BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Northeastern University College of Engineering
X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T093000
DTEND;TZID=America/New_York:20211216T103000
DTSTAMP:20260420T080400
CREATED:20211215T205218Z
LAST-MODIFIED:20211215T205218Z
UID:29766-1639647000-1639650600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Jaehyeon Ryu
DESCRIPTION:PhD Proposal Review: Engineering Functional Nanomesh for Advanced Neuroelectronics \nJaehyeon Ryu \nLocation: Zoom Link \nAbstract: Transparent electronics have emerged as promising platforms for neural interfacing by enabling simultaneous electrophysiological recording and optical measurements. Also\, there are high demands for stretchable devices due to their low modulus and compatible interface with irregular and soft neural tissue. However\, current transparent\, stretchable approaches are usually limited by their scalability for neuroelectronic applications. Here\, I present multi-functional nanomesh as an approach to achieve stretchable\, transparent microelectrode arrays (MEAs) with excellent scalability. By stacking mechanical supporting polymer\, gold\, and conductive polymer in a nanomesh structure on elastomer substrate\, multilayer nanomesh-based MEAs show excellent stretchability\, transparency\, and electrochemical properties with single neuron scale dimensions. The performance of these multi-functional nanomesh-based MEAs has been characterized through bench testing\, and I plan to perform in vivo validation in the remaining period of my thesis. These highly stretchable and transparent multilayer nanomesh MEAs are promising for applications ranging from neuroscience to biomedical devices.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-jaehyeon-ryu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T140000
DTEND;TZID=America/New_York:20211216T150000
DTSTAMP:20260420T080400
CREATED:20211206T145333Z
LAST-MODIFIED:20211207T164422Z
UID:29663-1639663200-1639666800@coe.northeastern.edu
SUMMARY:Distinguished Speaker Series in Robotics: Gregory S. Chirikjian
DESCRIPTION:Distinguished Speaker Series in Robotics \nGregory S. Chirikjian: Robot Imagination: Affordance-Based Reasoning about Unknown Objects \nLocation: ISEC Auditorium or Zoom Link \nAbstract: Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively. For humanoid robots to function as servants in the home and in hospitals they will need to demonstrate higher intelligence\, and must be able to function in ways that go beyond the stiff prescribed programming of their industrial counterparts. A new approach to service robotics is discussed here. The affordances of common objects such as chairs\, cups\, etc.\, are defined in advance. When a new object is encountered\, it is scanned and a virtual version is put into a simulation wherein the robot “imagines’’ how the object can be used. In this way\, robots can reason about objects that they have not encountered before\, and for which they have no training using. Videos of physical demonstrations will illustrate this paradigm\, which the presenter has developed with his students Hongtao Wu\, Meng Xin\, Sipu Ruan\, and others. \nBio: Gregory S. Chirikjian received undergraduate degrees from Johns Hopkins University in 1988\, and a Ph.D. degree from the California Institute of Technology\, Pasadena\, in 1992. From 1992 until 2021\, he served on the faculty of the Department of Mechanical Engineering at Johns Hopkins University\, attaining the rank of full professor in 2001. Additionally\, from 2004-2007\, he served as department chair. Starting in January 2019\, he moved the National University of Singapore\, where he is serving as Head of the Mechanical Engineering Department\, where he has hired 14 new professors so far. Chirikjian’s research interests include robotics\, applications of group theory in a variety of engineering disciplines\, applied mathematics\, and the mechanics of biological macromolecules. He is a 1993 National Science Foundation Young Investigator\, a 1994 Presidential Faculty Fellow\, and a 1996 recipient of the ASME Pi Tau Sigma Gold Medal. In 2008\, Chirikjian became a fellow of the ASME\, and in 2010\, he became a fellow of the IEEE. From 2014-15\, he served as a program director for the US National Robotics Initiative\, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at NSF. Chirikjian is the author of more than 250 journal and conference papers and the primary author of three books\, including Engineering Applications of Noncommutative Harmonic Analysis (2001) and Stochastic Models\, Information Theory\, and Lie Groups\, Vols. 1+2. (2009\, 2011). In 2016\, an expanded edition of his 2001 book was published as a Dover book under a new title\, Harmonic Analysis for Engineers and Applied Scientists. \n\nReceive Further Event Notifications \nPresented by the Institute for Experiential Robotics
URL:https://coe.northeastern.edu/event/distinguished-speaker-gregory-s-chirikjian/
LOCATION:ISEC Auditorium\, 805 Columbus Ave\, Boston\, MA\, 02115\, United States
GEO:42.3377049;-71.0870109
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=ISEC Auditorium 805 Columbus Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=805 Columbus Ave:geo:-71.0870109,42.3377049
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211216T150000
DTEND;TZID=America/New_York:20211216T160000
DTSTAMP:20260420T080400
CREATED:20211215T192630Z
LAST-MODIFIED:20211215T192630Z
UID:29764-1639666800-1639670400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Jinghan Zhang
DESCRIPTION:PhD Dissertation Defense: Domain Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications \nJinghan Zhang \nLocation: ISEC 362 or Zoom Link \nAbstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain\, e.g.\, video analytics\, software-defined radio\, and radar. This opens the opportunity for specialization to achieve the needed efficiency and/or performance.\nPlatforms integrating many accelerators (ACCs) is a primary approach for efficient\, high-performance stream computing.\nHowever\, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM).\nTo this end\, the concept of domain platforms is proposed\, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application.\nThis dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform\, where each ACC is specialized to accelerate a particular function.\nThere is a great challenge to select ACCs allocated in the domain platform\, considering the large design space and performance balance across many applications.\nHowever\, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g.\, one particular vision flow) for allocating a platform\, but not a set of similar applications.\nThis dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm\, which calculates an ACC score according to current allocation to guide mutation.\nAlternatively\, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE\, which avoids the complex application binding exploration.\nIn the experiments\, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform’s performance.\nRDP^3 using a machine learning method yields an even more significant speedup\, saving 90.8% of exploration time with only 0.0003% performance loss.\nDmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from manually and engineering experience guided into a general automatic process.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-jinghan-zhang/
END:VEVENT
END:VCALENDAR