EE

Lecture by Dr. Mathew Magimai Doss 3.00pm
2022-07-27 20:30 - 21:30 (Asia/Kolkata) | EE, MMCR
Department of Electrical Engineering, IEEE Signal Processing Society Bangalore Chapter, and Indian Speech Communication Association (IndSCA) cordially invite you to the following lecture
Title: Towards speech-based biomarkers
Speaker: Dr. Mathew Magimai Doss, Idiap Research Institute and EPFL, Switzerland
Host: Prof. Chandra Sekhar Seelamantula, EE, IISc
Date and time: July 27, 2022; 3 PM (Coffee will be served during the talk.)
Venue: Multimedia Classroom, Department of Electrical Engineering, Indian Institute of Science
Abstract:Speech communication is an essential part of our lives, which can undergo short-term and long-term changes at paralinguistic level due to several reasons such as, emotion, mood, stress, drinking, speech pathology, neurological speech and language disorders (e.g., Parkinson’s Disease (PD), Alzheimer’s Diseases). So, there is a thrust to develop speech-based methods to detect such short-term and long-term changes for clinical applications. In this talk, I will present a few research and development activities that I am involved in in that direction.Specifically, I will talk about (a) pathological speech processing, (b) joint modeling of speech and physiological signals, and (c) developmentof closed-loop deep brain stimulation with PD patient’s symptoms in loop.
Biography of the Speaker: Dr. Mathew Magimai Doss received the Bachelor of Engineering (B.E.) in Instrumentation and Control Engineering from the University of Madras, India in 1996; the Master of Science (M.S.) by Research in Computer Science and Engineering from the Indian Institute of Technology, Madras, India in 1999; the PreDoctoral diploma and the Docteur ès Sciences (Ph.D.) from the Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland in 2000 and 2005, respectively. He was a postdoctoral fellow at the International Computer Science Institute (ICSI),Berkeley, USA from April 2006 till March 2007. He is now a Senior Researcher at the Idiap Research Institute, Martigny, Switzerland. He is also a lecturer at EPFL. His main research interest lies in signal processing, statistical pattern recognition, artificial neural networks and computational linguistics with applications to speech and audio processing and multimodal signal processing. He is a Senior Area Editor of the IEEE Signal Processing Letters. He is also an Associate Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing.
All are invited.

PhD Thesis Colloquium
2022-07-12 15:30 - 17:00 | EE, MMCR
Name of the student: Tanmay Mishra
Faculty Advisor: Dr. Gurunath Gurrala
Date : 12th July 2022
Time: 10AM – 11.30AM
Venue: MMCR, 1st floor C-wing, EE Department, IISc
Abstract: Studying the dynamic behavior of non-linear complex power systems in a laboratory is very challenging. Early experimental platforms used micro-alternators to emulate the behavior of fixed steam turbine models. The micro-alternator is a three-phase synchronous generator with similar electrical constants (in per unit on machine rating) as those typically found in alternators in large power stations. It is an electrical scaled-down model of machines up to 1000 MW rating and is rated between 1 to 10 kVA. Researchers used these micro-machines up to the 90s to study large electric generators’ transient and steady-state performance. The Department of electrical engineering at the Indian Institute of Science (IISc) was also very active in experimental research in power engineering. The department still retained two-three kVA and one ten kVA micro-machine sets, but the control panels of these machines became obsolete as the manufacturer of these machines Mawdsley, London, doesn’t exist anymore. Advancements in simulation software packages and real-time simulators have primarily replaced the experimental models of electric power systems worldwide. The push for green energy technologies worldwide due to climate concerns has increased the presence of power electronic converters in the power grids. Reduction of overall inertia, frequent occurrence of electromechanical oscillations, electromagnetic transients, and control interaction modes has become a concern for the power grid operators. The need for understanding the physical insights of the oscillatory modes introduced by fast-acting power electronic converters, the need for developing practically feasible control algorithms for mitigating the interaction modes, and the need for developing dispatchability and grid support features like conventional generation sources have triggered the development of laboratory-scale experimental power grids across the world in the past decade.
In this thesis, initially, an attempt is made to revive the existing three kVA alternator controls. An IGBT-based buck converter static excitation system has been developed for the micro-alternator. This exciter also incorporates several limiters which were non-existent in the old analog control panels. An under-excitation limiter, over-excitation limiter, and V/Hz limiter as per IEEE standard 421.5 have been designed to protect the micro-alternator during abnormal conditions such as overloading, overheating, and over-fluxing of the machine. The detailed tuning procedure of limiters and TCR is discussed to comply with IEEE STD 421.2 and IEEE STD 421.5. A digital time constant regulator (TCR) is incorporated to modify the micro-alternator’s field’s time constant to mimic large synchronous machines’ dynamics as micro-machine time constants are very small. A custom 5 kVA micro-alternator was manufactured through a local vendor having parameters like the Mawdsley machines to facilitate the creation of multiple short circuits in the testbed.
A single micro-alternator can represent only one large alternator dynamics, thereby limiting the platform’s scalability. Emulating machines of different ratings using a single micro-machine would undoubtedly boost the capabilities of experimental platforms for investigating conventional and nonconventional source interactions in laboratories. To the best of our knowledge, only one such attempt was made in the literature, where a model reference control algorithm is proposed to mimic any rating alternator dynamics using a doubly excited laboratory micro-alternator. However, doubly excited micro-alternators are non-existent today. A generalized experimental platform using a non-linear output matching controller based on output feedback linearization is developed in this thesis for emulation of large turbo-alternators of different ratings, IEEE STD 421.5 excitation systems, and standard turbine governor models in the laboratory using the 5 kVA micro-alternator. IEEE Model 1.1 synchronous machine model in per unit on machine MVA rating with associated excitation system and governor-turbine models has been used as a reference model to be emulated. A single machine infinite bus (SMIB) setup with the 5 kVA micro-alternator and a 50 km 220 kV scaled lumped parameter frequency-dependent transmission line model is used for experimental validation. Synchronous generators of ratings, 128 MVA, 247.5 MVA, and 1000 MVA have been physically emulated using the setup. The dynamic responses of the large machines with thermal turbines (reheat, non-reheat), hydro turbine, and excitation systems; DC1A, AC4A, and ST1C have been reproduced under small and large disturbances.
A systematic scaling approach has been proposed to emulate a multi-machine system in the laboratory. Unlike in the SMIB system, the power levels of generators in a multi-machine system should be scaled to the laboratory level for emulation. Hence, every power system component (generator, transmission lines, transformer, loads) is scaled to a uniform level so that the laboratory machines don’t get overloaded. The developed non-linear control strategy for emulation has been extended to multi-machine systems. The Western System Coordinating CouncilJ 3-generator 9-bus test system has been used to validate the proposed concept. The feasibility of replicating WSCC system dynamics in a laboratory as a scaled-down model has been verified through simulations under small and large disturbances. Emulating large machine dynamics with different types of turbines, governors, and excitation controls using a singly excited micro-alternator enabling a generalized synchronous machine emulation platform is a first-of-its-kind effort in the literature to the best of our knowledge.
Note: Know how generated from the Source Emulation has been licensed to MCore Technologies Pvt Ltd, Bangalore for commercialization.
Acknowledgments: 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”.

