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DTSTART;TZID=Asia/Kolkata:20220104T150000
DTEND;TZID=Asia/Kolkata:20220104T170000
DTSTAMP:20260616T135300
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UID:239455-1641308400-1641315600@ee.iisc.ac.in
SUMMARY:PhD Thesis Defence of Katam Nishanth @ 9:30 am
DESCRIPTION:Research Supervisor: Prof. BS Rajanikanth\nTitle of the thesis: Plasma catalysis of diesel exhaust using industrial wastes: a study on NOX and THC removal\nTime and Date: 9.30 AM\, 4th January 2022 (Tuesday)\nVenue: MS Teams Link \nAbstract: Air pollution\, caused by large scale consumption of fossil fuels such as diesel\, has been the leading cause of several adverse environmental effects such as global warming\, higher acidity in rainwater\, lower yield of agriculture production and several health issues. Diesel has been the primary and inevitable fuel source of energy worldwide\, in both stationary power supplies and automobile applications. Several developing countries like India continue to rely heavily on usage of diesel fueled machinery and automobiles\, which has resulted in high soot\, particulate and hazardous gas emissions. The prominent gaseous pollutants of concern are the oxides of nitrogen (NOX) and total hydrocarbon content (THC) present in the diesel exhaust. Though efficient systems have been discovered for reducing soot and particulate emissions\, treatment techniques for removal of gaseous pollutants are yet to reach a similar level of progress. Therefore\, research efforts aimed at identifying treatment techniques for curbing hazardous gaseous pollutants are a welcoming step towards addressing the pertinent issue of air pollution. \nThe gaseous pollutants emitted from the diesel engine can be reduced by applying control strategies at the level of engine design (p= re-combustion) or as an aftertreatment technique of the exhaust stream (post-combustion). Although the pre-combustion control strategies are limited by the possible engine design modifications\, the post-combustion approach allows for greater flexibility and scope by utilizing a variety of plasma discharges\, catalysts and adsorbents. One such post-combustion strategy which involves treatment of NOX/THC using non-thermal plasma (NTP) generated from dielectric barrier discharge (DBD)\, has yielded promising results at the laboratory level. Non-thermal plasma produces an oxidative environment containing several charged species\, which include energetic electrons\, excited species\, ions\, and radicals\, at atmospheric pressure and ambient temperature conditions. Diesel exhaust exposed to such a non-thermal plasma environment has been found to cause the formation of higher oxides of nitrogen and oxidized hydrocarbon intermediates\, which necessitates exposing them further to adsorbents or catalysts for effective removal of the harmful pollutants. In recent years\, a treatment technique which involves filling a plasma reactor with catalytic materials that enhance reactions in the presence of plasma\, referred to as plasma catalysis\, has given promising results at laboratory level in terms of pollutant removal efficiency\, on par with conventional thermal catalysis. The highly reactive environment produced by the interaction between reactive species in the plasma and the surface of the catalytic material can facilitate reactions that usually occur only at high temperatures in conventional (thermal) catalysis. The literature on plasma catalysis for several gas treatment applications reveals the utilization of expensive\, commercially available catalytic materials. The expensive rare metals used in such catalysts and the need for replacement due to choking of the catalytic material\, makes their usage an economically non-viable option. It is at this juncture that the utilization of freely available industrial wastes as potential catalysts appears to be an economically feasible alternative. Such environmentally safe and inexpensive treatment techniques for NOX/THC abatement are a desirable and welcoming option for exhaust treatment in the long run. \nIn the current work\, gaseous pollutants from a stationary diesel engine exhaust were exposed to an electrical discharge plasma in a reactor packed with pellets made from industrial wastes\, in a carefully controlled laboratory condition. Oxides of nitrogen and the total hydrocarbons are the two components of the diesel exhaust that were studied as the gaseous pollutants. The pellets were made from solid industrial wastes such as foundry sand\, fly ash\, red mud\, oyster shells\, bagasse\, and mulberry residue. The plasma was either volume discharge type or surface discharge type during the study. The thesis then progresses with a study of the results of NOX and THC removal through plasma catalysis and performing qualitative analysis of experiments to ascertain the dominance of plasma catalysis over other pollutant removal processes\, such as plasma-cascaded adsorption and plasma-only treatment. \nIt was observed that among the solid industry wastes studied\, red mud showed better NOX and THC removal efficiencies compared to the other industrial waste pellets. Further\, plasma catalysis showed moderate to significant increase in NOX and THC removal when compared to the plasma-cascaded and plasma-only methods\, for all the pellets studied. This approach of using industrial waste pellets for plasma catalysis of diesel exhaust is the first of its kind in the NTP fraternity. The results will be presented in detail along with the possible reaction pathways associated with conversion or removal of NOX/THC under plasma catalysis.\n*******
