EE

Seminar by Visweshwar Chandrasekaran@ 3pm
2022-03-18 20:30 - 21:30 (Asia/Kolkata)
Title: Electric Drives in the Loop
Venue: MMCR EE, Teams Link
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.

M.Tech(Res) Thesis Colloquium of Meshineni Deepchand @ 10am
2022-03-15 15:30 - 16:30
Thesis Title: Non-contact Breathing and Heartbeat signal monitoring using FMCW radar
Research Supervisor: Dr. Rathna G N
Date & Time: March 15, 2022 (Tuesday) 10 AM
Venue: MMCR, EE (offline)
Abstract: 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.
All are welcome
Please follow the covid protocols

Ph.D. Thesis Defense of Sanjay Viswanath
2022-02-16 20:30 - 22:30
Advisor: Prof.. Muthuvel Arigovindan
Title: Spatially Adaptive Regularization for Image Restoration
Thesis Examiners: Prof. Suyash Awate, IIT Bombay, and Prof. Ajit Rajwade, IIT Bombay
Defense Examiner: Prof. Ajit Rajwade, IIT Bombay
Date and Time: 16th February (Wednesday): 3:00 pm – 5:00 pm
Venue: Microsoft Teams Live
Microsoft 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

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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.
In 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.
Our contributions in this part can be summarized as follows:
• 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.
• 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.
• 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.
• 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.
• We prove that the overall block coordinate descent method converges to a local minimum of the total cost function using Zangwill’s convergence theorem.
We 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.
In 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.
Compressive Sensing based methods have shown the advantage of l0-based sparsity enforcing functionals in restoration. For practical applications, lp, 0 <p ≤1 functionals have been found to perform better than l1 functionals. In the last part, we propose an lp-based generalization of the previous COROSA and H-COROSA formulations. We replace the corresponding l1 based functionals with lp norm enforced on the combined multi-order functionals. Additionally for H-COROSA, we also consider three forms of penalty for the spatial weights. We construct an iteration scheme that is a merging of the majorization-minimization method for lp norm and BCD method used in the first two parts of the thesis. Again, we use a similar multi-resolution method for initialization. We demonstrate the advantage of using lp norm using MRI reconstruction examples involving severe undersampling in Fourier domain.
ALL ARE CORDIALLY INVITED

PhD Thesis Colloquium of Ashiq Muhammed P E @ 10am
2022-02-09 15:30 - 17:00
Date/Time: 09 Feb 2022, at 10:00 to 11:30
Title: Improved Understanding of Standing Waves in Single Layer Coil and Elegant Methods to Estimate Transformer Winding Parameters
Abstract: Analyzing the effect of impulse voltages (like lightning, switching and VFTOs) on transformer winding has occupied centerstage in core electrical engineering research for over a century. These investigations gather great significance and relevance as it eventually governs the design of insulation in the winding. Notwithstanding the colossal contribution this domain has witnessed from stalwarts in the past century, a closer scrutiny surprisingly reveals that there still exists some grey areas that demands attention. Pursuing this line of thought, the first part of this thesis aims to clearly describe what this grey area is, and resolving it provides a deeper insight about fundamental understanding of surge response in transformer windings – with special emphasis on its standing wave phenomenon. Following this, in the latter part, elegant procedures are stitched together to determine a few electrical parameters of the transformer winding equivalent circuit – that have potential to help in assessing mechanical status of windings. Objectives of the thesis are –

Formulate an analytical method to determine the exact shape of standing waves for all modes in a uniform single layer coil as a solution of its governing partial differential equation
Estimate series capacitance of a uniform transformer winding from its measured driving point impedance
Determine effective air-core inductance of an iron-core uniform winding as a function of its axial length from measured driving point impedance

First part of the thesis revisits a century-old classical theory of standing waves on uniform single layer coils. Accurate information about natural frequencies and shapes of the corresponding standing waves are essential for gaining a deeper understanding of the response of coils to impulse excitations. Analytical studies on coils have largely been based on the assumption that standing waves are sinusoids in both space and time. However, this contradicts the results from numerical circuit analysis and practical measurements. So, this thesis attempts to bridge this discrepancy by revisiting the classical standing wave phenomena in coils. It not only assesses the reason for the aforementioned inconsistency, but also makes a contribution by analytically deriving the exact mode shape of standing waves for both neutral open/short conditions. For this, the coil is modelled as a distributed network of elemental inductances and capacitances, while an exponential function describes the spatial variation of mutual inductance between turns. Initially, an elegant derivation of the governing partial differential equation (in terms of voltage as the variable instead of flux) for surge distribution is presented and to the best of our knowledge, for the first time, an analytical solution for the same has been found by the variable-separable method to find the complete solution (sum of time and spatial terms). Hyperbolic terms in the spatial part of the solution have always been neglected but are included here, thus, yielding the exact mode shapes. For verification, both voltage and current standing waves computed from the analytical solution were plotted and compared with PSPICE simulation results on a 100-section ladder network representing a uniform single-layer coil. Then, practical measurements were conducted on a tailor-made large-sized single layer coil with a length of 2.2 m, diameter of 1 m and having 640 turns. It turns out that even in such simple single layer coils, the shape of standing waves of all modes deviates considerably from being sinusoidal. It was further observed that this deviation depends on spatial variation of mutual inductance, capacitive coupling, and order of the standing waves.
In the second part, an elegant method for determining the series capacitance (Cs) and air-core equivalent inductance of a uniform winding as a function of its axial length (termed as M0x in this thesis) of a uniform transformer winding, from its measured DPI magnitude, are discussed. Knowledge about the series capacitance of the winding is essential, which along with shunt capacitance, determines the initial impulse voltage distribution when a surge impinges on the winding. Unlike previously published approaches, the proposed method does not involve any cumbersome and time-consuming curve-fitting or running of optimization/search algorithms. Neither does it require winding geometry data. The proposed procedure for finding series capacitance relies on a property that is observable in the driving point impedance function of a lossless winding with an open neutral condition, viz., the ratio of the product of squares of open circuit natural frequencies to the product of squares of short circuit natural frequencies bears a particular relation to driving point impedance function coefficients. A simple procedure involving a deft manipulation and combination of a few well-known properties that correlate the roots of a polynomial to its coefficients are then utilized for determining series capacitance.
Knowledge about equivalent air-core inductance distribution as a function of its axial length (i.e., M0x) is useful for localizing a minor/incipient mechanical fault in the winding. A physically realizable empirical relationship to estimate M0x is initially proposed. The corresponding constants of the empirical relationship are then calculated from the measured driving point impedance. The proposed method requires three DPI measurements: one with neutral-end open and the other with neutral-end shorted. The third DPI is measured with a known external lumped capacitance connected between the neutral and ground. This method requires only the first few dominant natural frequencies observable in the first two of the DPIs. Feasibility of both proposed methods for estimating Cs and M0x was initially verified by simulation on an N-section ladder network and then by experiments on small-sized continuous-disk and interleaved-disk windings, and thereafter on a large-sized 33 kV, 3.5 MVA continuous-disk winding. Salient features of the proposed methods are – they are simple, elegant and involve minimum post-processing after measuring the DPI. Given its inherent simplicity and their relevance, the author is hopeful that industry will come forward to implement these procedures on an existing FRA measuring instruments – thus opening a new dimension to FRA measurements.
ALL ARE CORDIALLY INVITED
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PhD Thesis Defense of Mr. Praveen Kumar Pokala @ 11am
2022-01-05 16:30 - 18:00
Title: Robust Nonconvex Penalties for Solving Sparse Linear Inverse Problems and Applications to Computational Imaging
Thesis Supervisor: Prof. Chandra Sekhar Seelamantula
Examiner: Prof. Suyash Awate, IIT Bombay
Venue: MS Teams (Click here to join the meeting)
Date and time: January 5, 2022; 11 AM onward
Abstract: 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:
Nonconvex 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.
Existing 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.
Prior learning networks based on deep-unfolded architectures for solving nonconvex penalties have not been explored.
This thesis addresses the above aspects in three parts and considers applications to various computational imaging problems.
Part-1: Nonconvex Analysis-sparse Recovery
In this part, we solve the analysis-sparse recovery problem based on three regularization approaches:
Convexity-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.
Nonconvex 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.
Generalized 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.
We 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).
Part-2: Weighted Minimax Concave p-pseudonorm Minimization
In 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.
Part-3: Nonconvex Sparse Regularization and Deep-Unfolding
In 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.
Biography 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.
All are invited.

