BEGIN:VCALENDAR VERSION:2.0 PRODID:-//EE - ECPv5.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:EE X-ORIGINAL-URL:https://ee.iisc.ac.in X-WR-CALDESC:Events for EE BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD TZOFFSETFROM:+0530 TZOFFSETTO:+0530 TZNAME:IST DTSTART:20210101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220601T170000 DTEND;TZID=Asia/Kolkata:20220601T180000 DTSTAMP:20260403T233025 CREATED:20220530T233759Z LAST-MODIFIED:20220531T224824Z UID:239758-1654102800-1654106400@ee.iisc.ac.in SUMMARY:Lecture by Dr. Ayush Bhandari @ 11.30am DESCRIPTION:Title: Digital Acquisition via Modulo Folding: Revisiting the Legacy of Shannon-Nyquist\, Prony\, Schoenberg\, Pisarenko and Radon \nDate and time: June 1\, 2022; 11.30 AM\nVenue: Multimedia Classroom\, Electrical Engineering Department\, IISc \nCoffee will be served during the talk. \nAbstract: Digital data capture is the backbone of all modern day systems and “Digital Revolution” has been aptly termed as the Third Industrial Revolution. Underpinning the digital representation is the Shannon-Nyquist sampling theorem and more recent developments such as compressive sensing approaches. The fact that there is a physical limit to which sensors can measure amplitudes poses a fundamental bottleneck when it comes to leveraging the performance guaranteed by recovery algorithms. In practice\, whenever a physical signal exceeds the maximum recordable range\, the sensor saturates\, resulting in permanent information loss. Examples include (a) dosimeter saturation during the Chernobyl reactor accident\, reporting radiation levels far lower than the true value\, and (b) loss of visual cues in self-driving cars coming out of a tunnel (due to sudden exposure to light). \nTo reconcile this gap between theory and practice\, we introduce a computational sensing approach—the Unlimited Sensing framework (USF)—that is based on a co-design of hardware and algorithms. On the hardware front\, our work is based on a radically different analog-to-digital converter (ADC) design\, which allows for the ADCs to produce modulo or folded samples. On the algorithms front\, we develop new\, mathematically guaranteed recovery strategies. \nIn the first part of this talk\, we prove a sampling theorem akin to the Shannon-Nyquist criterion. Despite the non-linearity in the sensing pipeline\, the sampling rate only depends on the signal’s bandwidth. Our theory is complemented with a stable recovery algorithm. Beyond the theoretical results\, we also present a hardware demo that shows the modulo ADC in action. \nBuilding on the basic sampling theory result\, we consider certain variations on the theme. This includes different signal classes (e.g. smooth\, sparse and parametric functions) as well as sampling architectures\, such as One-Bit and Event-Triggered sampling. Moving further\, we reinterpret the USF as a generalized linear model that motivates a new class of inverse problems. We conclude this talk by presenting a research overview in the context of single-shot high-dynamic-range (HDR) imaging\, sensor array processing and HDR computed tomography based on the modulo Radon transform. \nAbout the speaker:  Ayush Bhandari received the Ph.D. degree from Massachusetts Institute of Technology (MIT)\, Cambridge\, MA\, USA\, in 2018\, for his work on computational sensing and imaging which is being shaped as a forthcoming\, co-authored book Computational Imaging in MIT Press. He is currently a faculty member with the Department of Electrical and Electronic Engineering\, Imperial College London\, U. K. He has held research positions at INRIA (Rennes)\, France\, Nanyang Technological University\, Singapore\, the Chinese University of Hong Kong and Ecole Polytechnique Fédérale de Lausanne (EPFL)\, Switzerland among other institutes. He was appointed the August–Wilhelm Scheer Visiting Professor (Department of Mathematics)\, in 2019 by the Technical University of Munich. \nHe has been a tutorial speaker at various venues including the ACM Siggraph (2014\,2015) and the IEEE ICCV (2015) and he was the keynote speaker at the Intl. Workshop on Compressed Sensing applied to Radar\, Multimodal Sensing and Imaging (CoSeRa)\, 2018. Some aspects of his work have led to new sensing and imaging modalities which have been widely covered in press and media (e.g. BBC news). Applied aspects of his research have led to more than 10 US patents. His scientific contributions have led to numerous prizes\, most recently\, the Best Paper Award at IEEE ICCP 2020 (Intl. Conf. on Computational Photography) and the Best Student Paper Award (senior co-author) at IEEE ICASSP 2019 (Intl. Conf. on Acoustics\, Speech and Signal Processing). In 2020\, his doctoral work was awarded the Best PhD Dissertation Award from the IEEE Signal Processing Society. In 2021\, he received the President’s Medal for Outstanding Early Career Researcher at Imperial College London. \nHost: Prof. Chandra Sekhar Seelamantula (EE) URL:https://ee.iisc.ac.in/event/lecture-by-dr-ayush-bhandari-11-30am/ LOCATION:EE\, MMCR END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220531T210000 DTEND;TZID=Asia/Kolkata:20220531T220000 DTSTAMP:20260403T233025 CREATED:20220529T232057Z LAST-MODIFIED:20220529T232212Z UID:239755-1654030800-1654034400@ee.iisc.ac.in SUMMARY:Lecture by Dr. Lokesh @3.30pm DESCRIPTION:Title: Artificial Intelligence in Clinical Neurology \nOrganizer: Aster-IISc AI lab \nSpeaker: Dr. Lokesh from Aster-CMI \nVenue: MMCR\, EE \nLInk: Teams https://tinyurl.com/4fv7rbh6 \nTime: 3.30pm to 4.30pm \nAll are welcome \n  URL:https://ee.iisc.ac.in/event/lecture-by-dr-lokesh-3-30pm/ LOCATION:EE\, MMCR END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220428T163000 DTEND;TZID=Asia/Kolkata:20220428T173000 DTSTAMP:20260403T233025 CREATED:20220426T020023Z LAST-MODIFIED:20220426T020102Z UID:239710-1651163400-1651167000@ee.iisc.ac.in SUMMARY:PhD Thesis Colloquium DESCRIPTION:Name of Student: Ruturaj Gavaskar. \nGuide: Prof. Kunal Narayan ChaudhuryDate:  April 28\, Thursday.               