EE

Thesis Defence of Mr. Paawan Kirankumar Dubal
2022-10-06 20:30 - 21:30 (Asia/Kolkata)
Thesis Title: Cyber Attack Resilient Breaker Failure Protection Scheme Using Wide Area measurements
Name of the Advisor: Prof. Sarasij Das
Degree Registered: M.Tech-Research
Date and Time: 6th October, 2022, 3:00 PM IST Online
Meeting Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZWZkYzViOWQtNWIzYi00MWQ1LWE1ZTMtNzcwOTUxM2JkNGM0%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%2240bc61c0-cc62-49a2-80af-a06745a651ac%22%7d
Abstract: Breaker Failure Protection (BFP) is a backup protection that comes into play when a circuit breaker fails to isolate the fault. If the circuit breaker fails to clear the fault, the BFP scheme commands other required breakers to isolate the fault. BFP schemes are usually incorporated in microprocessor-based digital relays. Commonly employed BFP schemes use overcurrent element (50BF) and Breaker Failure Initiation (BFI) signals as inputs. The BFI signal is issued to the BFP relays from other digital relays. Line current sensed via current transformer is fed to the BFP relay for the overcurrent element (50BF). When both 50BF and BFI are high, it waits for a specified time for the primary protection to operate. The 50BF element is usually set much lower than the rated load currents. So, it will be high during the normal loading conditions. A cyber-attack can be launched by issuing false BFI or blocking a legitimate BFI signal to the BFP relay. Operation of BFP scheme usually leads to disconnection of a larger amount of loads. As a result, mal-operation of BFP schemes can cause major disturbances in power systems. There is a need to make the BFP schemes resilient to cyber-attacks for reliable operation of power systems. Currently, there is a lack of literature on the cyber-attack resilient BFP schemes.
Hence, this thesis proposes a Wide-Area Measurement-Based Cyber-Resilient Breaker Failure Protection Scheme. The scope of the work is to develop an algorithm that will ascertain if the BFI received by the BFP relay is genuine. Blocking a legitimate BFI will cause the backup protection to operate and clear the fault. The proposition assumes that the BFP relay is not compromised in any manner. However, a fake BFI can be issued by other digital relays, which may cause unwanted BFP operations. In the proposed algorithm, when the BFI is received. The BFP relay will communicate the receipt of BFI to the Phasor Data Concentrator (PDC). The proposed algorithm will run at the PDC, which has access to the time-stamped measurements of the adjacent substations and the substation that triggered the algorithm. The decision of the proposed algorithm is communicated back to the BFP relay, which will allow the tripping if the BFI is genuine. Hence, we also propose modifications in the BF scheme in the BFP relay to incorporate the algorithm’s decision in issuing the final trip. The proposal running at PDC is a two-layer algorithm. The first layer randomly samples the bus voltages at the adjacent substations considering different groups of digital relays. The relay which has issued the BFI may be compromised. It makes relays of the same make and family more susceptible to a cyber-attack exploiting the same vulnerabilities. Hence we propose grouping of relays by their make and relay families. The first layer is meant to determine if there is a fault in the vicinity of the BFP relay that issued the trigger. The second layer provides discrimination between fault and cyber-attack by measuring the impedance observed at the two ends of the perceived-faulted line. Since the proposed solution is computationally lightweight, it adheres to the timing requirement of the BFP. The proposition requires healthy communication between the PMUs and the PDC. Nevertheless, the proposed method is fail-safe. It will resort to the conventional BFP scheme in case of loss of communication with the PDC. The proposed solution mitigates n number of cyber-attacks in a no-fault scenario. Additionally, the proposed solution can detect one cyber-attack if the attacker times the cyber-attack during a fault condition. PSCAD simulations were performed to validate the proposition on IEEE 118 bus system. Furthermore, the hardware was developed emulating the PMU-PDC communication as per IEEE C37.118-2 standard, and the execution time of the proposal was verified to ensure adherence to the timing requirement of the BFP.

