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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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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Kolkata:20240604T150000
DTEND;TZID=Asia/Kolkata:20240604T170000
DTSTAMP:20260527T032540
CREATED:20240603T044534Z
LAST-MODIFIED:20240603T044534Z
UID:241475-1717513200-1717520400@ee.iisc.ac.in
SUMMARY:Faculty Candidate Talk on Blind speaker separation from noisy speech mixtures
DESCRIPTION:Faculty Candidate Talk\nTitle: Blind speaker separation from noisy speech mixtures\nDate and Time: 3:00 PM: 4th JUNE\, 2024.\nLocation: MMCR\, EE dept (Online link)\n\nAbstract:\nBlind separation of speech mixtures into individual speaker signals is crucial for several speech processing applications\, including teleconferencing. These applications require blind speech separation (BSS)\, i.e.\, without any additional information about the speakers in the mixture or their count\, for both transcription and communication. This task becomes particularly difficult when the number of speakers in the mixture is unknown and recordings are made using a single microphone. In a recent work\, we developed a deep-learning-based system for BSS from noisy single-channel mixtures\, with an unknown number of speakers in the mixture. The work employs a transformer-based neural network architecture with an attractor generation scheme\, allowing it to count the speakers and separate their signals simultaneously. In my presentation\, I will share the results from experimental validation on simulated speech mixtures. Our findings show that the system can achieve 18 dB or more improvement in signal-to-distortion ratio and 99% accuracy in speaker counting for mixtures with up to three speakers. Additionally\, I will also discuss the insights gained into the model’s internal mechanics\, by examining the attention patterns computed in the transformers. We also observed that these findings apply universally across different transformer configurations used in other tasks\, such as ambisonic-to-ambisonic and multi-channel speech separation.\n\n\nBio: Srikanth Raj Chetupalli received the Master of Engineering and Doctor of Philosophy degrees from the Division of Electrical Sciences\, Indian Institute of Science (IISc.) Bengaluru\, India\, in 2011 and 2020\, respectively. He is currently a Postdoctoral Researcher with the International Audio Laboratories Erlangen (a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg and Fraunhofer Institute for Integrated Circuits IIS)\, Erlangen\, Germany. His research interests include speech processing\, multimicrophone processing\, spatial audio processing\, and in particular\, source extraction\, speech dereverberation\, acoustic parameter estimation\, and speaker diarization. He was the recipient of the Tata Consultancy Services Research Scholarship from 2015 to 2019.
URL:https://ee.iisc.ac.in/event/faculty-candidate-talk-on-blind-speaker-separation-from-noisy-speech-mixtures/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20240610T110000
DTEND;TZID=Asia/Kolkata:20240610T130000
DTSTAMP:20260527T032540
CREATED:20240606T110915Z
LAST-MODIFIED:20240610T034655Z
UID:241479-1718017200-1718024400@ee.iisc.ac.in
SUMMARY:Thesis defense on Estimation of flashovers in the EHV/UHV lines on the east coast due to lightning produced by the Bay of Bengal cyclones
DESCRIPTION:Name of the Candidate:  Anirban Chatterjee \nTitle of the Thesis:         Estimation of flashovers in the EHV/UHV lines on the east coast due to lightning produced by the Bay of Bengal cyclones \nDegree Registered:         MTech (Res) in Electrical Engineering \nTime and date:              11.00 AM\, 10th June 2024 \nVenue         :               MMCR Seminar Hall of EE Department \n                                    Meeting Link \nResearch Supervisor:      Udaya Kumar \nAbstract \nThe Bay of Bengal produces a considerable number of cyclones. Many of them invade the east coast of India. They can cause structural damage to towers\, substation flooding\, and conductor snapping. In many cases\, lightning causes several flashovers on the EHV/UHV grid and they are much more in numbers than the former. However\, no serious effort was made to estimate the possible number of flashovers caused by the lightning produced by such cyclones. The present work aims to fill this serious gap. \nEstimating such lightning-induced flashovers requires several aspects\, both electrical and cyclone-related. The lightning strike could be intercepted by the tower/ground wire\, or it can strike the phase conductor. The electro-geometric model (EGM)\, suggested in IEEE standards\, is employed to assess the normalized number of strokes striking the phase conductor and intercepted by the tower/ground wire. The associated probabilities are also estimated for typical EHV and UHV lines. \nThe simulations are carried out by modeling the lines in EMTP with a multi-story model for the tower and the voltage rise in the system is evaluated. Using this information and the BIL of the line\, the possibility of flashovers is assessed. \nThe cyclone’s trajectory\, the speed\, and the number of lightning flashes produced by them are assimilated from different sources. Modeling the cyclone as a disc like structure\,\, the line length shadowed as a function of time is calculated. In addition\, equivalent ground flash density per square km per hour is also calculated. Combining all this information\, the possible number of lighting-induced flashovers in the EHV/UHV grid along the East Coast is estimated.  It amounts to 100s of flashover in 400 kV lines and 1000s of flashover in 220kV line. \nFor engineering purposes\, the maximum number of possible flashovers are required. Based on the maximum number of ground flashes per hour across the cyclones for five years\, it was estimated. It amounts to a few to a few tens of flashovers in 765kV grid\, a few tens to a few hundred in 400kV grid and that for 220kV grid\, it amounted to a few hundred to a few thousands.