Time: 11-12 am.Venue: MS Teams (online).Link: https://tinyurl.com/bdfardzz \nTitle:  On plug-and-play regularization using linear denoisers.Abstract:  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. While several  forms of regularization and associated optimization methods have been  proposed in the imaging literature of 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 the  advances in image denoising in the last 20 years\, leading to the  development of powerful image denoisers. In this thesis\, we look at a  recent protocol called Plug-and-Play (PnP) method\, where powerful image  denoisers such as BM3D and DnCNN are deployed within iterative  algorithms for image regularization. Surprisingly\, the reconstructed  images 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. We have tried answering some of  these questions in this thesis.At a high level\, the contributions of the thesis are as follows. Based  on the theory of proximal operators\, we prove that a PnP algorithm in  fact minimizes a convex objective function provided the plugged denoiser  belongs to a broad class L of linear filters. In particular\, L has a  simple characterization and includes kernel and GMM denoisers. That we  are able to characterize the reconstruction (for class L denoisers) as  the solution of a convex optimization problem helps in settling some of  the above questions. For example\, this allows us to establish iterate  convergence for PnP regularization. Obtaining such a guarantee for  complex nonlinear denoisers such as BM3D and neural denoisers is  nontrivial. As a more profound application\, we are able to provide  guarantees on signal recovery for the compressed sensing problem. More  precisely\, under certain verifiable assumptions\, we are able to prove  that a signal can be recovered exactly (resp. stably) with high  probability from random clean (resp. noisy) measurements using PnP  regularization. To the best of our knowledge\, this is the first such  result where the underlying assumptions are verifiable. We will present  and discuss these and other theoretical findings in greater detail  during the colloquium. We will also present numerical results to  validate our findings. URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220422T213000 DTEND;TZID=Asia/Kolkata:20220422T223000 DTSTAMP:20260403T233025 CREATED:20220418T002346Z LAST-MODIFIED:20220418T002455Z UID:239701-1650663000-1650666600@ee.iisc.ac.in SUMMARY:M. Tech. (Research) Thesis Colloquium of Anwesha Roy DESCRIPTION:Title: Improved air-tissue boundary segmentation in real-time magnetic resonance imaging videos using speech articulator specific error criterion \nAbstract: Real-time Magnetic Resonance Imaging (rtMRI) is a tool used exhaustively in speech science and linguistics to understand the dynamics of the speech production process across languages and health conditions. rtMRI has two advantages over other methods which capture articulatory movement\, like X-ray\, Ultrasound and Electromagnetic articulography – it is non-invasive\, and it captures a complete view of the vocal tract including pharyngeal structures. The rtMRI video provides spatio-temporal information of speech articulatory movements\, which helps in modeling speech production. For this purpose\, a common step is to obtain the air-tissue boundary (ATB) segmentation in all frames of the rtMRI video. The accurate estimation of ATBs of the upper airway of the vocal tract is essential for many speech processing applications like speaker verification\, text-to-speech synthesis\, visual augmentation for synthesized articulatory videos\, and analysis of vocal tract movement. Thus\, it is necessary to have an accurate air-tissue boundary segmentation in every frame of the rtMRI videos. \nThe best performance in ATB segmentation of rtMRI videos in speech production\, in unseen subject conditions\, is known to be achieved by a 3-dimensional convolutional neural network (3D-CNN) model. In seen subject conditions\, both 3D-CNN and 2-dimensional deep convolutional encoder-decoder network (SegNet) show similar performance. However\, the evaluation of these models\, as well as other ATB segmentation techniques reported in literature\, has been done using Dynamic Time Warping (DTW) distance between the entire original and predicted boundaries or contours. Such an evaluation measure may not capture local errors in the predicted contour. Careful analysis of predicted contours reveals errors in regions like the velum part and tongue base section\, which are not captured in a global evaluation metric like DTW distance. In this thesis\, we automatically detect such errors and propose a novel correction scheme for them. We also propose two new evaluation metrics for ATB segmentation\, separately for each contour\, to explicitly capture errors in these contours. \nMoreover\, the state-of-the-art models use overall binary cross entropy as the loss function during model training. However\, such a global loss function does not give enough emphasis on regions which are more prone to errors. In this thesis\, together with global loss\, we explore the use of regional loss functions which focus on areas of the contours which have been analyzed as error prone in our analysis. Two different losses are considered in the regions around velum and tongue base – binary cross entropy (BCE) loss and dice loss. It is observed that dice-loss based models perform better than their BCE loss based counterparts. URL:https://ee.iisc.ac.in/event/m-tech-research-thesis-colloquium-of-anwesha-roy/ LOCATION:EE\, MMCR END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220421T163000 DTEND;TZID=Asia/Kolkata:20220421T173000 DTSTAMP:20260403T233025 CREATED:20220426T022712Z LAST-MODIFIED:20220426T022712Z UID:239712-1650558600-1650562200@ee.iisc.ac.in SUMMARY:MTech (Rresearch) Colloquium DESCRIPTION:Name of the Student:      Ahmad Arfeen \nGuide:                             Prof. Soma Biswas \nDate And Day:                21st April\, 2022\, Thursday \nTime:                               11:00 am \nVenue:                              EE\, MMCR \nTitle : Data Efficient Domain Generalization \nAbstract: For the task of image classification\, in general\, the test data is assumed to come from the same distribution as the training data. But this may not always hold in real-life scenarios. For example\, in night-time surveillance\, we may need to classify images captured using NIR cameras\, but the available model has been trained on RGB images. Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain\, by leveraging training samples from multiple source domains. In this thesis\, we address two challenging scenarios for the DG task. Currently\, the training process of majority of the state-of-the-art DG-methods is dependent on a large amount of labeled data. This restricts the application of the models in many real-world scenarios\, where collecting and annotating a large dataset is an expensive and difficult task.  \nThus\, as the first contribution\, we address the problem of Semi-supervised Domain Generalization (SSDG)\, where the training set contains only a few labeled data\, in addition to a large number of unlabeled data from multiple domains. This is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models\, since their performance degrades under such condition. To address this scenario\, we propose a novel Selective Mixing and Voting Network (SMV-Net)\, which effectively extracts useful knowledge from the set of unlabeled training data\, available to the model. Specifically\, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data\, which generalizes effectively across any unseen domain. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework.  \nThe second contribution of this thesis is a novel approach for the task of Zero-Shot Domain Generalization (ZSDG). This is very challenging since the query data can belong to an unseen class as well as unseen domain. For this task\, we address the challenge of class imbalance by learning class-specific classifier margins\, which not only maintain the semantic relationship of the classes in the embedding space\, but is also discriminative\, and thus improves the classification performance on the test data. Extensive experiments on multiple datasets justify the effectiveness of the proposed approach. \nALL ARE WELCOME URL:https://ee.iisc.ac.in/event/mtech-rresearch-colloquium/ LOCATION:EE\, MMCR END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220420T230000 DTEND;TZID=Asia/Kolkata:20220421T000000 DTSTAMP:20260403T233025 CREATED:20220411T231422Z LAST-MODIFIED:20220411T231422Z UID:239692-1650495600-1650499200@ee.iisc.ac.in SUMMARY:Seminar by Prof. Sairaj Dhople @ 5.30pm DESCRIPTION:Title: Power-system Modeling & Control for the Era of Inverter-based Resources \nSpeaker: Prof. Sairaj Dhople\, Electrical & Computer Engineering\, University of Minnesota\nTime: 20 April 2022\, 5:30 pm\nVenue: MMCR EE / Hybrid mode \nhttps://teams.microsoft.com/l/meetup-join/19%3ameeting_YWJjZTQyZTAtYTFjNi00YTVkLWE0NjktYmJkZDMzNjI4ZDFm%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%220c8fe27d-52c2-4e77-a28f-a759bd113fae%22%7d \nAbstract: Power networks all over the world are experiencing dramatic upheaval in compositional form and anticipated functionality. With retirement of fossil-fuel-driven synchronous generators\, integration of renewable energy\, and adoption of electrified transportation\, there is a pronounced change in the energy-conversion interfaces that form the backbone of the grid. Particularly\, energy processing in future grids will be dominantly handled by semiconductor-based power-electronics circuits termed inverter-based resources (IBRs). This talk will provide snapshots of how classical power-system modeling problems can (and will have to) be revised to accomodate these emerging technologies. In particular\, we will present insights on synchronization of IBRs with a variety of control methods\, provide a system-theoretic solution to normalizing dynamic models of diverse grid assets\, and overview a time-domain network-reduction method for large-scale electrical networks. Each topic will be presented with an effort to acknowledge the rich history of personalities\, methods\, and venues relevant to power engineering over the 20th century. Finally\, we will discuss how partnerships and collaboration across academia\, industry (system operators\, utilities\, manufacturers)\, and national labs will be critical to facilitate large-scale integration with performance guarantees. \nBio: Sairaj Dhople received the B.S.\, M.S.\, and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign\, Urbana\, IL\, USA\, in 2007\, 2009\, and 2012\, respectively. He is currently serving as Associate Professor with the Department of Electrical and Computer Engineering at the University of Minnesota. His research interests include modeling\, analysis\, and control of power electronics and power systems with a focus on renewable integration. He is the recipient of the National Science Foundation CAREER Award in 2015\, the Outstanding Young Engineer Award from the IEEE Power and Energy Society in 2019\, and the IEEE Power and Energy Society Prize Paper Award in 2021. URL:https://ee.iisc.ac.in/event/seminar-by-prof-sairaj-dhople-5-30pm/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220420T203000 DTEND;TZID=Asia/Kolkata:20220420T213000 DTSTAMP:20260403T233025 CREATED:20220426T025140Z LAST-MODIFIED:20220426T025140Z UID:239714-1650486600-1650490200@ee.iisc.ac.in