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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
BEGIN:STANDARD
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
TZNAME:IST
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230102T133000
DTEND;TZID=Asia/Kolkata:20230104T223000
DTSTAMP:20260529T022903
CREATED:20221128T001914Z
LAST-MODIFIED:20221128T003251Z
UID:240127-1672666200-1672871400@ee.iisc.ac.in
SUMMARY:Workshop on Protection and Stability of Renewable Dominated Power Grids
DESCRIPTION:Click here for the poster.\nTopics that will be covered in the workshop:\n\nOverview of Photovoltaic and Wind Generations\nConverter Controls for Renewables\nGrid Connection Requirements\nImpact of Renewables on Fault Analysis and Protection\nImpact of Renewables on System Stability\nTraining on Renewable Modelling in PSCAD\nAC Microgrids\nDC Microgrids\nCase Studies\n\nThe list of speakers: (Click here for the schedule.)\n\nProf Sukumar Brahma\, Clemson University\, USA\nProf Prasad Enjeti\, Texas A&M University\, USA\nDr Ritwik Majumder\, Mathworks\nProf Vinod John\, IISc\nProf Kaushik Basu\, IISc\nProf Gurunath Gurrala\, IISc\nDr. Vishnu Mahadeva Iyer\nProf Sarasij Das\, IISc\n\n 
URL:https://ee.iisc.ac.in/event/workshop-on-protection-and-stability-of-renewable-dominated-power-grids/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230102T163000
DTEND;TZID=Asia/Kolkata:20230102T173000
DTSTAMP:20260529T022903
CREATED:20221226T032757Z
LAST-MODIFIED:20221226T032848Z
UID:240207-1672677000-1672680600@ee.iisc.ac.in
SUMMARY:Thesis Defence of Mr. Ruturaj Gavaskar
DESCRIPTION:Title: On Plug-and-Play Regularization using Linear Denoisers.\nDegree Registered: PhD\nGuide: Prof Kunal Narayan Chaudhury\nDate: Jan 2\, 2023.\nTime: 11:00 am. \nVenue: Online.\nMS Teams: https://tinyurl.com/bdz95wmw\n(meeting ID: 483 231 041 151; Passcode: QDF8WS) \nAbstract: The problem of inverting a given measurement model comes up in several computational imaging applications. For example\, in CT and MRI\, we are required to reconstruct a high-resolution image from incomplete noisy measurements\, whereas in superresolution and deblurring\, we try to infer the ground truth from low-resolution or blurred images. Traditionally\, this is done by minimizing f + φ\, where f is a data-fidelity (or loss) function that is determined by the acquisition process\, and φ is a regularization (or penalty) function that is based on a subjective prior on the target image. The solution is obtained numerically using iterative algorithms such as ISTA or ADMM. \nWhile several forms of regularization and associated optimization methods have been proposed in the imaging literature over the last few decades\, the use of denoisers (aka denoising priors) for image regularization is a relatively recent phenomenon. This has partly been triggered by advances in image denoising in the last 20 years\, leading to the development of powerful image denoisers such as BM3D and DnCNN. In this thesis\, we look at a recent protocol called Plug-and-Play (PnP) regularization\, where image denoisers are deployed within iterative algorithms for image regularization. PnP consists of replacing the proximal map — an analytical operator at the core of ISTA and ADMM — associated with the regularizer φ with an image denoiser. This is motivated by the intuition that off-the-shelf denoisers such as BM3D and DnCNN offer better image priors than traditional hand-crafted regularizers such as total variation. While PnP does not use an explicit regularizer\, it still makes use of the data-fidelity function f. However\, since the replacement of the proximal map with a denoiser is ad-hoc\, the optimization perspective is lost — it is not clear if the PnP iterations can be interpreted as optimizing some objective function f + φ. Remarkably\, PnP reconstructions are of high quality and competitive with state-of-the-art methods. Following this\, researchers have tried explaining why plugging a denoiser within an inversion algorithm should work in the first place\, why it produces high-quality images\, and whether the final reconstruction is optimal in some sense.\nIn this thesis\, we try to answer such questions\, some of which have been the topic of active research in the imaging community in recent years. Specifically\, we consider the following questions. \n1. Fixed-point convergence: Under what conditions does the sequence of iterates generated by a PnP algorithm converge? Moreover\, are these conditions met by existing real-world denoisers? \n2. Optimality and objective convergence: Can we interpret PnP as an algorithm that minimizes f + φ for some appropriate φ? Moreover\, does the algorithm converge to a minimizer of this objective function? \n3. Exact and robust recovery: Under what conditions can we recover the ground truth exactly via PnP? And is the reconstruction robust to noise in the measurements? \nWhile early work