EE

EECS Symposium
2024-04-04 09:00 - 2024-04-05 17:00 (Asia/Kolkata) | IISc
The EECS division cordially invites you to the EECS Symposium on 4th & 5th April 2024.
For more details please see: https://eecs.iisc.ac.in/EECS2024
Registration link for the event: EECS Symposium 2024 Registration Form.

PhD Thesis defence on Imaging Inverse Problems
2024-03-26 09:00 - 11:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Title : Improved Derivative-based Regularizations for Imaging Inverse problems

Student : Manu Ghulyani

Advisor : Prof. Muthuvel Arigovindan

Thesis Examiner: Prof. Ajit Rajwade
Department of Computer Science and Engineering,
IIT Bombay.

Date and Time: 26.03.2024 (Tuesday), :.9:00 am

Venue : MMCR, Department of Electrical Engineering

Also online in Teams

Abstract:

Images undergo degradation during the capturing process due to physical limitations inherent to the capturing devices. Addressing this degradation and recovering high-quality images constitute the image recovery problem, a crucial concern with diverse applications across various fields such as biology, astronomy, and medicine. The enhancement of captured image resolution significantly influences these disciplines. Examples of this problem include tasks like reconstructing computed tomography images, magnetic resonance imaging, image deconvolution, and microscopic image reconstruction.

Image recovery is frequently approached using regularization techniques, with derivative-based regularizations being popular due to their ability to exploit image smoothness, yielding interpretable results devoid of introduced artifacts. Total Variation regularization (TV), proposed by Rudin, Osher, and Fatemi, is a seminal approach for image recovery. TV involves the norm of the image’s gradient, aggregated over all pixel locations. As TV encourages minimal values in the derivative norm, it leads to piece-wise constant solutions, resulting in what is known as the “staircase effect.” To mitigate this effect, the Hessian Schatten norm regularization (HSN) employs second-order derivatives, represented by the pth norm of eigenvalues in the image hessian vector, summed across all pixels. HSN demonstrates superior structure-preserving properties compared to TV. However, HSN solutions tend to be overly smoothed. To address this, we introduce a non-convex shrinkage penalty applied to the Hessian’s eigenvalues, deviating from the convex lp norm. While the analytical form of this penalty was unknown, we derived the algorithm using proximal operations. We established that the proposed regularization adhered to restricted proximal regularity, ensuring the algorithm’s convergence. The images recovered by this regularization were sharper than the convex counterparts.

In the subsequent work, we extend the concept of the Hessian-Schatten norm. By encompassing Schatten norms of the Hessian and introducing a smoothness constraint, we broaden the scope of Hessian Schatten norm. The resulting regularization can be derived as a Lagrange dual of the Hessian Schatten norm, akin to the total generalized variation. Furthermore, we present an efficient variable splitting scheme for solving image restoration challenges.

Total Generalized Variation (TGV) represents an important generalization of Total Variation. TGV involves multiple orders of derivatives, with higher-order TGV leading to improved recovered image quality. This enhancement has been validated through numerical experiments in image denoising. Consequently, a demand arises for an algorithm capable of solving TGV for any order. While various methods address TGV regularization, many are confined to second-order TGV, and only a few explore orders greater than three for image recovery with TGV regularization. To our knowledge, no algorithm resolves image recovery challenges employing TGV regularization for orders exceeding three under a general forward model. This challenge arises from the intricate nature of TGV representation. We surmount this obstacle by presenting two simple matrix based representations of TGV: the direct and compact forms. We prove the equivalence of both forms with the original TGV definition. Leveraging the compact representation, we propose a generalized ADMM-based algorithm to solve TGV regularization for any order.

ALL ARE WECOME.

PhD Oral Exam
2024-03-25 08:00 - 11:00
Title: Reduced Electrolytic Capacitor-Based Single-Phase Converters: Topologies, Control, and StabilityName of the Student: Anwesha MukhopadhyayName of the Advisor: Prof. Vinod JohnDegree Registered: Ph.D.Date and Time: 25th March, 2024, 08:00 AMTeams Meeting Link:Abstract: Single-phase power converters find wide applications as inverters. Applications ranging from a few hundred Watts for household solar micro-inverters, to multi-Megawatt levels for electric traction power train, single-phase converters are adopted worldwide.In single-phase power conversion, there is always a mismatch between the instantaneous input and output power, producing a second-harmonic ripple in the dc link current. Electrolytic capacitors are conventionally deployed for filtering the second-harmonic ripple due to their low cost and excellent energy density. However, their frequent premature failures often compromise the lifespan of the converters. In recent technologies demanding high power density, active filters have minimised the electrolytic capacitors in the circuit. However, the cost, efficiency, and power density trade-offs need scrutiny before adopting an active filter topology.Among the reported active filters (AF) for second-harmonic ripple mitigation, series capacitor stacked buffer (SSB) topology has emerged as a popular choice owing to its high efficiency and compactness. The use of low VA-rated switching devices enables achieving the high efficiency equivalent to passive filters. Despite its prospective utility in a range of applications, the model of the SSB, essential for implementing functional engineering control strategies under a wide range of operating conditions, is not discussed in existing literature.In the first part of the work, the plant model for controlling the buffer converter in voltage control mode as well as current control mode is developed. Using the proposed model, a closed-loop control scheme is developed, which ensures a fixed-frequency switching of the buffer converter. A step-by-step controller design procedure is elaborated, and the controller gain limit is identified to ensure closed-loop stability.Based on the developed SSB model, an average current mode control is implemented in the second part of the work. Unlike the existing methods of current mode control, in the proposed scheme, the current reference is estimated without using the dc-link current sensor, which is verified experimentally.The SSB-based existing topologies, though promising for many applications, are not realised with minimum switch counts. As opposed to four switch H-bridge-based buffer converter, two switch-based series capacitor stacked buffer converter topologies are synthesised in this part of the work. The generalised topology synthesis procedure and control challenges are identified. One of the proposed two-switch-based topologies named Series Capacitor Boost Hybrid Filter (SC-BOHF) is implemented and verified experimentally.Apart from the active solutions, an alternative dc bus filter structure, consisting of a combination of an inductor (L) and capacitor (C), tuned at the second harmonic (2ω) frequency, reduces the capacitance requirement, enhancing the likelihood of deployment of film capacitors. The proposed solid-state tuning restorer (SSTR) offers consistent filtering performance of the LC filter under frequency and parameter variations. As per the tuning requirement of the LC filter, SSTR acts as an electronic inductor or capacitor. It also ensures a graceful degradation in the filter characteristics during SSTR converter failure modes. The evolution of the SSTR configuration, analysis of its VA rating, and control requirements are studied in this work.The realisation that SSTR requires to behave as an electronic inductor and capacitor as per the sense of LC filter detuning motivated this part of the work, where a unified active capacitor and inductor (UACI) is proposed and implemented without using any dc capacitor. Based on the current reference, the proposed configuration emulates inductive or capacitive characteristics and smoothly transits from one characteristic to another. The operation of the proposed UACI is studied, and a closed-loop control scheme is developed.All the proposed methods are validated on hardware prototypes that have been developed as a part of the research. —————— ALL ARE WELCOME —————

