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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
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250307T150000
DTEND;TZID=Asia/Kolkata:20250307T170000
DTSTAMP:20260526T080037
CREATED:20250304T120025Z
LAST-MODIFIED:20250304T120025Z
UID:241969-1741359600-1741366800@ee.iisc.ac.in
SUMMARY:EE Talk: Cybersecurity for the Power Grid in the AI Age - 7th March 3PM\, MMCR
DESCRIPTION:IEEE PES Student Branch Chapter\, IISc and POWERGRID Center of Excellence in Cyber Security (PGCoE) is pleased to invite you for a technical talk by Prof. Manimaran Govindarasu\, Iowa State University\, USA. The technical talk details are as follows. \n\nTitle: Cybersecurity for the Power Grid in the AI Age \nVenue: C-241\, MMCR\, 1st floor\, Electrical Engineering Department \nDate and Time: 07th March 2025\, 03:00 PM IST \nMode: Hybrid (Online and Offline).   MS Team link \nTalk Abstract: Power grid is a complex cyber-physical system (CPS) that forms the lifeline of our modern society. Reliable\, secure\, and resilient operation of nation’s energy infrastructure is of paramount importance to national security and economic wellbeing. In recent years\, there has been a growing trend of cyber threats/attacks\, both in numbers and sophistication\, targeting critical infrastructure systems around the globe (e.g.\, Stuxnet\, Ukraine power grid attacks\, and Colonial Pipeline attack). This evolving cyber threat landscape\, coupled with the leveraging of AI tools by the adversaries\, underscores the importance and urgency for CPS security solutions that go beyond the traditional IT cybersecurity by leveraging grid’s cyber-physical properties and harnessing the power of AI models to be able to prevent\, detect\, and mitigate stealthy cyber attacks. This talk will present an overview of cybersecurity threats to critical energy infrastructures\, then it will present a holistic life-cycle model for CPS security with some illustrative example solutions for attack prevention\, detection\, and mitigation. The talk will also briefly discuss CPS security testbeds for attack-defense evaluations and then conclude with some future research directions. \n\nSpeaker Bio: Manimaran Govindarasu is on the faculty of Iowa State University since 1999\, and he currently holds the titles of Anson Marston Distinguished Professor in Engineering and Harploe Professor in Electrical and Computer Engineering. Prior to joining Iowa State\, he received his Ph.D. in Computer Science and Engineering from Indian Institute of Technology\, Madras (Chennai). His research experience includes Cybersecurity for the Smart Grid and Critical Infrastructures\, and Real-time Systems and Networks. He has co-authored over 300 peer-reviewed research publications\, received multiple conference best paper awards\, presented several dozen conference invited talks\, tutorials\, and industry short-courses\, hands-on training sessions\, and mentored over 50 graduate students for their dissertation/thesis research. He is currently serving as the Chair of Cybersecurity Working Group for Power Grid in IEEE Power & Energy Society and has served as an Associate Editor and guest co-editor for several flagship IEEE publications. His research is supported over the years by US NSF\, DOE\, DHS\, and DOD. He is a Fellow of the IEEE and an ABET Program Evaluator.