URL:https://ee.iisc.ac.in/event/phd-thesis-defence-of-katam-nishanth/
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DTSTART;TZID=Asia/Kolkata:20220105T163000
DTEND;TZID=Asia/Kolkata:20220105T180000
DTSTAMP:20260616T135300
CREATED:20220104T012855Z
LAST-MODIFIED:20220104T013652Z
UID:239459-1641400200-1641405600@ee.iisc.ac.in
SUMMARY:PhD Thesis Defense of Mr. Praveen Kumar Pokala @ 11am
DESCRIPTION:Title: Robust Nonconvex Penalties for Solving Sparse Linear Inverse Problems and Applications to Computational Imaging\nThesis Supervisor: Prof. Chandra Sekhar Seelamantula\nExaminer: Prof. Suyash Awate\, IIT Bombay\nVenue: MS Teams (Click here to join the meeting)\nDate and time: January 5\, 2022; 11 AM onward \nAbstract: Sparse linear inverse problems require the solution to the l-0-regularized least-squares cost\, which is not computationally tractable. Approximate and computationally tractable solutions are obtained by employing convex/nonconvex relaxations of the l-0-pseudonorm. One such approximation is obtained by considering the l-1-norm\, which is a convex relaxation of the l-0-pseudonorm. However\, l-1 regularization is known to result in biased estimates due to over-relaxation of the l-0-pseudonorm but it comes with the advantage of convexity of the regularized least-squares cost. Several nonconvex approximations of the l-0 pseudonorm have been proposed to overcome the bias introduced by the l-1-norm and to ensure better sparsity. However\, certain aspects of nonconvex sparse regularization have not been explored. Some of these are as follows: \nNonconvex sparse priors have been explored in the synthesis-sparse framework\, but not in the analysis-sparse framework due to the unavailability of proximal operators in closed-form in the analysis setting. \nExisting nonconvex approaches attach the same regularization weights across all the components of a sparse vector and treat them as fixed hyperparameters. Considering different weights for the entries and adapting them iteratively is likely to result in a superior performance. \nPrior learning networks based on deep-unfolded architectures for solving nonconvex penalties have not been explored. \nThis thesis addresses the above aspects in three parts and considers applications to various computational imaging problems. \nPart-1: Nonconvex Analysis-sparse Recovery \nIn this part\, we solve the analysis-sparse recovery problem based on three regularization approaches: \nConvexity-preserving nonconvex regularization: We propose the analysis variants of the generalized Moreau envelope and generalized minimax concave penalty (GMCP) over a complex domain. Since the cost is a real-valued function defined over a complex domain\, it is nonholomorphic\, i.e.\, it does not satisfy Cauchy-Riemann (CR) conditions. To circumvent this problem\, we rely upon on Wirtinger calculus to derive the proximal operator for the analysis l-1 prior and develop an efficient optimization strategy employing projected proximal algorithms. The projection transform maps the analysis-sparse recovery problem into an equivalent constrained synthesis-sparse formulation. \nNonconvex sparse regularization: We consider the problem of nonconvex analysis sparse recovery in which the signal is assumed to be sparse in a redundant analysis operator. Standard nonconvex sparsity promoting priors do not have a proximal operator in closed-form under a redundant analysis operator and therefore\, proximal approaches cannot be applied directly. This led us to develop two alternatives — Moreau envelope regularization and projected transformation. \nGeneralized weighted l-1 regularization: We develop a generalized weighted l-1 regularization strategy\, which allows for efficient weight-update strategies for iteratively reweighted l-1-minimization under tight frames. Further\, we impose sufficient conditions on the weight function that leads to a reweighting strategy\, which follows the interpretation originally given by Candès et al.\, but is more efficient than theirs. Since the objective function is nonholomorphic\, we resort to Wirtinger calculus for deriving the update equations. We develop an algorithm called generalized iteratively reweighted soft-thresholding algorithm (GIRSTA) and its fast variant\, namely\, generalized fast iteratively reweighted soft-thresholding algorithm (GFIRSTA). We provide convergence guarantees for GIRSTA and empirical convergence results for GFIRSTA. \nWe demonstrate the efficacy of the proposed regularization strategies in comparison with the benchmark techniques considering compressive-sensing magnetic resonance image (CS-MRI) reconstruction under a redundant analysis operator\, more specifically\, shift-invariant discrete wavelet transform (SIDWT). \nPart-2: Weighted Minimax Concave p-pseudonorm Minimization \nIn this part\, we develop techniques for accurate low-rank plus sparse matrix decomposition (LSD) and low-rank matrix recovery. We proposed weighted minimax-concave penalty (WMCP) as the nonconvex regularizer and show that it admits a certain equivalent representation