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X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
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TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230102T133000
DTEND;TZID=Asia/Kolkata:20230104T223000
DTSTAMP:20260529T022852
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/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230102T163000
DTEND;TZID=Asia/Kolkata:20230102T173000
DTSTAMP:20260529T022852
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/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230102T213000
DTEND;TZID=Asia/Kolkata:20230102T223000
DTSTAMP:20260529T022852
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
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