Phd Thesis Defense
2024-03-07 10:00 - 12:00
Title: Pulse Width Modulation Techniques of Two-level Inverter Fed Asymmetrical Six-phase Machine Drive in Linear and Overmodulation Regions
Name of the Student: Sayan Paul
Name of the Advisor: Dr. Kaushik Basu
Degree Registered: Ph.D.
Date and Time: 07th March, 2024, 10:00 AM
Teams Meeting Link
ALL ARE CORDIALLY INVITED.
Abstract:
Multi-phase machines (MPMs) have more than three windings in their stator, rotor, or both. With the broader adoption of power-electronic converters for efficient driving of the machines, MPMs are gaining attention in different applications due to their certain advantages over three-phase machines. One such advantage is higher fault tolerance due to having higher phase redundancy, which makes it suitable for safety-critical applications like electric vehicles (EVs), ship propulsions, electric aircraft, etc. Another advantage is that MPMs allow power splitting across multiple phases. Hence, the power rating per phase drive unit becomes low, making it suitable for high-power applications like railway traction, pumps, compressors, etc. Recent literature also proposes using the same multi-phase converter fed MPM, otherwise used for propulsion, as an onboard battery charger; it substantially reduces space, weight, and cost. During charging mode, the leakage inductance of the machine provides the required inductance for the grid connection, and MPM’s higher degrees of freedom are used to lock the rotor electronically. An asymmetrical six-phase machine (ASPM) is one of such MPMs and is very common in EVs. This thesis aims to devise the pulse-width modulation (PWM) techniques of a two-level six-phase inverter fed ASPM to improve the overall drive efficiency.
ASPM has two sets of balanced three-phase windings, which are spatially shifted by 30 degrees (electrical angle). In one of the popular configurations, the two three-phase winding sets are connected in star fashion with two isolated neutral points. This machine is conventionally analyzed in two two-dimensional (2D) orthogonal subspaces. One of these subspaces is associated with electromagnetic energy transfer and torque production. The other subspace doesn’t transfer energy through the air gap and the equivalent circuit in this plane, consisting of winding resistance and leakage inductance, provides a low impedance. Therefore, excitation of this non-energy-transferring subspace causes a large current and associated copper loss. Any PWM technique of ASPM aims to synthesize the desired voltages in the energy-transferring plane and minimize the applied voltage in the non-energy-transferring subspace.
Linear modulation techniques (LMTs) of ASPM apply zero average voltage in the non-energy-transferring subspace and synthesize the desired voltages in the energy-transferring plane on an average over a switching cycle. It is desired that these LMTs should avoid more than two switching transitions of an inverter leg within a carrier period to limit the instantaneous switching loss. Through an innovative approach, our work finds a way to account for all possible infinitely many LMTs that follow the rule of at most two transitions per leg. But each of them results in a different current ripple performance. Ripple current is inevitable in PWM converters and should be minimized through modulation to reduce the associated copper loss. The total ripple current RMS of ASPM is contributed by both energy-transferring and non-transferring planes. One machine parameter also impacts this performance, which is the ratio of high-frequency inductances in these two subspaces. For all reference voltage vectors and the whole feasible range of the machine parameter, our work finds the techniques with minimum current ripple RMS among the above infinite possible LMTs through numerical optimization. A hybrid PWM strategy is proposed with these optimal techniques, which outperforms all existing techniques in terms of current ripple performance.
Overmodulation (OVM) techniques of ASPM attain higher voltage gain in energy-transferring subspace than LMTs by applying non-zero average voltage in the non-energy transferring subspace. This operation doesn’t cause any torque ripple, but the applied voltage in non-energy transferring subspace should be minimized to reduce unwanted current and associated loss. The existing OVM technique in the literature minimizes this average voltage from the space-vector perspective with a pre-defined set of four active vectors. To find the best technique, one needs to perform the above minimization problem with all possible sets of active vectors, which can give higher voltage gain. So, this requires the evaluation of a large number of cases. In this thesis, we have formulated the above minimization problem in terms of average voltage vectors of two three-phase inverters, where active vectors need not be specified beforehand. Thus, the analysis is more general. Following the above analysis, eight switching sequences in one part and two in another part of OVM are derived, which attain the minimum average voltage injection in the non-energy transferring subspace.
Although the above OVM sequences apply the same average voltages in the two subspaces, they have different high-frequency ripple currents due to having different switching strategies. The current ripple study of the OVM techniques of ASPM is missing in the literature. Hence, one of our works in the thesis studies the current ripple performances of the above PWM sequences in the OVM region, which apply minimum average voltage in the non-energy-transferring subspace. The article finds the sequence with the best ripple performance for a given reference vector in the OVM region and the machine parameter. After that, a PWM technique is proposed, which substantially improves the high-frequency current ripple RMS compared to two existing OVM techniques for a feasible machine parameter.
Finally, simple carrier-comparison-based implementation methods of the proposed LMTs and OVM sequences will be discussed. The six-phase inverter is split into two three-phase inverters, and the proposed strategy implements the PWM sequences per three-phase inverter basis. In carrier-based implementations, the duty signal of the top switch of an inverter leg is compared with a triangular carrier. The bottom switch’s gating pulse complements the top switch’s pulse with a fixed dead time. The duty signal of the top switch of any leg has two components- a modulation signal and a common-mode signal. And two 180-degree phase-shifted carrier signals are required to implement the proposed sequences. The energy-transferring plane of ASPM is divided into twenty-four equivalent sectors; the carrier signals and the expressions of modulation and common-mode signals differ from one sector to another. Henceforth, a sector-independent algorithm is proposed in this thesis to derive these signals with a lower computational burden and smaller size of program memory.
The above theoretical analyses are validated through MATLAB Simulink simulation and experiments on a hardware prototype at a power level of 4 kW.

IISc EE Open Day 2024
2024-02-24 09:00 - 17:00 | IISc
Click here for IISc EE Open Day

