sign monitoring is the second part of the talk, including the heartbeat dynamics, respiration, and blood pressures of both central and pulmonary circulations, with the new near-field coherent sensing (NCS) approach, which not only provides unparalleled RF vital-sign signal quality and sensing capability, but also does not require skin touch or motion restraint to greatly improve the applicability to people and animals. The systems in this talk can be implemented in the applications of high precision indoor locating, assisted living, RF bio-tomography, biometrics for security, wearable sensors, and clinical researches. The talk will include the supporting RF theory, the design methods and the hardware/software experimental system, but its content will be aimed for the general audience in engineering.
Biography: Xiaonan Hui is a Ph.D. candidate in the School of Electrical and Computer Engineering at Cornell University. He works with Prof. Edwin Kan and focuses on radio-frequency systems for Cyber Physical System (CPS) and Internet of Things (IoT) applications. His recent works on vital-sign acquisition for people as well as animals were published on high-impact journals and conferences, attracting not only more than 30 news agencies, but also generating broad industrial interests for automotive, medical, pharmaceutical, and digital agricultural applications. Moreover, his high-precision indoor localization works provide an innovative method for IoT tracking, robotic localization, and civil structure integrity monitoring. He is the principal investigator of Cornell Scale-up and Prototype Grants, the winner of Cornell ECE Outstanding Ph.D. Thesis Research Award, and other 3 fellowships. He also serves as the reviewer for Springer Nature, IEEE journals and conferences in the areas of electromagnetic systems, vital-sign sensing and wireless communications. More of his academic information can be found in his website: www.xiaonanhui.com
Host: ECE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Thu, Mar 05, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Frederic Sala, Stanford Computer Science Department
Talk Title: Structure to the Rescue: Breaking Data Barriers in Machine Lear
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The current machine learning zeitgeist is that models are only as good as the data they are fed, so that limitations in the data---and especially mismatches with the ML algorithm---present fundamental barriers to model performance. However, for ML to continue its growth and be safely and widely deployed across domains with significant societal impact, such limitations must be minimized. In this talk, I will describe two ways to exploit structure in data to overcome apparent obstacles, with theoretical guarantees.
First, I will argue that geometry is a barrier to producing quality representations used by models. The root cause is a mismatch between the geometric structure of the data and the geometry of the model---but the issue can be resolved by adopting matching non-Euclidean geometries, relying on, for example, hyperbolic geometry for hierarchical data. Next, motivated by the fact that labeling large datasets is a major bottleneck in supervised learning, I will discuss a weak supervision framework for automating the process of labeling, overcoming the lack of hand-labeled data. This is done by encapsulating different aspects of manual labeling into heuristics whose structure is characterized by learnable accuracies and correlations. I will describe extensions of this framework to handle multitask, time-series, and other forms of structured data. This framework is widely used in industry, helping drive applications used by millions daily.
Biography: Frederic Sala is a postdoctoral scholar in the Stanford Computer Science Department, advised by Chris Ré. His research interests include machine learning, data-driven systems, and information and coding theory, and in particular problems related to the analysis and design of algorithms that operate on diverse and challenging forms of data. He received the Ph.D. and M.S. degrees in Electrical Engineering from UCLA, where he received the Distinguished Ph.D. Dissertation in Signals & Systems Award from the UCLA Electrical Engineering Department, the NSF graduate fellowship, and the Edward K. Rice Outstanding Master's Student Award.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
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ECE Seminar: Collaborative Perception and Learning Between Robots and the Cloud
Thu, Mar 05, 2020 @ 02:15 PM - 03:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sandeep Chinchali, PhD Candidate, Dept of CS, Stanford University
Talk Title: Collaborative Perception and Learning Between Robots and the Cloud
Abstract: Augmenting robotic intelligence with cloud connectivity is considered one of the most promising solutions to cope with growing volumes of rich robotic sensory data and increasingly complex perception and decision-making tasks. While the benefits of cloud robotics have been envisioned long before, there is still a lack of flexible methods to trade-off the benefits of cloud computing with end-to-end systems costs of network delay, cloud storage, human annotation time, and cloud-computing time. To address this need, I will introduce decision-theoretic algorithms that allow robots to significantly transcend their on-board perception capabilities by using cloud computing, but in a low-cost, fault-tolerant manner.
Specifically, for compute-and-power-limited robots, I will present a lightweight model selection algorithm that learns when a robot should exploit low-latency on-board computation, or, when highly uncertain, query a more accurate cloud model. Then, I will present a collaborative learning algorithm that allows a diversity of robots to mine their real-time sensory streams for valuable training examples to send to the cloud for model improvement. The utility of these algorithms will be demonstrated on months of field data and experiments on state-of-the-art embedded deep learning hardware. I will conclude this talk by outlining a number of future research directions on the systems and theoretical aspects of networked system control, some of which extend beyond cloud robotics.
Biography: Sandeep Chinchali is a computer science PhD candidate at Stanford, advised by Sachin Katti and Marco Pavone. Previously, he was the first principal data scientist at Uhana, a Stanford startup working on data-driven optimization of cellular networks, now acquired by VMWare. His research on networked control has led to proof-of-concept trials with major cellular network operators and was a finalist for best student paper at Robotics: Science and Systems 2019. Prior to Stanford, he graduated from Caltech, where he worked on robotics at NASA's Jet Propulsion Lab (JPL). He is a recipient of the Stanford Graduate Fellowship and National Science Foundation (NSF) fellowships.
Host: Host: Professor Konstantinos Psounis
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