PhD Thesis Defence of Katam Nishanth @ 9:30 am
2022-01-04 15:00 - 17:00
Research Supervisor: Prof. BS Rajanikanth
Title of the thesis: Plasma catalysis of diesel exhaust using industrial wastes: a study on NOX and THC removal
Time and Date: 9.30 AM, 4th January 2022 (Tuesday)
Venue: MS Teams Link
Abstract: 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.
The 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.
In 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.
It 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.
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PhD Thesis Colloquium of Shamibrota Kishore Roy
2021-12-23 15:30 - 16:30
Title: Characterization and Modelling of Switching Dynamics of SiC MOSFETs
Name of the Advisor: Dr Kaushik Basu
Date and Time: 23rd December 2021, 10:00 AM
Place: Offline (MMCR EE,) online: Meeting Link: Click here to join the meeting
Abstract: Silicon Carbide (SiC) MOSFET is a wide bandgap (WBG) power device commercially available in the voltage range of 600-1700V. With superior switching, conduction, and thermal performance, it is in close competition with the state-of-the-art Si IGBTs in this voltage range.
In power electronic converters, semiconductor devices operate as switches. They can be turned on or off using a control signal. Unlike ideal switches, practical devices require a finite amount of time to transit between on and off states. This is termed as switching transient. Non-zero finite product of voltage and current during switching transient results in switching loss. Characterization and modelling of switching dynamics help gain insight into the switching process and estimate switching loss. Estimated loss can be used to determine switching frequency and selection of power devices. Also, switching dynamics is strongly impacted by the device and circuit parasitics. Insight into the switching process helps in the proper design of gate driver and power circuit layout.
The switching transient of SiC MOSFET is fast compared to its Si counterpart, resulting in reduced switching loss. However, it excites device and circuit parasitics that may lead to prolonged oscillations, spurious turn on, high device stress, EMI-related issues, Etc. The nonlinearity of the device characteristics and impact of circuit parasitics makes the switching transient of SiC MOSFET more involved than its Si counterpart. So, the characterization and modelling of switching dynamics of SiC MOSFET are essential.
Experimental, simulation and analytical approaches are used to study the switching dynamics and estimate switching loss. The experimental approach is inaccurate, requires expensive measurement set-ups, and is not suitable for the early stages of power converter design. The behavioural modelling approach is a widely used simulation-based approach (i.e., Spice simulation) where the circuit-based model of the device is used along with lumped parameter model of the external circuit. These models are simple and can capture the switching transient with sufficient accuracy. However, it does not provide insight into the switching process, and applying it for a large set of devices and operating conditions will be time-consuming. The analytical model belongs to another class of switching transient models. It is based on the simplified approximate solution of a set of coupled non-linear differential equations obtained from the behavioral model and results in analytical closed-form solutions or reduced order coupled non-linear equations. This model is computationally efficient and can be implemented easily in freely available programming platforms such as C or Python. Also, the parameters required for analytical models can be obtained from the device datasheet. This modelling approach is beneficial at the early stages of the converter design when switching loss and junction temperature need to be evaluated over several operating points for many available devices from different manufacturers. This work focuses on developing analytical models.
The first part of the work proposes an analytical model to capture the switching dynamics (turn on and off) of SiC MOSFET. In the existing literature, simplified modelling of channel current and device capacitances were used, resulting in underestimating switching transition time and loss. On the contrary, the proposed approach considers the detailed non-linear model of channel current and device capacitances along with circuit parasitics. It accurately estimates transition time, switching loss, (dv/dt), (di/dt), and transient over-voltage.
In soft switched converters (i.e., DAB), hard turn on is avoided by the converter operation, and the switching loss is solely dictated by the turn off loss. The addition of external capacitance across the device prolongs the voltage rise period and reduces overlap between voltage and current during turn off transient. This is termed as zero voltage switching (ZVS). However, the selection of external capacitance is not straightforward. A large external capacitance reduces switching loss, (dv/dt), (di/dt), and transient over-voltage but may also result in higher dead-time loss and reduced switching frequency. Also, this may lead to partial soft switching for light load conditions if the dead-time is not sufficient. In this work, an analytical model to capture capacitor-assisted turn-off switching transient is also presented where the detailed non-linear modelling of the SiC MOSFET is used. This leads to a better estimation of switching transition time, actual loss, (dv/dt), (di/dt), and transient over voltage. Also, a step-by-step design procedure of the optimal external snubber capacitor was proposed. It ensures the soft-switching condition is satisfied, and the maximum (dv/dt) rate is within a predefined limit for a specified DC bus voltage and range of load currents. This procedure also helps in the selection of proper dead-time to avoid partial soft-switching conditions.
Double pulse test (DPT) based experimental measurement is used to first validate the behavioural model. Then, the behavioural model is used to verify the correctness of the proposed analytical models. This indirect verification approach is necessary as it is not possible to measure the actual switching loss directly from the experimental measurements. Two 1.2-kV SiC MOSFETs of different current ratings are used for validation. It has been observed that there can be a significant difference between the experimentally measured switching loss and actual loss, and the difference is more prominent for low external gate resistances. Also, the turn off loss of SiC MOSFET is small compared to the turn-on loss.
Fast switching transient of SiC MOSFET is significantly impacted by circuit parasitics. Circuit parasitic inductances are dependent on both device package (device lead, wire bond etc.) and circuit layout (PCB layout), whereas circuit parasitic capacitances are contributed solely by the circuit layout. Proposed switching transient models require circuit parasitics as input, and the values are not usually available in the device datasheet. Measurement is the only way to accurately estimate some device package-dependent circuit parasitics when the internal package geometry is unknown. In this context, a set of simple measurement techniques are proposed to determine important circuit parasitics necessary for switching dynamics study. The accuracy of the proposed technique is verified through behavioural simulation, and experimental results of the hard turn off and capacitor assisted soft turn off dynamics of SiC MOSFET over a range of operating conditions for two 1.2-kV discrete SiC MOSFET of different current ratings and two different PCB layouts. Measured circuit parasitic when used in switching transient model, correctly predicted both hard turn-off and capacitor assisted soft turn off switching dynamics over a wide range of operating conditions.
An interactive software based on the proposed analytical model is also developed in Python environment. The developed software takes device parameters and circuit parasitics as input and estimates transition time, switching loss, (dv/dt), (di/dt) and transient over-voltage as a function of load current.
ALL ARE CORDIALLY INVITED