EE Faculty Colloquium
2024-02-16 16:00 - 17:30
Title: Challenges & Opportunities in Dysarthric Speech ProcessingSpeaker: Dr. Prasanta Kumar Ghosh, Dept of Electrical Engineering, Indian Institute of ScienceVenue: MMCR, EETime: 4pm, Friday, 16 FebAbstract:Dysarthria is a speech condition caused by motor impairments. This talk will give an overview of Dysarthric Speech Processing (DSP) arising due to Amyotrophic Lateral Sclerosis (ALS) and Parkinson’s Disease (PD). It will present our latest research results and bring out the challenges and opportunities in DSP.Speaker’s Bio:Prasanta Kumar Ghosh received his Ph.D. in Electrical Engineering from University of Southern California (USC), Los Angeles, USA in 2011. Prior to that he obtained his M.Sc.(Engineering) in Electrical Communication Engineering from Indian Institute of Science (IISc), Bangalore and B.E.(ETCE) in Electronics from Jadavpur University, Kolkata in 2006 and 2003 respectively. He has been a Research Intern at Microsoft Research India, Bangalore in the area of audio-visual speaker verification from March to July in 2006. During 2011-2012 he was with IBM India Research Lab (IRL) as a researcher. Currently, he is an assistant professor in the department of Electrical Engineering (EE) at IISc. Before joining as assistant professor, he was a faculty fellow in the department of EE under the INSPIRE faculty fellowship program.Prasanta Kumar Ghosh was awarded the INSPIRE faculty fellowship from Department of Science and Technology (DST), Govt. of India in 2012. He was the winner of the first prize in Mr. BRV Varadhan Post-Graduate student paper contest in IEEE Bangalore chapter, in 2005. He received the best M.Sc. (Engg.) thesis award for the year 2006-07 in the Electrical Sciences division at IISc. He was awarded Center of Excellence in Teaching’s award for excellence in teaching in the category of EE for the year 2010-11 in USC. He has also received the best teaching assistantship (TA) awards for the years 2007-08 and 2008-09 and the honorable mention for the best paper award in the EE, USC. He was also awarded Ming Hsieh Institute (MHI) Ph.D. scholar for the year 2010-11 in EE, USC. His research interests include non-linear signal processing methods for speech and audio, speech production and its relation to speech perception, and automatic speech recognition inspired by the speech production and perception link.————–

[EE Defense Talk]- Graph Based Approaches for Diarization of Conversational Speech,
2024-02-06 11:30 - 13:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
The thesis defense talk of Ms. Prachi Singh (PhD candidate, EE dept.) with the following details
Title Graph Clustering Approaches for Speaker Diarization of Conversational Speech
Date and time February 6, 2024 (11:30am)
Venue MMCR, EE, (C241). And in Teams
Abstract
In this era of advanced machine intelligence, real-world speech applications still find it challenging to deal with conversations involving multiple speakers. An essential first step in speech information extraction from conversational speech is the task of finding “who spoke when”, also referred to as speaker diarization. The focus of this talk is to describe our efforts in investigating graph representation learning and clustering techniques for this problem. While graph models have been used in several other domains, our work on its application to temporal segmentation of speech is the first of its kind.
The talk is divided into three main parts. In the first part of this talk, I will describe a novel proposal on self-supervised learning to perform joint representation learning and clustering, called self-supervised clustering (SSC) for diarization. On the learned representations, we explore path integral clustering (PIC), a graph-based clustering algorithm. The PIC is an unsupervised agglomerative graph clustering method that performs clustering based on the edge connections of a node, called path integral. The proposed SSC with path integral clustering (SSC-PIC) is shown to achieve state-of-the-art performance for benchmark datasets.
The second part of the talk is an extension of SSC-PIC to incorporate metric learning. We design a neural version of the probabilistic linear discriminant analysis (PLDA) approach with learnable parameters to compute a log-likelihood score between embeddings from two segments of the recording. We propose a joint self-supervised representation learning and metric learning approach called Selfsup-PLDA-PIC.
In the third part of the talk, we develop a supervised learning setup using labeled conversational data for training. In this setting, we propose a supervised clustering approach called Supervised HierArchical gRaph Clustering (SHARC) for speaker diarization. This approach uses Graph Neural Networks (GNN) to capture the similarity between the speaker embeddings and performs hierarchical clustering. An extension of this work is the joint training of the speaker embedding extractor along with the GNN module, referred to as end-to-end SHARC (E-SHARC). I will also illustrate how to extend the E-SHARC model for diarization of overlapped speech recordings.
The talk will conclude with a summary of our key contributions, highlighting the pros and cons of using graph-based models for speaker diarization.

–—————–
Coffee/tea will be served before the talk. All are welcome.

Lecture series on Electric Vehicles
2024-01-29 14:00 - 2024-02-01 15:30
Dr Gautham Ram Chandra Mouli from TU Delft will visit us next week as part of an IISc-TU Delft collaborative effort. He will deliver a short (4-part) lecture series on electric vehicles from Jan 29th to Feb 1st from 2.00 pm to 3.30 pm.
The venue will be Room B303 in the Department of EE.
Please sign up for the lecture series using the below link before 28/01/2024.
https://forms.gle/8tQn5Mbaoh6s7tXdA

EE Seminar: Multicore Digital Processors in Power Electronic Applications
2024-01-24 17:00 - 18:00
Title: Multicore Digital Processors in Power Electronic Applications

Speaker: Professor Mukul Chandorkar, IIT Bombay
Time: 5-6 pm 24/01/2024 Wednesday
Venue: MMCR EE IISc
Abstract: Multicore digital processors have been available for industrial equipment control applications for several years. We will examine current state-of-the-art multicore processor offerings, their features, and how they fit in with demanding power electronic control applications. The use of RTOS will also be touched upon.
All are welcome.

Talk by CPRI Chair Prof Tarlochan Sidhu
2024-01-19 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Title: IEC Standard Enabling Digitization of Power Systems – Opportunities, Challenges and Possible Solutions

Speaker:
Professor
Department of Electrical, Computer and Software Engineering
Faculty of Engineering and Applied Science
Ontario Tech University, Canada

Date: 19th January 2024, 4 PM

Venue: MMCR, EE Dept, IISc

Abstract:
Introduction of IEC61850 standard and related communication infrastructure has the capability to facilitate a smart grid enabling digitization and smooth integration of renewables in power systems. This standard has provided many new opportunities for improving protection, control, monitoring and automation functions within electric power systems. However, there are still a number of challenging aspects that need innovative solutions not directly discussed in the standard.
This talk will start with an overview of the IEC61850 standard which is rapidly being applied not only in power systems but also in other areas. This will be followed by a brief description of some aspects not included in the standard and are open questions. Details of possible issues and solutions especially those related to process bus such as lost samples will be provided. Interesting and innovative improvements for protection and automation that can be achieved within the new environment using IEC61850 will be discussed. Implementation and some test results for the above-mentioned solutions and improvements will be shared.

Short Biography:
Tarlochan S. Sidhu received Ph.D degree from the University of Saskatchewan, Canada. He worked for the Regional Computer Center, Chandigarh, India, Punjab State Electricity Board, India, and Bell-Northern Research Ltd., Canada. From July 1990 to June 2002, he was with the Department of Electrical Engineering, University of Saskatchewan, where he served as Professor and Graduate Chairman of the Department. From July 2002 to Dec 2011, he was Professor and Chair of the Electrical and Computer Engineering Department at the University of Western Ontario, Canada. He also held the NSERC/Hydro One Networks Senior Industrial Research Chair in Power Systems Engineering. From Jan. 2012 to Feb 2020, he was the Dean of Faculty of Engineering and Applied Science at the Ontario Tech University where he is currently a Professor.

He has regularly contributed to the activities of IEEE Power Systems Relaying Committee in various capacities, and he was awarded the 2001 Distinguished Service Award of this Committee. He has published more than 300 papers in various journals and conferences, and some of these papers have won major awards. He is regularly invited to give lectures/tutorials around the world on the subject of power system protection, automation and monitoring. He has served regularly as a consultant to power system industries both nationally and internationally. Prof. Sidhu is a Fellow of the Institution of Engineers (India), a Fellow of the Institution of Engineering and Technology (U.K), a Fellow of the Engineering Institution of Canada, a Fellow of the Canadian Academy of Engineering and a Fellow of the Institute of Electrical and Electronics Engineers (USA).