ALL ARE CORDIALLY INVITED

Thesis Defence of Mr. Ahmad Arfeen @ 11am
2022-09-26 16:30 - 17:30 | EE, MMCR
Title:  Data Efficient Domain Generalization
Faculty Advisor: Prof. Soma Biswas
Examiner: Prof. Venkatesh Babu R. (CDS, IISc)
Date: Monday, September 26, 2022
Time: 11:00 AM
Venue:  MMCR (EE dept)
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, with focus on data efficiency. 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.
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 maintains 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.
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ALL ARE WELCOME

Online Lecture by Dr. Mohammad Hedayati @5pm
2022-09-21 22:30 - 23:30
Title: High Voltage DC Circuit Breakers (HVDC CB)
Speaker: Dr. Mohammad Hedayati, Dyson Technology, UK.
Time and Venue: 5 pm, MS Teams/Online mode (Link provided below)
Abstract: The main challenge of the DC meshed grid is the lack of suitable fault protection devices. The AC circuit breaker cannot be used in the DC system as in DC there is no zero crossing to extinguish the arc. Hence the DC circuit breakers need to create a virtual current zero crossing. There are different types of DC CB, and three of them (solid state, mechanical, hybrid circuit breaker) are explained in this talk. The advantages and disadvantages of each technology are then pointed out.
Speaker Bio: Dr. Mohammad Hedayati did his Master and PhD in department of Electrical Engineering, IISc between 2008 to 2016. Then he moved to University of Aberdeen as a postdoc, where he was working on DC CB for DC meshed grids in 2016. In 2018 he joined University of Bristol working on GaN Devices Reliability and health monitoring and switching characteristics. In 2021 he joined Dyson technology, during this time he was designing high speed (150kRPM) motor drive inverters for vacuum cleaner and personal care applications. In October 2022 he will be joining Jaguar Land Rover (owned by Tata group) to design EV motor drive inverters.
All are welcome.
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Thesis Colloquium of Anurenjan P. R.
2022-09-09 20:00 - 21:00 | EE, MMCR
Venue, Date and Time: MMCR, EE, IISc. 9-9-2022, 2.30-3.30pm.

Teams link – https://tinyurl.com/3bpmjxkx .
Title: Dereverberation of speech using frequency domain linear prediction
Faculty Advisor: Dr. Sriram Ganapathy
Abstract : The speech-based technologies are radically changing the way we interact with systems and how we access information. In many of these applications, the users prefer to interact with the system through a far-field microphone without the nuance of a handheld or body-worn device. Examples of such applications are automated meeting analysis, speech-based dictation systems, hands-free interfaces for controlling consumer-products, IoT, virtual assistants in mobile phones and smart speakers. The major challenge in capturing speech from the far-field is the degradation of the signal quality due to reverberation. Reverberation refers to the delayed and weighted summation of the direct component of the speech signal with the reflected versions. This talk is focused on developing methods for speech dereverberation, i.e., restoring the functional quality of reverberated speech, using the signal analysis technique of frequency domain linear prediction (FDLP).
The FDLP is the frequency domain dual of the conventional Time Domain Linear Prediction (TDLP). Just as the TDLP estimates the spectrum of a signal, the FDLP estimates the temporal envelopes of the signal using an autoregressive model. We apply the FDLP approach to the sub-bands of speech signal that are distributed in the mel scale.
This talk will describe two broad directions for addressing issues in the far-field speech using the FDLP approach. In the first part of the talk, we explore a front-end design for automatic speech recognition (ASR) applications that suppresses the reverberation artifacts in the FDLP envelope. In the second part of the thesis, we develop a speech enhancement model using the envelope and carrier decomposition given by the FDLP technique.
In the design of the ASR front end, I will discuss a novel approach for 3-D acoustic modeling framework, where the spatio-spectral features from all the sub-band channels are extracted. The features that are input to the 3-D CNN are extracted by modeling the signal peaks in the spatio-spectral domain using a multi-variate autoregressive modeling approach. In the subsequent part of this section, I will describe a neural model for speech dereverberation using the long-term sub-band envelopes of speech. The neural dereverberation model estimates the envelope gain, which when applied to reverberant signals, allows the suppression of the late reflection components. The de-reverberated envelopes are used for feature extraction in speech recognition. The key novelty in this model is the joint learning of the reverberation and the ASR system. In these ASR experiments using the proposed framework, we illustrate significant performance gains over previously proposed front ends.
The second part of the thesis deals with the FDLP based speech dereverberation for enhancement applications, where the goal is to restore the audible quality of the speech signal. For this task, we decompose the sub-band speech signal into the constituent envelope and carrier part. A dereverberation neural model is designed that attempts to enhance the envelope and carrier signals jointly. Further, joint learning of the speech enhancement model with the end-to-end ASR model is proposed with a single neural framework. The proposed model therefore can generate improved audio quality and provide robust representations for far-field ASR. Finally, I will illustrate the subjective quality improvement of the audio signal as well as the improvement in ASR performance obtained by the proposed envelope-carrier model.
Acknowledgement
This work was partly supported by project grants from Samsung Research India, Bangalore and the College of Engineering, Trivandrum, Kerala.
Bio: Mr. Anurenjan is a PhD student at the LEAP lab, Electrical Engineering, IISc. He is also currently working as Assistant Professor in College of Engineering, Trivandrum. Mr. Anurenjan completed his Bachelors in Technology from Government Engineering College, Barton Hill, Trivandrum, Kerala in 2006 and his Masters in Technology from College of Engineering, Trivandrum, Kerala in 2008. He joined the LEAP lab as a PhD candidate under AICTE-QIP program in the year 2017. He hails from Trivandrum district of Kerala. He is interested in signal processing, machine learning and speech processing. Mr. Anurenjan is a student member of IEEE SPS and the ISCA. During his free hours, Mr. Anurenjan likes to play badminton and swimming.
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All are invited. Coffee/Tea will be served before the talk.