PhD Thesis Colloquium of Kiran Kumar Challa @ 3pm
2021-12-17 20:30 - 22:00
Title: Estimation of Synchronous Generator Parameters from Digital Relay Records and Development of an Experimental Testbed for Validation
Faculty Adviser: Dr. Gurunath Gurrala.
Date : 17th December 2021
Time: 3pm – 4.30pm
Mode: Hybrid mode, both Teams and Physical
Venue: MMCR, 1st floor C-wing, EE Department, IISc
Teams Link: Click here to join the meeting.
Abstract: The development of dynamic power system components models became increasingly important in the modern grids dominated by high penetration of renewables because of the increased dependency of planning and operational decisions on dynamic simulation studies. The parameters of synchronous machines and associated control models play significant role in the overall model of the grid, which need to be updated regularly by the utilities. So, the parameters of the power plants are calibrated/estimated either using off-line testing or online measurements from phasor measurement units (PMU) or digital fault recorders (DFR). Development of individual generator models is feasible only if the PMU/DFR data is available for each generator in a power plant. Otherwise, they can provide only aggregate model of a generating plant as PMU/DFRs are usually placed in substations. Digital protective relay (DPR) records are available for individual generators in any generating plant. This thesis explores the possibilities of utilizing DPR records of individual generators for parameter estimation. About 200 relay records have been collected from a hydro plant and a thermal plant in Karnataka. It is found that most of the records contain at the most 3 seconds data. Existing methods of parameter estimation using PMU/DFR data failed to work with the short duration records. There is no prior work reported in the literature which uses short relay records for parameter estimation of the synchronous generators. Constrained iterated unscented Kalman filter (CIUKF) and enhanced scattered search (eSS) algorithms are proposed for the parameter estimation using DPR records in this thesis. The parameters of a turbo alternator and its excitation system (210 MW) are estimated from the relay records collected using the proposed algorithms and the results are found be accurate. This is a first of its kind effort in the literature to the best of our knowledge. It is also found that the relay records should contain pre-fault data, during fault data and some post-fault data for accurate estimation. However, from the collected records only a small percentage of the records are found to be useful. To generate realistic data in the laboratory an experimental test bed development, replicating the field implementation aspects of the digital relays, is proposed in this thesis. A realistic scaled-down generalized substation model for translational research in smart grids is developed, which can be configured to operate in 7 widely used substation bus bar schemes with prevalent current transformer (CT) configurations. All the potential transformers (PT) and CT measurements, circuit breaker (CB), isolator and earth switch status signals are made available to configure any protection strategy like bus-bar protection, unit protection schemes, etc. precisely the same way they get implemented in the field. A systematic procedure for the development of an experimental scaled-down frequency-dependent transmission line model of a 230 kV transmission line is proposed. A lumped parameter frequency dependent transmission line model using modal transformation is derived for a 230 kV transmission line and scaled-down to 220 V. Clarke and inverse Clarke transformations are implemented using specially designed 1-phase transformers. The inductances of the scaled-down model are realized using amorphous cores. A new algorithm is proposed to fit a reduced-order R-L equivalent circuit to the frequency response of the modal impedances of the transmission lines. A close enough fitting is achieved with lesser number of passive elements using the proposed method compared to the widely used vector fitting algorithm. This kind of physical realization of a frequency dependent power transmission line model in the laboratory is first of its kind effort in the literature to the best of our knowledge.
Note: Know how generated from the implementation of the generalized substation panels and transmission line models has been licensed to MCore Technologies Pvt Ltd, Bangalore for commercialization.
Acknowledgements: This work is supported by Fund for Improvement of Science and Technology (FIST) program, DST, India, No.SR/FST/ETII-063/2015 (C) and (G) under the project “Smart Energy Systems Infrastructure – Hybrid Test Bed”. Acknowledge partial funding support from Robert Bosch Centre for Cyber Physical Systems (RBCCPS), IISc. Also acknowledge the Tata Trust Travel Grant.