Title: Power Architecture and Battery Management Systems Research in EVTOLs (Electric Vertical Take-off and Landing)
2024-01-02 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
EE SeminarSpeaker: Prof. Abhijit KulkarniAalborg University, Denmark.Title: Power Architecture and Battery Management Systems Research in EVTOLs (Electric Vertical Take-off and Landing)Date: Tuesday, 2nd January 2024Time: 4.00 pmVenue: EE MMCR (C241)Power Architecture and Battery Management Systems Research in EVTOLs (Electric Vertical Take-off and Landing)Abstract:Urban air mobility (UAM) is an emerging mode of transportation that aims to reduce the traffic congestion using green technologies, which will also reduce environmental pollution. In this presentation, we will discuss the energy storage system and the power architecture for electric vertical take-off and landing (eVTOL) aircraft, which are popularly under development for UAM. The energy storage system comprising of the Li-ion batteries need to be sized optimally and operated to maximize their cycle time. The presentation introduces the sizing details and a reconfigurable battery architecture that paves the way to optimize and improve the operation of the batteries for different flight conditions. The sizing/configuration of the battery packs has an impact on the dc bus voltage, drive train topology and system weight. In this presentation, an optimization method is discussed to select the dc bus configuration that improves the efficiency and payload capacity of the eVTOL.The safe operation of an eVTOL energy system is dependent on the battery management system used. In this presentation, the importance of battery impedance and a novel method to extract battery temperature using online impedance measurement is discussed. A low-complexity method is introduced to develop the battery equivalent circuit model online, that can be used as a digital twin within the battery management system for system diagnostics and protection.Biography of the speaker:Abhijit Kulkarni received the Ph.D. degree in electrical engineering from the Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India, in 2016. From July 2016 to July 2017, he was a Post-Doctoral Research Associate with the University of Illinois at Chicago, Chicago, IL, USA. Subsequently, he worked as a Scientist with Honeywell Technology Solutions Lab. Pvt. Ltd., Bengaluru. He is currently an Assistant Professor at Aalborg University, Denmark.All are welcome.

Courses offered by EE faculty during Jan-April Semester 2024
2023-12-29 08:00 - 2024-01-16 17:00
Courses offered by EE faculty during Jan-April Semester 2024
EE Courses_JanTerm2024

Ph.D. Thesis Defence: Resource-Aware State-Triggered Networked Control Systems
2023-12-22 10:00 - 11:30
Ph.D. Thesis Defence.Speaker: Anusree RajanSupervisor: Pavankumar TallapragadaDate and Time: Friday, 22 December 2023, at 10 amVenue (Hybrid): MMCR (C241), EE DepartmentMS Teams LinkTitle: Resource-Aware State-Triggered Networked Control SystemsAbstract:Networked control systems are very popular nowadays, with different fields of applications such as environmental monitoring, industrial automation, military surveillance, and disaster management. State-triggered control is a commonly used control method in the field of networked control systems owing to its advantage of efficient utilization of resources while simultaneously achieving control objectives. In this control method, the communication times are opportunistic and implicitly determined by a triggering rule. In addition, state-triggered control can be designed with provable guarantees for a variety of systems, including nonlinear systems, distributed systems, and multi-agent systems, and for a variety of control objectives, such as stabilization, filtering, trajectory tracking, distributed optimization, multi-agent consensus, and model predictive control.However, the question of how to theoretically analyze the resource usage by a state-triggered control system is not well understood even in the simplest settings. Understanding inter-event times generated by a triggering rule is necessary for higher level planning and scheduling for control over shared or constrained resources as well as for the analytical quantification of the usage of communication or other resources compared to a time-triggered controller. This motivates the first part of the thesis, in which, we provide a systematic way to analyze the evolution of inter-event times in planar linear systems, under a general class of scale-invariant event triggering rules. We provide a sufficient condition for the convergence or non-convergence of inter-event times to a steady state value. We also provide a sufficient condition for the asymptotic average inter-event time to be a constant for all non-zero initial states of the system. Then, under a special case, we comment on the asymptotic behaviour of the inter-event times, including on whether the inter-event times converge to a periodic sequence. Later, we extend our analysis of inter-event times to linear systems under region-based self-triggered control. In this control method, the state space is partitioned into a finite number of conic regions and each region is associated with a fixed inter-event time. We provide several necessary conditions and sufficient conditions for the local convergence of inter-event times to a constant or to a given periodic sequence.In the second part of this thesis, we consider a design problem. Most of the existing event- or self-triggered controllers are designed using sampled-data zero-order-hold (ZOH) control input. However, many communication protocols used in networked control systems, such as TCP and UDP, have a minimum packet size. So, ZOH control may lead to under-utilization of each packet while also increasing the number of communication instances. On the other hand, use of non-ZOH control leads to better utilization of the minimum payload of each packet while also reducing the overall number of communication instances. With these motivations, we propose a new control method called event-triggered parametrized control (ETPC). In this control method, between two consecutive events, each control input to the plant is a linear combination of a set of linearly independent scalar functions. At each event, the coefficients of the parameterized control input are chosen to minimize the error in approximating a continuous time control signal and then they are communicated to the actuator. We, first, showcase this method by focusing on the specific problem of stabilization of linear systems. We design two event-triggering rules that guarantee global asymptotic stability of the origin of the closed loop system under some conditions on the model uncertainty. Later, we use a similar idea to propose an event-triggered polynomial control method for trajectory tracking of unicycle robots. We design an event-triggered parametrized controller for trajectory tracking by a unicycle robot and provide guarantees for uniform ultimate boundedness of the tracking error.Due to time limitations, the defence will focus on the second part of the thesis.——————————- All are Welcome —————————-

[EE/CPS Seminar] Apurv Shukla - Differentially Private Online Resource Allocation
2023-12-21 11:00 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
EE/CPS SeminarSpeaker: Apurv ShuklaPostdoctoral Associate at Texas A&M UniversityTitle: Differentially Private Online Resource AllocationDate: Thursday, 21 December 2023Time: 11amVenue: EE MMCR (C241)Abstract:We consider an online resource allocation problem when thedecision-maker wishes to preserve the privacy of incoming arrivals andrewards. We present a family of algorithms based on private meanestimation and primal-dual schemes that achieve optimal utilityguarantees. We establish utility and privacy bounds on the behavior ofalgorithms in this family under minimal assumptions on the arrivalprocess. These results are further corroborated by a set of numericalexperiments that demonstrate the tightness of our bounds.Biography of the speaker:Apurv is a Postdoctoral Associate at Texas A&M working with Prof. Le Xieand Prof. PR Kumar. His research interest lies in learning and controlwith applications in power systems. He obtained his PhD from ColumbiaUniversity and his bachelor’s degree from IIT Kharagpur.