SUMMARY:Seminar by Prof. Sanjib Kumar Panda DESCRIPTION:meeting link: Teams meeting link \nTitle: Single-phase inverter control techniques for interfacing renewable energy sources with micro-grid – Parallel connected inverter topologies with active and reactive power flow control along with grid current shaping \n\n\n\n Speaker: Professor Sanjib Kumar Panda \n Time: 20 April 2022\, 3:00 pm \n Venue: MMCR EE \n Abstract: Renewable energy sources (RESs) have been receiving significant attention recently worldwide as a sustainable alternative type of energy supply in the energy mix. Inverters are being used to convert the dc voltage into ac voltage before being injected into the grid or isolated loads. In this presentation\, a novel current control technique is proposed to control both active and reactive power flow from a renewable energy source feeding a micro-grid system through a single-phase parallel connected inverter. The parallel-connected inverter ensures active and reactive power flow from the grid with low current THD even in the presence of non-linear load. A p-q theory-based approach is used to find the reference current of the parallel-connected converter to ensure desired operating conditions at the grid terminal. The proposed current controller is simple to implement and gives superior performance over the conventional current controllers such as rotating frame PI controller or stationary frame Proportional Resonant (PR) controller. The stability of the proposed controller is ensured by the direct Lyapunov method. A new technique based on the Spatial Repetitive Controller (SRC) is also proposed to improve the performance of the current controller by estimating the grid and other periodic disturbances. Detailed experimental results are presented to show the efficacy of the proposed current control scheme along with the proposed non-linear controller to control the active and reactive power flow in a single-phase micro-grid under different operating conditions \nSpeaker’s Bio: Sanjib Kumar Panda (Student 98’\, Member ’92\, SM 00’\, F 21’) received a Bachelor of Engineering Degree with 1st Class Honours from Sardar Vallabhabhai Regional College of Engineering and Technology\, Surat\, India\, in 1983. He was awarded the Gold Medal for securing the highest marks not only amongst the B. Engg. (Electrical) but also for securing the highest marks amongst all the B. Engg. (Civil\, Mechanical and Electrical) students. He also earned a Masters of Technology Degree from the Institute of Technology\, Banaras Hindu University\, Varanasi\, India in 1987. He was awarded the Gold Medal for securing the highest marks amongst all the M. Tech. (Electrical) students. Subsequently\, he earned a PhD. Degree from the University of Cambridge\, U.K.\, in 1991. He was awarded the Nehru Cambridge Fellowship and Overseas Research Studentship from the Cambridge Commonwealth Trust for Cambridge University for his PhD studies\, 1987-1991. \nHe joined the Department of Electrical and Computer Engineering at the National University of Singapore as a Lecturer in 1992. He is currently serving as an Associate Professor and Director of the Power & Energy Research Area. He has served as Director (Education) at the Design Technology Institute\, a joint-venture between NUS and TU/e\, The Netherlands and funded by EDB\, Singapore. He has served as the Group Head of the Drives\, Power and Control Group from 2007-2009. He was appointed as Area Director\, Power & Energy Research Group of the Department of Electrical & Computer Engineering at NUS on 1st January 2010 and serving in this position till date. \nDr. Panda has won the Annual Teaching Excellence Award at the National University of Singapore in 2004 and 2009. Besides these two University Level Awards\, he has also been awarded several Teaching Awards at the Faculty of Engineering and at the Department of Electrical and Computer Engineering Department consistently since the year 2000. \nDr. Panda has carried out extensive research in various areas of control of electric drives and power electronic converters. He has co-authored 1 book\, several book chapters\, published more than 450 papers in international refereed journals and conferences and holds 6 patents to his credit. Dr. Panda has an h-index of 44 and has citations almost close to 10\,000. He has received research funding to the tune of S$25mil over the past 15 years or so. Dr. Panda is the co-founder of three start-up companies: (1) ENBED Pte. Ltd.\, (2) REPMIX Pte. Ltd. and (3) SPCSCAN Pte. Ltd. along with his PhD students and research staff. His current research interests are in high-performance control of motor drives\, control of distributed renewable energy sources and their integration with grid\, condition monitoring\, preventive and predictive maintenance. \nDr. Panda has been an active member of the IEEE. He has served in various capacities as Chapter Officer in the IEEE Singapore Section’s Joint Power Electronics and Industry Applications Society Chapter since 1994 till date. He has served in various capacities in the IEEE Singapore Section during the period 2000-2004 and served as the Section Chairman during the period 2004. He was the recipient of the IEEE 3rd Millennium Medal. He was the Organizing Chairman for the International IEEE Power Electronics and Drives Systems Conference (PEDS) in 2003. Dr. Panda served as the founding Chairman for the International Conference on Sustainable Energy Technologies (ICSET) in 2008. He was awarded the Best Volunteer Award by the IEEE Singapore Section in 2006. He was awarded the Best Volunteer Award by IEEE R-10 in 2014. Since 2012\, Dr. Panda has been a volunteer serving in the Membership and Chapter Development for the IEEE PELS and presently serving as R-10 Coordinator. The IEEE PELS has seen the consistent membership growth rate of more than 