Thesis Colloquium of Manish Tathode @3.30pm
2022-08-29 21:00 - 22:30 | EE, MMCR
Title: Fast and Compact Voltage Equalizer for Satellite Applications.Advisor: Prof. Vinod John.Date and Time: Monday, 29th August 2022, 3.30 pm.Location: MMCR, EE Department.Meeting Link: click here for Meeting Link
Abstract: Lithium-ion batteries have now become an critical constituent of Electrical Power System of solar-powered satellites due to their high energy density, wider operating temperature range and better radiation tolerance. For compact realization and better space utilization, the series-parallel connected Li-ion batteries are operated with currents close to the design limit of the cells, speeding up the increase in the inherent initial imbalance in the individual cell voltages in a series connected stack, demanding fast equalization. Active multicell-to-multicell equalization achieve fast equalization by efficient charge transfer among multiple cells in the series connected stack. PS-MAHB equalizer is a multicell-to-multicell equalizer, with its open-loop control maintains high equalization current throughout the equalization. Its soft-switched operation and modularization abilities make it an attractive choice for space applications. However, it lacks the necessary protective features and required redundancy essential for its use in space applications. Hence, a Modified PS-MAHB (MPS-MAHB) equalizer is developed by incorporating necessary protection features and redundancy in the PS-MAHB equalizer. The Failure Mode Impact Analysis of the MPS-MAHB equalizer reveals that during the most likely switch short circuit failure mode, the faulty part of the equalizer is disconnected by the protective device and the redundancy does not let the cell get out of the equalization.The existing static phase shift-based control of the equalizer causes direct dependency of the equalization currents on the cell voltages and limits the equalization current levels to lower than the design equalization current value when the cell voltages are lower. Thus, the control works with reduced rate of equalization, and causes the under-utilization of the equalizer hardware for significant duration of time in the charge-discharge cycle. A dynamic phase shift-based control is proposed to maximize the equalization current through the cells irrespective of the cell voltages which further increases the rate of equalization and improves the equalizer hardware utilization. In the simulation, a significant improvement in the equalization rate compared to the static phase shift control is verified with the proposed dynamic phase shift-based control.The compact hardware realization of the equalizer hardware and the voltage sensors addresses the space-volume constraints in satellite applications. The equalizer hardware is realized as 4-cell equalizer modules, and the compactness of the equalizer hardware is achieved by pushing the switching frequency to 1MHz reducing the values and sizes of the passive components. The challenges faced during the PCB design of the 4-cell equalizer module are addressed by the design. A non-isolated high-precision op-amp based voltage sensing scheme is developed to target the equalization band close to 10mV. The concept of easy-to-design motherboard-based interface is introduced, which does not require any changes in the design of 4-cell equalizer module and the voltage sensor board, irrespective of the cell connector geometry.The experimental results verify the operation of the equalizer showing the convergence of cell voltages from the initial imbalance of 300mV to the band of 10mV. The impact of the non-ideal dynamic response of the Li-ion cell voltage on the voltage-sensing-based control algorithm is discussed along with the necessary modifications incorporated in the control.We request your presence to the colloquium.All are welcome.