EE Summer School 2022
2022-07-04 13:30 - 2022-07-08 22:30 | EE, IISc
Visit https://ee.iisc.ac.in/summerschool2022/ for details

2nd talk of the "TCE Lecture Series on Power Systems"
2022-07-01 15:30 - 16:30 | EE, MMCR
Title: Stability Modelling and Analysis of Converter Driven Power System
Speaker: Prof Bikash Pal
Date and Time: 1st July, 2022, 10 am
Venue: MMCR, EE
Abstract:The number of power electronics converters connected to electrical networks has been growing exponentially as they are part of all new generation connected to the grid. While the rapid control and fast electronic switching available with this technology offer flexibility in network operation, the dynamic interactions between several of them threaten the operational stability of the transmission grid is a concern. It is required to develop a methodology for identifying the risks associated with the stability and control interaction before a new power electronic device (e.g. Windfarm, interconnector, STATCOM) is introduced to the network.
The talk will focus on an analytical framework in impedance domain to quantify the interaction between the new plant and the rest of the network for setting additional grid connection study specifications which will include detail technical study to check and mitigate the risks associated with new power electronics interfaced generation. The framework developed is to support MMC technology, control delay, system strength and FRT capability of dynamic voltage support devices and windfarm through technical case study conducted at the research group of Bikash Pal at Imperial College London. Future research challenges and opportunities will be highlighted.
Speaker Bio: Bikash Pal is a Professor of Power Systems at Imperial College London (ICL). He is research active in power system stability, control, and estimation. Currently he is leading a six university UK-China research consortium on Resilient Operation of Sustainable Energy Systems (ROSES) as part of EPSRC-NSFC Programme on Sustainable Energy Supply. He led UK-China research consortium project on Power network stability with grid scale storage (2014-2017): He also led an eight- university UK-India research consortium project (2013-2017) on smart grid stability and control. His research is conducted in strategic partnership with ABB, SIEMENS, GE Grid Solutions, UK, and National Grid, UK. UK Power Networks. SIEMENS R&D collaborated with him to develop fast power flow and volt-var control tools in Spectrum Power, an advanced module for distribution management system solution from SIEMENS. This is now commissioned in distribution control centres in Columbia, Bosnia Norway and Azerbaijan serving 15 million customers in these countries. GE commissioned sequel of projects with him to analyse and solve wind farm HVDC grid interaction problems (2013-2019). Prof Pal was the chief technical consultant for a panel of experts appointed by the UNFCCC CDM (United Nations Framework Convention on Climate Change Clean Development Mechanism). He has offered trainings in Chile, Qatar, UAE, Malaysia and India in power system protections, stability and control topics. He has developed and validated a prize winning 68-bus power system model, which now forms a part of IEEE Benchmark Systems as a standard for researchers to validate their innovations in stability analysis and control design. He was the Editor-in-Chief of IEEE Transactions on Sustainable Energy (2012-2017) and Editor-in-Chief of IET Generation, Transmission and Distribution (2005-2012). He is Vice President, PES Publications (2019-). In 2016, his research team won the President’s outstanding research team award at Imperial College London (ICL). He is Fellow of IEEE for his contribution to power system stability and control. He is an IEEE Distinguished Lecturer in Power distribution system estimation and control. He has published about 125 papers in IEEE Transactions and authored four books in power system modelling, dynamics, estimations and control. He was Otto Monstead Professor at Denmark Technical University (DTU) (2019) and Mercator Professor sponsored by German Research Foundation (DFG) at University of Duisburg-Essen in 2011. He worked as faculty at IIT Kanpur, India. He holds a Visiting Professorship at Tsinghua University, China.

Talk by Prof Bikash Pal @10am
2022-07-01 15:30 - 16:30 | EE, MMCR
2nd talk of the “TCE Lecture Series on Power Systems”
Date and Time: 1st July, 2022 from 10 am
Venue: MMCR, EE
Title: Stability Modelling and Analysis of Converter Driven Power System
Abstract: The number of power electronics converters connected to electrical networks has been growing exponentially as they are part of all new generation connected to the grid. While the rapid control and fast electronic switching available with this technology offer flexibility in network operation, the dynamic interactions between several of them threaten the operational stability of the transmission grid is a concern. It is required to develop a methodology for identifying the risks associated with the stability and control interaction before a new power electronic device (e.g. Windfarm, interconnector, STATCOM) is introduced to the network
The talk will focus on an analytical framework in impedance domain to quantify the interaction between the new plant and the rest of the network for setting additional grid connection study specifications which will include detail technical study to check and mitigate the risks associated with new power electronics interfaced generation. The framework developed is to support MMC technology, control delay, system strength and FRT capability of dynamic voltage support devices and windfarm through technical case study conducted at the research group of Bikash Pal at Imperial College London. Future research challenges and opportunities will be highlighted.
Speaker Bio: Bikash Pal is a Professor of Power Systems at Imperial College London (ICL). He is research active in power system stability, control, and estimation. Currently he is leading a six university UK-China research consortium on Resilient Operation of Sustainable Energy Systems (ROSES) as part of EPSRC-NSFC Programme on Sustainable Energy Supply. He led UK-China research consortium project on Power network stability with grid scale storage (2014-2017): He also led an eight- university UK-India research consortium project (2013-2017) on smart grid stability and control. His research is conducted in strategic partnership with ABB, SIEMENS, GE Grid Solutions, UK, and National Grid, UK. UK Power Networks. SIEMENS R&D collaborated with him to develop fast power flow and volt-var control tools in Spectrum Power, an advanced module for distribution management system solution from SIEMENS. This is now commissioned in distribution control centres in Columbia, Bosnia Norway and Azerbaijan serving 15 million customers in these countries. GE commissioned sequel of projects with him to analyse and solve wind farm HVDC grid interaction problems (2013-2019). Prof Pal was the chief technical consultant for a panel of experts appointed by the UNFCCC CDM (United Nations Framework Convention on Climate Change Clean Development Mechanism). He has offered trainings in Chile, Qatar, UAE, Malaysia and India in power system protections, stability and control topics. He has developed and validated a prize winning 68-bus power system model, which now forms a part of IEEE Benchmark Systems as a standard for researchers to validate their innovations in stability analysis and control design. He was the Editor-in-Chief of IEEE Transactions on Sustainable Energy (2012-2017) and Editor-in-Chief of IET Generation, Transmission and Distribution (2005-2012). He is Vice President, PES Publications (2019-). In 2016, his research team won the President’s outstanding research team award at Imperial College London (ICL). He is Fellow of IEEE for his contribution to power system stability and control. He is an IEEE Distinguished Lecturer in Power distribution system estimation and control. He has published about 125 papers in IEEE Transactions and authored four books in power system modelling, dynamics, estimations and control. He was Otto Monstead Professor at Denmark Technical University (DTU) (2019) and Mercator Professor sponsored by German Research Foundation (DFG) at University of Duisburg-Essen in 2011. He worked as faculty at IIT Kanpur, India. He holds a Visiting Professorship at Tsinghua University, China.