15% for the R-10. Dr. Panda also proposed the Regional Distinguished Lecture (RDL) Program for the IEEE PELS and the initiated as a part of the R-10 RDL Speakers to be implemented in June 2020. Dr. Panda is the Organizing Chair for the IEEE ECCE-ASIA 24-27th May\, 2021 to be held at Singapore. Dr. Panda has also served as a Member in the Program Committee in the IEEE Section Congress 2014 at Amsterdam\, The Netherlands. He also presented in the IEEE Section Congress 2017 at Sydney\, Australia. Dr. Panda has been serving as an Associate Editor of the IEEE Transactions in Power Electronics\, the Journal of Emerging and Selected Topics in Power Electronics since 2012 till date. Dr. Panda has been elevated to the IEEE Fellowship w.e.f. form 1st Jan. 2021. Dr. Panda is the IEEE PELS DL for the period 1st Jan. 2022 – 31st December 2023. \n All are welcome. URL:https://ee.iisc.ac.in/event/seminar-by-prof-sanjib-kumar-panda/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220420T203000 DTEND;TZID=Asia/Kolkata:20220420T213000 DTSTAMP:20260403T233025 CREATED:20220418T002901Z LAST-MODIFIED:20220418T003005Z UID:239705-1650486600-1650490200@ee.iisc.ac.in SUMMARY:Seminar by Prof. Sanjib Kumar Panda @ 3pm DESCRIPTION:Title: Single-phase inverter control techniques for interfacing renewable energy sources with micro-grid – Parallel connected inverter topologies with active and reactive power flow control along with grid current shaping \nTime: 20 April 2022\, 3:00 pm \nVenue: MMCR EE \nAbstract: Renewable energy sources (RESs) have been receiving significant attention recently worldwide as a sustainable alternative type of energy supply in the energy mix. Inverters are being used to convert the dc voltage into ac voltage before being injected into the grid or isolated loads. In this presentation\, a novel current control technique is proposed to control both active and reactive power flow from a renewable energy source feeding a micro-grid system through a single-phase parallel connected inverter. The parallel-connected inverter ensures active and reactive power flow from the grid with low current THD even in the presence of non-linear load. A p-q theory-based approach is used to find the reference current of the parallel-connected converter to ensure desired operating conditions at the grid terminal. The proposed current controller is simple to implement and gives superior performance over the conventional current controllers such as rotating frame PI controller or stationary frame Proportional Resonant (PR) controller. The stability of the proposed controller is ensured by the direct Lyapunov method. A new technique based on the Spatial Repetitive Controller (SRC) is also proposed to improve the performance of the current controller by estimating the grid and other periodic disturbances. Detailed experimental results are presented to show the efficacy of the proposed current control scheme along with the proposed non-linear controller to control the active and reactive power flow in a single-phase micro-grid under different operating conditions \nSpeaker’s Bio: Sanjib Kumar Panda (Student 98’\, Member ’92\, SM 00’\, F 21’) received a Bachelor of Engineering Degree with 1st Class Honours from Sardar Vallabhabhai Regional College of Engineering and Technology\, Surat\, India\, in 1983. He was awarded the Gold Medal for securing the highest marks not only amongst the B. Engg. (Electrical) but also for securing the highest marks amongst all the B. Engg. (Civil\, Mechanical and Electrical) students. He also earned a Masters of Technology Degree from the Institute of Technology\, Banaras Hindu University\, Varanasi\, India in 1987. He was awarded the Gold Medal for securing the highest marks amongst all the M. Tech. (Electrical) students. Subsequently\, he earned a PhD. Degree from the University of Cambridge\, U.K.\, in 1991. He was awarded the Nehru Cambridge Fellowship and Overseas Research Studentship from the Cambridge Commonwealth Trust for Cambridge University for his PhD studies\, 1987-1991. \nHe joined the Department of Electrical and Computer Engineering at the National University of Singapore as a Lecturer in 1992. He is currently serving as an Associate Professor and Director of the Power & Energy Research Area. He has served as Director (Education) at the Design Technology Institute\, a joint-venture between NUS and TU/e\, The Netherlands and funded by EDB\, Singapore. He has served as the Group Head of the Drives\, Power and Control Group from 2007-2009. He was appointed as Area Director\, Power & Energy Research Group of the Department of Electrical & Computer Engineering at NUS on 1st January 2010 and serving in this position till date. \nDr. Panda has won the Annual Teaching Excellence Award at the National University of Singapore in 2004 and 2009. Besides these two University Level Awards\, he has also been awarded several Teaching Awards at the Faculty of Engineering and at the Department of Electrical and Computer Engineering Department consistently since the year 2000. \nDr. Panda has carried out extensive research in various areas of control of electric drives and power electronic converters. He has co-authored 1 book\, several book chapters\, published more than 450 papers in international refereed journals and conferences and holds 6 patents to his credit. Dr. Panda has an h-index of 44 and has citations almost close to 10\,000. He has received research funding to the tune of S$25mil over the past 15 years or so. Dr. Panda is the co-founder of three start-up companies: (1) ENBED Pte. Ltd.