URL:https://ee.iisc.ac.in/event/ee-talk-cybersecurity-for-the-power-grid-in-the-ai-age-7th-march-3pm-mmcr/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250319T160000
DTEND;TZID=Asia/Kolkata:20250319T173000
DTSTAMP:20260526T080037
CREATED:20250317T091519Z
LAST-MODIFIED:20250317T091519Z
UID:241988-1742400000-1742405400@ee.iisc.ac.in
SUMMARY:Thesis Defense (Online)
DESCRIPTION:Title: Parallel Algorithms for Efficient Utilization of Multiprocessor Architectures for Transient Stability \nStudent: Francis C Joseph \nFaculty Advisor: Dr. Gurunath Gurrala. \nExaminer: Prof Saikat Chakrabarti\, IIT Kanpur \nDate : 19th March 2025 \nTime: 4 PM – 5.30 PM \nMode: ONLINE \nTEAMS LINK \nABSTRACT: \nComputer hardware capabilities have been enormously increasing over the years. Multi-core processors\, graphic processing units (GPUs)\, and field programmable gate array (FPGA) accelerators have grown significantly recently. They have opened new computational paradigms such as edge computing\, fog computing\, grid computing\, distributed computing\, cloud computing\, and exascale supercomputing. However\, efficiently utilising most of these computational paradigms in traditional engineering disciplines\, such as power engineering\, is challenging. In this thesis\, efficient algorithms for multiprocessor-based high-performance computing and edge computing platforms for two power system applications are developed\, power system stability assessment and power quality measurements respectively. Faster than real-time transient stability assessment of large power grids using time domain simulations with detailed models is computationally challenging. Today\, the commercial tools used for this application in Energy Management Systems (EMS) worldwide rely on parallel batch processing methods\, which don’t efficiently utilise the architecture of the computational paradigms. For transient stability simulations\, this thesis explores a time parallel algorithm\, Parareal in Time\, which belongs to a class of temporal decomposition methods for time parallel solutions of differential equations. Two effective implementation approaches\, Master Worker and Distributed\, are analysed for large systems\, and scaling tests are performed using a state space model with a Message Passing Interface (MPI) in a multiprocessor environment. One of the findings was that the performance of the Parareal depends on the accuracy and the computational cost of the coarse solver used for initialisation and subsequent correction steps. A potential coarse solver\, Modified Euler (ME)\, a well-known solver for transient stability simulations even in commercial packages\, has been explored to adapt its step size by controlling the Local Truncation Error (LTE) to achieve the desired accuracy. An LTE estimator using a Multistage Homotopy Analysis Method (MHAM)\, which gives an approximate solution to a set of non-linear equations in the form of a power series\, is proposed to control the LTE at each integration step to enable adaptation of the ME step size. The proposed MHAM-assisted adaptive ME solver is faster and has comparable accuracy to the conventional fixed and adaptive Modified Euler solver for large systems’ transient stability simulations. Since MHAM is lighter than the ME solver and the LTE estimate is sufficient for step size adaptation\, an adaptive MHAM coarse solver is proposed for the Parareal. However\, MHAM provides a non-zero auxiliary parameter `c’ to select a family of solutions. Hence\, an optimisation framework is also proposed to automatically select this parameter based on the system’s dynamics. Based on many case studies on test systems of different sizes\, it is found that maintaining the LTE lower than the Parareal convergence tolerance improves the speedup of the Master-Worker paradigm; however\, for the distributed implementation\, maintaining LTE higher than the convergence tolerance gives improved speedup. An approach to include unscheduled events which arise in power system operation due to the operation of protective relays is also proposed for Parareal. The impact of frequency estimation on Parareal is evaluated using three estimation methods. It was found that the network admittance-based method has the lowest execution time. Many different types of disturbance types are performed on systems of different sizes and see that Parareal can maintain its performance. In Parareal implementation\, each coarse time segment is assigned to one processor in the MPI environment. Multiple processors in a node can be assigned to a coarse time segment to improve speedup. Therefore\, a shared memory-based space parallel transient stability solver is also considered for further performance enhancement. Space parallelisation of transient stability simulation involves breaking the network into subnetworks and solving each part independently while ensuring the original network’s convergence. Therefore\, a Multi Area Thevenin Equivalent (MATE) based parallel solver implementation on a shared memory platform is proposed\, and both space parallelisation and task parallelisation are explored. It is shown that the space parallelism can closely match the ideal speedup and can be exceeded by space + task parallelism while the network is well-partitioned. It can be further improved when combined with time parallelism. A hybrid time-space solver using OpenMP MATE\, space + task parallelism\, and MPI Parareal is proposed using two scheduling schemes: homogeneous and heterogeneous for both communication paradigms. The homogeneous scheduling enabled a faster-than-real time solution even for the PEGASE 13659 bus system and provided multiple combinations to achieve it. The heterogeneous can increase the performance of the hybrid solver when homogeneous scheduling is unavailable. A particular case for Hybrid Master with a single core worker was used to showcase the initialisation phase’s time reduction by reducing the coarse solver’s computational time. The current state-of-the-art chips also provide multicore architectures for edge computing applications. One such low-cost\, open-source\, heterogeneous\, resource-constrained hardware platform is called Parallella. The unique hardware architecture of the Parallella provides many edge computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. This Parallella device was used as a measurement device for edge computing applications research in smart grids\, and it could sample 3 voltages and four currents at a 32 kHz sampling rate. The thesis explores one application of such a device to measure the harmonics and compute various Power Quality (PQ) indices. A parallel implementation of multichannel FFT on Epiphany for the streaming data is developed in this regard. Epiphany 16-core architecture has very limited memory resources\, and the order in which the cores are to be accessed significantly impacts the execution. Proper decomposition of the FFT algorithm tasks and scheduling the tasks for efficient core and memory usage are crucial\, requiring a good understanding of the Epiphany architecture. The obtained PQ measurements from the proposed implementation are comparable to those of a commercial power analyser.