PhD Colloquium of Sounak Nandi
2022-06-30 20:30 - 21:30
Thesis Title: Experimental and Theoretical Investigations on High Voltage Polymeric Insulators
Research Supervisor: Dr Subba Reddy B
Date and Time: Thursday 30th June 2022, 3.00 pm
Venue: Online. Click here to join the meeting
Abstract: High Voltage Ceramic and glass Insulators are widely used by various transmission and distribution utilities for several decades across the globe. Recently composite or silicone rubber insulators have evolved and are now replacing ceramic/glass insulators due to their improved advantages, however these Insulators suffer from degradation over a period in service.
The primary objective of the investigation relates to the performance of silicon rubber/polymer insulator under various climatic conditions, both experimentally and comprehend theoretically. Experimental studies were conducted to understand the degradation of insulators under different climatic conditions which prevail in the Country.
Studies on polymer insulators under sub-zero and under extreme high temperature conditions were attempted experimentally and their performance evaluated. During experimentation the leakage current was continuously monitored also material analysis which is very important factor and essential to correlate with the morphological changes of the insulator surface was studied. The experimental investigations demonstrate that there is a need to conduct multi-stress experimentation under specific climatic conditions before the Insulators are being installed in the field.
The next portion of the theses work deals with failure mechanism of Fibre Reinforced Plastic (FRP) Rod. Some portion of the work deals with mathematical analysis being extended to condition monitoring of dielectric surfaces and understanding the performance of FRP rods under high AC/DC voltages. Further, experimental Investigations are performed on FRP Rods to analyse the behaviour witnessed like the field failures reported on Silicon rubber Insulators.
Condition monitoring of dielectric surfaces is very important; hence it was felt necessary to analyse the field performance of transmission/distribution composite Insulators. To understand further a mathematical analysis based on Chaos has been evaluated for leakage current data and quantization of comparative degradation for a dielectric surface, later Empirical Mode Decomposition is also used for understanding leakage current and implied degradation under minimal data condition.
Subsequently, Surface electric field of insulators exposed to HVDC is studied considering the temporal boundary conditions which may arise due to the capacitive-resistive transients, some experimental investigations are also conducted to compare the simulated results.
The last portion of the thesis emphases on the study of bulk conductivity of polymer material. The Electric Field dependence of conductivity on the application of voltage and subsequent space charge distribution is attempted.
— All are Welcome —
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PhD Thesis Colloquium of Unni V S @11am
2022-06-24 16:30 - 17:30
Date: June 24, 2022.Time: 11am Venue: MS Teams (online).Link: https://tinyurl.com/yckf6en2
Title: Efficient and Convergent Algorithms for High-Fidelity Hyperspectral Image Fusion.Abstract: Hyperspectral (HS) imaging refers to acquiring images with hundreds of bands corresponding to different wavelengths of light. HS imaging has a wide range of applications such as remote sensing, industrial inspection, environmental monitoring, etc. A fundamental consideration with multiband sensors is that the amount of incident energy is limited and this creates an intrinsic tradeoff between spatial resolution and the number of bands—current optical sensors can either generate images with high resolution but a small number of bands or images with a large number of bands but reduced resolution. For example, HS images have hundreds of bands but low spatial resolution, whereas the opposite is true for multispectral (MS) images. An extreme case is a panchromatic (PAN) image with very high spatial resolution but just a single band. Image fusion refers to techniques where multiband images with high spatial resolution are synthetically generated using image processing algorithms. It includes pansharpening (MS+PAN), hyperspectral sharpening (HS+PAN), and HS-MS fusion (HS+MS). Reconstructing a fused image from the observed images is ill-posed and needs regularization. Diverse regularization methods have been proposed over the years for general imaging problems, many of which perform very well for fusion. This includes vector total variation, sparsity and dictionary-based penalties, generalized Gaussian- and GMM-based priors, etc. This thesis proposes novel regularization models and algorithms that can outperform state-of-the-art image fusion techniques. We can broadly group these into two classes—explicit and implicit regularization.Explicit regularization refers to the design of hand-crafted penalty functions that impose desirable properties (e.g., smoothness) on the reconstruction; this is used along with the observed data for fusion. We propose a convex regularizer that is motivated by nonlocal patch-based methods for image restoration. Our regularizer accounts for long-distance correlations in hyperspectral images, considers patch variation for capturing texture information, and uses the higher resolution image for guiding the fusion process. Unlike local pixel-based methods, where variations along just horizontal and vertical directions are penalized, we use a wider search window in terms of nonlocality and directionality. This is shown to yield state-of-the-art results. The catch is that the resulting optimization problem is non-differentiable and we cannot use simple gradient-based algorithms. However, we show that by expressing patch variation as filtering operations and judiciously splitting the original variables and introducing latent variables, we develop a provably convergent iterative algorithm, where the subproblems can be solved efficiently using FFT-based convolution and soft-thresholding.In the implicit approach, we rely on a recent paradigm known as plug-and-play (PnP) regularization, where powerful off-the-shelf denoisers are used for regularization purposes. While this has been shown to give state-of-the-art results for general restoration tasks, it has not so much been explored for fusion. In fact, we faced few technical challenges in applying PnP for hyperspectral fusion. Firstly, existing denoisers are slow when applied to multiband images and we need to apply such denoisers several times with the PnP framework. Secondly, convergence is generally not guaranteed for PnP regularization since the mechanism is ad-hoc. Along with efficiency and good denoising performance, we need to come up with a denoiser with specific properties that can guarantee convergence. We proposed a couple of approaches to solve this problem. In the first approach, we have developed a high-dimensional kernel denoiser with low cost yet good denoising performance, which can guarantee PnP convergence. The overall algorithm is fast and competitive with state-of-the-art methods. In the second approach, we leverage the power of deep learning to develop a trained patch denoiser which has a couple of advantages over conventional end-to-end learning: (1) Unlike end-to-end networks which require excessive ground-truth data for training, we can be trained the denoiser from patches extracted from the observed images. For example, in HS+MS fusion, the MS image captures the same scene and has the same spatial resolution as the target image. We train the denoiser by sampling clean patches from the MS image and corrupting them with noise.(2) Compared to end-to-end learning, where the training is done with a fixed forward model, our method can be deployed for different forward models. This is possible thanks to the decoupling of the inversion (of the forward model) and denoising steps in PnP.We use the trained denoiser for PnP regularization and establish convergence of the PnP iterations under a technical assumption which we verify numerically. As far as the reconstruction quality is concerned, our method outperforms state-of-the-art variational and deep learning fusion techniques.

Lecture by Mr. Sidhu Sridhara Rao @3pm
2022-06-08 20:30 - 21:30 | EE, MMCR
Click here for Online Teams Meeting link.
Venue: MMCR, EE, IISc, Bangalore / Hybrid mode
Time: 8 June 2022, 3 pm
Title: Demystifying Cybersecurity Program
Speaker: Mr. Sidhu Sridhara Rao, Head of Information Security, Well Fargo.
Abstract: “What comes to one’s mind when we say the word ‘cybersecurity’?
Vulnerabilities, Breaches, incident response, hackers, hacktivists, nation state actors, cyber criminals etc.
With the rapid digitalization of the global economic activity during the pandemic, the world is even more connected than it was 2 years ago. And the benefits of digitalization to organizations will continue to drive this journey. However, as the world is more and more interconnected, the risks of that digital partnerships become even more important for resiliency of the organization’s business. There is no single organization in the world that is not dependent of extended enterprise partner ecosystem to run their business to serve their clients. As such the attack surface of the organizations continue to grow due to digitalization.
There is lot to cybersecurity than vulnerabilities, breaches, incident response. The purpose of this session is to throw some light on to all the elements that constitutes a comprehensive cybersecurity program, what type of skill sets are needed where and how the global economy can benefit from it. This session will cover industry agnostic generic cybersecurity framework that is easy to understand. This framework also has linkages to various regulatory frameworks around security and privacy.
The audience can be from broad spectrum of disciplines. It’s not necessary that they have core computer science background.”
Bio:
Sidhu leads the Information and Cyber Security Services Group at Wells Fargo India reporting into the Global Chief Information Security Officer. Prior to joining Wells Fargo, Sidhu served as “Risk Management Fellow (Banking and Securities)” at Deloitte. As Regional Leader of the Finance Risk Transformation Services practice of the firm, Sidhu led global M&A projects in the financial services sector. He holds multiple certifications in IT Risk Management and IT Governance. Sidhu is a regular keynote speaker, panel member and moderator on cyber security, innovation and financial services matters in India and abroad. He has been invited by global media organizations like The Economist, Economic Times, Financial Times, Information Security Media Group, Indian agencies like ASSOCHAM, FICCI, and global premier business Institutions like the IIMs and ISB for his expertise in the industry. Sidhu was awarded Editor’s Choice CISO of the year in 2018. Sidhu takes personal interest in coaching and mentoring women in middle management levels across Asia, Europe and the Americas.

Request everyone to attend the event.