Thesis Colloquium of Ms. Mani Madhoolika Bulusu
2022-08-24 21:30 - 22:30 | EE, MMCR
Degree Registered: M.Tech.(Research)
Advisor: Prof. Chandra Sekhar Seelamantula
Title: Interpolation of Digital Elevation Models Using Generative Adversarial Networks
Venue: Multimedia Classroom (MMCR), Department of Electrical Engineering, IISc
Date & Time: August 24, 2022; 4 PM onward (coffee will be served during the talk)
Abstract: A digital elevation model (DEM) is a three-dimensional representation of elevation data of a terrain such as a lunar terrain acquired using a Chandrayaan rover or a terrestrial terrain acquired by a reconnaissance aircraft. Terrestrial DEMs are used in hydrological modeling, geomorphology, and glaciology. Lunar DEMs can be used to locate natural resources and to identify prospective landing sites for exploratory missions. Hence, high-quality, reliable DEMs are of great significance. DEMs are generally captured using LiDAR (Light Detection and Ranging), stereophotogrammetry, and time-of-flight cameras. However, a reliable DEM cannot be constructed without adequate landmark points/features. This is the case with smooth terrains, occlusions, and multiple voids. The measurements are nonuniform in general. Hence, there is a need for interpolation and void-filling techniques to estimate the elevation with a high accuracy.
Inverse distance weighting (IDW), De Launay triangulation, and Kriging are some of the popular benchmark algorithms for interpolating scattered and nonuniformly spaced data. Manual parameter tuning, inability to recover high-frequency information, and high computational complexity are some of the issues that these traditional interpolation techniques suffer from. Deep Learning (DL) has proven to be effective in providing excellent results in the field of image processing and computer vision, specifically in the tasks of super-resolution, image in-painting, extrapolation, and segmentation. With the massive success of DL in several image processing and computer vision applications, its applicability has been explored for solving the DEM interpolation problem as well. However, convolutions are not readily defined if the measurements are nonuniform. Hence, the recent DL based research on DEM interpolation has only focused only on regularly spaced data.
We address the realistic problem of DEM interpolation from irregularly spaced measurements, with the density of measurements varying spatially. This is a new and unexplored direction in the deep learning setting. We propose a new and robust DL architecture based on Generative Adversarial Networks (GANs) to perform interpolation and result in a uniform DEM with a user-specified resolution. The generator comprises three modules: Learnable Distance Weighting (LDW), DEM in-painting, and Continuous Convolution (CC). We designed the novel LDW module as a learnable counterpart to the popular IDW algorithm that operates on the distances between the measurements and grid locations. This reduces the problem to that of inpainting post the LDW transformation. The proposed method is evaluated on synthetically generated data and on standard publicly available NASA (LOLA LRO) datasets using the mean relative error and PSNR as performance metrics. Extensive experiments justify the effectiveness and accuracy of the proposed approach in comparison with the benchmark techniques. We conclude the presentation by discussing possible future directions for DL based DEM interpolation.
All are invited.

Lecture by Prof. Mainak Sengupta @4.00pm
2022-08-19 21:30 - 22:30 | EE, MMCR
Title: Induction heating : basic principles and power converters
Speaker: Prof. Mainak Sengupta, Professor, Department of Electrical Engineering, IIEST Shibpur
Venue: Multimedia Classroom (Hybrid Mode), Department of Electrical Engineering, Indian Institute of Science
Date and Time: Friday 19/08, 4:00 pm
Meeting Link: Click here to join the meeting
Abstract: Induction heating is a non-contact and “clean” electrical heating process. Induction Heating units and furnaces are used for melting (500Hz or so), surface heat treatment (10 s of kHz), induction welding, foil heating etc. and even cooking. The concepts involved are elementary yet interesting. The phenomenon of resonance may be used in an interesting manner (either parallel or series). The converters used might have a current source or a voltage source configuration. Some analytical and experimental studies shall be discussed.
Speaker Biography: Prof. Mainak Sengupta did his B.E (EE) from Jadavpur University in Calcutta in 1992. He did his MTech (Power Electronics and Machine Drives, Electrical Engg.) In January 1994 under Prof. K. Venkataratnam. In January 1994 he joined Ph D studies under Prof. K. Venkataratnam and Prof. Tapas K. Bhattacharya. He obtained his PhD degree in September 1999. In between, after completing the experimental work, he joined as a Lecturer in EE, in the then Bengal Engineering College (Deemed University) and is since teaching there. He has been a Professor since June 2010.