[Talk] TCE Lecture Series on Power Systems
2023-12-11 11:00 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
Title: Control and Management of Electric Grid with Inverter-Based Resources (IBRs)
Speaker:
Prof Sukumar Kamalasadan
Professor and Distinguished Scholar
Department of Electrical & Computer Engineering
The University of North Carolina at Charlotte
Abstract:
In this talk, the management and control of the electric grid with renewables is discussed. Functions that include renewable energy resources, energy storage, advanced management systems, and the operational framework is illustrated. Further, operational methods including newer management and control tools are presented with a special emphasis on supply-side management functions. Finally, evolving methods and pathways of electric grid management that integrate data sets generated from sensors and meters are discussed with a special emphasis on the overall reliability and resiliency of the electric grid with renewable energy resources.

Short Biography:
Sukumar Kamalasadan is a Professor and Distinguished Scholar of electric power engineering at the University of North Carolina at Charlotte (UNCC) and the Director of power energy and intelligent systems lab (PEISL) within the Energy Production and Infrastructure Center (EPIC) at UNCC. He received his Ph.D. degree in electrical engineering from the University of Toledo, OH in 2004. His research interests include inverter-based resources modeling and integration, data-driven approaches to power grid modernization, smart grid, microgrid, power system operation and optimization, and power system dynamics, stability, and control. Prof. Kamalasadan’s research for the last 20 years has resulted in tools and methods that have a high-level impact on electric utility modernization with a fleet-wide deployment of his tools that enabled modern grid management and control towards 100% integration of renewable energy. His research work has secured more than $12M in grants and contracts notably from the US Department of Energy, National Science Foundation (NSF), Siemens Energy, Duke Energy Corporation, Schweitzer Engineering Lab, and several other industries. He is the chief architect of Duke Energy Smart Grid Laboratory at UNCC, a $5M facility. Prof. Kamalasadan has co-authored more than 250 refereed journal and conference articles and has received several awards from IEEE and NSF including the National Science Foundation CAREER award. He has delivered more than 100 talks in the form of tutorials, keynotes, panels, and webinars/workshops at various international IEEE conferences.

[EE Ph. D. Colloq.] - Dual Mode Operation of Grid-tied Inverters: Modeling, Islanding Detection, and Transfer of Control
2023-12-01 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Ph. D. Thesis ColloquiumStudent: Sugoto MaulikAdvisor: Prof. Vinod JohnDegree: PhDDate and Time: 03:00 PM, 1st December 2023Place: MMCR EE, IISc.=========================================Title: Dual Mode Operation of Grid-tied Inverters: Modeling, Islanding Detection, and Transfer of ControlAbstract: Increased penetration of renewable energy sources like solar PVs and wind is fundamentally altering the power flow dynamics in distribution networks. These localized forms of generation add redundancy to the power system and increase its load-handling capacity. However, these advantages come at the cost of reduced stability and altered protection requirements. These distributed forms of generation (DGs) are interfaced with the power grid via power electronic converters operating at high bandwidths compared to conventional sources. While these offer higher performance, but consequently lower the stability margins. An analytical framework is thus necessary for modeling and stability analysis of such systems. The dynamics involved in modeling a grid-tied DG system span a wide spectrum of frequencies. While simplified modeling can lead to inaccuracies, an all-inclusive model leads to complex and unintuitive models. This work proposes a systematic approach to model the behavior of 3-phase AC grid-tied DG systems using dynamic phasors. Dynamic phasors allow for a state-space representation of the relevant dynamics.The developed state space model is used for the following:1. Islanding detectionIslands are formed in 3-phase distribution networks when an active DG is disconnected from the grid. If undetected, the DG continues to energize its local loads, leading to safety concerns. In this work, a state-feedback approach is developed for islanding detection, which places a system pole in the right half plane (RHP). This ensures the destabilization of the islanded network and a zero non-detection zone. Methods for tuning of the control parameters to meet the system islanding detection requirements are proposed. The scheme is designed and implemented experimentally.2. Transfer of ControlPost-island detection, the DG is required to disconnect from the grid while ensuring uninterrupted power flow to its local loads. A control scheme involving a voltage control loop and grid current feed-forward is developed to achieve a fast transfer from grid-following to grid-forming mode of operation. The introduced voltage control loop ensures that rated voltage is maintained across the loads, and the grid current feed-forward is used to minimize the transients during the transfer process. The method is designed and implemented in conjunction with the islanding detection scheme and verified experimentally with local loads.3. Stability analysis of grid-tied DG systemsOwing to the formation of microgrids and weak grids in the distribution network, the stability assessment of such networks becomes essential. This assessment is performed by extending the dynamic phasor-based model for islanded systems to model grid-tied systems as well. The developed model includes the dynamics of the PLL, grid, DG current levels, and load. In addition to passive loads, considered in the relevant literature, the proposed model also incorporates the effect of constant power and constant current type power electronic loads. It is demonstrated, analytically and experimentally, that the presence of local loads has a stabilizing impact on the synchronization stability of a DG. Additionally, an upper limit on the bandwidth of power-electronic type constant power loads is derived, affirming the observation that high bandwidth loads lead to reduced system stability.All the proposed methods are validated on hardware prototypes that have been developed as a part of the work. —————— ALL ARE WELCOME —————

Talk: India Semiconductor Mission by Prof Tummala Rao
2023-11-29 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Title: Next Gen, Global- level and Large-scale Device, Packaging and Systems R&D and Workforce development in India.
Speaker: Professor Tummala Rao is a distinguished IISc alumnus and an ISM, India advisor. Former IBM Fellow and Founding Director of Georgia Tech 3D Systems Packaging Research Center.
Date and Time: Wednesday, November 29, 2023. 4:00 PM
Venue: MMCR Department of Electrical Engineering IISc
Teams Link
Abstract: India has not been a global player in semiconductors and packaging. But it can be with the new ISM initiative in manufacturing and R&D. Prof. Rao Tummala believes that India can transform its electronics by transforming its academics into industry-centric R&D culture, based on the Georgia Tech model, by performing massive global level R&D that India is capable of. But this R&D must be consistent with global industry needs in integrated systems packaging by highly- innovative, young, and energetic faculty and great executive Directors that India already has at its IITs and IISc. The proposal describes the strategic, next- gen global level R&D, education and skill development programs, infrastructure needed for these and the resulting IP creation and exponential growth in startups to attract many global companies to make India globally competitive. Indian Government is committed to invest in next gen R&D at its academic institutions with the state-of-the-art facilities, as well as state of the art facilities for technology development at ISRC thereby attracting companies both for R&D and manufacturing in both semiconductors and packages. With these investments, India, for the first time, has all the programs necessary for it to be a global player in the short term and global leader in the long term in integrated systems. If such an investment by the GOI is matched by the industry in a consortium mode, India can transform itself from its current design-centric to system centric with expertise and resources spanning from system design and architectures to system integration, assembly and test to offer system foundry for the world.
A proposal was developed by more than 50 academic faculty from India’s top 13 academic institution and 20 colleges and universities under the advice and leadership of Prof. Rao Tummala, as a champion for vision, strategy, and programs, based on a highly successful Georgia Tech model for leading-edge R&D from concept to commercialization, education of large number cross-disciplinary students, and industry partnership with about 100 global companies—all simultaneously. The Indian proposal is expected to result in 12 India-wide Centers of Excellence in 12 different strategic technologies funded by GOI and potentially another 12 COEs funded by the private industry. The proposal also involves partnership with more than 50 global semiconductor and packaging companies from around the globe and 24 global academic experts from Purdue, Georgia Tech, Penn State, USC, University of Arkansas, Maryland, Florida International and Illinois in the proposed 12 strategic research areas (SRAs).
The proposed R&D and workforce development programs are the best examples of how government, universities and global companies can work together for mutual benefit. For the academic community, the Indian universities will begin to perform global R&D and educate the needed workforce for the emerging semiconductor, packaging, and systems industry – the two most important strategic needs for the global industry. In so doing, it will develop global academic faculty leaders and provide jobs for their students in India. For global academic collaborators from US, Indian R&D provides additional opportunities, in addition to educated students from India as their Ph. D students for the global industry, India will develop most comprehensive, large scale global- level R&D, unlike most, if not all countries, and attract large number of companies to come to India, just for R&D in a decade. For its investment, Indian government can claim to have set up the complete ecosystem from research to technology development to manufacturing, just like in advanced countries, in semiconductors and packaging to grow its economy to capture a significant portion of $2T market, to become 3rd largest economy by 2030.
This program requires large number of faculty and students from electrical, mechanical, materials and chemical engineering disciplines
Biography: Education: B.E: Indian Institute of Science. Ph.D.: U of Illinois