PhD Thesis Colloquium of Pravin Nair @11am
2022-06-07 16:30 - 17:30
Date: June 7, 2022.
Time: 11-12 am.
Venue: MS Teams (online).
Link: https://tinyurl.com/2p8pwa3c
Title: Provably convergent algorithms for denoiser driven image regularization.
Abstract: Some fundamental reconstruction tasks in image processing can be posed as an inverse problem where we are required to invert a given forward model. For example, in deblurring and superresolution, the ground-truth image needs to be estimated from blurred or low-resolution images, whereas in CT and MR imaging, we need to reconstruct a high-resolution image from few linear measurements. Such inverse problems are invariably ill-posed—they exhibit non-unique solutions, and the process of direct inversion is unstable. Some kind of image model (or prior) on the ground-truth is required to regularize the inversion process. For example, a classical solution involves minimizing f+g, where the loss term f is derived from the forward models and the regularizer g is used to constrain the search space. The challenge is to come up with a formula for g that can yield high-fidelity reconstructions. This has been the center of research activity in image reconstruction for the last two decades.
“Regularization using denoising” is a recent breakthrough in which a powerful denoiser is used for regularization purpose, instead of having to specify some hand-crafted g (however the loss f is used). This was empirically shown to yield significantly better results than staple f+g minimization. In fact, the results are generally comparable and often superior to state-of-the-art deep learning methods. In this thesis, we study two such popular models for image regularization—Plug-and-Play (PnP) and Regularization by Denoising (RED). In particular, we focus on the convergence aspect of these iterative algorithms which is not well understood even for simple denoisers. This is important since lack of convergence guarantee can result in spurious reconstructions in imaging applications. The contributions of this thesis in this regard are as follows:
(1) We show that for a class of non-symmetric linear denoisers that includes kernel denoisers such as nonlocal means, one can associate a convex regularizer $g$ with the denoiser. More precisely, we show that any such linear denoiser can be expressed as the proximal operator of convex function, provided we work with a non-standard inner product (not the Euclidean inner product). A direct implication of this observation is that (a simple variant of) the PnP algorithm based on this linear denoiser amounts to solving an optimization problem of the form f+g, though it was not originally conceived this way. Consequently, if f is convex, both objective and iterate convergence are guaranteed for the PnP algorithm. Apart from the convergence guarantee that it brings in, we go on to show that this observation has algorithmic value as well. For example, in the case of linear inverse problems such as superresolution, deblurring and inpainting (where f is quadratic), we can reduce the problem of minimizing f+g to a linear system. In particular, we show how using Krylov solvers we can solve this system efficiently in just few iterations. Surprisingly, the reconstructions are found to be comparable with state-of-the-art deep learning methods. To the best of our knowledge, the possibility of achieving near state-of-the-art image reconstructions using a linear solver has not been demonstrated before.
(2) In general, state-of-the-art PnP and RED algorithms work with trained CNN denoisers such as DnCNN. Unlike linear denoisers, it is difficult to place PnP and RED algorithms within an optimization framework for CNN denoisers. Nonetheless, we can still try understanding the convergence of iterates, i.e., do these algorithms stabilize eventually. Again, for convex loss f, we show that this question can be resolved using the theory of monotone operators, namely, that nonexpansivity of the denoiser is sufficient for iterate convergence of PnP and RED. Using numerical examples, we show that existing CNN denoisers are not nonexpansive and can cause PnP and RED algorithms to diverge. The question is can we train nonexpansive denoisers? Unfortunately, this is computationally challenging—simply checking nonexpansivity of a CNN is known to be intractable. As a result, existing algorithms for training nonexpansive CNNs either cannot guarantee nonexpansivity or are computation intensive. We show that this problem can be solved by moving away from CNN denoisers to unfolded deep denoisers. In particular, we are able to construct unfolded networks that are efficiently trainable and come with convergence guarantees for PnP and RED, and whose regularization capacity can be matched with CNN denoisers.
We will discuss our findings in greater detail during the colloquium and present numerical results to validate of our theoretical findings.

PhD Thesis Colloquium of Jerrin Thomas Panachakel
2022-06-06 20:00 - 21:00
Title of the thesis: Machine Learning for Decoding Imagined words and Altered State of Consciousness from EEG
Date and Time: June 6, 2022 (Monday) 2.30 PM

Microsoft Teams meeting link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_YzRlNjZhN2ItNzYzMC00MmNhLWE5ZmUtY2NhY2U0ODBhNzIz%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%2213e2f8ed-a5d8-408f-b912-af1da693f745%22%7d

Abstract: The thesis explores several architectures for accurately decoding the cognitive activity from EEG recorded during speech imagery and Rajayoga meditation. The major contributions of the thesis are listed below:

Neural Correlates of Phonological Category in Speech Imagery EEG

We have shown that neural correlates of phonological categories exist in the EEG recorded during speech imagery. These correlates lead to significant differences in the mean phase coherence (MPC) values of the EEG across several cortical regions.
We have also shown that MPC values can be used for accurately classifying the EEG recorded during speech imagery based on the phonological category of the prompts. The proposed architecture for this task has an accuracy of 84.9%.

Decoding Imagined Words from EEG

One of the challenges in designing systems for decoding imagined words from EEG is the limited availability of data. We have presented three architectures for decoding imagined words from EEG. All three architectures alleviate this problem of limited availability of data.
The transfer learning-based architecture employs MPC and magnitude-squared coherence values along with a ResNet50-based classifier. This architecture achieves an accuracy of 92.8% on a publicly available EEG dataset in classifying speech imagery.

Classification of Altered State of Consciousness from Resting State

We have presented three architectures for classifying the altered state of consciousness during Rajayoga meditation from the resting state.
Both CSP-LDA-LSTM (common spatial pattern-linear discriminant analysis-long short-term memory) and SVD-DNN (singular value decomposition-deep neural network) architectures are able to capture subject-invariant features.
The best intra-subject accuracy obtained is 98.2% and the best inter-subject accuracy is 96.4%.

Publications from the Thesis:

Journals

Panachakel, Jerrin Thomas, and Ramakrishnan Angarai Ganesan. “Decoding Imagined Speech From EEG Using Transfer Learning.” IEEE Access 9 (2021): 135371-135383.
Panachakel, Jerrin Thomas, and Angarai Ganesan Ramakrishnan. “Decoding covert speech from EEG – a comprehensive review.” Frontiers in Neuroscience (2021): 392.

Conferences

Panachakel, Jerrin Thomas, et al. “Can we identify the category of imagined phoneme from EEG?.” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021.
Panachakel, Jerrin Thomas, and Ramakrishnan Angarai Ganesan. “Classification of phonological categories in imagined speech using phase synchronization measure.” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021.
Panachakel, Jerrin Thomas, Ramakrishnan Angarai Ganesan, and T. V. Ananthapadmanabha. “Decoding imagined speech using wavelet features and deep neural networks.” 2019 IEEE 16th India Council International Conference (INDICON). IEEE, 2019.
Panachakel, Jerrin Thomas, Ramakrishnan Angarai Ganesan, and T. V. Ananthapadmanabha. “Common Spatial Pattern Based Data Augmentation Technique for Decoding Imagined Speech.” 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021.
Panachakel, Jerrin Thomas, et al. “Binary classification of meditative state from the resting state using EEG.” 2021 IEEE 18th India Council International Conference (INDICON). IEEE, 2021.
Panachakel, Jerrin Thomas, et al. “Automated classification of EEG into meditation and non-meditation epochs using common spatial pattern, linear discriminant analysis, and LSTM.” TENCON 2021-2021 IEEE Region 10 Conference (TENCON). IEEE, 2021.

ALL ARE WELCOME!

Lecture by Harmeet Singh @ 2pm
2022-06-02 19:30 - 20:30 | EE, MMCR
Title: Technology Advancements in Buck Converters
Time: 2 June 2022, 2:00 pm
Venue: MMCR EE
Abstract: The D-CAP™ ( “Direct connection to the output CAPacitor”) mode control in power converters provides many attractive features, such as ease of use with no loop compensation, minimum external components, and fast transient response which reduces output capacitance and high efficiency. In the presentation, I would talk about the advancement in the variable frequency control modes of buck converters, especially TI’s DCAP architecture. I would also talk about how the integration of input decoupling and boot capacitors in the IC helps to solve electromagnetic interference (EMI). I will discuss how TI has been able to reduce the size of external differential and common mode EMI filters with the integration of the Active EMI filter (AEF) inside the IC. Lastly, I will briefly touch upon the advancement w.r.t to IC package development.
Speaker’s Bio: Harmeet Singh is a Principal Analog Field Application Engineer at Texas Instruments, India. He is a member of, the Group Technical staff. He is responsible for the growth of Analog revenue in Grid infrastructure in India. Before joining TI, he worked as an R&D manager in Samtel, Ghaziabad, handling the power division of the Plasma Display department. Prior to that, he worked as Joint Manager R&D in PUNCOM, heading the SMPS division and responsible for the design of AC-DC, DC-DC SMPS and 48V telecom power plants for various telecom equipment. He holds a Bachelor’s degree in Electronics and Telecommunications Engineering from Punjab Engineering College (PEC), Chandigarh.
All are welcome.