URL:https://ee.iisc.ac.in/event/thesis-defense-online/
LOCATION:Online\, India
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250320T113000
DTEND;TZID=Asia/Kolkata:20250320T130000
DTSTAMP:20260526T080037
CREATED:20250317T041048Z
LAST-MODIFIED:20250317T041048Z
UID:241984-1742470200-1742475600@ee.iisc.ac.in
SUMMARY:Colloquium
DESCRIPTION:Date & Time: 20th March 2025\, at 11:30 am \nSpeaker: Mr. Varun Krishna P S \nVenue: EE\, MMCR [1st Floor\,  C241]\n  \nTitle: Self-Supervised Learning Approaches for Content Factor Extraction from Raw Speech \n  \nAbstract: \nThe rapid expansion of the digital data has led to a growing interest in self-supervised learning (SSL) techniques\, particularly for speech processing tasks where labeled data is often scarce. SSL enables models to learn meaningful representations directly from raw data by capturing inherent structures and patterns without requiring explicit supervision. To be effective\, speech representations must not only capture content-related information—such as phonetic\, lexical\, and semantic features—but also remain robust against speaker variations\, co-articulation effects\, channel distortions\, and background noise. The focus of this talk is to describe our efforts in developing self-supervised learning techniques in extracting semantic content from raw speech while remaining invariant to non-semantic speech factors. \n  \nIn the first part of the talk\, we propose the Hidden Unit Clustering (HUC) framework\, which integrates contrastive learning with deep clustering techniques to enhance representation quality. A speaker normalization strategy is incorporated to mitigate speaker variability\, ensuring that the extracted representations focus primarily on the content-related information. Additionally\, a heuristic data sampling method is introduced to generate pseudo-targets for deep clustering\, further refining the learned representations. The framework is evaluated across multiple SSL models\, demonstrating significant improvements in phonetic and semantic benchmarks\, as well as superior performance in the low-resource ASR settings. \n  \nThe second part of the talk focuses on the efforts to achieve context-invariant representations to address the challenges posed by co-articulation effects and variations in speaker and channel characteristics. To achieve this\, a pseudo-con loss framework is proposed\, leveraging pseudo-targets to guide the contrastive learning and enhance robustness. This approach serves as an effective auxiliary module that can be seamlessly integrated into SSL models based on deep clustering. Extensive evaluations demonstrate state-of-the-art performance across multiple ZeroSpeech 2021 sub-tasks\, as well as significant improvements in phoneme recognition and ASR performance. \n  \nIn the final part of the talk\, we explore the integration of adversarial learning to obtain semantic representations that are invariant to non-semantic factors. A gradient-reversal mechanism is employed to suppress non-semantic variations explicitly within the SSL models\, thereby refining the learned representations. The proposed adversarial learning approach effectively disentangles content from non-semantic factors\, leading to robust semantic representations. Experimental results confirm that the proposed approach enhances the ability of speech processing models to generalize across different acoustic conditions while preserving the linguistic information. \n  \n  \nBio: \nVarun Krishna is a PhD scholar at the Department of Electrical Engineering\, Indian Institute of Science Bengaluru. He obtained his B.Tech from the Department of Electronics and Communications from NITK\, Surathkal\, in 2017. Later completed his M.Tech in Signal Processing from the Department of Electrical Engineering\, Indian Institute of Science\, Bengaluru\, in 2019. His research interests include self-supervised representation learning\, large language models\, and generative AI. \n 
URL:https://ee.iisc.ac.in/event/colloquium-3/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250325T150000
DTEND;TZID=Asia/Kolkata:20250325T170000
DTSTAMP:20260526T080037
CREATED:20250319T120706Z
LAST-MODIFIED:20250319T120726Z
UID:241990-1742914800-1742922000@ee.iisc.ac.in
SUMMARY:PhD Oral Examination
DESCRIPTION: The oral examination of Lalgy Gopi will be held at the Multi-Media Class Room (MMCR)\, EE Department\, between 3 pm and 5 pm on Tuesday\, March 25.