MTech(Research) Colloquium of Paawan Kirankumar Dubal @3.30pm
2021-12-16 21:00 - 22:30
Guide: Dr. Sarasij Das
Title: Cyber Attack Resilient Breaker Failure Protection Scheme Using Wide Area measurements
Name of the Student: Paawan Kirankumar Dubal
Date and Time: 16th December, 2021, 3:30 PM
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Abstract: Breaker Failure Protection (BFP) is a backup protection that comes into play when a circuit breaker fails to isolate the fault. If the circuit breaker fails to clear the fault, the BFP scheme commands other required breakers to isolate the fault. BFP schemes are usually incorporated in microprocessor-based digital relays. Commonly employed BFP schemes use overcurrent element (50BF) and Breaker Failure Initiation (BFI) signals as inputs. The BFI signal is issued to the BFP relays from other digital relays. Line current sensed via current transformer is fed to the BFP relay for the overcurrent element (50BF). When both 50BF and BFI are high, it waits for a specified time for the primary protection to operate. The 50BF element is usually set much lower than the rated load currents. So, it will be high during the normal loading conditions. A cyber-attack can be launched by issuing false BFI or blocking a legitimate BFI signal to the BFP relay. Operation of BFP scheme usually leads to disconnection of a larger amount of loads. As a result, mal-operation of BFP schemes can cause major disturbances in power systems. There is a need to make the BFP schemes resilient to cyber-attacks for reliable operation of power systems. Currently, there is a lack of literature on the cyber-attack resilient BFP schemes.
Hence, this thesis proposes a Wide-Area Measurement-Based Cyber-Resilient Breaker Failure Protection Scheme. The scope of the work is to develop an algorithm that will ascertain if the BFI received by the BFP relay is genuine. Blocking a legitimate BFI will cause the backup protection to operate and clear the fault. The proposition assumes that the BFP relay is not compromised in any manner. However, a fake BFI can be issued by other digital relays, which may cause unwanted BFP operations. In the proposed algorithm, when the BFI is received. The BFP relay will communicate the receipt of BFI to the Phasor Data Concentrator (PDC). The proposed algorithm will run at the PDC, which has access to the time-stamped measurements of the adjacent substations and the substation that triggered the algorithm. The decision of the proposed algorithm is communicated back to the BFP relay, which will allow the tripping if the BFI is genuine. Hence, we also propose modifications in the BF scheme in the BFP relay to incorporate the algorithm’s decision in issuing the final trip. The proposal running at PDC is a two-layer algorithm. The first layer randomly samples the bus voltages at the adjacent substations considering different groups of digital relays. The relay which has issued the BFI may be compromised. It makes relays of the same make and family more susceptible to a cyber-attack exploiting the same vulnerabilities. Hence we propose grouping of relays by their make and relay families. The first layer is meant to determine if there is a fault in the vicinity of the BFP relay that issued the trigger. The second layer provides discrimination between fault and cyber-attack by measuring the impedance observed at the two ends of the perceived-faulted line. Since the proposed solution is computationally lightweight, it adheres to the timing requirement of the BFP. The proposition requires healthy communication between the PMUs and the PDC. Nevertheless, the proposed method is fail-safe. It will resort to the conventional BFP scheme in case of loss of communication with the PDC. The proposed solution mitigates n number of cyber-attacks in a no-fault scenario. Additionally, the proposed solution can detect one cyber-attack if the attacker times the cyber-attack during a fault condition. PSCAD simulations were performed to validate the proposition on IEEE 118 bus system. Furthermore, the hardware was developed emulating the PMU-PDC communication as per IEEE C37.118-2 standard, and the execution time of the proposal was verified to ensure adherence to the timing requirement of the BFP.
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PhD Colloquium of Asha Radhakrishnan @3pm
2021-12-14 20:30 - 21:30
Title of the Thesis: Protection of Transmission and Distribution Networks in Presence of Converter – Interfaced Renewable Generators
Advisor: Dr. Sarasij Das
Degree Registered: PhD (Eng.)
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Abstract: Power generation from conventional sources like fossil fuels is widely being supplemented by renewable power sources. An increasing degree of penetration of renewable generators is observed in transmission and distribution levels. Power electronic converters are commonly used as interfaces to control the power output of these generators. Fast control and complex dynamics of the power electronic interfaces pose significant challenges to the legacy protection schemes employed in the transmission and distribution networks. This research focuses on addressing the impact of the wide penetration of CIGs on selected protection schemes in transmission and distribution networks.
Distance protection is one of the most popular methods employed for the primary protection of transmission lines. Mho elements are used extensively in distance protection, which employs positive sequence memory-polarization (PSMP) technique to give the most reliable performance during close-in faults. The use of memory voltage for polarization leads to the dynamic expansion of the mho circle in distance relays. The dynamic expansion of the mho characteristics increases resistive coverage in the case of synchronous generators. In this research work, the performance of PSMP mho elements in the presence of CIGs has been studied. Simulations have been performed on the benchmark IEEE-39 bus system with a connected Vdc – Q control-based PV plant. Performances during Zone 1 forward faults and Zone 3 reverse faults have been studied. The results indicate the possibility of a significant reduction in the resistive coverage of the PSMP mho distance element during forward faults and loss of dependability during reverse faults. A novel solution to the observed problem has been proposed and validated using PSCAD simulations on the IEEE 39-bus system. The proposed solution is found to effectively achieve a predictable performance of PSMP mho relays in the presence of PV generators as well as synchronous generators.
Back-up protection of transmission lines is important to be in place to ensure dependable protection during failure of primary protection. Breaker failure protection (BFP) is an important backup protection. It is employed to take appropriate action to clear a fault when the breaker that is normally expected to clear the fault fails to do so for any reason. Fault current contribution of CIGs is usually comparable with load currents. Low fault current contribution by utility-scale CIGs may lead to significant loss of security of BFP because of the existing practice of using lower setting for 50BF overcurrent element. This research work proposes a voltage-dependent adaptive setting of 50BF element to enhance the security of BFP schemes while maintaining dependability. Use of voltage helps in differentiating loads from fault situations. In traditional power systems, CT subsidence current is known to delay the reset of BFP schemes. The impact of low fault contribution by CIGs on the reset time of BFP has been studied. Mathematical expression for CT subsidence current, which influences the reset time, has been derived. It is observed that the BFP reset may not be delayed if the fault current seen by the breaker is low due to the presence of CIG. The performance of the proposed 50BF setting and the findings on the subsidence current are supported using PSCAD simulations.
The Rate of Change of Frequency (RoCoF) relays are employed to arrest frequency collapse of a grid in the event of sudden loss of major generation. With large-scale CIGs replacing the synchronous generators, the primary frequency response of the system is often affected due to the decrease in the system inertia. The rate of change of frequency at the inception of an event is observed to increase for a system with high penetration of CIGs. The settings of RoCoF relays are therefore required to be revised to account for the faster dynamics of CIGs. With varying degree of CIG penetrations, the settings may change further. This work proposes a new method to detect the system changes by considering voltage as the parameter. The proposed method has been validated using PSCAD simulations performed on the IEEE39-bus system. Different degrees of CIG penetrations have been considered to test the performance of the proposed method.
Penetration of Distributed Generations (DGs) has made traditional distribution protection schemes mostly ineffective. Sophisticated protection schemes cannot be implemented using fuses, reclosers and Miniature Circuit Breakers (MCBs). Economics limits the use of protective relays in distribution systems. Voltage based protection is often not economical for distribution systems. Smart Meters (SMs) are available at various load points in a distribution system. SMs are equipped with measuring, calculation, and communication capabilities. This work proposes the utilization of SMs in distribution system protection. The possible applications of SMs in high impedance faults (HIF), overcurrent, reverse power, series arcing, and under-voltage protection of distribution systems are identified in this work. Voltage-based protection can also be implemented using SMs. This work also proposes a voltage-based HIF location method. The fault signature of HIF is significant at the SMs which are nearer to the fault. The proposed method uses the SMs to compute an index to capture the fault signature. The HIF is then located in a zone defined by SMs adjacent to the SM with the highest index. The performance of the proposed method has been evaluated considering electric arc furnaces, DGs and power electronics-interfaced loads. The effectiveness of the proposed SM-based protection has been demonstrated by simulating a European low voltage test feeder comprising of 906 buses in PSCAD. The proposed algorithm has also been implemented on a commercial smart energy meter to demonstrate its feasibility.
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EE PhD Thesis defense of Rejesh N A @3pm
2021-12-07 20:30 - 21:30
Guide: Prof.. Muthuvel Arigovindan
Title: Novel Regularized Image Reconstruction Methods for Sparse Photoacoustic Tomography
Examiners: Prof. Michael Liebling, IDIAP Switzerland, and Prof. Hari Varma, IIT Bombay
Date and Time: 7th December 2021 (Tuesday): 3:00 pm – 5:30 pm
Venue : Online through Microsoft Teams. Click here to join the meeting