3rd talk of the "TCE Lecture Series on Power Systems"
2022-08-17 21:30 - 22:30 | EE, MMCR
Speaker: Prof Ramakrishna Gokaraju of University of Saskatchewan, Canada
Title: High-Speed Digital Relaying & Transient Stability Prediction/Controlled Islanding to Prevent Large-Scale Blackouts (Poster)
Abstract: Keeping the lights “on”, an axiom in power systems engineering has taken on a new level of complexity with increasing pressure on the existing
network to deliver more power over existing infrastructure. The first part of the presentation will discuss High-Speed Digital Relaying Scheme for EHV/UHV transmission systems (345 kV and above) with half-cycle operating times. The second part of the presentation will discuss a scheme
for real-time transient stability prediction in larger grids, and a Remedial Action Scheme (RAS) scheme applying intentional islanding to prevent large-scale blackouts. The proposed controlled islanding consists of two parts as “when” and “where” to island. The proposed methodology simplifies the communication between central “when” unit and each generator protection relay by using status flags communicated with IEC 61850 RGOOSE protocol. The proposed “when” methodology is combined with the “where” method based on graph theory to test the overall controlled islanding scheme.

Bio: Ramakrishna Gokaraju received his Bachelor of Engineering degree (with Distinction) in Electrical and Electronics Engineering from the National Institute of Technology, Trichy, India in April 1992. He received the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Calgary, Calgary, AB, Canada, in 1996 and 2000, respectively. From 1992-‘94 he worked with the Larsen & Toubro, Chennai, India as a graduate engineer and then later with the IIT, Kanpur & NIT, Rourkela as a Project Associate/Research Engineer. From 1999-2002, he was a Research Scientist with the Alberta Research Council and a Staff Software Engineer with IBM Toronto Lab. He joined the Department of Electrical & Computer Engineering at the University of Saskatchewan in 2003 and is currently a professor in the department. His current research works are in high speed digital relaying, controlled/active islanding in electric grids, wild fire mitigation due to electrical faults, and computer modelling of the new emerging nuclear-based Small Modular Reactors (SMRs) for electricity and other energy applications.

Thesis Defence of Shamibrota Kishore Roy @3.30pm
2022-08-16 21:00 - 22:30 | EE, MMCR
Title: Characterization and Modelling of Switching Dynamics of SiC MOSFETs
Name of the Student: Shamibrota Kishore Roy
Name of the Advisor: Dr Kaushik Basu
Degree Registered: PhD
Date and Time: 16 August 2022, 03:30 PM (IST)
Place: MMCR EE
Meeting Link: Click here to join the meeting
Abstract: Silicon Carbide MOSFETs (SiC MOSFETs) fall into the class of wide band gap (WBG) power devices. These devices are commercially available in the voltage range of 600-3300V and are superior to the state-of-the-art Si insulated gate bipolar junction transistors (IGBTs) due to their better electrical and thermal performances.

In power electronic converters, semiconductor devices operate as switches. They can be turned on or off using a control signal. Unlike ideal switches, practical devices require a finite amount of time to transit between on and off states. This is termed switching transient. Non-zero finite product of voltage and current during switching transient results in switching loss. Characterization and modelling of switching dynamics help gain insight into the switching process and estimate switching loss. It is useful over experimental measurement techniques like double pulse test (DPT) or calorimetric measurements in the early stages of power converter design. Estimated loss through the switching transient model can be used to determine switching frequency and selection of power devices. Also, switching dynamics is strongly impacted by the device and circuit parasitic. Insight into the switching process helps in the proper design of gate driver and power circuit layout. Superior material properties of SiC MOSFET lead to a smaller die size compared to the state-of-the-art Si-based power devices. It results in faster switching transients and lower switching loss. However, it excites device and circuit parasitic that may lead to prolonged oscillation, high device stress, spurious turn-on and EMI-related issues Etc. So, the benefit of using SiC MOSFET as power devices come with numerous design challenges resulting in slow commercial adaptation. It is predicted that the overall market share of WBG devices (SiC and GaN together) will be roughly 10% of the total market for power semiconductors by 2025.