Industry: 25 years at IBM

IBM Fellow and Director of Advanced Packaging Lab
Pioneered Industry 1st Plasma Display
Pioneered industry’s first LTCC for all RF applications.
Developed Industry’s 1st 100-144 integrated chip package, very much like today’s chip let package.

Academia: 28 Years at Georgia Tech as Distinguished & Chair Professor and Founding Director

Founding Director of first and only NSF Eng. Research Center in packaging in US pioneering System on Package vision
Created a model at Georgia Tech for a very large and successful global industry consortium.
Educated 10,000 engineers in more than 20 different packaging courses.
Graduated 900 Ph. D & MS packaging engineers to supply to almost all electronic companies in US.

Professional Awards

IEEE named him Father of Modern Packaging and created IEEE Rao Tummala Electronic Packaging Award, a technical field award.
Wrote > 800 papers, 7 Textbooks and > 100 US patents.
Member of National Academy of Eng.in US and India, Fellow of IEEE, &IMAPS
President of IEEE CPMT 2000-2004 and President of IMAPS 1998-2000
Distinguished Alumni of IISc, U of Illinois and Distinguished faculty of Georgia Tech

India; Now Advisor to Government of India and Championing large scale R&D and industry consortium in India.

All are welcome.

[Talk] Technology trends and innovation in HV transmission industry,
2023-11-29 10:00 - 11:00 | HV seminar Hall
Talk
Title: Technology trends and innovation in HV transmission industry
Speaker: Dr. Manoj Pradhan, Global R & D Manager HVDC, Hitachi Energy, Sweden
Date: WEDNESDAY, 29 November 2023
Time: 10:00-11:00 AM
Venue: Seminar Hall, HVE BUILDING
Coffee will be served
ABSTRACT
In this presentation, Dr. Pradhan, R&D manager from Hitachi Energy will share technology trends in HV transmission industry and especially role of HVDC technology in enabling energy transition towards integration of renewable resources. He will provide a comprehensive view of how Hitachi Energy is playing a leading role in advancing the world’s energy system to be more sustainable, flexible and secure. ​As the pioneering technology leader, we collaborate with customers and partners to enable a sustainable energy future.
Biography

Manoj Pradhan joined ABB in 2008, is now global R&D portfolio manager for HVDC at Hitachi Energy (formerly ABB), located in Sweden. He is responsible for technology, product and solution readiness for both offshore and onshore HVDC projects.
Mr. Pradhan has represented Hitachi Energy in several international multiparty joint industrial initiatives (JIP). He is actively involved and leading discussions in several clean energy forum viz., European commission working group on offshore renewable, EU Horizon InterOPERA and OceanGrid initiatives to enable EU’s renewable energy ambition.
He is a great promoter of sustainability and digitalization of power infrastructure. He has also led new business development projects in EV charging sector to align players in the ecosystem to make offerings to accelerate EV adoption. He has led cross business and customer engagement initiatives in decarbonization of steel industry. He has been board member of large research consortium on digitalization, digital cellulose center.
He holds MSc. and PhD degrees in Electrical Sciences from Indian Institute of Science Bangalore, India (2000-2004). He was senior researcher and postdoctoral research fellow at IIT Madras, ETH Zurich, Switzerland, University of Queensland, Australia. He is author of > 25 article in IEEE journals and international conferences and Inventor of > 25 granted patents and several high impact publications in CIGRE and IEEE. Mr. Pradhan holds management diploma from Wharton business school, USA and diploma in global innovation management from Chalmers University of Technology, Sweden.
ALL ARE WELCOME

Physics-Aware AI for Enabling Grid Resiliency
2023-11-24 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Title: Physics-Aware AI for Enabling Grid Resiliency

Speaker:
Dr. Anurag K Srivastava
Raymond J. Lane Professor and Chairperson,
Lane Department of Computer Science and Electrical Engineering
Director, Smart Grid REsearch and Analytics Lab (SG-REAL)
West Virginia University, Morgantown, WV, USA

Date: 24th November 2023, 3 pm

Venue: MMCR, EE Dept, IISc

Abstract:
Availability of data from massive sensors deployment in the cyber-physical electric grid enables new monitoring and control applications. Advancement in artificial intelligence (AI) provides an opportunity to develop data-driven technique utilizing these datasets. Some possible applications include, early alarm and diagnosis, predicative analysis, distributed and decentralized control. New applications need to consider physics-induced limits and high-performance requirement in a dynamic environment. Availability of additional sensor data brings its own challenges including data anomalies, real time processing, data fusion, data management and cyber-security management. Differentiating between data anomalies, cyber events and physical system events can be very challenging due to the similar signatures. This talk will focus on limits of AI for dynamic power grid applications, example of Physics-Aware Machine Learning (PAAI) applications to enhance situational awareness, and associated challenges and opportunities for enabling power grid resiliency.

Speaker Bio: Anurag K. Srivastava is a Raymond J. Lane Professor and Chairperson of the Computer Science and Electrical Engineering Department at the West Virginia University. He is also an adjunct professor at the Washington State University and senior scientist at the Pacific Northwest National Lab. He received his Ph.D. degree in electrical engineering from the Illinois Institute of Technology in 2005. His research interest includes data-driven algorithms for power system operation and control including cyber-resiliency analysis. Dr. Srivastava high impact research projects resulted in tools installed at the utility control center supported for more than $50M by US Department of Energy, National Science Foundation, Siemens Corporate Research, Electric Power Research Institute, Schweitzer Engineering Lab, Power System Engineering Research Center, Office of Naval Research and several National Labs. He is an IEEE Fellow and the author of more than 360 technical publications including three books on power system security and resiliency as well as 3 patents.