Summary: Among all tissue imaging modalities, photo-acoustic tomography (PAT), has been getting increasing attention in the recent past due to the fact that it has high contrast, high penetrability, and has the capability of retrieving high resolution. By using the combination of optical absorption and acoustic wave propagation, PAT has been able to image tissues at relatively large depths with high resolution compared to purely optical modalities. Upon shining with a laser pulse, the substance under investigation absorbs optical energy and undergoes thermoelastic expansion; as a result, the spatial distribution of the concentration of the substance gets translated into the distribution of pressure-rise. This initial pressure rise travels outwards as ultrasound waves which are collected by ultrasound transducers placed at the boundary. From the ultrasound signal measured by the transducers as a function of time, a PAT reconstruction method recovers an estimate of the initial pressure-rise by solving the associated inverse problem. The inverse problem is however challenging. It is challenging because the image has to be recovered for the entire cross-sectional plane, whereas the samples of the acoustics pressure are available only from the points lying in the periphery of the imaging specimen where the transducers are located. In this thesis, we make contributions in two widely used types of reconstructions methods known as the time-reversal method, and the model-based method.
We summarize our contributions in three parts in the following.
In the first part, we develop an improved model-based method. Model-based reconstruction methods in PAT express the measured pressure samples as a linear transformation on the initial pressure-rise and perform a regularized reconstruction. Model-based methods yield superior image quality even in the situation where measured data size is small. We propose a model-based image reconstruction method for PAT involving a novel form of regularization and demonstrate its ability to recover good quality images from datasets of significantly reduced size. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images, high intensities and high second-order derivatives are jointly sparse. This regularization is combined with a data fidelity cost, and the required image is obtained as the minimizer of this cost. As this regularization is non-convex, the efficiency of the minimization method is crucial in obtaining artefact-free reconstructions. We develop a custom minimization method for efficiently handling this non-convex minimization problem. Further, as non-convex minimization requires a large number of iterations and the PAT forward model in the data-fidelity term has to be applied in the iterations, we propose a computational structure for efficient implementation of the forward model with reduced memory requirements. We evaluate the proposed method on both simulated and real measured data sets and compare them with a recent reconstruction method that is based on well-known total variation regularization.
Appropriate tuning of the regularization weight, λ, plays a crucial role in determining the quality of reconstructed images in PAT. To make any regularization method practicable, we need to have a way to determine the λ from the measured data. Unfortunately, an appropriately tuned value of the regularization weight varies significantly with the variation in the noise level, as well as, with the variation in the high-resolution contents of the image, in a way that has not been well understood. In the part of the work described above, we did not address this problem as the focus has been to demonstrate the suitability of the intensity-augmented regularization for PAT image recovery; in the experimental demonstration, we determined the required regularization weight by using the models that generated data. In the second part of the thesis, we develop a semi-automatic method for determining the regularization weight from measured data. As a first step, we introduce a relative smoothness constraint with a parameter; this parameter computationally maps into the actual regularization parameter, but its tuning does not vary significantly with variation in the noise level, as well as with the variation in the high-resolution contents of the image. Next, we construct an algorithm that integrates the task of determining this mapping along with obtaining the reconstruction. Finally, we demonstrate experimentally that we can run this algorithm with a nominal value of the relative smoothness parameter—a value independent of the noise level and the structure of the underlying image—to obtain good quality reconstructions. We compare the structural similarity (SSIM) scores of reconstructions obtained this way to that of reconstructions in which the regularization weight was determined using the models themselves; we show that the SSIM scores are comparable. This means that, from a practical point of view, our work solves the problem of determining the required regularization weight from measured images.
In the first two parts, we assumed that the forward model that measures the signal from the target object be ideal. In particular, we assumed that the excitation pulse and transducers’ impulse response are Dirac deltas. We focused only on the non-ideality of the transducer configuration, i.e., we handled the case where the transducer locations do not densely sample the detection surface as required by the well-known back-projection method to work. Both excitation pulse and transducer impulse response have a finite width, and this leads to some distortions in the reconstructed image. In the last part of the thesis, we propose a pre-processing method for correcting the distortions in the context of using time-reversal methods which are similar to the back-projection method. To this end, we formulate the broadening of the PA signals as a convolution between the impulse response of the system and the input excitation pulse. A deconvolution method using Tikhonov regularization is proposed to correct the PA signals before applying the time-reversal method. This resulted in improved resolution in the reconstructed images. A two-level deconvolution with the Tikhonov regularization method is also proposed to remove the blurring caused by the finite bandwidth of transducers and by the broad excitation pulses. We evaluate the usefulness of our method using numerical simulations and demonstrate that the reconstructed images from the deconvolved PA signals remain unaffected by the change in pulse widths or pulse shapes, as well as by the limited bandwidth of the ultrasound detectors.
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PhD Thesis Defense of Mr. Jitendra Kumar Dhiman
2021-11-24 16:30 - 17:30
Date and Time: November 24, 2021, 11 AM.
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Title of the thesis: Spectrotemporal Processing of Speech Signals Using the Riesz Transform
Examiner: Prof. S. R. Mahadeva Prasanna, IIT Dharwad and IIT Guwahati
Abstract: Speech signals have time-varying spectra. Spectrograms have served as a useful tool for the visualization and analysis of speech signals in the joint time-frequency plane. In this thesis, we consider 2-D analysis of speech spectrograms. We consider a spectrotemporal patch and model it as a 2-D amplitude-modulated and frequency-modulated (AM-FM) sinusoid. Demodulation of the spectrogram yields the 2-D AM and FM components, which correspond to the slowly varying vocal-tract envelope and the excitation, respectively. For solving the demodulation problem, we rely on the complex Riesz transform, which is a 2-D extension of the 1-D Hilbert transform. The demodulation viewpoint brings forth many interesting properties of the speech signal. The spectrotemporal carrier helps us identify the regions that are coherent and those that are not. Based on this idea, we introduce the coherencegram corresponding to a given spectrogram. The temporal evolution of the pitch harmonics can also be characterized by the orientation at each time-frequency coordinate, resulting in the orientationgram. We show that these features collectively enable solutions for the important problems of voiced/unvoiced segmentation, aperiodicity estimation, periodic/aperiodic signal separation, and pitch tracking. We compare the performance of the proposed methods with benchmark methods. The spectrotemporal amplitude characterizes the time-varying magnitude response of the vocal-tract filter. We show how the formants and their bandwidths manifest in the spectrotemporal amplitude. It turns out that the formant bandwidths are mildly overestimated, which are perceptible when one performs speech synthesis using the estimated parameters. We propose a method for correcting the formant bandwidths, which also restores the speech quality. Finally, we use the curated spectrotemporal amplitude, pitch, aperiodicity, and voiced/unvoiced decisions for the task of speech reconstruction in a spectral synthesis model and a neural vocoder, namely, WaveNet. We show that conditioning WaveNet on the spectrotemporal features results in high-quality speech synthesis. The quality of the synthesized speech is assessed using both objective and subjective measures.
We rely on the Perceptual Evaluation of Speech Quality (PESQ) measure and standard Mean Opinion Score (MOS) test for objective and subjective evaluation, respectively. The performance of the proposed parameters is evaluated in a vocoder framework that uses the spectral synthesis model for speech reconstruction. The objective evaluation shows that the performance of the Riesz transform-based speech parameters is on par with the baseline systems. Using the spectral synthesis model, we report an average PESQ score in the range from 2.30 to 3.45 over a total of 200 speech waveforms taken from the CMU-ARCTIC database comprising both male and female speakers. In comparison, WaveNet-based speech reconstruction gave an average PESQ score of 3.65.
Subjective evaluation was carried out through listening tests conducted in an acoustic test chamber on volunteers in the age group of 21 to 30. The average MOS score was 4.30 when the Riesz transform-based features were used in WaveNet for speech reconstruction, which was also comparable with the baseline systems: STRAIGHT and WORLD. Both objective and subjective evaluations also showed that the quality of reconstructed speech waveforms was superior with the proposed features in a WaveNet vocoder than in the spectral synthesis model.
An audio demonstration is available at the GitHub link: http://jitendradhiman.github.io/Demo
Biography of Jitendra Kumar Dhiman: Jitendra Kumar Dhiman received his B.Tech. degree in Electronics and Telecommunication Engineering from the Institution of Electronics and Telecommunication Engineering, Delhi, India, in 2010, and M.Tech. degree in Signal Processing from Indian Institute of Technology Hyderabad in 2013. Subsequently, he joined as a project assistant in Spectrum Lab (EE Department, IISc) and worked on prosody modification of speech signals, and then as a PhD student working on spectrotemporal models for speech processing. His research interests include speech and audio signal processing and machine learning. He will soon be joining Samsung Research and Innovation, Bangalore (SRIB) as Chief Engineer.