Talk by Prof Sukumar Brahma @ 9.30am
2022-06-02 15:00 - 16:30 | EE, MMCR
Dear all,

We are starting “TCE Lecture Series on Power Systems” at the EE Dept of IISc with the funding support from Tata Consulting Engineers Ltd. In this regard, we cordially invite you to the inaugural talk to be given by Prof Sukumar Brahma, FIEEE of Clemson University, USA on 2nd June, 2022 from 10 am IST at the MMCR of EE Dept. The talk title, abstract and the brief bio of the speaker are given below. This talk can also be attended online. The link is given in the attached poster.

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Title: Challenges to Power System Protection in Presence of Renewables
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Abstract: Power system protection has been conceived and refined through decades of innovation and experience. However, the underlying assumptions behind all protection design and implementation have been that the faulted power system behaves as a linear system, and load currents can be neglected compared to fault currents. These assumptions are under scrutiny as more and more renewables connect at transmission and distribution levels. Renewable generation like solar and wind connect through power converters. The response of the renewable generation to faults depends largely on the converter controls. The controls restrict the fault currents to values comparable to load currents, and can also control the power factor of the fault current. Such response creates problems with various protection functions, and system analysis that underpin the design of some of the protection functions. This lecture will describe the challenges in detail for both transmission and distribution systems, including microgrids, and offer insight into some potential solutions.
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Speaker Bio: Sukumar Brahma received his Bachelor of Engineering from Gujarat University in 1989, Master of Technology from Indian Institute of Technology, Bombay in 1997, and PhD in from Clemson University in 2003; all in Electrical Engineering. He joined Clemson university as the Dominion Energy Distinguished Professor of Power Engineering in August 2018. He also serves as the director of the industry-funded Clemson University Electric Power Research Association (CUEPRA). Before joining Clemson he was William Kersting Endowed Chair Professor at New Mexico State University, USA. Dr. Brahma has chaired IEEE Power and Energy Society’s Power and Energy Education Committee, Life Long Learning Subcommittee and Distribution System Analysis Subcommittee. He is a member of the Power System Relaying and Control Committee (PSRCC), where he has been contributing to and leading working groups that produce reports, guides and standards in the area of power system protection. He has been an editor for IEEE Transactions on Power Delivery, and served as Guest Editor-in-Chief for the Special Issue on Frontiers of Power System Protection for the journal. His research, widely published and funded by the National Science Foundation, US Department of Energy, utilities, and other government agencies has focused on different aspects of power system modeling, analysis, and protection. Fundamentally, it spans across diverse areas of electrical engineering and computer science, integrating signal processing, machine learning, control and communication to holistically approach the emerging problems in the power and energy domain. Current research, funded by the US Department of Energy, investigates and addresses protection and fault location issues in integration of renewables with power systems and develops new paradigms in protection of smart grid, at both transmission and distribution levels.

Dr. Brahma is a Distinguished Lecturer of the IEEE. He was elected IEEE Fellow “for contributions to power system protection with distributed and renewable generation”.

Lecture by Dr. Ayush Bhandari @ 11.30am
2022-06-01 17:00 - 18:00 | EE, MMCR
Title: Digital Acquisition via Modulo Folding: Revisiting the Legacy of Shannon-Nyquist, Prony, Schoenberg, Pisarenko and Radon
Date and time: June 1, 2022; 11.30 AM
Venue: Multimedia Classroom, Electrical Engineering Department, IISc
Coffee will be served during the talk.
Abstract: Digital data capture is the backbone of all modern day systems and “Digital Revolution” has been aptly termed as the Third Industrial Revolution. Underpinning the digital representation is the Shannon-Nyquist sampling theorem and more recent developments such as compressive sensing approaches. The fact that there is a physical limit to which sensors can measure amplitudes poses a fundamental bottleneck when it comes to leveraging the performance guaranteed by recovery algorithms. In practice, whenever a physical signal exceeds the maximum recordable range, the sensor saturates, resulting in permanent information loss. Examples include (a) dosimeter saturation during the Chernobyl reactor accident, reporting radiation levels far lower than the true value, and (b) loss of visual cues in self-driving cars coming out of a tunnel (due to sudden exposure to light).
To reconcile this gap between theory and practice, we introduce a computational sensing approach—the Unlimited Sensing framework (USF)—that is based on a co-design of hardware and algorithms. On the hardware front, our work is based on a radically different analog-to-digital converter (ADC) design, which allows for the ADCs to produce modulo or folded samples. On the algorithms front, we develop new, mathematically guaranteed recovery strategies.
In the first part of this talk, we prove a sampling theorem akin to the Shannon-Nyquist criterion. Despite the non-linearity in the sensing pipeline, the sampling rate only depends on the signal’s bandwidth. Our theory is complemented with a stable recovery algorithm. Beyond the theoretical results, we also present a hardware demo that shows the modulo ADC in action.
Building on the basic sampling theory result, we consider certain variations on the theme. This includes different signal classes (e.g. smooth, sparse and parametric functions) as well as sampling architectures, such as One-Bit and Event-Triggered sampling. Moving further, we reinterpret the USF as a generalized linear model that motivates a new class of inverse problems. We conclude this talk by presenting a research overview in the context of single-shot high-dynamic-range (HDR) imaging, sensor array processing and HDR computed tomography based on the modulo Radon transform.
About the speaker: Ayush Bhandari received the Ph.D. degree from Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2018, for his work on computational sensing and imaging which is being shaped as a forthcoming, co-authored book Computational Imaging in MIT Press. He is currently a faculty member with the Department of Electrical and Electronic Engineering, Imperial College London, U. K. He has held research positions at INRIA (Rennes), France, Nanyang Technological University, Singapore, the Chinese University of Hong Kong and Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland among other institutes. He was appointed the August–Wilhelm Scheer Visiting Professor (Department of Mathematics), in 2019 by the Technical University of Munich.
He has been a tutorial speaker at various venues including the ACM Siggraph (2014,2015) and the IEEE ICCV (2015) and he was the keynote speaker at the Intl. Workshop on Compressed Sensing applied to Radar, Multimodal Sensing and Imaging (CoSeRa), 2018. Some aspects of his work have led to new sensing and imaging modalities which have been widely covered in press and media (e.g. BBC news). Applied aspects of his research have led to more than 10 US patents. His scientific contributions have led to numerous prizes, most recently, the Best Paper Award at IEEE ICCP 2020 (Intl. Conf. on Computational Photography) and the Best Student Paper Award (senior co-author) at IEEE ICASSP 2019 (Intl. Conf. on Acoustics, Speech and Signal Processing). In 2020, his doctoral work was awarded the Best PhD Dissertation Award from the IEEE Signal Processing Society. In 2021, he received the President’s Medal for Outstanding Early Career Researcher at Imperial College London.
Host: Prof. Chandra Sekhar Seelamantula (EE)

Lecture by Dr. Lokesh @3.30pm
2022-05-31 21:00 - 22:00 | EE, MMCR
Title: Artificial Intelligence in Clinical Neurology
Organizer: Aster-IISc AI lab
Speaker: Dr. Lokesh from Aster-CMI
Venue: MMCR, EE
LInk: Teams https://tinyurl.com/4fv7rbh6
Time: 3.30pm to 4.30pm
All are welcome