To overcome the design challenges and fully utilize the benefits of fast switching SiC MOSFETs, a better understanding of dynamics is essential. However, the switching dynamics of SiC MOSFET is different compared to its Si counterpart. This is due to the highly non-linear device characteristics. Also, the fast-switching transient of SiC MOSFET excites the circuit parasitic and makes the switching dynamics highly involved. This work focuses on the characterization and modelling of switching transient of SiC MOSFET. Simulation and analytical modelling approaches are used to model the switching dynamics and estimate switching loss. The behavioral modelling approach is a widely used simulation-based approach (i.e., Spice simulation) and it can capture the switching transient with sufficient accuracy. This approach uses lumped parameter model (circuit model) of the device and external circuit and can be simulated in a circuit simulator like MATLAB/Simulink. This implies the numerical solution of a set of coupled non-linear differential equations. On the other hand, the analytical modelling approach is based on the simplified approximate solution of a set of coupled non-linear differential equations obtained from the behavioral model. In order to obtain the approximate solution, the entire switching process is divided into different modes with clearly defined transition conditions. Different approximations are used in each mode to arrive at analytical closed-form solutions or reduced order coupled non-linear differential equations. This model is computationally efficient and can be implemented easily in freely available programming platforms such as C or Python. Also, the parameters required for analytical models can be obtained from the device datasheet. This modelling approach is beneficial for the converter design when switching loss and junction temperature need to be evaluated over several operating points for many available devices from different manufacturers.

In the first part of the work, a behavioral model is developed to capture the switching transient of SiC MOSFET. It considers the detailed channel current model and captures the gradual transition effect from ohmic to saturation region. A piecewise non-linear model of gate-drain capacitance is used and a comprehensive non-linear model of drain-source and diode capacitances is considered. Also, the effect of the circuit parasitic are taken into account. A double pulse test (DPT) based experimental measurement is used for validation.

In the next part, analytical models to capture the switching dynamics (hard turn on and off) of SiC MOSFET are proposed. These models are based on the behavioral model developed in the first part of the work. In the existing literature, simplified modelling of channel current and device capacitances was used, resulting in underestimation of switching transition time and loss. On the contrary, the proposed approach considers the detailed non-linear model of channel current and device capacitances along with circuit parasitic. It accurately estimates transition time, switching loss, (dv=dt), (di=dt), and transient over-voltage. Double pulse test (DPT) based experimental measurement and behavioral simulations are used for validation.

In soft switched converters (i.e., DAB), hard turn-on is avoided by the converter operation, and the switching loss is solely dictated by the turn-off loss. The addition of external capacitance across the device prolongs the voltage rise period and reduces overlap between voltage and current during turn-off transient. This is termed zero voltage switching (ZVS). However, the selection of external capacitance is not straightforward. A large external capacitance reduces switching loss, (dv/dt), (di/dt), and transient over-voltage but may also result in higher dead-time loss and reduced switching frequency. Also, this may lead to partial soft switching for light load conditions if the dead-time is not sufficient. In this work, an analytical model to capture capacitor-assisted turn-off switching transient is also presented where the detailed non-linear modelling of the SiC MOSFET is used. This leads to a better estimation of switching transition time, actual loss, (dv/dt), (di/dt), and transient overvoltage. Also, a step-by-step design procedure for the optimal external snubber capacitor was proposed. It ensures the soft-switching condition is satisfied, and the maximum (dv/dt) rate is within a predefined limit for a specified DC bus voltage and range of load currents. This procedure also helps in the selection of proper dead-time to avoid partial soft-switching conditions. Double pulse test (DPT) based experimental measurement and behavioral simulations are used to validate the analytical model.
The fast switching transient of SiC MOSFET is significantly impacted by circuit parasitic. Circuit parasitic inductances are dependent on both device package (device lead, wire bond etc.) and circuit layout (PCB layout), whereas circuit parasitic capacitances are contributed solely by the circuit layout. Proposed switching transient models require circuit parasitic as input, and the values are not usually available in the device datasheet. Measurement is the only way to accurately estimate some device package-dependent circuit parasitic when the internal package geometry is unknown. In this context, a set of simple measurement techniques are proposed to determine important circuit parasitic necessary for switching dynamics study. The accuracy of the proposed technique is verified through behavioral simulation, and experimental results of the hard turn off and capacitor assisted soft turn off dynamics of SiC MOSFET over a range of operating conditions for two 1.2-kV discrete SiC MOSFET of different current ratings and two different PCB layouts. Measured circuit parasitic when used in switching transient model, correctly predicted both hard turn-off and capacitor assisted soft turn off switching dynamics over a wide range of operating conditions.
An interactive software based on the proposed analytical model is also developed in Python environment. The developed software takes device parameters and circuit parasitic as input and estimates transition time, switching loss, (dv/dt), (di/dt) and transient over-voltage as a function of load current.
ALL ARE CORDIALLY INVITED