M.Tech.(Research) Thesis Defense of Mr. Vinayak Killedar
2021-11-17 19:30 - 20:30 | Online, India
Title of the thesis: Solving Inverse Problems Using a Deep Generative Prior
Supervisor: Prof. Chandra Sekhar Seelamantula (EE)
Examiner: Prof. Sumohana Channappayya (EE, IIT Hyderabad)
Abstract: The objective in an inverse problem is to recover a signal from its measurements, given the knowledge of the measurement operator. In this thesis, we address the problems of compressive sensing (CS) and compressive phase retrieval (CPR) using a generative prior model with sparse latent sampling. These problems are ill-posed and have infinite solutions. Structural assumptions such as smoothness, sparsity and non-negativity are imposed on the solution to obtain a unique solution.
The standard CS and CPR formulations impose a sparsity prior on the signal. Recently, generative modeling approaches have removed the sparsity constraint and shown superior performance over traditional CS and CPR techniques in recovering signals from fewer measurements. Generative model uses a pre-trained network, the generator of a Generative Adversarial Network (GAN) or the decoder of a Variational Autoencoder (VAE) to model the distribution of the signal and impose a Set-Restricted Eigenvalue Condition (S-REC) on the measurement operator. The S-REC property places a condition on the l-2 norm of the difference in signal and measurement domain for signals coming from the set S. Solving CS and CPR using generative models have some limitations. The reconstructed signal is constrained to lie in the range-space of the generator. The reconstruction process is slow because the latent space is optimized through gradient-descent (GD) and requires several restarts. It has been argued that the distribution of natural images is not confined to a single manifold, but a union of submanifolds. To take advantage of this property, we propose a sparsity-driven latent space sampling (SDLSS) framework, where sparsity is imposed in the latent space. The effect is to divide the latent space into subspaces such that the generator models map each subspace into a submanifold. We propose a proximal meta-learning (PML) algorithm to optimize the parameters of the generative model along with the latent code. The PML algorithm reduces the number of gradient steps required during testing and imposes sparsity in the latent space. We derive the sample complexity bounds within the SDLSS framework for the linear CS model, which is a generalization of the result available in the literature. The results demonstrate that, for a higher degree of compression, the SDLSS method is more efficient than the state-of-the-art deep compressive sensing (DCS) method. We consider both linear and learned nonlinear sensing mechanisms, where the nonlinear operator is a learned fully connected neural network or a convolutional neural network and show that the learned nonlinear version is superior to the linear one.
As an application of the nonlinear sensing operator, we consider compressive phase retrieval, wherein the problem is to reconstruct a signal from the magnitude of its compressed linear measurements. We adapt the S-REC imposed on the measurement operator and propose a novel cost function. The SDLSS framework along with PML algorithm is applied to optimize the sparse latent space such that the adapted $mathcal{S}$-REC loss and data-fitting error are minimized. The reconstruction process is fast and requires few gradient steps during testing compared with the state-of-art deep phase retrieval technique.
Experiments are conducted on standard datasets such as MNIST, Fashion-MNIST, CIFAR-10, and CelebA to validate the efficiency of SDLSS framework for CS and CPR. The results show that, for a given dataset, there exists an effective input latent dimension for the generative model. Performance quantification is carried out by employing three objective metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and reconstruction error (RE) per pixel, which are averaged across the test dataset.
About the speaker: Vinayak Killedar obtained a B.E. (ECE) degree from M. S. Ramaiah Institute of Technology (MSRIT), Bangalore in 2008. During 2008-2010, he worked for Robert Bosch Engineering and Business Solution (RBEI), Coimbatore. He joined the M.Tech.(Signal Processing) program in National Institute of Technology (NIT) Calicut and graduated in 2013. He worked for Continental AG during 2014-2018 in the areas of autonomous driving and radar signal processing. Subsequently, he joined the Spectrum Lab, Department of Electrical Engineering, Indian Institute of Science for M.Tech.(Research) and specialized in Compressed Sensing and Machine Learning. He is presently a Senior Technical Specialist at Ansys, Kempten, Germany.