\, (2) REPMIX Pte. Ltd. and (3) SPCSCAN Pte. Ltd. along with his PhD students and research staff. His current research interests are in high-performance control of motor drives\, control of distributed renewable energy sources and their integration with grid\, condition monitoring\, preventive and predictive maintenance. \nDr. Panda has been an active member of the IEEE. He has served in various capacities as Chapter Officer in the IEEE Singapore Section’s Joint Power Electronics and Industry Applications Society Chapter since 1994 till date. He has served in various capacities in the IEEE Singapore Section during the period 2000-2004 and served as the Section Chairman during the period 2004. He was the recipient of the IEEE 3rd Millennium Medal. He was the Organizing Chairman for the International IEEE Power Electronics and Drives Systems Conference (PEDS) in 2003. Dr. Panda served as the founding Chairman for the International Conference on Sustainable Energy Technologies (ICSET) in 2008. He was awarded the Best Volunteer Award by the IEEE Singapore Section in 2006. He was awarded the Best Volunteer Award by IEEE R-10 in 2014. Since 2012\, Dr. Panda has been a volunteer serving in the Membership and Chapter Development for the IEEE PELS and presently serving as R-10 Coordinator. The IEEE PELS has seen the consistent membership growth rate of more than 15% for the R-10. Dr. Panda also proposed the Regional Distinguished Lecture (RDL) Program for the IEEE PELS and the initiated as a part of the R-10 RDL Speakers to be implemented in June 2020. Dr. Panda is the Organizing Chair for the IEEE ECCE-ASIA 24-27th May\, 2021 to be held at Singapore. Dr. Panda has also served as a Member in the Program Committee in the IEEE Section Congress 2014 at Amsterdam\, The Netherlands. He also presented in the IEEE Section Congress 2017 at Sydney\, Australia. Dr. Panda has been serving as an Associate Editor of the IEEE Transactions in Power Electronics\, the Journal of Emerging and Selected Topics in Power Electronics since 2012 till date. Dr. Panda has been elevated to the IEEE Fellowship w.e.f. form 1st Jan. 2021. Dr. Panda is the IEEE PELS DL for the period 1st Jan. 2022 – 31st December 2023. \nAll are welcome. URL:https://ee.iisc.ac.in/event/seminar-by-prof-sanjib-kumar-panda-3pm/ LOCATION:EE\, MMCR END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220418T163000 DTEND;TZID=Asia/Kolkata:20220418T173000 DTSTAMP:20260403T233025 CREATED:20220418T001523Z LAST-MODIFIED:20220418T002022Z UID:239695-1650299400-1650303000@ee.iisc.ac.in SUMMARY:MTech Research Thesis Defense of Mr. Jaswanth Reddy Katthi @ 11am DESCRIPTION:Location : Electrical Engineering\, MMCR (C241)\, Online via Teams (if network connection allows) https://tinyurl.com/2p8exxys \nTitle : Deep Learning Methods for Audio-EEG Analysis \nAbstract : The perception of speech and audio is one of the defining features of humans. Much of the brain’s underlying processes\, as we perceive acoustic signals\, are unknown\, and significant research efforts are needed to unravel them. The non-invasive recordings capturing the brain activations like electroencephalogram (EEG) and magnetoencephalogram (MEG) are commonly deployed to capture the brain responses to auditory stimuli. But these non-invasive techniques capture artifacts and noise that are not related to the stimuli\, which distort the downstream stimulus-response analysis.  The current state-of-art models used for normalization and pre-processing of EEG data utilize the linear canonical correlation analysis (CCA) or the temporal response function (TRF) based approach. However\, these methods assume a simplistic linear relationship between the audio features and the EEG responses and therefore\, may not alleviate the recording artifacts and interfering signals in EEG. This talk proposes novel methods using deep learning advances to improve the audio-EEG analysis. \nWe propose a deep learning framework for audio-EEG analysis in intra-subject and inter-subject settings. The deep learning based intra-subject analysis methods are trained with a Pearson correlation-based cost function between the stimuli and EEG responses. This model allows the transformation of the audio and EEG features in a common sub-space that is maximally correlated. The correlation-based cost function can be optimized with the learnable parameters of the model trained using standard gradient-descent based methods. This model is referred to as the deep CCA (DCCA) model. Several experiments\, performed on the EEG data recorded on subjects listening to naturalistic speech and music stimuli\, show that the deep methods obtain improved representations than the linear methods\, thereby resulting in statistically significant improvements in correlation values. \nFurther\, we propose a neural network model with shared encoders that align the EEG responses from multiple subjects listening to the same audio stimuli. This inter-subject model boosts the signals common across the subjects related to the stimuli and suppresses the subject-specific artifacts. This model is referred to as the deep multi-way canonical correlation analysis (DMCCA). The combination of inter-subject analysis using DMCCA and intra-subject analysis using DCCA is shown to provide the best stimulus-response in audio-EEG experiments. \nFinally\, the talk will discuss about an ambitious experiment\, where we attempted to recreate acoustic signal directly from EEG responses. While the audio is not fully recoverable\, the parts of the signal that can be recovered from the non-invasive EEG recordings throws light into the characteristics of audio captured in the EEG data. URL:https://ee.iisc.ac.in/event/mtech-research-thesis-defense-of-mr-jaswanth-reddy-katthi-11am/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220418T133000 DTEND;TZID=Asia/Kolkata:20220422T223000 DTSTAMP:20260403T233025 CREATED:20220405T095254Z LAST-MODIFIED:20220406T004010Z UID:239689-1650288600-1650666600@ee.iisc.ac.in SUMMARY:Information for M.Tech aspirants in Electrical Engineering DESCRIPTION:Department of Electrical Engineering \nIndian Institute of Science\, Bangalore \nImportant information to the applicants called for interview \nDear Applicant\, \nThis page is relevant to you only if you had applied for admission to M Tech EE programme and have been invited for an interview at the Department of Electrical Engineering\, IISc in April 2022. \nBased on your performance in GATE\, you have been shortlisted and invited to appear for a technical interview offline. There will not be any test. The final selection will be based on the performances in GATE and interviews. \nPlease note the following information: \nInterview will be held during 18 to 22 April 2022. So\, kindly adhere to the date(s) and time allotted for your interview. \nParticipation in interview is mandatory to be eligible for selection process. \nKindly carry your interview call letter from Academic section\, ID proof\, photostat copies of certificates\, Category Certificate\, National Qualifying exam Score card /Certificate and mark statements from 10th Std onwards for verification \nYou are requested to follow the Covid-19 related guidelines issued by the government \nWith our very best wishes\, \nChairman\, Department of Electrical Engineering \n  \nFor any queries mail to office.ee@iisc.ac.in. Or call at 22932361/3170 URL:https://ee.iisc.ac.in/event/information-for-m-tech-aspirants-in-electrical-engineering/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220408T150000 DTEND;TZID=Asia/Kolkata:20220410T010000 DTSTAMP:20260403T233025 CREATED:20220325T033019Z LAST-MODIFIED:20220328T230746Z UID:239680-1649430000-1649552400@ee.iisc.ac.in SUMMARY:EECS Resesarch Students Symposium 2022 DESCRIPTION:Click on the image to visit symposium website. Click here for the poster URL:https://ee.iisc.ac.in/event/eecs-resesarch-students-symposium-2022/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220318T233000 DTEND;TZID=Asia/Kolkata:20220319T010000 DTSTAMP:20260403T233025 CREATED:20220315T041418Z LAST-MODIFIED:20220315T041527Z UID:239665-1647646200-1647651600@ee.iisc.ac.in SUMMARY:Aspiring IIScian’s Meet 2022 on 18 March 2022 DESCRIPTION:Click on the poster for details URL:https://ee.iisc.ac.in/event/aspiring-iiscians-meet-2022-on-18-march-2022/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220318T203000 DTEND;TZID=Asia/Kolkata:20220318T213000 DTSTAMP:20260403T233025 CREATED:20220314T000219Z LAST-MODIFIED:20220314T000328Z UID:239660-1647635400-1647639000@ee.iisc.ac.in SUMMARY:Seminar by Visweshwar Chandrasekaran@ 3pm DESCRIPTION:Title: Electric Drives in the Loop \nVenue: MMCR EE\, Teams Link \n Speaker’s bio: Visweshwar Chandrasekaran received the B.E. degree in Electrical and Electronics Engineering from Anna University\, Chennai\, India\, in 2011\, M.S. degree in Electrical Engineering from the University of Minnesota\, Minneapolis\, in 2013\, and is currently pursuing a PhD degree in Electrical Engineering from the University of Minnesota\, Minneapolis. He has been working at Trane Technologies since 2014 and has recently started leading the Power Electronics group focused on new technology introduction. His research interests are in Variable Speed Drives\, Power Hardware-in-the-Loop and Real-Time Simulations. URL:https://ee.iisc.ac.in/event/seminar-by-visweshwar-chandrasekaran-3pm/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220315T153000 DTEND;TZID=Asia/Kolkata:20220315T163000 DTSTAMP:20260403T233025 CREATED:20220311T014519Z LAST-MODIFIED:20220314T000413Z UID:239654-1647358200-1647361800@ee.iisc.ac.in SUMMARY:M.Tech(Res) Thesis Colloquium of Meshineni Deepchand @ 10am DESCRIPTION:Thesis Title: Non-contact Breathing and Heartbeat signal monitoring using FMCW radar \nResearch Supervisor: Dr. Rathna G N \nDate & Time: March 15\, 2022 (Tuesday) 10 AM \nVenue: MMCR\, EE (offline) \nAbstract: Non-contact breathing and heartbeat signals monitoring are the tasks of extracting them without contact sensors. It became even more critical in COVID 19\, and hence it is crucial to estimate them correctly. FMCW (Frequency Modulated Continuous Wave) radar is employed to estimate these two signals without contact. Radar captures chest displacement and body movement. Because of this\, breathing and heartbeat signals are distorted. The reduction of false peaks and peak estimation is crucial for breathing rate calculation. In this thesis\, firstly\, we propose a novel way for tracing body movement and eliminating the traced segment for breathing and heart rate calculation. In the second part\, we efficiently reduced false peaks using maximal overlap discrete wavelet transform (MODWT) to decompose and reconstruct the filtered breathing signal for estimating breathing rate. We also compared our algorithm with the task force monitoring (TFM) device as a reference and discussed its performance. \nAll are welcome\nPlease follow the covid protocols URL:https://ee.iisc.ac.in/event/m-techres-thesis-colloquium-of-meshineni-deepchand/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220216T203000 DTEND;TZID=Asia/Kolkata:20220216T223000 DTSTAMP:20260403T233025 CREATED:20220216T001817Z LAST-MODIFIED:20220216T002011Z UID:239594-1645043400-1645050600@ee.iisc.ac.in SUMMARY:Ph.D. Thesis Defense of Sanjay Viswanath DESCRIPTION:Advisor: Prof.. Muthuvel Arigovindan\nTitle: Spatially Adaptive Regularization for Image Restoration\nThesis Examiners: Prof.  Suyash Awate\,  IIT Bombay\,   and  Prof. Ajit Rajwade\, IIT Bombay\nDefense Examiner:  Prof.  