URL:https://ee.iisc.ac.in/event/thesis-defense-on-estimation-of-flashovers-in-the-ehv-uhv-lines-on-the-east-coast-due-to-lightning-produced-by-the-bay-of-bengal-cyclones/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20240613T160000
DTEND;TZID=Asia/Kolkata:20240613T173000
DTSTAMP:20260527T032540
CREATED:20240605T042540Z
LAST-MODIFIED:20240605T042540Z
UID:241477-1718294400-1718299800@ee.iisc.ac.in
SUMMARY:EE Talk on Advancements in Power Electronics for Sustainable and Resilient Energy Systems.
DESCRIPTION:Title: Advancements in Power Electronics for Sustainable and Resilient Energy Systems.\n \nSpeaker: Prof Avik Bhattacharya \nIIT Roorkee \n \nDate 13/6/2024\n \nTime: 4:00 pm\n \nVenue: MMCR EE\n \nAbstract: This talk delves into the pivotal role of power electronics in advancing the sustainability\, reliability\, and efficiency of modern energy systems. Key topics include sustainable microgrids\, which utilize advanced power electronic converters to seamlessly integrate renewable energy sources and storage systems for resilient\, self-sufficient power solutions. The presentation addresses power quality improvement techniques\, leveraging power electronics to mitigate issues such as voltage sags\, harmonics\, and frequency variations\, ensuring stable and high-quality power delivery. The development of multilevel solar inverters\, which enhance photovoltaic system efficiency and reduce electromagnetic interference\, will be explored\, showcasing their design and operational benefits. The rapid expansion of electric vehicles (EVs) underscores the necessity for efficient fast charging infrastructure\, where power electronics play a crucial role in reducing charging times and enhancing the reliability of charging networks. Lastly\, the discussion highlights resilient space converters\, emphasizing innovative power electronic designs that ensure robustness against extreme conditions and cyber threats. Integrating these cutting-edge power electronic technologies significantly advances the pursuit of a more sustainable\, reliable\, and resilient energy future.
