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X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
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TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221209T213000
DTEND;TZID=Asia/Kolkata:20221209T223000
DTSTAMP:20260529T022908
CREATED:20221207T000049Z
LAST-MODIFIED:20221207T000049Z
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SUMMARY:Seminar by Dr. Subhash Lakshminarayana
DESCRIPTION:Title: IoT-Based Load-Altering Attacks Against Power Grids\nDate: 09-12-2022\, Friday\nTime: 4:00 PM – 5:00 PM\nVenue: MMCR of EE Department\, Room No: C-241 (First floor) \nAbstract: Large-scale Internet of Things (IoT)-based load-altering attacks can have a major impact on power grid operations such as causing unsafe frequency excursions and destabilizing the grid’s control loops. In this talk\, I will present my recent research on enhancing the resilience of power grids to IoT-based load-altering attacks. First\, I will present a novel analytical framework to investigate the impact of IoT-based static/dynamic load-altering attacks (S/DLAAs) on the power grid’s dynamic response using the theory of second-order dynamical systems. The results help identify the victim nodes from which that attacker can launch the most impactful attacks and offer insights into how the temporal fluctuations of load and renewable energy sources impact the grid’s vulnerabilities to LAAs. Finally\, I will present results on the detection and mitigation of such attacks. \nBiography: Dr. Subhash Lakshminarayana is an associate professor at the University of Warwick. His research interests include cyber-physical system security and wireless communications. He serves as an associate editor in the IET Smart Grid journal. His research is funded by Innovate UK\, EPSRC-PETRAS National Centre of Excellence for Cybersecurity of IoT Systems UK\, and the EUTOPIA European Alliance.
URL:https://ee.iisc.ac.in/event/seminar-by-dr-subhash-lakshminarayana/
LOCATION:EE\, MMCR
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221213T203000
DTEND;TZID=Asia/Kolkata:20221213T213000
DTSTAMP:20260529T022908
CREATED:20221212T221650Z
LAST-MODIFIED:20221212T221650Z
UID:240170-1670963400-1670967000@ee.iisc.ac.in
SUMMARY:Lecture by Dr. Raveesh Magod
DESCRIPTION:Title: Powering the Future – Research and Current Trends Shaping the Next Generations of Power Management ICs \nDate and Time: 13th December 2022 at 3pm \nVenue: MMCR EE \nAbstract: Power management has been a key enabler for a variety of ever increasing modern day electronic applications like smartphones\, data center servers\, electric vehicles and smart grids to name a few. High-efficiency\, small form-factor\, reliability and lower cost will continue to be critical requirements of power regulation for such applications. In this talk\, details about what constitutes this broad area of power management along with research vectors and market trends defining the next-generation ICs in power conversion is presented. Specifically i) ultra-low power consumption converters ii) Gallium Nitride (GaN) and Silicon Carbide (SiC) based power converters and iii) low noise and low EMI power conversion\, are identified and described as three major focus areas. Personal research contributions\, insights into latest research and industry products\, and future performance trendlines are presented in detail. \nSpeaker Bio: Raveesh Magod received the M.S. and Ph.D. degree in Electrical engineering from Arizona State University\, Tempe\, AZ\, USA\, in 2014 and 2018 respectively. In 2017\, he joined Jack Kilby Labs\, Texas Instruments\, Dallas\, TX\, USA\, where he is Member of Technical Staff and has been involved in R&D of wide range of power converter products ranging from nanopower voltage regulators to high power-density DC-DC converters and recently has been focusing on GaN based power converters. From 2015 to 2016\, he was an analog design intern at Texas Instruments\, Tucson\, AZ\, USA\, where he designed low-power voltage supervisors and low quiescent current LDOs. From 2010 to 2012\, he was a Design Engineer at Sankalp Semiconductor (A HCL technologies company)\, Hubli\, India\, developing low-power CMOS interface solutions. He was the co-recipient of A.K. Chowdhary Best Paper award at the International conference on VLSI Design\, 2021 and has five granted/pending U.S. patents to his name. He also serves as a technical program committee member for the CICC and a reviewer for multiple reputed IEEE journals.