on PnP has attempted to answer some of these questions\, many of the underlying assumptions are either strong or unverifiable. This is essentially because denoisers such as BM3D and DnCNN are mathematically complex\, nonlinear and difficult to characterize. A first step in understanding complex nonlinear phenomena is often to develop an understanding of some linear approximation. In this spirit\, we focus our attention on denoisers that are linear. In fact\, there exists a broad class of real-world denoisers that are linear and whose performance is quite decent; examples include kernel filters (e.g. NLM\, bilateral filter) and their symmetrized counterparts. This class has a simple characterization that helps to keep the analysis tractable and the assumptions verifiable. Our main contributions lie in resolving the aforementioned questions for PnP algorithms where the plugged denoiser belongs to this class. We summarize them below. \n• We prove fixed-point convergence of the PnP version of ISTA under mild assumptions on the measurement model. \n• Based on the theory of proximal maps\, we prove that a PnP algorithm in fact minimizes a convex objective function f + φ\, subject to some algorithmic modifications that arise from the algebraic properties of the denoiser. Notably\, unlike previous results\, our analysis applies to non-symmetric linear filters. \n• Under certain verifiable assumptions\, we prove that a signal can be recovered exactly (resp. robustly) from clean (resp. noisy) measurements using PnP regularization. As a more profound application\, in the spirit of classical compressed sensing\, we are able to derive probabilistic guarantees on exact and robust recovery for the compressed sensing problem where the sensing matrix is random. An implication of our analysis is that the range of the linear denoiser plays the role of a signal prior and its dimension essentially controls the size of the set of recoverable signals. In particular\, we are able to derive the sample complexity of compressed sensing as a function of distortion error and success rate. \nWe validate our theoretical findings numerically\, discuss their implications and mention possible future research directions.
URL:https://ee.iisc.ac.in/event/thesis-defence-of-mr-ruturaj-gavaskar/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230102T213000
DTEND;TZID=Asia/Kolkata:20230102T223000
DTSTAMP:20260529T022903
CREATED:20221229T225034Z
LAST-MODIFIED:20221229T225125Z
UID:240213-1672695000-1672698600@ee.iisc.ac.in
SUMMARY:Lecture by Prof. Manoj Saranathan
DESCRIPTION:Department of Electrical Engineering\, Indian Institute of Science\nand\nIEEE Signal Processing Society\, Bangalore Chapter\ncordially invite you to a lecture on \nAdvances in imaging and segmentation of thalamic nuclei with applications \nby \nProf. Manoj Saranathan\, Professor of Radiology at UMass Chan Medical School in Worcester\, Massachusetts \nDate and time: January 2\, 2023\, 4 PM\nVenue: Multimedia Classroom\, Department of Electrical Engineering (EE)\, IISc. \nCoffee will be served at 3.45 PM. \nAbstract: The thalamus is a subcortical deep brain structure increasingly implicated in a number of neurodegenerative and neuropsychiatric conditions. It is subdivided into regions called nuclei which are linked to specific cortical and sensory regions of the brain. However\, thalamic nuclei have largely been ignored in most imaging studies as they are mostly invisible in conventional MRI. In this talk\, I will present our work on MRI methods to improve visualization of thalamic nuclei as well as cutting edge thalamic nuclei segmentation methods. I will then show examples of characterization of atrophy of specific thalamic nuclei in neurodegenerative diseases as well as for cutting edge neurosurgical treatment of epilepsy and essential tremor. \nBiography of the speaker: Manoj Saranathan is an MRI physicist with over twenty-five years of experience in MR physics\, pulse sequence development\, image reconstruction\, and image processing spanning industry and academia. His current research interests are focused on ultra high-resolution imaging and segmentation of deep brain structures like thalamus and hippocampus and the specificity of their involvement in pathology such as alcoholism\, multiple sclerosis\, Alzheimer’s disease\, and essential tremor. Another area of interest is high spatio-temporal resolution dynamic contrast enhanced MRI for quantification of physiologic function and cancer. One of his methods\, DISCO\, is now a product available on all GE MRI scanners since 2017\, and is widely used for prostate and breast imaging worldwide. He has a PhD in Bioengineering from the University of Washington\, Seattle and is currently a Professor of Radiology at UMass Chan Medical School in Worcester\, Massachusetts. \nHosts: Prof. P. S. Sastry and Prof. Chandra Sekhar Seelamantula\, EE\, IISc \nAll are invited.