that is more amenable to weight adaptation. Similarly\, an equivalent representation to the weighted matrix gamma norm (WMGN) enables weight adaptation for the low-rank part. The optimization algorithms are based on the alternating direction method of multipliers. The optimization frameworks relying on the two penalties\, WMCP and WMGN\, coupled with a novel iterative weight-update strategy\, result in accurate low-rank plus sparse matrix decomposition and low-rank matrix recovery techniques. Further\, we derive an algorithm\, namely\, iteratively reweighted MGN (iReMaGaN) algorithm\, which has a superior low-rank matrix recovery performance. The proposed algorithms are shown to satisfy descent properties and convergence guarantees. On the applications front\, we consider the problems of foreground-background separation and image denoising. Simulations and validations on standard datasets show that the proposed techniques outperform the benchmark techniques. Next\, we extended the idea to obtain a generalized l-p-penalty\, namely\, minimax concave p-pseudonorm (MCpN) based on a novel p-Huber function as the sparsity promoting function\, and its weighted counterpart\, weighted MCpN (WMCpN) as a regularizer for solving the sparse linear inverse problem. WMCpN is a generalization of which several penalties\, namely\, l-1-norm\, minimax concave penalty (MCP)\, l-p penalty\, weighted l-1-norm\, and weighted l-p penalty become special cases. However\, MCpN and WMCpN regularizers do not have closed-form proximal operators\, which makes the optimization problem challenging. To overcome this hurdle\, we develop an equivalent representation that is more amenable to optimization and allows for an analytical weight-update strategy. MCpN is a special case of WMCpN where all the weights are fixed and equal. The optimization algorithms are based on the alternating direction method of multipliers. Considering the application of interferometric phase estimation\, we demonstrate that MCpN and WMCpN result in accurate interferometric phase estimation. Simulations and experimental validations on standard datasets show that the proposed techniques outperform the benchmark techniques. \nPart-3: Nonconvex Sparse Regularization and Deep-Unfolding \nIn the final part\, we transition from fixed analytical priors to data-driven priors. To begin with\, we develop a deep-unfolded architecture\, namely\, FirmNet\, for sparse recovery. FirmNet has two parameters — one that controls the noise variance\, and the other that allows for explicit sparsity control. We show that FirmNet is better than Learned-ISTA (LISTA) by at least three-fold in terms of the probability of error in support (PES)\, and about 2 to 4 dB higher reconstruction SNR. Further\, we solve the problem of reflectivity inversion\, which deals with estimating the subsurface structure from seismic data through FirmNet. As an application\, we consider the problem of seismic reflectivity inversion. We demonstrate the efficacy of FirmNet over the benchmark techniques for the reflectivity inversion problem by testing on synthetic 1-D seismic traces and 2-D wedge models. We also report validations on simulated 2-D Marmousi2 model and real data from the Penobscot 3D survey off the coast of Nova Scotia\, Canada. Next\, we propose convolutional FirmNet (ConFirmNet)\, which is an extension of the FirmNet approach to solve the problem of convolutional sparse coding. As an application\, we build a ConFirmNet based sparse autoencoder (ConFirmNet-SAE) and demonstrate suitability for image denoising and inpainting. Further\, we also show that training ConFirmNet-SAE with the Huber loss imparts robustness to outliers. ConFirmNet-SAE also proves to be robust to mismatch between training and test noise conditions than convolutional learned iterative soft-thresholding algorithm (CLISTA). Finally\, we propose a sparse recovery formulation that employs a nonuniform\, nonconvex synthesis sparse model comprising a combination of convex and nonconvex regularizers\, which results in accurate approximations of the l-0 pseudo-norm. The resulting iterative optimization employs proximal averaging. When unfolded\, the iterations give rise to a nonuniform sparse proximal average network (NuSPAN) that can be optimized in a data-driven fashion. We demonstrate the efficacy of NuSPAN also for solving the problem of seismic reflectivity inversion. \nBiography of the candidate: Praveen Kumar Pokala received his B.Tech. degree in Electronics and Telecommunication Engineering from Jawaharlal Nehru Technological University\, Hyderabad\, India\, in 2006 and M. Tech degree in Signal Processing from Indian Institute of Technology (IIT)\, Guwahati\, India\, in 2009. Subsequently\, he worked as an Assistant Professor in LPU university\, Jalandhar\, India and GITAM university\, Hyderabad\, India. He is currently pursuing Ph.D. in the Department of Electrical Engineering\, Indian Institute of Science\, Bangalore. His current research interests are machine learning\, deep learning\, and nonconvex optimization algorithms\, with applications to inverse problems in computational imaging. He is presently a Senior Lead Engineer at Qualcomm R&D\, Bangalore. \nAll are invited. \n 
URL:https://ee.iisc.ac.in/event/phd-thesis-defense-of-mr-praveen-kumar-pokala-11am/
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