URL:https://ee.iisc.ac.in/event/ee-talk-on-advancements-in-power-electronics-for-sustainable-and-resilient-energy-systems/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20240624T163000
DTEND;TZID=Asia/Kolkata:20240624T173000
DTSTAMP:20260527T032540
CREATED:20240614T114411Z
LAST-MODIFIED:20240624T065448Z
UID:241483-1719246600-1719250200@ee.iisc.ac.in
SUMMARY:PhD Thesis Colloquium of Nareddy Kartheek Kumar Reddy
DESCRIPTION:Name of the Candidate: Mr. Nareddy Kartheek Kumar Reddy\n\nResearch Supervisor: Prof. Chandra Sekhar Seelamantula\n\nDate and time: June 24\, 2024; 4.30 PM\nCoffee/tea will be served during the talk.\n\nVenue: Multimedia Classroom (MMCR)\, EE Department\, IISc.\n\nTitle: Tight Frames\, Non-convex Regularizers\, and Quantized Neural Networks for Solving Linear Inverse Problems\n\nAbstract:\nThe recovery of a signal/image from compressed measurements involves formulating an optimization problem and solving it using an efficient algorithm. The optimization objective involves data fidelity\, which is responsible for ensuring conformity of the reconstructed signal to the measurement\, and a regularization term to enforce desired priors on  the signal. More recently\, the optimization based solvers have been replaced by deep neural networks.\n\nThis thesis considers three aspects of inverse problems in computational imaging: (i) Choice of data-fidelity term for compressed-sensing image recovery; (ii) Non-convex regularizers in the context of linear inverse problems; and (iii) Explainable deep-unfolded networks and the effect of quantization of model parameters.\n\n\nPart-1: Tight-Frame-Based Data Fidelity for Compressed Sensing\nThe choice of the sensing matrix is crucial in compressed sensing. Random Gaussian sensing matrices satisfy the restricted isometry property\, which is crucial for solving the sparse recovery problem using convex optimization techniques. However\, tight-frame sensing matrices result in minimum mean-squared-error recovery given oracle knowledge of the support of the sparse vector. If the sensing matrix is not tight\, could one achieve the recovery performance assured by a tight frame by suitably designing the recovery strategy? ­    This is the key question addressed in this part of the thesis.  We consider the analysis-sparse l1-minimization problem with a generalized l2-norm-based data-fidelity and show that it effectively corresponds to using a tight-frame sensing matrix. The new formulation offers improved performance bounds when the number of non-zeros is large. One could develop a tight-frame variant of a known sparse recovery algorithm using the proposed formalism. We solve the analysis-sparse recovery problem in an unconstrained setting using proximal methods. Within the tight-frame sensing framework\, we rescale the gradients of the data-fidelity loss in the iterative updates to further improve the accuracy of analysis-sparse recovery. Experimental results show that the proposed algorithms offer superior analysis-sparse recovery performance. Proceeding further\, we also develop deep-unfolded variants\, with a convolutional neural network as the sparsifying operator. On the application front\, we consider compressed sensing image recovery. Experimental validations on Set11\, BSD68\, Urban100\, and DIV2K datasets show that the proposed techniques outperform the state-of-the-art techniques\, where the performance is measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM).\n\nPart 2: Proximal Averaging Methods for Image Restoration and Recovery\nSparse recovery methods are iterative and most techniques typically rely on proximal gradient methods. While the commonly used sparsity promoting penalty is the l1-norm\, which is convex\, alternatives such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty have also been employed to obtain superior results. Combining various penalties to achieve robust sparse recovery is possible\, but the challenge lies in optimal parameter selection. Given the connection between deep networks and unrolling of iterative algorithms\, it is possible to unify the unfolded networks arising from different formulations. We propose an ensemble of proximal networks for sparse recovery\, where the ensemble weights are learnt in a data-driven fashion. The proposed network performs superior to or on par with the individual networks in the ensemble for synthetic data under various noise levels and sparsity conditions. We demonstrate an application to image denoising based on the convolutional sparse coding formulation.\n\nPart 3: Deep Unfolded Networks\, Quantization\, and Explainability\n\nDeep-unfolded networks (DUNs) have set new performance benchmarks in compressed sensing and image restoration. DUNs are built from conventional iterative algorithms\, where an iteration is transformed into a layer/block of a network with learnable parameters. This work focuses on enhancing the explainability of DUNs by investigating potential reasons behind their superior performance over traditional iterative methods. Our findings reveal that the learned matrices in DUNs are unstable because their singular values exceed unity. However\, the overall DUN gives rise to a recovery accuracy higher than the optimisation techniques. This goes to show that although the linear/affine components of the DUN are unstable\, the overall network is stable\, which leads us to conclude that it is the nonlinearities\, more precisely\, the activation functions\, that are responsible for restoring stability. This study illustrates an intriguing property of deep unfolded networks\, which is not observed in standard optimization schemes.