URL:https://ee.iisc.ac.in/event/phd-oral-examination/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20250327T140000
DTEND;TZID=Asia/Kolkata:20250327T170000
DTSTAMP:20260526T080037
CREATED:20250327T064327Z
LAST-MODIFIED:20250327T064327Z
UID:241993-1743084000-1743094800@ee.iisc.ac.in
SUMMARY:PhD Thesis Defense
DESCRIPTION:Name of the Candidate:  Lalit Manam\nResearch Supervisor: Venu Madhav Govindu\nDate and Time: March 27\, 2025\, Thursday\, 2:00 PM\nVenue: C-241\, First Floor\, Multimedia Classroom (MMCR)\, EE\nTitle: Global Methods for Camera Motion Estimation\nAbstract:\nIn computer vision\, Structure-from-Motion (SfM) aims to recover a 3D reconstruction of a scene from a collection of images of the scene. SfM has been of interest to the 3D computer vision community for the last few decades\, with a wide range of scientific\, industrial and domestic applications. A key component of global approaches to SfM is estimating the motion of individual cameras\, i.e. their rotation and translation with respect to a frame of reference. The camera motions are generally not available a priori\, making their estimation a crucial component. This thesis focuses on accurate\, reliable and efficient estimation of camera motions in SfM. We examine a number of issues concerning the estimation of camera motions\, namely input quality\, choice of cost function and the underlying graph representation\, and develop methods to address these issues.\nInput Quality: We examine the problem of translation averaging\, where all camera translations are simultaneously estimated\, given relative directions between them as the input. The accuracy of relative camera directions is limited due to multiple factors relating to how they are obtained. We take recourse to keypoint correspondences between image pairs from which relative motions between the cameras are estimated. We propose a modular framework to iteratively reweight keypoint correspondences based on their global consistency via translation averaging methods. Our proposed framework improves relative translation directions in comparison to the translation averaging scheme used.\nChoice of Cost Function: In translation averaging\, recovering camera translations from relative directions involves two types of optimization costs\, either comparing directions or displacements. These cost functions are often relaxed to obtain simpler optimization approaches. We observe that neither cost performs the best under varying distributions of the underlying camera translations and the noise present in the input directions. We propose a principled approach to recursively fuse the estimates obtained from both the relaxed costs using a principled uncertainty model. Our method leads to improvement in camera translation estimates compared to the individual costs.\nGraph Representation: We leverage the underlying graph representation that arises due to relationships between cameras in two applications to improve camera motion estimates. In the first application\, we introduce the idea of sensitivity in translation averaging\, which analyzes the change in camera translations with small perturbations to input directions. We develop two different formulations to theoretically analyze the sensitivity/conditioning of the problem based solely on the inputs. We propose efficient algorithms to remove the ill-conditioned configuration of inputs\, which is abundant in real data. Removal of ill-conditioned inputs significantly improves the translation estimates\, revealing the benefits of our analysis.\nThe second application that leverages graph representation solves the tasks of graph sparsification and disambiguation of repeated structures in a unified manner. We present a scoring mechanism to identify redundant and false edges and remove them with a threshold that is optimal under an edge selection cost. We design efficient algorithms which can be applied as a preprocessing step to any SfM pipeline. Our approach handles both tasks in a unified manner\, making it practical for use. Applying our methods reduces reconstruction time due to the sparsification of graphs and avoids superimposed reconstructions because of repeated structures being disambiguated.
URL:https://ee.iisc.ac.in/event/phd-thesis-defense/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
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