URL:https://ee.iisc.ac.in/event/lecture-by-prof-manoj-saranathan/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230106T153000
DTEND;TZID=Asia/Kolkata:20230106T163000
DTSTAMP:20260529T022903
CREATED:20221227T011631Z
LAST-MODIFIED:20221227T011659Z
UID:240210-1673019000-1673022600@ee.iisc.ac.in
SUMMARY:Seminar by Prof. Niladri Chakraborty
DESCRIPTION:Title: Emulator-based Microgrid studies vis a vis Some Indian Renewable interconnected Microgrids. \nTime: 10 am 6/1/23 \nVenue: EE MMCR \nAbstract: Mini / Micro Grid in Stand Alone or Grid integrated forms are coming up very fast in the conventional power system. Many a time\, these are renewable sources integrated. Design considerations\, Plant automation\, Remote monitoring and O & M strategies play important roles in their operational exploitation. However\, for economics and systematic\, technically sound energy businesses\, it is prudent to have emulator-based studies done before these systems are commercially realized. Attempts will be made to showcase an emulator-based microgrid available in the Department of Power Engineering\, Jadavpur University\, Kolkata. At the same time\, generalized Design considerations\, Plant automation\, Remote monitoring\, O & M strategies and System studies will be briefly highlighted.  Introduction of different types of renewable integrated Mini / Microgrids encountered during consultancy services or for technical studies available elsewhere in the pan-Indian domain will be made perceptible to the audience. \n Bio: Prof Niladri Chakraborty was born in Kolkata on 27/08/1964. He received his Bachelor’s (1986) and Masters’s Degree (1989) in Electrical Engineering from Jadavpur University\, Kolkata\, India. He was the recipient of the prestigious Commonwealth Scholarship in the United Kingdom and obtained the Diploma of Imperial College and Doctor of Philosophy from the University of London in 1999. He was elected as the youngest Dean of the Faculty of Engineering and Technology at Jadavpur University in 2010. He has served twice as Head of the Department of Power Engineering and as Joint Director of the School of Energy Studies at Jadavpur University. He was often elected to the Executive Council and The Court\, the highest policy-making academic body of Jadavpur University. He has also received Scholarships from the Royal Society (UK)-DST and Abdul Kalam Award\, besides many other awards from numerous academic activities. Dr Chakraborty has served as one of the Lead Scientists in India’s National Communication to United Nations on Climate Change (NATCOM\, India\, UNFCCC) and a Specialized Energy Expert in formulating the Futuristic Energy Action Plan for the Government of West Bengal\, India. He has also completed many research projects obtained from different funding agencies and earned revenue for Jadavpur University as a consultant. \n Prof. Chakraborty’s recent research interests include Energy Economics\, Renewable integrated Microgrid\, Environmental Measurement and Material Modelling. Dr Chakraborty has in his credit to be the author of about 75 referred journal publications and nearly 130 International Conference Publications. Prof. Chakraborty has guided 13 PhDs (4 more are in writing-up status) and 41 Masters of Engineering Thesis.
URL:https://ee.iisc.ac.in/event/seminar-by-prof-niladri-chakraborty/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230111T223000
DTEND;TZID=Asia/Kolkata:20230111T233000
DTSTAMP:20260529T022903
CREATED:20230110T223726Z
LAST-MODIFIED:20230110T223914Z
UID:240244-1673476200-1673479800@ee.iisc.ac.in
SUMMARY:Lecture by Prof. Anubha Gupta
DESCRIPTION:Department of Electrical Engineering\, Indian Institute of Science  \n\n\nand  \n\n\nIEEE Signal Processing Society\, Bangalore Chapter  \n\n\n  \n\n\ncordially invite you to a lecture on  \n\n\nProper Definition of Dirichlet Conditions and Convergence of Fourier Representations  \n\n\n By  \n\n\n Prof. Anubha Gupta  \n\n\nIndraprastha Institute of Information Technology -Delhi  \n\n\n Date and time: January 11\, 2023\, 5 PM  \n\n\n Venue: Multimedia Classroom\, Department of Electrical Engineering (EE)\, IISc.  \n\n\n Coffee will be served during the talk.  \n\n\n Abstract:  Fourier theory is the backbone of Signal Processing (SP) and Communication Engineering. It has been widely used in almost all branches of science and engineering in numerous applications since its inception. However\, Fourier representations such as Fourier series (FS) and Fourier transform (FT) may not exist for some signals that fail to fulfil a predefined set of Dirichlet conditions (DCs). We note a subtle gap in explaining these conditions as available in the popular SP literature. For example\, the original DCs require a signal to have bounded variations over one time period for the convergence of FS\, where there can be at most countably infinite number of maxima and minima\, and at most countably infinite number of discontinuities of finite magnitude. However\, a large body of the literature replaces this statement with the requirements of a finite number of maxima and minima over one time period\, and a finite number of finite discontinuities over one time period. Due to the latter\, some signals fulfilling the original DCs are incorrectly perceived as not having convergent FS representation. A similar problem holds in the description of DCs for the FT. This talk is based on our recent lecture notes published in IEEE Signal Processing Magazine (Sep 2022 issue)\, wherein we provide the required clarifications and a lucid but precise description and explanation on the DCs along with a lot of suitable examples.  \n\n\n Brief Bio:  \n\n\n \n\n\nAnubha Gupta (anubha@iiitd.ac.in) received her B.Tech and M.Tech from Delhi University\, India in 1991 and 1997 in Electronics and Communication Engineering. She received her PhD. from Indian Institute of Technology (IIT)\, Delhi\, India in 2006 in Electrical Engineering. She did her second master’s as a full-time student from the University of Maryland\, College Park\, USA from 2008-2010 in Education. She worked as Assistant Director with the Ministry of Information and Broadcasting\, Govt. of India (through Indian Engineering Services) from 1993 to 1999 and\, as faculty at NSUT-Delhi (2000-2008) and IIIT-Hyderabad (2011-2013)\, India. Currently\, she is working as a Professor at IIIT-Delhi\, where she served as the Dean\, Academic Affairs from June 2019 to June 2020. She has authored/co-authored more than 100 technical papers in scientific journals and conferences. She received SERB POWER Fellowship\, 2021 from DST. Govt. of India. She has published research papers in both engineering and education. A lot of exciting work is being taken up in her lab: SBILab (Lab: http://sbilab.iiitd.edu.in/index.html \n\n\n\n\n\nSBILab – Indraprastha Institute of Information Technology\, Delhi\nSignal Processing and Biomedical Imaging Lab. Led By Prof. Anubha Gupta of IIIT-Delhi (Started in Jan 2014) SBILab focuses on Signal Processing areas including applications of Wavelet Transforms\, Machine (Deep) Learning\, and Compressed Sensing\, Sparse Reconstruction\, fMRI/EEG/MRI/DTI Signal and Image Processing\, Genomics Signal Processing\, Signal Processing for Communication Engineering\, and …\nsbilab.iiitd.edu.in\n\n\n\n\n\n). Her research interests include applications of machine learning in cancer genomics\, cancer imaging\, biomedical signal and image processing including fMRI\, MRI\, EEG\, ECG signal processing\, and Wavelets in deep learning. Dr. Gupta is a senior member of IEEE Signal Processing Society (SPS) and a member of IEEE Women in Engineering Society. She is a technical committee member of BISP committee of IEEE SPS Society for Jan 2022– Dec 2024.   \n\n\n  \n\n\nHost: Prof. Chandra Sekhar Seelamantula\, EE\, IISc.  \n\n\n  \n\n\nAll are invited. 
URL:https://ee.iisc.ac.in/event/lecture-by-prof-anubha-gupta/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230112T203000
DTEND;TZID=Asia/Kolkata:20230112T213000
DTSTAMP:20260529T022904
CREATED:20230109T051231Z
LAST-MODIFIED:20230109T051231Z
UID:240242-1673555400-1673559000@ee.iisc.ac.in
SUMMARY:Thesis Colloquium of Sayan Paul
DESCRIPTION:Thesis Title: Pulse Width Modulation Techniques of Inverter Fed Split-Phase Machine Drive in Linear and Overmodulation Regions \nDegree registered: PhD \nGuide: Prof. Kaushik Basu \nDate and Time: 12th January 2023\, 03:00 PM \nPlace: MMCR EE\, \nMeeting Link: Teams Meeting Link \nMulti-phase machines (MPMs) have more than three windings in their stator\, rotor\, or both. With the broader adoption of power-electronic converters for efficient driving of the machines\, MPMs are gaining attention in different applications due to their certain advantages over three-phase machines. One such advantage is higher fault tolerance due to higher phase redundancy\, which makes it suitable for safety-critical applications like electric vehicles (EVs)\, ship propulsions\, electric aircraft\, etc. Another advantage is that MPMs allow power splitting across multiple phases. Hence\, the power rating per phase drive unit becomes low\, making it suitable for high-power applications like railway traction\, pumps\, compressors\, etc. Recent literature also proposes using the same multi-phase converter fed MPM\, otherwise used for propulsion\, as an onboard battery charger; it substantially reduces space\, weight\, and cost. During charging mode\, the leakage inductance of the machine provides the required inductance for the grid connection\, and MPM’s higher degrees of freedom are used to lock the rotor electronically. An asymmetrical six-phase machine (ASPM) or split-phase machine is one such MPMs and is very common in EVs. This thesis aims to devise the pulse-width modulation (PWM) techniques of a two-level six-phase inverter fed ASPM to improve the overall drive efficiency. \nASPM has two sets of balanced three-phase windings\, which are spatially shifted by 30 degrees (electrical angle). In one of the popular configurations\, the two three-phase winding sets are connected in star fashion with two isolated neutral points. This machine is conventionally analyzed in two two-dimensional (2D) orthogonal