Ajit Rajwade\, IIT Bombay\nDate and Time: 16th February (Wednesday): 3:00 pm – 5:00 pm\nVenue: Microsoft Teams Live\nMicrosoft Teams meeting link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NjBkZTE1NmEtNzQ5Ny00NzJkLTllNTgtM2ViNWZiZDQzNzA4%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%22d7e91daa-7e70-4e9c-b565-b900dfd5b5b5%22%7d \n\n\n\n\n\n\n\n\n\nJoin conversation\nteams.microsoft.com\n\n\n\n\n\n\n Summary: Image restoration/reconstruction refers to the estimation of underlying image from measurements generated by imaging devices. This problem is generally ill-posed due to the fact that measurements are corrupted because of the physical limitations of the imaging device\, and the inherent noise involved in the measurement process. There are three main classes of methods in the current literature. The first class of methods are based on regularization framework that enforces an ad-hoc prior on the restored image. The second class of methods use regression-based learning paradigms\, where a training set of clean images and the corresponding distorted measurements are used to generate a trained prior. The third class of methods adopt trained priors similar to the ones utilized in second class of methods\, but within the regularization framework. This third class of methods\, the trained regularization methods\, are getting increasing attention because of their versatility as regularization methods\, while also encompassing natural priors obtained from training. However\, the need for training data can limit their applicability. In this thesis\, we propose spatially adaptive regularization methods where the adaptation information is retrieved from the measured data that undergoes reconstruction. Due to the adaptation\, the enforced prior is more natural than the existing regularization methods. At the same time\, our methods do not require training data. \nIn the first part\, we propose a novel regularization method that adaptively combines the well-known second order regularization\, called Hessian-Schatten (HSN) norm regularization\, and first order TV (TV-1) functionals with spatially varying weights. The relative weight involved in combining the first- and second-order terms becomes an image\, and this weight is determined through minimization of a composite cost function\, without user intervention. \nOur contributions in this part can be summarized as follows: \n• We construct a composite regularization functional containing two parts: (i) the first part is constructed as the sum of TV-1 and HSN with spatially varying relative weights; (ii) the second part is an additional regularization term for preventing rapid spurious variations in the relative weights. The total composite cost functional is convex with respect to either the required image or the relative weight\, but it is non-convex jointly. \n• We construct a block coordinate descent method involving minimizations w.r.t. the required image and the relative weight alternatively with the following structure: the minimization w.r.t. the required image is carried out using Alternating Direction Method of Multipliers (ADMM) \, and the minimization w.r.t. the relative weight is carried out as a single step exact minimization using a formula that we derive. \n• Since the total cost is non-convex\, the reconstruction results are highly dependent on the initialization for the block-coordinate descent method. We handle this problem using a multi-resolution approach\, where a series of coarse-to-fine reconstructions are performed by minimization of cost functionals defined through upsampling operators. Here\, minimization w.r.t. the relative weight and the required image is carried out alternatively\, as we progress from coarse to final resolution levels. At the final resolution level\, the above-mentioned block coordinate descent method is applied. \n• Note that the sub-problem of minimization w.r.t. to the required image involves spatially varying relative weights. Further\, this sub-minimization problem in the above-mentioned multi-resolution loop involves upsampling operators. Hence\, the original ADMM method proposed by Papafitsoros et al. turns out to be unsuitable. We propose an improved variable splitting method and computational formulas to handle this issue. \n• We prove that the overall block coordinate descent method converges to a local minimum of the total cost function using Zangwill’s convergence theorem. \nWe name our method as Combined Order Regularization with Optimal Spatial Adaptation (COROSA). We provide restoration examples involving deconvolution of TIRF images and reconstruction of Magnetic Resonance Imaging (MRI) images from under-sampled Fourier data. We demonstrate that COROSA outperforms existing regularization methods and selected deep learning methods. \nIn the second part\, we make COROSA more adaptive by replacing the HSN with a spatially varying weighted combination of Eigenvalues of the Hessian. This means that the resulting regularization will be in the form of a spatially varying weighted sum of three terms involving the gradient and two Eigenvalues of Hessian. This allows the functional to restore fine image structures through directional weighting\, in terms of the local Eigenvalues. We again adopt a BCD scheme that alternates between the spatially varying weight estimation and image computation\, as done in the first part. However\, both steps are more complex with the new form. The first task of weight estimation is more complex as it involves three terms. The second task of image computation is more complex\, because there is no known proximal operator for regularization involving unequally weighted Hessian Eigenvalues. We solve the first problem by constructing a novel iterative method\, and the second problem by deriving a novel proximal formula. Here too\, we adopt a multi-resolution approach to initialize the BCD method. We call our method the Hessian Combined Order Regularization with Optimal Spatial Adaptation (H-COROSA). We experimentally compare H-COROSA with well-known regularization methods and selected learning based methods for MRI reconstruction from under-sampled Fourier data. \nCompressive Sensing based methods have shown the advantage of l0-based sparsity enforcing functionals in restoration. For practical applications\, lp\, 0