PhD Thesis Colloquium
2022-04-28 16:30 - 17:30
Name of Student: Ruturaj Gavaskar.
Guide: Prof. Kunal Narayan ChaudhuryDate: April 28, Thursday. Time: 11-12 am.Venue: MS Teams (online).Link: https://tinyurl.com/bdfardzz
Title: On plug-and-play regularization using linear denoisers.Abstract: The problem of inverting a given measurement model comes up in several computational imaging applications. For example, in CT and MRI, we are required to reconstruct a high-resolution image from incomplete noisy measurements, whereas in superresolution and deblurring, we try to infer the ground-truth from low-resolution or blurred images. While several forms of regularization and associated optimization methods have been proposed in the imaging literature of the last few decades, the use of denoisers (aka denoising priors) for image regularization is a relatively recent phenomenon. This has partly been triggered by the advances in image denoising in the last 20 years, leading to the development of powerful image denoisers. In this thesis, we look at a recent protocol called Plug-and-Play (PnP) method, where powerful image denoisers such as BM3D and DnCNN are deployed within iterative algorithms for image regularization. Surprisingly, the reconstructed images are of high quality and competitive with state-of-the-art methods. Following this, researchers have tried explaining why plugging a denoiser within an inversion algorithm should work in the first place, why it produces high-quality images, and whether the final reconstruction is optimal in some sense. We have tried answering some of these questions in this thesis.At a high level, the contributions of the thesis are as follows. Based on the theory of proximal operators, we prove that a PnP algorithm in fact minimizes a convex objective function provided the plugged denoiser belongs to a broad class L of linear filters. In particular, L has a simple characterization and includes kernel and GMM denoisers. That we are able to characterize the reconstruction (for class L denoisers) as the solution of a convex optimization problem helps in settling some of the above questions. For example, this allows us to establish iterate convergence for PnP regularization. Obtaining such a guarantee for complex nonlinear denoisers such as BM3D and neural denoisers is nontrivial. As a more profound application, we are able to provide guarantees on signal recovery for the compressed sensing problem. More precisely, under certain verifiable assumptions, we are able to prove that a signal can be recovered exactly (resp. stably) with high probability from random clean (resp. noisy) measurements using PnP regularization. To the best of our knowledge, this is the first such result where the underlying assumptions are verifiable. We will present and discuss these and other theoretical findings in greater detail during the colloquium. We will also present numerical results to validate our findings.

M. Tech. (Research) Thesis Colloquium of Anwesha Roy
2022-04-22 21:30 - 22:30 | EE, MMCR
Title: Improved air-tissue boundary segmentation in real-time magnetic resonance imaging videos using speech articulator specific error criterion
Abstract: Real-time Magnetic Resonance Imaging (rtMRI) is a tool used exhaustively in speech science and linguistics to understand the dynamics of the speech production process across languages and health conditions. rtMRI has two advantages over other methods which capture articulatory movement, like X-ray, Ultrasound and Electromagnetic articulography – it is non-invasive, and it captures a complete view of the vocal tract including pharyngeal structures. The rtMRI video provides spatio-temporal information of speech articulatory movements, which helps in modeling speech production. For this purpose, a common step is to obtain the air-tissue boundary (ATB) segmentation in all frames of the rtMRI video. The accurate estimation of ATBs of the upper airway of the vocal tract is essential for many speech processing applications like speaker verification, text-to-speech synthesis, visual augmentation for synthesized articulatory videos, and analysis of vocal tract movement. Thus, it is necessary to have an accurate air-tissue boundary segmentation in every frame of the rtMRI videos.
The best performance in ATB segmentation of rtMRI videos in speech production, in unseen subject conditions, is known to be achieved by a 3-dimensional convolutional neural network (3D-CNN) model. In seen subject conditions, both 3D-CNN and 2-dimensional deep convolutional encoder-decoder network (SegNet) show similar performance. However, the evaluation of these models, as well as other ATB segmentation techniques reported in literature, has been done using Dynamic Time Warping (DTW) distance between the entire original and predicted boundaries or contours. Such an evaluation measure may not capture local errors in the predicted contour. Careful analysis of predicted contours reveals errors in regions like the velum part and tongue base section, which are not captured in a global evaluation metric like DTW distance. In this thesis, we automatically detect such errors and propose a novel correction scheme for them. We also propose two new evaluation metrics for ATB segmentation, separately for each contour, to explicitly capture errors in these contours.
Moreover, the state-of-the-art models use overall binary cross entropy as the loss function during model training. However, such a global loss function does not give enough emphasis on regions which are more prone to errors. In this thesis, together with global loss, we explore the use of regional loss functions which focus on areas of the contours which have been analyzed as error prone in our analysis. Two different losses are considered in the regions around velum and tongue base – binary cross entropy (BCE) loss and dice loss. It is observed that dice-loss based models perform better than their BCE loss based counterparts.

MTech (Rresearch) Colloquium
2022-04-21 16:30 - 17:30 | EE, MMCR
Name of the Student: Ahmad Arfeen
Guide: Prof. Soma Biswas
Date And Day: 21st April, 2022, Thursday
Time: 11:00 am
Venue: EE, MMCR
Title : Data Efficient Domain Generalization
Abstract: For the task of image classification, in general, the test data is assumed to come from the same distribution as the training data. But this may not always hold in real-life scenarios. For example, in night-time surveillance, we may need to classify images captured using NIR cameras, but the available model has been trained on RGB images. Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain, by leveraging training samples from multiple source domains. In this thesis, we address two challenging scenarios for the DG task. Currently, the training process of majority of the state-of-the-art DG-methods is dependent on a large amount of labeled data. This restricts the application of the models in many real-world scenarios, where collecting and annotating a large dataset is an expensive and difficult task.
Thus, as the first contribution, we address the problem of Semi-supervised Domain Generalization (SSDG), where the training set contains only a few labeled data, in addition to a large number of unlabeled data from multiple domains. This is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models, since their performance degrades under such condition. To address this scenario, we propose a novel Selective Mixing and Voting Network (SMV-Net), which effectively extracts useful knowledge from the set of unlabeled training data, available to the model. Specifically, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data, which generalizes effectively across any unseen domain. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework.
The second contribution of this thesis is a novel approach for the task of Zero-Shot Domain Generalization (ZSDG). This is very challenging since the query data can belong to an unseen class as well as unseen domain. For this task, we address the challenge of class imbalance by learning class-specific classifier margins, which not only maintain the semantic relationship of the classes in the embedding space, but is also discriminative, and thus improves the classification performance on the test data. Extensive experiments on multiple datasets justify the effectiveness of the proposed approach.
ALL ARE WELCOME

Seminar by Prof. Sairaj Dhople @ 5.30pm
2022-04-20 23:00 - 2022-04-21 00:00
Title: Power-system Modeling & Control for the Era of Inverter-based Resources
Speaker: Prof. Sairaj Dhople, Electrical & Computer Engineering, University of Minnesota
Time: 20 April 2022, 5:30 pm
Venue: MMCR EE / Hybrid mode
https://teams.microsoft.com/l/meetup-join/19%3ameeting_YWJjZTQyZTAtYTFjNi00YTVkLWE0NjktYmJkZDMzNjI4ZDFm%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%220c8fe27d-52c2-4e77-a28f-a759bd113fae%22%7d
Abstract: Power networks all over the world are experiencing dramatic upheaval in compositional form and anticipated functionality. With retirement of fossil-fuel-driven synchronous generators, integration of renewable energy, and adoption of electrified transportation, there is a pronounced change in the energy-conversion interfaces that form the backbone of the grid. Particularly, energy processing in future grids will be dominantly handled by semiconductor-based power-electronics circuits termed inverter-based resources (IBRs). This talk will provide snapshots of how classical power-system modeling problems can (and will have to) be revised to accomodate these emerging technologies. In particular, we will present insights on synchronization of IBRs with a variety of control methods, provide a system-theoretic solution to normalizing dynamic models of diverse grid assets, and overview a time-domain network-reduction method for large-scale electrical networks. Each topic will be presented with an effort to acknowledge the rich history of personalities, methods, and venues relevant to power engineering over the 20th century. Finally, we will discuss how partnerships and collaboration across academia, industry (system operators, utilities, manufacturers), and national labs will be critical to facilitate large-scale integration with performance guarantees.
Bio: Sairaj Dhople received the B.S., M.S., and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign, Urbana, IL, USA, in 2007, 2009, and 2012, respectively. He is currently serving as Associate Professor with the Department of Electrical and Computer Engineering at the University of Minnesota. His research interests include modeling, analysis, and control of power electronics and power systems with a focus on renewable integration. He is the recipient of the National Science Foundation CAREER Award in 2015, the Outstanding Young Engineer Award from the IEEE Power and Energy Society in 2019, and the IEEE Power and Energy Society Prize Paper Award in 2021.