subspaces. One of these subspaces is associated with electromagnetic energy transfer and torque production. The other subspace doesn’t transfer energy through the air gap and the equivalent circuit in this plane\, consisting of winding resistance and leakage inductance\, provides a low impedance. Therefore\, excitation of this non-energy-transferring subspace causes a large current and associated copper loss. Any PWM technique of ASPM aims to synthesize the desired voltages in the energy-transferring plane and minimize the applied voltage in the non-energy-transferring subspace. \nLinear modulation techniques (LMTs) of ASPM apply zero average voltage in the non-energy-transferring subspace and synthesize the desired voltages in the energy-transferring plane on an average over a switching cycle. It is expected that these LMTs should avoid more than two switching transitions of an inverter leg within a carrier period to limit the instantaneous switching loss. Through an innovative approach\, our work finds a way to account for all possible infinitely many LMTs that follow the rule of at most two transitions per leg. But each of them results in a different current ripple performance. Ripple current is inevitable in PWM converters and should be minimized through modulation to reduce the associated copper loss. The total ripple current RMS of ASPM is contributed by both energy-transferring and non-transferring planes. One machine parameter also impacts this performance: the ratio of high-frequency inductances in these two subspaces. For all reference voltage vectors and the whole feasible range of the machine parameter\, our work finds the techniques with minimum current ripple (RMS) among the above infinite possible LMTs through numerical optimization. A hybrid PWM strategy is proposed with these optimal techniques\, which outperforms all existing techniques regarding current ripple performance. \nOvermodulation (OVM) techniques of ASPM attain higher voltage gain in energy-transferring subspace than LMTs by applying non-zero average voltage in the non-energy transferring subspace. This operation doesn’t cause any torque ripple\, but the applied voltage in non-energy transferring subspace should be minimised to reduce unwanted current and associated loss. The existing OVM technique in the literature minimizes this average voltage from the space-vector perspective with a pre-defined set of four active vectors. To find the best technique\, one needs to perform the above minimization problem with all possible sets of active vectors\, which can give higher voltage gain. So\, this requires the evaluation of a large number of cases. In this thesis\, we have formulated the above minimization problem in terms of average voltage vectors of two three-phase inverters\, where active vectors need not be specified beforehand. Thus\, the analysis is more general. Following the above analysis\, eight switching sequences in one part and two in another part of the OVM region are derived\, which attain the minimum average voltage injection in the non-energy transferring subspace. \nAlthough the above OVM sequences apply the same average voltages in the two subspaces\, they have different high-frequency ripple currents due to different switching strategies. The current ripple study of the OVM techniques of ASPM is missing in the literature. Hence\, one of our works in the thesis studies the current ripple performances of the above optimal PWM sequences in the OVM region\, which apply minimum average voltage in the non-energy-transferring subspace. We find the sequence with the best switching current ripple performance for a given reference vector in the OVM region and the machine parameter. After that\, a PWM technique is proposed\, which substantially improves the high-frequency current ripple performance (RMS) compared to two existing OVM techniques for a given machine parameter value. \nFinally\, simple carrier-comparison-based implementation methods of the proposed LMTs and OVM sequences are found. The six-phase inverter is split into two three-phase inverters\, and the proposed strategy implements the PWM sequences per three-phase inverter basis. In carrier-based implementations\, the duty signal of the top switch of an inverter leg is compared with a triangular carrier. The bottom switch’s gating pulse complements the top switch’s pulse with a fixed dead time. The duty signal of the top switch of any leg has two components- a modulation signal and a common-mode signal. Two 180-degree phase-shifted carrier signals are required to implement the proposed sequences. The energy-transferring plane of ASPM is divided into twenty-four equivalent sectors; the carrier signals and the expressions of modulation and common-mode signals differ from one sector to another. Henceforth\, a sector-independent algorithm is proposed in this thesis to derive these duty signals that substantially reduce the computational burden. \nThe proposed techniques are validated through simulation in MATLAB/Simulink and experiments on a hardware prototype at a power level of 4 kW.