Ph.D. Thesis Colloquium
2023-11-14 14:30 - 16:30 | HV seminar Hall
Thesis Title : Developmental Studies on a Multistage Induction Coilgun-Based Electromagnetic
Launcher
Student Name : Ranashree Ram
Abstract
The archetypal chemical propellant-based launchers (e.g., guns, missiles, spacecraft launchers, etc.) with their hot trailing plume has been widely deployed over the decades for various applications. However, because of certain disadvantages of these systems and the physical limitations associated with their designs, electromagnetic launchers (EMLs) seem to offer an alternative way forward as the next-generation hypervelocity (>3 km/s) launchers. The multistage induction coilgun is one such futuristic class of EMLs that works on the principle of electromagnetic induction between an array of coils (or drive coils), which are wound on a long barrel of appropriate length, and an electrically conducting projectile (or armature) placed inside the barrel. Previously charged high-voltage capacitor banks are sequentially discharged into the coils through solid-state switches leading to the generation and flow of very high pulsed currents (kA) through the coils. Time-varying magnetic flux thus produced by the pulsed currents through the coils interact with the projectile inside and induce a resultant current on it. The propulsive electromagnetic force exerted on the projectile is a product of the current through the coil, the induced current on the projectile, and the mutual inductance. The “turn on” and “turn off” of the coils of the various stages must be precisely and appropriately synchronized during the multistage operation to achieve a higher projectile velocity and this makes its successful design and operation a challenge. Owing to its high confidentiality in defense and space applications, not much can be known from the published works. In the present work a four-stage induction coilgun has been designed and developed in the laboratory. The research work presented in the thesis aims to understand the factors contributing to achieving a higher muzzle velocity for a projectile of a given mass while launching a payload with the coilgun. The projectile of a coilgun can be either sleeve-type (ring-shaped projectile) or solenoid-type (multi-turn projectile).
The author also designed and fabricated a high-speed infrared transmitter-receiver-based sensor (with 25 ns rise and fall time) to quickly sense the moving projectile (or armature) inside the barrel. The triggering instant of the subsequent stage coils of a multistage coilgun critically depends on the projectile’s position inside the barrel. The projectile will fail to achieve the highest muzzle velocity if the subsequent stage coils are not optimally triggered in a sequence. The fast-moving projectile through the barrel necessitates the fast sensing of its position inside the barrel. In addition, the author has also designed, developed, and fabricated a high-speed gate driver circuit with a peak 25 kV DC isolation for the signal circuit from the high voltage power circuit within a compact space of the printed circuit board (PCB) to trigger the high-voltage SCRs used for triggering the pulsed power source of each stage of the coilgun..
The large current flowing through each stage coil creates EMI problems in the coilgun. The EMI issues corrupt the sensor data, which prevents successful sensing of the projectile’s position. Also, EMI causes the SCRs to trigger the coils spuriously even when the projectile has not reached its optimal triggering position inside the coil. Synchronizing the triggering of stages by preventing the EMI issues is a significant challenge and is very important in successfully operating a multistage induction coilgun. The author could successfully synchronize the stages of the coilgun by preventing spurious triggering of the SCRs using appropriate EMI mitigation techniques.
The influence of the capacitance of the capacitor bank used in the high voltage pulsed power supply on the optimum triggering position of the projectile inside the drive coil of the coilgun has been analyzed. An empirical relationship between the projectile velocity and the charging voltage of the capacitor bank has been formulated for the first time in this thesis. The subject of the study presented in this thesis also focuses on analyzing the parameters on which the efficiency of an induction coilgun depends and how it can be optimized. Study has been performed to optimize the shape and dimensions of the projectile to achieve the highest muzzle velocity. The dependency of the projectile motion on the flow of induced current in the subsequent stages has been analyzed. The study also focuses on establishing an approach to choosing a proper distance between the stages in a multistage induction coilgun.
A comprehensive and explicit analysis has been performed to study and explain the reasons behind the differences in the optimum triggering positions of the projectile inside each stage coil and the achieved muzzle velocities for different arrangements of the drive coil current directions in a multistage induction coilgun.

MS Teams Link:

EE Talk: The Role of Privacy-Preserving Computation Frameworks Enabling Cooperation in Business and Legal Contracts
2023-11-09 16:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Title: The Role of Privacy-Preserving Computation Frameworks Enabling Cooperation in Business and Legal Contracts
Abstract: Collaborative data analytics and the usage of advanced software controls are core elements for transformation of the industrial systems and automotive systems of today to future ‘smart’ manufacturing and mobility. In many scenarios within Industry 4.0 systems, innovation is limited due to data being siloed between competing business organizations or entities in an organization. Similarly, the concern for lack of sufficient protection mechanisms limit several transformations within the mobility/automotive domain. Effective protection mechanisms can enable the usage of data sources without the loss of competitive edge and software controls without the fear of misuse. While modification of traditional IT security methods have been actively proposed for these domains, we argue these are typically insufficient. In this talk, we discuss the role of privacy-preserving computation frameworks as a technical supplement to business and legal contracts to enable cooperation. Additionally, we highlight constraints for these systems (with a focus on automotive) that make the use of traditional methods challenging.
Speaker: Shalabh Jain is a Senior Research Scientist with the Security and Privacy group at the Bosch Research and Technology Center in Pittsburgh. Prior to joining Bosch Research he worked as a hardware engineer for Texas Instruments. He has been actively working in the domain of security and privacy for automotive systems, cloud systems, Industry 4.0 and IoT networks. He is an associate at the Carnegie Bosch Institute and responsible for mentoring postdoctoral fellows in the domain security and privacy. He is a privacy evangelist within Bosch, co-founding the Privacy Engineering Guild and organizing the annual Bosch Privacy Engineering Summit. Additionally, he is the North America representative for Open Bosch Research, working on establishing collaborations between startups and Bosch Corporate Research. He leads the Pittsburgh chapter of Automotive Security Research Group (ASRG), a non-profit organization for building the next generation of security experts in the automotive domain. He is He received his PhD and masters from University of Maryland, College Park and his Bachelors degree from IIT Roorkee.
Venue and Time: EE 1st floor, MMCR, 9th November, Thursday 4 PM – 5 PM

EE Seminar: Charging of Electric vehicles: Direct DC, vehicle to grid & heavy duty
2023-10-18 10:00 - 11:00
Date & Time: Wednesday, 18/10/2023, 10 am – 11 am
Venue: MMCR, Dept. of Electrical Engineering (Hybrid Mode)
For online participants: Meeting Link

Title of the Talk: Charging of Electric vehicles: Direct DC, vehicle to grid & heavy duty

Abstract: In this talk, we will investigate three critical technologies for electric vehicle charging, namely, direct DC charging with solar and battery, vehicle-to-grid (V2G) technology and charging of heavy-duty vehicles. First, we will look at (bidirectional) power converter topologies for solar-powered EV chargers and how to increase power density and efficiency. Then, we will explore how EV batteries can be used as energy storage for the grid and how charging algorithms can help facilitate the mass EV-grid integration and reduce battery capacity fade. Finally, we will study electrification of heavy-duty vehicles such as (trolley) busses and modular design of chargers for megawatt level charging of trucks. To conclude, we will look at examples of current research at TU Delft in EV charging space.