Thesis Defence of Mr. Rupam Pal @ 11am
2022-08-03 16:30 - 17:30 | EE, MMCR
Degree Registered; PhD
Guide: Prof. Udaya Kumar
Thesis Title: Influence of soil’s electrical parameters on lightning stroke-current evolution and fields in the close range
Date: 3rd August 2022; Time: 11.00 AM
Mode: It will be in hybrid mode. To join online: Click here to join the meeting
Venue: MMCR Room, department of electrical engineering.
Abstract: The lightning return stroke forms one of the severest natural sources of electromagnetic interference for systems, both in the air and soil. Several physical fields govern this complex physical phenomenon, and most of the engineering applications resort to much simplifications. Several pertinent aspects are somewhat unclear, and it is not practical to conduct the field measurements to answer them. One such important aspect, which is of practical relevance, is the influence of soil’s electrical properties on the stroke current evolution and the fields in the close range. It is investigated in the present work.
Among different models for the lightning return stroke, only the ‘Self-consistent return stroke’ model is found to be suitable for the intended work. This model employs a macroscopic electrical representation of the underlying physical phenomenon to emulate the stroke current evolution. However, this model has considered only a perfectly conducting earth and relied on the time-domain thin-wire formulation to solve for the associated dynamic electromagnetic fields. On the other hand, a more realistic representation of the soil, including its dispersive nature and non-linearity, is required for the present work. This necessitated suitable adoption of the ‘Finite difference time domain’ (FDTD) method for modeling the channel and its corona sheath, soil-ionization, and soil-dispersion.
The developed FDTD formulation is used to investigate and ascertain the role of soil’s electrical properties on the stroke current evolution and the field in the soil. For the first time, it is shown that the soil’s electrical conductivity has some noticeable influence on the stroke current magnitude, and the ionization phenomenon in soil tends to reduce this influence. The dispersive nature of the soil’s conductivity, and permittivity to a lesser extent, significantly reduces the field in the soil. The current concentration near the surface, which is expected for the skin-effect phenomenon, is altered at later periods by the field produced by the channel current. Also, the normal field in the soil changes its polarity. The vertical stratification of the soil, which is expected near the water body-soil interface, influences the field in the soil quite significantly. A strike to a model mountain leads to an entirely different field structure beyond its base. Similarly, a strike to a tall tower produces a field in the soil, which is bipolar near the tower base. These are quite novel findings, and many of them are somewhat unexpected.
In summary, significant contributions have been made towards the FDTD formulations for modeling lightning phenomena and finding the role of soil’s electrical parameters on lightning stroke current evolution and the resulting field.