Seminar by Prof. Sanjib Kumar Panda
2022-04-20 20:30 - 21:30
meeting link: Teams meeting link
Title: Single-phase inverter control techniques for interfacing renewable energy sources with micro-grid – Parallel connected inverter topologies with active and reactive power flow control along with grid current shaping

Speaker: Professor Sanjib Kumar Panda
Time: 20 April 2022, 3:00 pm
Venue: MMCR EE
Abstract: Renewable energy sources (RESs) have been receiving significant attention recently worldwide as a sustainable alternative type of energy supply in the energy mix. Inverters are being used to convert the dc voltage into ac voltage before being injected into the grid or isolated loads. In this presentation, a novel current control technique is proposed to control both active and reactive power flow from a renewable energy source feeding a micro-grid system through a single-phase parallel connected inverter. The parallel-connected inverter ensures active and reactive power flow from the grid with low current THD even in the presence of non-linear load. A p-q theory-based approach is used to find the reference current of the parallel-connected converter to ensure desired operating conditions at the grid terminal. The proposed current controller is simple to implement and gives superior performance over the conventional current controllers such as rotating frame PI controller or stationary frame Proportional Resonant (PR) controller. The stability of the proposed controller is ensured by the direct Lyapunov method. A new technique based on the Spatial Repetitive Controller (SRC) is also proposed to improve the performance of the current controller by estimating the grid and other periodic disturbances. Detailed experimental results are presented to show the efficacy of the proposed current control scheme along with the proposed non-linear controller to control the active and reactive power flow in a single-phase micro-grid under different operating conditions
Speaker’s Bio: Sanjib Kumar Panda (Student 98’, Member ’92, SM 00’, F 21’) received a Bachelor of Engineering Degree with 1st Class Honours from Sardar Vallabhabhai Regional College of Engineering and Technology, Surat, India, in 1983. He was awarded the Gold Medal for securing the highest marks not only amongst the B. Engg. (Electrical) but also for securing the highest marks amongst all the B. Engg. (Civil, Mechanical and Electrical) students. He also earned a Masters of Technology Degree from the Institute of Technology, Banaras Hindu University, Varanasi, India in 1987. He was awarded the Gold Medal for securing the highest marks amongst all the M. Tech. (Electrical) students. Subsequently, he earned a PhD. Degree from the University of Cambridge, U.K., in 1991. He was awarded the Nehru Cambridge Fellowship and Overseas Research Studentship from the Cambridge Commonwealth Trust for Cambridge University for his PhD studies, 1987-1991.
He joined the Department of Electrical and Computer Engineering at the National University of Singapore as a Lecturer in 1992. He is currently serving as an Associate Professor and Director of the Power & Energy Research Area. He has served as Director (Education) at the Design Technology Institute, a joint-venture between NUS and TU/e, The Netherlands and funded by EDB, Singapore. He has served as the Group Head of the Drives, Power and Control Group from 2007-2009. He was appointed as Area Director, Power & Energy Research Group of the Department of Electrical & Computer Engineering at NUS on 1st January 2010 and serving in this position till date.
Dr. Panda has won the Annual Teaching Excellence Award at the National University of Singapore in 2004 and 2009. Besides these two University Level Awards, he has also been awarded several Teaching Awards at the Faculty of Engineering and at the Department of Electrical and Computer Engineering Department consistently since the year 2000.
Dr. Panda has carried out extensive research in various areas of control of electric drives and power electronic converters. He has co-authored 1 book, several book chapters, published more than 450 papers in international refereed journals and conferences and holds 6 patents to his credit. Dr. Panda has an h-index of 44 and has citations almost close to 10,000. He has received research funding to the tune of S$25mil over the past 15 years or so. Dr. Panda is the co-founder of three start-up companies: (1) ENBED Pte. Ltd., (2) REPMIX Pte. Ltd. and (3) SPCSCAN Pte. Ltd. along with his PhD students and research staff. His current research interests are in high-performance control of motor drives, control of distributed renewable energy sources and their integration with grid, condition monitoring, preventive and predictive maintenance.
Dr. Panda has been an active member of the IEEE. He has served in various capacities as Chapter Officer in the IEEE Singapore Section’s Joint Power Electronics and Industry Applications Society Chapter since 1994 till date. He has served in various capacities in the IEEE Singapore Section during the period 2000-2004 and served as the Section Chairman during the period 2004. He was the recipient of the IEEE 3rd Millennium Medal. He was the Organizing Chairman for the International IEEE Power Electronics and Drives Systems Conference (PEDS) in 2003. Dr. Panda served as the founding Chairman for the International Conference on Sustainable Energy Technologies (ICSET) in 2008. He was awarded the Best Volunteer Award by the IEEE Singapore Section in 2006. He was awarded the Best Volunteer Award by IEEE R-10 in 2014. Since 2012, Dr. Panda has been a volunteer serving in the Membership and Chapter Development for the IEEE PELS and presently serving as R-10 Coordinator. The IEEE PELS has seen the consistent membership growth rate of more than 15% for the R-10. Dr. Panda also proposed the Regional Distinguished Lecture (RDL) Program for the IEEE PELS and the initiated as a part of the R-10 RDL Speakers to be implemented in June 2020. Dr. Panda is the Organizing Chair for the IEEE ECCE-ASIA 24-27th May, 2021 to be held at Singapore. Dr. Panda has also served as a Member in the Program Committee in the IEEE Section Congress 2014 at Amsterdam, The Netherlands. He also presented in the IEEE Section Congress 2017 at Sydney, Australia. Dr. Panda has been serving as an Associate Editor of the IEEE Transactions in Power Electronics, the Journal of Emerging and Selected Topics in Power Electronics since 2012 till date. Dr. Panda has been elevated to the IEEE Fellowship w.e.f. form 1st Jan. 2021. Dr. Panda is the IEEE PELS DL for the period 1st Jan. 2022 – 31st December 2023.
All are welcome.