URL:https://ee.iisc.ac.in/event/thesis-colloquium-of-sayan-paul/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230120T213000
DTEND;TZID=Asia/Kolkata:20230120T223000
DTSTAMP:20260529T022904
CREATED:20230117T002035Z
LAST-MODIFIED:20230117T002448Z
UID:240253-1674250200-1674253800@ee.iisc.ac.in
SUMMARY:EE Faculty Colloquium by Prof. Kaushik Basu
DESCRIPTION:Venue: MMCR\, EE \nTime: 4pm\, 20 January 2023 \nAbstract:Silicon Carbide MOSFETs (SiC MOSFETs) fall into wide band gap (WBG) power devices. These devices are commercially available in the voltage range of 600-3300V and compete with the state-of-the-art Si-insulated gate bipolar junction transistors (IGBTs). Superior material properties  of SiC MOSFET lead to smaller die sizes. This results in faster switching transients and lower switching loss. However\, it excites device and circuit parasitic that may lead to prolonged oscillation\, high device stress\, spurious turn-on and EMI-related issues. So\, the benefit of using SiC MOSFET as a power device comes with numerous design challenges resulting in slow commercial adaptation. It is predicted that the overall market share of WBG devices (SiC and GaN together) will be roughly 10% of the total market for power semiconductors by 2025. To overcome the design challenges and fully utilise the benefits of fast-switching SiC MOSFETs\, a better understanding of switching dynamics is essential. However\, the switching dynamics of SiC MOSFET are different compared to its Si counterpart due to the highly non-linear device characteristics and participation of circuit parasitic in the process. In this talk\, we will discuss our recent work on developing an analytical model of the switching dynamics for hard and soft transitions of SiC MOSFET by simplifying the complex non-linear dynamics predicted by the behavioural model. The developed model\, given the device-related parameters extracted from the data sheet\, estimated or measured circuit parasitic\, can predict lost switching energy\, rate of change of device voltage etc.\, necessary for a successful power converter design through computation with sufficient accuracy. Based on this model a Python based interactive software tool has been developed. The results of this work are applied to two WBG-based advanced power converter development: A 3kW onboard charger for 2 Wheelers and a 200kW SiC-based inverter for high-bandwidth power amplifiers.Speaker’s Bio:Kaushik Basu received the BE. Degree from the Bengal Engineering and Science University\, Shibpore\, India\, in 2003\, the M.Sc. degree in electrical engineering from the Indian Institute of Science (IISc)\, Bangalore\, India\, in 2005\, and the PhD degree in electrical engineering from the University of Minnesota\, Minneapolis\, in 2012\, respectively. He was a Design Engineer with Cold Watt India in 2006 and an Electronics and Control Engineer with Dynapower Corporation USA from 2013-to 15. He is an Associate Professor in the Department of Electrical Engineering at the IISc. He served as the Technical Program Committee Vice-Chair of IEEE ECCE 2019 and 2022. In 2019\, he received the Prof. Priti Shankar Teaching Award from IISc. As a co-author\, he received the Second Best Prize Paper Award from IEEE Transactions on Transportation Electrification in 2021.  He is IEEE senior member and is the founding chair of both IEEE PELS and IES Bangalore Chapter. He is an Associate Editor of IEEE Transactions on Power Electronics\, IEEE Transactions on Industrial Electronics\, and Springer Journal of Power Electronics. His research interests include most aspects of Power Electronic converter design from a few kW to a few MW for applications ranging from space\, grid integration of renewables and storage to fast charging of electric vehicles.
URL:https://ee.iisc.ac.in/event/ee-faculty-colloquium-by-prof-kaushik-basu/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230124T163000
DTEND;TZID=Asia/Kolkata:20230124T173000
DTSTAMP:20260529T022904
CREATED:20230125T033041Z
LAST-MODIFIED:20230125T033041Z
UID:240264-1674577800-1674581400@ee.iisc.ac.in
SUMMARY:Ph.D thesis Defense of Mr.Sounak Nandi
DESCRIPTION:Title of Thesis: Experimental and Theoretical Investigations on High Voltage Polymeric Insulators.  \nResearch Supervisor:  Subba Reddy B  \nDate and Time: Tuesday 24th Jan 2023\, 11am  \nVenue: ON Line: Meeting link:  \nAbstract  \nHigh Voltage Ceramic and glass Insulators have been widely used by various transmission and distribution utilities for several decades across the globe. Recently composite or silicone rubber insulators have evolved and are now replacing ceramic/glass insulators due to their improved advantages; however\, these Insulators suffer from degradation over a period of service.   \nThe Primary objective of the investigation relates to the study of silicon rubber/polymer insulators under various climatic conditions. Exhaustive experimental studies were conducted to understand the degradation of insulators under different climatic conditions which prevail in the Country.   \nStudies on polymer insulators under sub-zero and under extremely high-temperature conditions were attempted experimentally to evaluate their performance. During experimentation\, the leakage current was continuously monitored. Later\, material analysis\, which is a very important aspect and essential to correlate with the morphological changes of the insulator surface\, was examined. The experimental investigations demonstrate that there is a need to conduct multi-stress experimentation under specific climatic conditions before the Insulators are installed in the field.   \nThe next portion of the thesis work deals with the failure mechanism of a Fibre Reinforced Plastic (FRP) Rod. Some portion of the work deals with mathematical analysis being extended to condition monitoring of dielectric surfaces and understanding the performance of FRP rods under high AC voltages. Further\, experimental investigations are performed on FRP Rods to analyze the behaviour witnessed\, as the field failures reported on Silicon rubber Insulators\, interesting results are reported.   \nCondition monitoring of dielectric surfaces is very important; hence it was felt necessary to analyze the field performance of transmission/distribution composite Insulators. To understand further\, a mathematical analysis based on Chaos has been evaluated for leakage current data and quantization of comparative degradation for a dielectric surface is presented. Later\, Empirical Mode Decomposition is also used for understanding leakage current and implied degradation under minimal data conditions.  \nSubsequently\, the Surface electric field of insulators exposed to HVDC is studied considering the temporal boundary conditions which may arise due to the capacitive-resistive transients. Some experimental investigations are also conducted to compare the simulated results.  \n\nThe last portion of the thesis emphases on the study of bulk conductivity of polymer material. The Electric Field dependence of conductivity on the application of voltage and subsequent space charge distribution is attempted.  In short\, the thesis work reports some new findings on the experimental\, simulation and theoretical studies pertaining to the high voltage polymeric insulators used for EHV/UHV Transmission.  \n\nAll are welcome
URL:https://ee.iisc.ac.in/event/ph-d-thesis-defense-of-mr-sounak-nandi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230125T163000
DTEND;TZID=Asia/Kolkata:20230125T173000
DTSTAMP:20260529T022904
CREATED:20230125T032338Z
LAST-MODIFIED:20230125T032338Z
UID:240262-1674664200-1674667800@ee.iisc.ac.in
SUMMARY:PhD Thesis Defense of Dhruv Jawali
DESCRIPTION:Advisors: Prof. Chandra Sekhar Seelamantula (EE) & Prof. Supratim Ray (CNS)\n\nExaminer: Prof. Vikram M. Gadre (EE)\, IIT Bombay\nTitle of the thesis: Learning Filters\, Filterbanks\, Wavelets\, and Multiscale Representations\n\nDate & Time: January 25\, 2023; 11:00 AM onward (Coffee will be served during the defense)\nVenue: Multimedia Classroom (MMCR)\, Department of Electrical Engineering\, IISc\nAbstract:\n\nThe problem of filter design is ubiquitous. Frequency selective filters are used in speech/audio processing\, image analysis\, convolutional neural networks for tasks such as denoising\, deblurring/deconvolution\, enhancement\, compression\, etc. While traditional filter design methods use a structured optimization formulation\, the advent of deep learning techniques and associated tools and toolkits enables the learning of filters through data-driven optimization. In this thesis\, we consider the filter design problem in a learning setting in both data-dependent and data-independent flavors. Data-dependent filters have properties governed by a downstream task\, for instance\, filters in a convolutional dictionary used for the task of image denoising. On the contrary\, data-independent filters have constraints imposed on their frequency responses\, such as lowpass\, having diamond-shaped support\, satisfying perfect reconstruction property\, ability to generate wavelet functions\, etc.\nThe contributions of this thesis are four-fold: (i) the formulation of filter\, filterbank\, and wavelet design as regression problems\, allowing them to be designed in a learning framework; (ii) the design of contourlet-based scattering networks for image classification; (iii) the design of a deep unfolded network using composite regularization techniques for solving inverse problems in image processing; and (iv) a multiscale dictionary learning algorithm that learns one or more multiscale generator kernels to parsimoniously explain certain neural recordings.We begin by developing learning approaches for designing filters having data-independent specifications\, for instance\, filters with a specified frequency response\, including an ideal filter. The problem of designing such filters is formulated as a regression problem\, using a training set comprising cosine signals with frequencies sampled uniformly at random. The filters are optimized using the mean-squared error loss\, and generalization bounds are provided. We demonstrate the applicability of our approach for filters such as lowpass\, bandpass\, and highpass in 1-D\, and diamond\, fan and checkerboard support filters in 2-D. We then show how the methodology extends easily for designing 1-D and 2-D cosine modulated filterbanks.\nSecond\, we consider the problems of 1-D filterbank and wavelet design through learning. Wavelets have proven to be highly successful in several signal and image processing applications. Wavelet design has been an active field of research for over two decades\, with the problem often being approached analytically. We draw a parallel between convolutional autoencoders and wavelet multiresolution approximation and show how the learning angle provides a coherent computational framework for solving the design problem. We design data-independent wavelets by interpreting the corresponding perfect reconstruction filterbanks as autoencoders (what we refer to as “filterbank autoencoders”)\, which precludes the need for customized datasets. In fact\, we show that it is possible to design them efficiently using high-dimensional Gaussian vectors as training data. Generalization bounds show that a near-zero training loss implies that the learnt filters satisfy the perfect reconstruction property with a very high probability. We show that desirable properties of a wavelet such as orthogonality\, compact support\, smoothness\, symmetry\, and vanishing moments can all be incorporated into the proposed framework by means of architectural constraints or by introducing suitable regularization functionals to the MSE cost. Notably\, our approach not only recovers the well-known Daubechies family of orthogonal wavelets and the Cohen-Daubechies-Feauveau (CDF) family of symmetric biorthogonal wavelets\, which are used in JPEG-2000 compression\, but also learns new wavelets outside these families.