\n\nWe also consider quantization of the network weights for efficient model deployment in resource-constrained devices. Quantization makes neural networks efficient both in terms of memory and computation during inference and also renders them compatible with low-precision hardware deployment. Our learning algorithm is based on a variant of the ADAM optimizer in which the quantizer is part of the forward pass. The gradients of the loss function are evaluated corresponding to the quantized weights while doing a book-keeping of the high-precision weights. We demonstrate applications for compressed image recovery and magnetic resonance image reconstruction. The proposed approach offers superior reconstruction accuracy and quality than state-of-the-art unfolding techniques\, and the performance degradation is minimal even when the weights are subjected to extreme quantization.\n\nImpact of the research: The novel techniques proposed in this thesis led to improved accuracy in linear inverse problems — sparse signals recovery\, compressed image recovery\, image deconvolution\, and image denoising. The tight-frame based algorithms require fewer iterations to converge\, thus reducing the reconstruction time. The quantized neural networks\, on the other hand\, improved the inference time and reduced the model footprint for efficient deployment on the edge. Analysis of deep-unfolded networks has shown that the learnt weights follow a Gaussian distribution suggesting more efficient initialisation schemes than weights derived from ISTA. We also identified potential local instabilities in a deep learning setting\, which are avoided in a conventional optimization setting. The role of the nonlinearity is to restore stability. The analysis showed that while deep unfolded networks have potential instabilities\, they can be useful for solving inverse problems.\n\n\nBiography of the Candidate:\nNareddy Kartheek Kumar Reddy is a PhD student in the Spectrum Lab\, Department of Electrical Engineering at the Indian Institute of Science (IISc). He received a Bachelor of Technology (Honors) degree from Indian Institute of Technology Kharagpur in 2016. Subsequently\, he worked as a Senior Engineer at Honeywell Technology Solutions from 2016 to 2018\, where he focused on developing device drivers for SD card and NAND Flash devices which went into production in Honeywell’s flagship weather radar RDR7000.\n\nKartheek joined IISc as a Masters student in Signal Processing\, and subsequently upgraded to PhD after receiving the prestigious Prime Minister’s Research Fellowship in 2019. He is twice recipient of the Qualcomm Innovation Fellowship\, once during 2020 & again in 2023. Kartheek enjoys traveling\, reading books and manga\, watching anime\, and playing video games in his leisure time.
URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-nareddy-kartheek-kumar-reddy/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20240625T100000
DTEND;TZID=Asia/Kolkata:20240625T170000
DTSTAMP:20260527T032540
CREATED:20240624T045303Z
LAST-MODIFIED:20240624T065530Z
UID:241485-1719309600-1719334800@ee.iisc.ac.in
SUMMARY:EE Talk on Analog Semiconductor innovations in the era of Artificial Intelligence
DESCRIPTION:Title: Analog Semiconductor innovations in the era of Artificial Intelligence \n\n\nSpeaker: Dr Sombuddha Chakraborty \n\n\nAnalog Design Manager\, TI Kilby Labs (Power) \nDate 25/6/2024 \n\n\nTime: 10:00 am \n\n\nVenue: MMCR EE \n  \n\n\nAbstract: With the dramatic rise of computation related to the proliferation of AI\, powering the GPU hardware poses new challenges and opportunities for power and analog semiconductor companies. This talk will discuss the various semiconductor innovations in development across the industry and how they are shaping data-centre power delivery solutions. \nBio: \nSombuddha Chakraborty (Senior Member\, IEEE) received his M.S. and Ph.D. in electrical engineering from the University of Minnesota\, Minneapolis\, MN\, USA\, in 2003 and 2006\, respectively. Before this\, he received his BE from Bengal Engineering College in 2001. \nSince 2014\, he has been the Design Manager of Power Technology at Texas Instrument’s advanced product development team called Kilby Labs in Santa Clara\, CA. His work and research interests include high-density AC/DC and DC/DC power management systems for computing\, automotive\, and industrial applications using leading-edge processes\, package\, integration\, and circuit techniques to enhance power delivery efficiency. \nSombuddha holds around 30 US Patents and around like number of IEEE publications. He is involved in various IEEE consortiums and serves in IEEE editorial boards.
URL:https://ee.iisc.ac.in/event/ee-talk-on-analog-semiconductor-innovations-in-the-era-of-artificial-intelligence/
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