Seminar by Prof. Sanjib Kumar Panda @ 3pm
2022-04-20 20:30 - 21:30 | EE, MMCR
Title: Single-phase inverter control techniques for interfacing renewable energy sources with micro-grid – Parallel connected inverter topologies with active and reactive power flow control along with grid current shaping
Time: 20 April 2022, 3:00 pm
Venue: MMCR EE
Abstract: Renewable energy sources (RESs) have been receiving significant attention recently worldwide as a sustainable alternative type of energy supply in the energy mix. Inverters are being used to convert the dc voltage into ac voltage before being injected into the grid or isolated loads. In this presentation, a novel current control technique is proposed to control both active and reactive power flow from a renewable energy source feeding a micro-grid system through a single-phase parallel connected inverter. The parallel-connected inverter ensures active and reactive power flow from the grid with low current THD even in the presence of non-linear load. A p-q theory-based approach is used to find the reference current of the parallel-connected converter to ensure desired operating conditions at the grid terminal. The proposed current controller is simple to implement and gives superior performance over the conventional current controllers such as rotating frame PI controller or stationary frame Proportional Resonant (PR) controller. The stability of the proposed controller is ensured by the direct Lyapunov method. A new technique based on the Spatial Repetitive Controller (SRC) is also proposed to improve the performance of the current controller by estimating the grid and other periodic disturbances. Detailed experimental results are presented to show the efficacy of the proposed current control scheme along with the proposed non-linear controller to control the active and reactive power flow in a single-phase micro-grid under different operating conditions
Speaker’s Bio: Sanjib Kumar Panda (Student 98’, Member ’92, SM 00’, F 21’) received a Bachelor of Engineering Degree with 1st Class Honours from Sardar Vallabhabhai Regional College of Engineering and Technology, Surat, India, in 1983. He was awarded the Gold Medal for securing the highest marks not only amongst the B. Engg. (Electrical) but also for securing the highest marks amongst all the B. Engg. (Civil, Mechanical and Electrical) students. He also earned a Masters of Technology Degree from the Institute of Technology, Banaras Hindu University, Varanasi, India in 1987. He was awarded the Gold Medal for securing the highest marks amongst all the M. Tech. (Electrical) students. Subsequently, he earned a PhD. Degree from the University of Cambridge, U.K., in 1991. He was awarded the Nehru Cambridge Fellowship and Overseas Research Studentship from the Cambridge Commonwealth Trust for Cambridge University for his PhD studies, 1987-1991.
He joined the Department of Electrical and Computer Engineering at the National University of Singapore as a Lecturer in 1992. He is currently serving as an Associate Professor and Director of the Power & Energy Research Area. He has served as Director (Education) at the Design Technology Institute, a joint-venture between NUS and TU/e, The Netherlands and funded by EDB, Singapore. He has served as the Group Head of the Drives, Power and Control Group from 2007-2009. He was appointed as Area Director, Power & Energy Research Group of the Department of Electrical & Computer Engineering at NUS on 1st January 2010 and serving in this position till date.
Dr. Panda has won the Annual Teaching Excellence Award at the National University of Singapore in 2004 and 2009. Besides these two University Level Awards, he has also been awarded several Teaching Awards at the Faculty of Engineering and at the Department of Electrical and Computer Engineering Department consistently since the year 2000.
Dr. Panda has carried out extensive research in various areas of control of electric drives and power electronic converters. He has co-authored 1 book, several book chapters, published more than 450 papers in international refereed journals and conferences and holds 6 patents to his credit. Dr. Panda has an h-index of 44 and has citations almost close to 10,000. He has received research funding to the tune of S$25mil over the past 15 years or so. Dr. Panda is the co-founder of three start-up companies: (1) ENBED Pte. Ltd., (2) REPMIX Pte. Ltd. and (3) SPCSCAN Pte. Ltd. along with his PhD students and research staff. His current research interests are in high-performance control of motor drives, control of distributed renewable energy sources and their integration with grid, condition monitoring, preventive and predictive maintenance.
Dr. Panda has been an active member of the IEEE. He has served in various capacities as Chapter Officer in the IEEE Singapore Section’s Joint Power Electronics and Industry Applications Society Chapter since 1994 till date. He has served in various capacities in the IEEE Singapore Section during the period 2000-2004 and served as the Section Chairman during the period 2004. He was the recipient of the IEEE 3rd Millennium Medal. He was the Organizing Chairman for the International IEEE Power Electronics and Drives Systems Conference (PEDS) in 2003. Dr. Panda served as the founding Chairman for the International Conference on Sustainable Energy Technologies (ICSET) in 2008. He was awarded the Best Volunteer Award by the IEEE Singapore Section in 2006. He was awarded the Best Volunteer Award by IEEE R-10 in 2014. Since 2012, Dr. Panda has been a volunteer serving in the Membership and Chapter Development for the IEEE PELS and presently serving as R-10 Coordinator. The IEEE PELS has seen the consistent membership growth rate of more than 15% for the R-10. Dr. Panda also proposed the Regional Distinguished Lecture (RDL) Program for the IEEE PELS and the initiated as a part of the R-10 RDL Speakers to be implemented in June 2020. Dr. Panda is the Organizing Chair for the IEEE ECCE-ASIA 24-27th May, 2021 to be held at Singapore. Dr. Panda has also served as a Member in the Program Committee in the IEEE Section Congress 2014 at Amsterdam, The Netherlands. He also presented in the IEEE Section Congress 2017 at Sydney, Australia. Dr. Panda has been serving as an Associate Editor of the IEEE Transactions in Power Electronics, the Journal of Emerging and Selected Topics in Power Electronics since 2012 till date. Dr. Panda has been elevated to the IEEE Fellowship w.e.f. form 1st Jan. 2021. Dr. Panda is the IEEE PELS DL for the period 1st Jan. 2022 – 31st December 2023.
All are welcome.

MTech Research Thesis Defense of Mr. Jaswanth Reddy Katthi @ 11am
2022-04-18 16:30 - 17:30
Location : Electrical Engineering, MMCR (C241), Online via Teams (if network connection allows) https://tinyurl.com/2p8exxys
Title : Deep Learning Methods for Audio-EEG Analysis
Abstract : The perception of speech and audio is one of the defining features of humans. Much of the brain’s underlying processes, as we perceive acoustic signals, are unknown, and significant research efforts are needed to unravel them. The non-invasive recordings capturing the brain activations like electroencephalogram (EEG) and magnetoencephalogram (MEG) are commonly deployed to capture the brain responses to auditory stimuli. But these non-invasive techniques capture artifacts and noise that are not related to the stimuli, which distort the downstream stimulus-response analysis. The current state-of-art models used for normalization and pre-processing of EEG data utilize the linear canonical correlation analysis (CCA) or the temporal response function (TRF) based approach. However, these methods assume a simplistic linear relationship between the audio features and the EEG responses and therefore, may not alleviate the recording artifacts and interfering signals in EEG. This talk proposes novel methods using deep learning advances to improve the audio-EEG analysis.
We propose a deep learning framework for audio-EEG analysis in intra-subject and inter-subject settings. The deep learning based intra-subject analysis methods are trained with a Pearson correlation-based cost function between the stimuli and EEG responses. This model allows the transformation of the audio and EEG features in a common sub-space that is maximally correlated. The correlation-based cost function can be optimized with the learnable parameters of the model trained using standard gradient-descent based methods. This model is referred to as the deep CCA (DCCA) model. Several experiments, performed on the EEG data recorded on subjects listening to naturalistic speech and music stimuli, show that the deep methods obtain improved representations than the linear methods, thereby resulting in statistically significant improvements in correlation values.
Further, we propose a neural network model with shared encoders that align the EEG responses from multiple subjects listening to the same audio stimuli. This inter-subject model boosts the signals common across the subjects related to the stimuli and suppresses the subject-specific artifacts. This model is referred to as the deep multi-way canonical correlation analysis (DMCCA). The combination of inter-subject analysis using DMCCA and intra-subject analysis using DCCA is shown to provide the best stimulus-response in audio-EEG experiments.
Finally, the talk will discuss about an ambitious experiment, where we attempted to recreate acoustic signal directly from EEG responses. While the audio is not fully recoverable, the parts of the signal that can be recovered from the non-invasive EEG recordings throws light into the characteristics of audio captured in the EEG data.

Information for M.Tech aspirants in Electrical Engineering
2022-04-18 13:30 - 2022-04-22 22:30
Department of Electrical Engineering
Indian Institute of Science, Bangalore
Important information to the applicants called for interview
Dear Applicant,
This page is relevant to you only if you had applied for admission to M Tech EE programme and have been invited for an interview at the Department of Electrical Engineering, IISc in April 2022.
Based on your performance in GATE, you have been shortlisted and invited to appear for a technical interview offline. There will not be any test. The final selection will be based on the performances in GATE and interviews.
Please note the following information:
Interview will be held during 18 to 22 April 2022. So, kindly adhere to the date(s) and time allotted for your interview.
Participation in interview is mandatory to be eligible for selection process.
Kindly carry your interview call letter from Academic section, ID proof, photostat copies of certificates, Category Certificate, National Qualifying exam Score card /Certificate and mark statements from 10th Std onwards for verification
You are requested to follow the Covid-19 related guidelines issued by the government
With our very best wishes,
Chairman, Department of Electrical Engineering

For any queries mail to office.ee@iisc.ac.in. Or call at 22932361/3170

EECS Resesarch Students Symposium 2022
2022-04-08 15:00 - 2022-04-10 01:00
Click on the image to visit symposium website. Click here for the poster

Aspiring IIScian’s Meet 2022 on 18 March 2022
2022-03-18 23:30 - 2022-03-19 01:00
Click on the poster for details

Seminar by Visweshwar Chandrasekaran@ 3pm
2022-03-18 20:30 - 21:30
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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teams.microsoft.com

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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