PhD Thesis Defense of Mr. Shome Subhra Das
2021-11-17 16:45 - 18:00
Date and Time: November 17, 2021 (Wednesday) 11.15 AM
Click here to join the meetingExternal Examiner: Prof. Gaurav Harit, IIT Jodhpur

Title: Techniques for estimating the direction of pointing gestures using depth images in the presence of orientation and distance variations from the depth sensor
Abstract: Currently, we interact with computers, robots, drones, and virtual reality interfaces using pointing devices such as mouse, touchpad, joystick, virtual reality wand, drone controller, etc. These devices have one or more of the following limitations: being cumbersome, non-immersive, immobile, and having a steep learning curve. The target of this work is to explore ways to replace existing pointing devices with pointing gesture-based interfaces.
This thesis addresses two problems, namely estimating the direction being indicated by a pointing gesture (PDE) and detection of pointing gestures. The proposed techniques use a single depth sensor and use only the hand region. To our knowledge, this is the maiden attempt at creating depth and orientation tolerant, accurate methods for estimating the pointing direction using only depth images of the hand region. The proposed methods achieve accuracies comparable to or better than those of existing methods while avoiding their limitations.
Significant contributions of the thesis:
(i) Proposing a real-time technique for estimating the pointing direction using a nine-axis inertial motion unit (IMU) and an RGB-D sensor. It is the first method to compute the pointing direction (PD) by finding the axis vector of the index finger. It is also the first method to fuse information from the IMU and depth sensor to obtain the PD. Further, this is the first method to obtain the ground-truth pointing direction of pointing gestures using depth data of the index finger region.
(ii) Creation of a large (100k+ samples) dataset with accurate ground truth for PDE from depth images. Each sample consists of the segmented depth image of a hand, the fingertip location (2D + 3D), the pointing vector (as a unit vector and in terms of the yaw and pitch values), and the mean depth of the hand. This is the first public dataset for depth image based PDE that has accurate ground truth and a large number of samples.
(iii) Proposing a new 3D convolutional neural network-based method to estimate pointing direction. This is the first deep learning-based method for PDE that uses only the depth images of the hand region for PDE, without the use of RGB data. It is tolerant to variation in orientation and depth of the hand with respect to the camera and is suitable for real-time applications.
(iv) Proposing another technique for estimating the pointing direction using global registration of the test data point cloud with a pointing hand model captured using Kinect fusion-based method. It is tolerant to the variation in the orientation and depth of the hand w.r.t. the RGB-D sensor. It does not have the limitation of the previously proposed methods since it does not require the attachment of any device such as IMU nor does it require any dataset for training. It achieves less net angular error than most techniques in the literature using only the hand region.
(v) Creation of a large dataset of positive and negative samples for detection of pointing gestures from depth images of the hand region. A technique is also proposed using deep learning to distinguish pointing gestures from other hand gestures. This achieves higher accuracy than the only other existing technique by Cordo et al. for detection of pointing gestures from depth images of the hand.

Thesis Defence of Indla Rajitha Sai Priyamvada
2021-10-14 16:00 - 2021-10-15 05:00
Thesis Title: Analysis and Enhancement of Stability of Power Systems with Utility-scale Photovoltaic Power Plants
Guide: Dr. Sarasij Das
Abstract: Owing to the negative impact of carbon emissions on the environment, power systems are experiencing a paradigm shift in power generation. The fossil fuel-based generators that utilize synchronous machines are increasingly being replaced by the renewables such as Photovoltaic (PV) generators. Utility-scale PV power plants are coming up in the various parts of the world. Power electronic interface, control strategies and lack of inherent rotational element are the main factors that distinguish PV generation from Synchronous Generators (SGs). In addition, the time constants of the PV control loops and Phase Locked Loop (PLL) are of the same order unlike the SGs. The power electronic interface offers a better control over the electrical energy generated by the PV generators. However, the power electronic interface brings new challenges to power system stability. This research work focuses on addressing transient and small-signal stability issues of grid connected utility scale PV power plants.
In conventional power systems, swing equation of SGs and (extended) equal area criterion are used to assess the transient stability of power system. However, the same analysis techniques may not be applicable for PV generators. In this research work, transient stability assessment criteria are developed for grid connected PV generator with two different control strategies viz., Vdc-Q control and PQ control (with/without support functionalities). The proposed criteria are developed considering the outer and inner control loop, PLL and filter dynamics of PV generator. PSCAD simulations are carried out on a two-bus system and a modified IEEE-39 bus system to validate the proposed criterion. The stability criteria are found to effectively assess the stability of grid connected utility scale PV generators.
The power transfer capability of transmission network is limited by thermal limits, voltage limits and stability limits. Power transfer capability of transmission lines emanating from PV generators considering thermal and voltage limits is explored well in the literature. However, there is a lack of literature on stability constrained power transfer capability limit. In this research work, adaptive control-based tuning laws are proposed for grid connected PV generators to improve the stability constrained power transfer capability. The adaptive tuning laws are derived based on the Lyapunov energy function analysis. The Lyapunov functions are formulated using the summation of squares of the PI block errors and difference between the PI parameter values from their optimal values. Time domain simulations are carried out on a two-bus system and a modified IEEE-39 bus system to validate the proposed tuning laws. From time domain simulations, it is observed that the proposed tuning laws are found to effectively improve the stability limit on power transfer to the voltage limit.
The increased penetration of PV generations into power systems has also brought qualitative changes in small signal stability of power systems. Two new categories of oscillation modes are introduced into power systems which have participation from PV state variables. As the mode shape of the two new categories of oscillation modes is different from that of SG modes, the power system stabilizer design should be revisited. In this research work, control-based power system stabilizer is developed considering the controllability and observability of the new categories of oscillation modes. The effectiveness of the developed stabilizer in providing sufficient damping to the new categories of oscillation modes is validated through PSCAD simulations on a modified IEEE-39 bus system.
As power systems are large interconnected systems, the increased penetration of PV generation has resulted in notable interaction among PV generators and SGs. Investigation of the interaction among generators is important to understand the dynamic behaviour of overall power system when subjected to disturbances. This research work is carried out to understand the interaction among PV and SGs. The interaction is analysed through investigation of interaction among oscillation modes of PV generation and SG. A mathematical formulation to quantify the interaction among the oscillation modes of PV generations and SGs is proposed. A modified IEEE-39 bus system is considered to carry out the interaction study and validate the results obtained from mathematical formulations.