URL:https://ee.iisc.ac.in/event/lecture-by-dr-raveesh-magod/
LOCATION:EE\, MMCR
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221220T163000
DTEND;TZID=Asia/Kolkata:20221220T173000
DTSTAMP:20260529T022908
CREATED:20221215T225256Z
LAST-MODIFIED:20221215T225552Z
UID:240183-1671553800-1671557400@ee.iisc.ac.in
SUMMARY:Thesis Colloquium of Mr. Debarpan Bhattacharya
DESCRIPTION:Degree Registered: MTech Resesarch\nTitle: A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications\nAdvisor: Prof. Sriram Ganapathy\nDate and Time: Tuesday\, Dec 20th \, 11am\nVenue:  MMCR\, Electrical Engineering\, IISc.\n\nAbstract: Deep neural networks are the most widely used examples of sophisticated mapping functions from feature space to class labels. In the recent years\, several high impact decisions in domains such as finance\, healthcare\, law and autonomous driving\, are made with deep models. In these tasks\, the model decisions lack interpretability\, and pose difficulties in making the models accountable. Hence\, there is a strong demand for developing explainable approaches which can elicit how the deep neural architecture\, despite the astounding performance improvements observed in all fields\, including computer vision\, natural language processing\, generates the output decisions.\n\nThe current frameworks for explainability of model learning are based on gradients (eg. GradCAM\, guided-gradCAM\, Integrated gradients etc) or based on locally linear assumptions (eg. LIME). Some of these approaches require the knowledge of the deep model architecture\, which may be restrictive in many applications. Further\, most of the prior works in the literature highlight the results on a set of small number of examples to illustrate the performance of these XAI methods\, often lacking statistical evaluation.\n\nThis talk proposes a new approach for explainability based on mask estimation approaches\, called the Distillation Approach for Model-agnostic Explainability (DAME). The DAME is a saliency-based explainability model that is post-hoc\, model-agnostic\, and applicable to any architecture/domain. The DAME is a student-teacher modeling approach\, where the teacher model is the original model for which the explainability is sought\, while the student model is the mask estimation model. The input sample is augmented with various data augmentation techniques to produce numerous samples in the immediate vicinity of the input. Using these samples\, the mask estimation model is learned to learn the saliency map of the input sample for predicting the labels. A distillation loss is used to train the DAME model\, and the student model tries to locally approximate the original model. Once the DAME model is trained\, the DAME generates a region of the input (either in space or in time-domain for images and audio samples\, respectively) that best explains the model predictions. \n\nWe also propose an evaluation framework\, for both image and audio tasks\, where the XAI models are evaluated in a statistical framework on a set of held-out of examples with the Intersection-over-Union (IoU) metric. We have validated the DAME model for vision\, audio and biomedical tasks. Firstly\, we deploy the DAME for explaining a ResNet-50 classifier pre-trained on ImageNet dataset for the object recognition task. Secondly\, we explain the predictions made by ResNet-50 classifier fine-tuned on Environmental Sound Classification (ESC-10) dataset for the audio event classification task. Finally\, we validate the DAME model on the COVID-19 classification task using cough audio recordings. In these tasks\, the DAME model is shown to outperform existing benchmarks for explainable modeling. \n\nThe talk will also illustrate results on various multimodal datasets and conclude with a discussion on the limitations of the DAME approach along with the potential future directions.\n\n######################################################################\n\nAll are welcome\,\n\n\n\n 
URL:https://ee.iisc.ac.in/event/thesis-colloquium-of-mr-debarpan-bhattacharya/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221230T163000
DTEND;TZID=Asia/Kolkata:20221230T173000
DTSTAMP:20260529T022908
CREATED:20221205T053506Z
LAST-MODIFIED:20221205T053816Z
UID:240135-1672417800-1672421400@ee.iisc.ac.in
SUMMARY:Invited Talk by Prof Anjan Bose
DESCRIPTION:Title: Maintaining Reliability and Resiliency while Decarbonizing the Power Grid \nAbstract: The world has focused strongly\, through the Paris Accords\, on ridding the electricity generation mix of fossil fuels and replacing that with sustainable non-carbon resources. The emerging new generation mix\, however\, is changing the characteristics of the grid thus requiring the modification of the ways we plan\, design\, operate and control the grid. The new engineering tools and procedures must be ready as the penetration of renewable energy resources keeps increasing. These methods to provide highly reliable electricity to society were developed over decades but now must be updated more urgently. In this address we outline where these threats are coming from and how we maintain the highly reliable systems despite the changes. In addition\, the increase in extreme weather events is necessitating more efficient ways to recover from grid damage thus increasing the resiliency of the system in addition to maintaining reliability. \nAuthor’s Bio: Prof Anjan Bose (Life Fellow\, IEEE) received the B.Tech. degree from IIT Kharagpur\, Kharagpur\, India\, the M.S. degree from the University of California\, Berkeley\, CA\, USA\, and the Ph.D. degree from Iowa State University\, Ames\, IA\, USA. He has worked for industry\, academe\, and government for 40 years in electric power engineering. He is currently a Regents Professor and holds an Endowed Distinguished Professor in power engineering at Washington State University\, Pullman\, WA\, USA\, where he also served as the Dean for the College of Engineering and Architecture\, from 1998 to 2005. He is a member of the U.S. National Academy of Engineering and a Foreign Fellow of the Indian National Academy of Engineering. He received the Herman Halperin Award and the Millennium Medal from the IEEE and was recognized as a Distinguished Alumnus by IIT Kharagpur and Iowa State University
URL:https://ee.iisc.ac.in/event/invited-talk-by-prof-anjan-bose/
LOCATION:EE\, MMCR
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