\nThird\, we extend the ideas used for 1-D filterbank and wavelet learning to 2-D filterbank and wavelet design. A variety of efficient representations of natural images\, such as wavelets and contourlets can be formulated as corresponding filterbank design problems. The design constraints on the continuous-domain wavelets have corresponding filter-domain manifestations. While most learning problems require specialized datasets\, we employ 2-D random Gaussian matrices as training data and optimize filter coefficients considering the MSE loss. Design specifications such as orthogonality of the filterbank\, perfect reconstruction property\, symmetry\, and vanishing moments are enforced through an appropriate parameterization of the convolutional units. We demonstrate several examples of learning biorthogonal and orthogonal filterbanks and wavelets having a specified number of vanishing moments\, both point vanishing moments and directional vanishing moments\, and symmetry constraints. Sparse recovery via composite regularization is an interesting approach proposed recently in the literature. One could design non-convex regularizers through a convex combination of sparsity-promoting penalties with known proximal operators. We develop a new algorithm\, namely\, convolutional proximal-averaged thresholding algorithm (C-PATA) for {\it composite-regularized} convolutional sparse coding (CR-CSC) based on the recently proposed idea of proximal averaging. We develop an autoencoder structure based on the deep-unfolding of C-PATA iterations into neural network layers\, which results in the composite-regularized neural network (CoRNet) architecture. The convolutional learned iterative soft-thresholding algorithm becomes a special case of CoRNet. We demonstrate the efficacy of CoRNet considering applications to image denoising and inpainting\, and compare the performance with state-of-the-art techniques such as BM3D\, convolutional LISTA\, and fast and flexible convolutional sparse coding (FFCSC).The data-independent filter design technique is employed to learn a contourlet transform used within a hybrid scattering network. Hybrid scattering networks are convolutional neural networks (CNNs) where the first few layers implement a fixed windowed scattering transform\, while the rest of the network is learned. Scattering networks outperform state-of-the-art deep learning models for limited-data classification tasks although the performance gains are not much for large datasets. The 2-D Morlet filterbank used in Mallat’s scattering network is replaced by a contourlet filterbank\, which provides sparser representations and better frequency-domain directional separation. The contourlet transform comprises a multiresolution pyramidal filterbank cascaded with directional filters. We construct directional filters using diamond-shaped quincunx filterbanks and consider two pyramidal filter variants — square-shaped\, and filters with radially isotropic frequency domain support. The performance of all variants is evaluated for natural image classification tasks on CIFAR-10 and ImageNet datasets. We show that the radial contourlet variant achieves competitive performance compared with the Morlet scattering transform on large-dataset classification tasks while performing better for the limited-dataset scenario.We then switch over to the problem of learning data-dependent filters for sparse recovery by employing a combination of sparsity promoting regularizers. Sparse recovery via such composite regularization approaches is an interesting framework proposed recently in the literature. One could design non-convex regularizers through a convex combination of sparsity-promoting penalties with known proximal operators. We developed a new algorithm\, namely\, convolutional proximal-averaged thresholding algorithm (C-PATA) for composite-regularized convolutional sparse coding (CR-CSC) based on proximal averaging. We develop an autoencoder structure based on the deep-unfolding of C-PATA iterations into neural network layers\, which results in the composite-regularized neural network (CoRNet) architecture. The convolutional learned iterative soft-thresholding algorithm becomes a special case of CoRNet. We demonstrate the efficacy of CoRNet considering applications to image denoising and inpainting and compare the performance with state-of-the-art techniques such as BM3D\, convolutional LISTA\, and fast and flexible convolutional sparse coding (FFCSC).Finally\, we conclude by developing a data-dependent method to learn filters generating a multiscale convolutional dictionary. First\, the multiscale convolutional dictionary learning (MCDL) algorithm is proposed to extract a representative waveform shape from a given dataset. The proposed algorithm is based on the popularly used convolutional dictionary learning formulation with a crucial difference — we assume that the learned atoms are scaled versions of a single generator kernel. We evaluate kernel recovery for synthetic data under noiseless and noisy data conditions. A smoothness regularizer on the learned atom is used to aid better kernel recovery under noisy conditions. Kernel recovery is shown to be robust to model choices of scales and the assumed support size of the kernel without any restrictive assumptions. The proposed approach is applied to visualizing the typical patterns present within human electrocorticogram (ECoG) measurements. The validation is carried out using publicly available ECoG data recorded from a single Parkinson’s disease patient.This thesis thus presents a cogent framework for learning filters\, filterbanks\, wavelets\, convolutional and multiscale dictionaries.\nBiography of the candidate: Dhruv Jawali received the Bachelor of Technology (B. Tech) degree from the Department of Computer Science and Engineering\, National Institute of Technology Goa\, India\, in 2014. He worked as a software developer at the Samsung Research Institute\, Bangalore during 2014-2015. He enrolled into the PhD program at the IISc Mathematics Initiative (IMI) Department\, Indian Institute of Science (IISc) in August 2015\, and has been working at the Spectrum Lab\, Department of Electrical Engineering ever since. His research interests include wavelet theory\, deep neural networks\, and sparse signal processing. He is currently employed as an instructor at Scaler Academy specializing in Data Science and Machine Learning.\n\nAll are invited.
URL:https://ee.iisc.ac.in/event/phd-thesis-defense-of-dhruv-jawali/
LOCATION:EE\, MMCR
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