Seminar by Dr. Prem Ranjan
2021-10-11 21:00 - 22:00
Title: Sustainability through High Voltage Engineering and Research
Abstract : Through this talk, we will take a glance at applications of high voltage engineering in three different sustainable technologies. (a) Direct application of high voltage and pulsed power will be shown for economical generation of nanoparticles (NPs) through wire explosion process (WEP). Control of NPs size, phase and formation mechanism will be discussed through modelling studies and different material characterisation techniques. Application of WEP-synthesized semiconductor NPs will be discussed for wastewater treatment. (b) Then, we will go through the need of high voltage electric system in more electric aircraft (MEA) to reduce the carbon footprint. Different tools available to evaluate the arc faults and damage caused to the neighbouring systems will be detailed through mathematical and experimental tools. (c) Drive towards sustainable environment is leading to search of SF6 alternatives in power equipment, which are responsible for more than 80% of total SF6 emission. Research towards SF6 alternatives in gas insulated systems to reduce the global warming potential will be discussed in brief. Finally, the prospective of high voltage engineering and research in some other areas will be discussed.
Speaker Biodata: Prem Ranjan is working as a postdoc researcher at High Voltage Lab, The University of Manchester, UK, since Nov. 2019. He obtained the B. Tech. degree in Electrical and Electronics Engineering from NIT Calicut, India in 2015 and the integrated MS, PhD degrees in Electrical Engineering from IIT Madras, Chennai, India in 2019. He worked as an exchange researcher at Nagaoka University of Technology, Japan for 4 months during 2016 and 2017. His research interests are focused on sustainable applications of high voltage engineering including exploding wire, gas insulation (SF6 alternatives), arc-tracking in more electric aircraft and condition monitoring of power apparatus.

PhD Thesis Defence of Aravind Illa
2021-09-29 16:30 - 17:30
Thesis Title: Acoustic-Articulatory Mapping: Analysis and Improvements with Neural Network Learning Paradigms
Abstract: Human speech is one of many acoustic signals we perceive, which carries linguistic and paralinguistic (e.g: speaker identity, emotional state) information. Speech acoustics are produced as a result of different temporally overlapping gestures of speech articulators (such as lips, tongue tip, tongue body, tongue dorsum, velum, and larynx) each of which regulates constriction in different parts of the vocal tract. Estimating speech acoustic representations from articulatory movements is known as articulatory-to-acoustic forward (AAF) mapping i.e., articulatory speech synthesis. While estimating articulatory movements back from the speech acoustics is known as acoustic-to-articulatory inverse (AAI) mapping. These acoustic-articulatory mapping functions are known to be complex and nonlinear.
Complexity of this mapping depends on a number of factors. These include the kind of representations used in the acoustic and articulatory spaces. Typically these representations capture both linguistic and paralinguistic aspects in speech. How each of these aspects contributes to the complexity of the mapping is unknown. These representations and, in turn, the acoustic-articulatory mapping are affected by the speaking rate as well. The nature and quality of the mapping varies across speakers. Thus, complexity of mapping also depends on the amount of the data from a speaker as well as number of speakers used in learning the mapping function. Further, how the language variations impact the mapping requires detailed investigation. This thesis analyzes few of such factors in detail and develops neural network based models to learn mapping functions robust to many of these factors.
Electromagnetic articulography (EMA) sensor data has been used directly in the past as articulatory representations (ARs) for learning the acoustic-articulatory mapping function. In this thesis, we address the problem of optimal EMA sensor placement such that the air-tissue boundaries as seen in the mid-sagittal plane of the real-time magnetic resonance imaging (rtMRI) is reconstructed with minimum error. Following optimal sensor placement work, acoustic-articulatory data was collected using EMA from 41 subjects with speech stimuli in English and Indian native languages (Hindi, Kannada, Tamil and Telugu) which resulted in a total of ~23 hours of data, used in this thesis. Representations from raw waveform are also learnt for AAI task using convolutional and bidirectional long short term memory neural networks (CNN-BLSTM), where the learned filters of CNN are found to be similar to those used for computing Mel-frequency cepstral coefficients (MFCCs), typically used for AAI task. In order to examine the extent to which a representation having only the linguistic information can recover ARs, we replace MFCC vectors with one-hot encoded vectors representing phonemes, which were further modified to remove the time duration of each phoneme and keep only phoneme sequence. Experiments with phoneme sequence using attention network achieve an AAI performance that is identical to that using phoneme with timing information, while there is a drop in performance compared to that using MFCC.
Experiments to examine variation in speaking rate reveal that, the errors in estimating the vertical motion of tongue articulators from acoustics with fast speaking rate, is significantly higher than those with slow speaking rate. In order to reduce the demand for data from a speaker, low resource AAI is proposed using a transfer learning approach. Further, we show that AAI can be modeled to learn acoustic-articulatory mappings of multiple speakers through a single AAI model rather than building separate speaker-specific models. This is achieved by conditioning an AAI model with speaker embeddings, which benefits AAI in seen and unseen speaker evaluations. Finally, we show the benefit of estimated ARs in voice conversion application. Experiments revealed that ARs estimated from speaker independent AAI preserves linguistic information and suppress speaker-dependent factors. These ARs (from unseen speaker and language) are used to drive target speaker specific AAF to synthesis speech, which preserves linguistic information and target speaker’s voice characteristics.

PhD Thesis Colloquium of Praveen Kumar Pokala @ 4pm
2021-08-06 21:30 - 22:30
Event : Thesis Colloquium
Title : Robust Nonconvex Penalties for Solving Sparse Linear Inverse Problems and Applications to Computational Imaging
Speaker : Praveen Kumar Pokala
Degree Registered : PhD
Advisor : Prof. Chandra Sekhar Seelamantula
Date : 06/08/2021
Venue : Online
Abstract : 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:
Nonconvex 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. Existing 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.
Prior learning networks based on deep-unfolded architectures for solving nonconvex penalties have not been explored. This thesis addresses the above aspects in three parts and considers applications to various computational imaging problems.
Part-1: Nonconvex Analysis-sparse Recovery
In this part, we solve the analysis-sparse recovery problem based on three regularization approaches:
Convexity-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.
Nonconvex 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.
Generalized 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.
We 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).
Part-2: Weighted Minimax Concave p-pseudonorm Minimization
In 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.
Part-3: Nonconvex Sparse Regularization and Deep-Unfolding
In 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.
Speaker Biodata : 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.

Events earlier to Aug 2021
2021-08-01 05:30 - 2021-08-02 05:30
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