Thesis Colloquium of Mr. Shubham Rawat @ 2.00pm
2022-07-29 19:30 - 20:30 | EE, MMCR
Title: A Novel Passive Regenerative Snubber for the Phase-Shifted Full-Bridge Converter: Analysis, Design and Experimental Verification
Name of the Advisor: Dr Kaushik Basu
Degree Registered: MTech (Research)
Date and Time: 29 July 2022, 02:00 PM
Place: MMCR EE
Meeting Link: Click here to join the meeting
Abstract: The development of Wide Bandgap (WBG) devices has enabled power electronic converters to operate at much higher frequencies, voltages and power. Working at a higher switching frequency minimises the size of magnetics but results in significant switching losses and electromagnetic interference (EMI) noise. Thus, it necessitates the use of soft-switching techniques to reduce these losses. Phase-Shifted Full-Bridge (PSFB) Converter is the most widely used soft-switching topology in the high-voltage and high-power, unidirectional, DC-DC conversion. The phase shift PWM control utilises the converter parasitic to achieve zero voltage switching (ZVS) turn ON. The gating technique allows the magnetic energy stored in the leakage inductance of the isolation transformer to charge and discharge the output capacitances of the inverter leg.
However, the converter suffers from severe voltage overshoots across the rectifier bridge during the zero to the active state transition. The resonant circuit formed between the transformer leakage inductance and the parasitic diode capacitance of the rectifier is responsible for the high-voltage ringing. Many passive and active snubbers are presented in the literature to mitigate the high voltage overshoots across the diode bridge. While passive snubbers are relatively simple to implement than active snubbers, they are lossy. On the other hand, the active snubbers require additional gate driver circuitry and complex control.
The first part of the thesis proposes a novel passive regenerative snubber to overcome the mentioned drawbacks of the existing snubbers. The proposed snubber is ideally lossless with no control complexity. The work covers a detailed analysis of the PSFB operation with the proposed snubber while obtaining closed-form expressions for the converter state variables at the end of each topological stage. The study considers all the major converter parasitic, such as transformer leakage and magnetising inductances, and parasitic capacitances of the converter. Given the new snubber, the thesis also lays out a step-by-step PSFB design procedure utilising the analysis carried out in the first part of the work. The design aimed to develop a 100 kHz PSFB for an input voltage of 360-400 V and the output power range of 0.5-1.5 kW at a fixed output voltage of 48 V. The design approach focuses on the two design objectives. All inverter switches must achieve ZVS turn ON, and the converter gain must achieve the necessary gain to maintain desired constant output voltage for all possible operating conditions.
A hardware prototype is built and tested per the given specification. The experimental results validate the effectiveness of the snubber in reducing the voltage overshoot. Further, the analysis and design accuracy is verified using the measured state variables. Finally, the work presents the overall efficiency and the loss distribution among the converter components.

Lecture by Dr. Prasanth Venugopal @ 5.00pm
2022-07-27 22:30 - 23:30 | EE, MMCR
Title: SaRe ♫♪♪ Battery Electronics of the Future: Safe and Reliable
Speaker: Dr. Prasanth Venugopal, Assistant Professor, University of Twente
Date and Time: 27 July 2022, 5:00 pm
Venue: Multimedia Classroom (Hybrid Mode), Department of Electrical Engineering, Indian Institute of Science
Meeting Link: Click here to join the meeting

Abstract: Li-ion battery (LiB) has disrupted the world of energy storage thereby creating a pathway for a sustainable future relying on intermittent renewable energy sources. It is expected that the market for LiB will exceed 400 GWh per year from 2025 onwards. The talk will summarize an overview of the R&D program at the University of Twente in advanced battery power electronics and battery evaluation. This includes the development of a new generation of advanced BMS concepts based on advanced power electronics. In addition, an overview will be presented of performance and aging including implications on safety and second-life batteries.
Speaker Biography: Prasanth Venugopal received the B.Tech. degree in electrical and electronics engineering from Amrita Vishwa Vidyapeetham University, Coimbatore, India, in 2010, and the M.Sc. degree in electrical engineering and the Ph.D. degree from the Delft University of Technology, Delft, The Netherlands, in 2012, and 2018, respectively. His MSc. and Ph.D. theses were related to wireless charging of EVs. He worked in the semiconductor and passive components industry from 2016-2020 in Munich, Germany. From November 2016 to December 2018, he was with Qualcomm Halo, Munich, Germany, as a Senior Electrical Engineer in the field of power electronic systems and applications related to wireless charging of Electric Vehicles. He then went on to work at TDK Europe as a Technical Specialist/Manager for xEV applications until May 2020. From June 2020, he is appointed as a Tenured Assistant Professor in the Power Electronics and EMC group at U Twente.
He has so far published 29 papers with 6 journals and has filed 6 patents which are in various stages of acceptance. His current areas of interest are Electric Vehicles, Wireless Charging, Power Electronics Integration – Semiconductors, and Passive Component Technologies.

Lecture by Dr. Mathew Magimai Doss 3.00pm
2022-07-27 20:30 - 21:30 | 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.