Speaker Bio: Gautham Ram is an Assistant Professor of Electric Mobility in the Department of Electrical Sustainable Energy, Faculty of EEMCS at TU Delft. His current research focuses on electric vehicles, EV charging, batteries and power electronics. He is currently involved in many research projects at national and EU level concerning electric mobility such as TULIPS, Drive2X, FLOW, FlexEC, FLEXInet. He is the coordinator and lecturer for the Massive Open Online Course (MOOC) on Electric cars on edX.org with ~215,000 learners from 175 countries. He received his PhD from TU Delft in 2018 for the development of a solar-powered V2G electric vehicle charger and was later a postdoctoral researcher from 2017-19. Google scholar: https://scholar.google.co.in/citations?user=ri-Hp7cAAAAJ&hl=en

[EE Faculty Colloquium] Overview of Research on Pulse Width Modulation, Motor Control and Electrical Machines
2023-09-29 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Title: Overview of Research on Pulse Width Modulation, Motor Control and Electrical Machines at EE, IISc
Speaker: Prof. G. Narayanan, Professor, Electrical Engineering, IISc
Venue: MMCR, EE & Online Teams link
Time: 4pm, 29 September 2023
Abstract:
The talk will provide a brief overview of research on pulse width modulation (PWM), motor control and electrical machines, which the speaker has been a part of, in the department of electrical engineering over the past 2-3 decades. Research highlights will be presented on the following aspects:

Space vector based PWM for motor drives and four-quadrant voltage-source converters
Offline optimized PWM for induction motor drives
High power converters for motor drives and active front-end converters
Power-electronic control of induction motor and its variants
Effect of inverter dead-time and its compensation
Induction motor drive-based emulation of wind turbine
Power electronic based emulation of electrical machines

Recent and on-going activities on the following with be discussed briefly:

Permanent-magnet-free high-performance motors based on reluctance principle
High-speed switched reluctance motors and contact-free electromagnetic bearings
Multi-disciplinary initiatives involving multiple departments, multiple institutions / organizations across technical domains

Speaker’s Bio:
G. Narayanan received his BE from College of Engineering Guindy, Anna University, Chennai, in 1992; MTech from IIT Kharagpur in 1994; and PhD from IISc in 2000. He has been on the faculty of EE, IISc, since August 2003. He researches in the areas of power electronics, motor drives and electrical machines. He has taught / has been teaching multiple courses in these broad domains.

* All are welcome *
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[EE Thesis Defense] - "Speech Dereverberation Using Autoregressive Models of Sub-band Envelopes", Anurenjan,
2023-09-29 10:00 - 11:00 | MMCR, Hall C 241, 1st floor, EE department
Title Dereverberation of Speech Using Autoregressive Models of Sub-band Envelopes.
Speaker Anurenjan P. R.
Faculty Advisor: Dr. Sriram Ganapathy
Examiner : Prof. Umesh S. (IITM)
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 autoregressive models of sub-band envelopes. signal analysis
The technique of frequency domain linear prediction (FDLP) is used for finding the autoregressive model parameters. 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 working as Assistant Professor in Government Engineering College, Idukki. 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 member of IEEE SPS and the ISCA. During his free hours, Mr. Anurenjan likes to play badminton and goes for swimming.
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All are invited. Coffee/Tea will be served before the talk.

PhD Thesis Colloquium - Subhas Chandra Das, EE (ERP)
2023-09-27 16:00 - 17:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Title: Experimental Investigations on Switching Behaviour of Traction-Grade IGBTs over Wide Operating Conditions

Subhas Chandra Das (PhD -ERP)

Supervisor: Prof. G. Narayanan
Date and Time: 27 Sep 2023 (Wednesday), 4 pm – 5 pm
Venue: MMCR, EE Department (Hybrid mode)

Teams meeting link:

Abstract

Insulated gate bipolar transistors (IGBTs) are the dominant power semiconductor devices in high power applications, such as, locomotive traction and megawatt-level renewable energy systems. Power electronic converters in such applications are expected to have a long-life span of about 20-30 years. Hence, efficiency and reliability of these converters are very important. IGBT switching behavior has a direct influence on both power conversion efficiency and system reliability.
The various switching characteristics parameters of IGBTs, which are available in the respective device datasheets, are limited to certain operating conditions. For an example, the switching characteristic parameters are available for only one or two DC link voltages; however, in applications such as diesel-electric locomotives, IGBTs have to operate over a wide range of the DC link voltages. Similarly, the characteristic parameters are available at only one or two junction temperatures (e.g. 25 oC and 125 oC); but, the IGBTs in traction and wind energy systems have to operate over wide range of temperatures including sub-zero ambient temperatures.
In this work, switching behavior of IGBTs of four different makes are studied experimentally over a wide range of operating conditions. The load current is considered upto 1.667p.u., where 1 p.u corresponds to the rated current of the IGBTs. The range of DC link voltage considered is from 0.571 p.u. to 1.321p.u., where 1.0 p.u. is the nominal voltage of the application. The junction temperature range is considered from -35 oC to +125 oC. The following are the major highlights of the research work:
1. Generation of experimental data on switching behavior of IGBTs over wide range of operating conditions as mentioned above.
2. The experimental data, which are generated, complement the technical information available in device datasheets.
3. The experimental investigation are carried out on four traction-grade IGBTs of different makes and of comparable ratings to ensure that the findings of the study are applicable to reasonable cross-section of the available commercial devices.
4. Experimental study on the switching behavior of an IGBT converter leg, having top and bottom devices of two different makes, and its comparison with the switching behavior of a converter leg, having complementary devices of the same make.
5. Experimental study of the rise and fall times of the device switching voltages and currents, both during turn-on and turn-off, over the complete range of operating conditions.
6. Evaluation of turn-on and turn-off switching energy losses as functions of load current, DC link voltages and junction temperatures, which are valid over the complete operating range.
7. Experimental study of reverse recovery characteristics of anti-parallel diode of IGBTs with varying DC link voltage, load current and junction temperatures.
8. Experimental investigation on the effect of variations in DC link voltage, load current and junction temperatures on device peak stress parameters, namely, peak device voltage, peak device current, peak rate of change of device voltage, and peak rate of change of device current.
9. Experimental study of sub-intervals of the turn-on switching delay, turn-off switching delays and parameters related to the switching delay intervals over the complete operating range.
10. Correlation of the various turn-on and turn-off switching parameters with junction temperatures based on the experimental data generated.
11. Study of the consistency of the above correlations across different traction-grade devices of comparable ratings and different makes.
12. Critical review of various thermo-sensitive electrical parameters (TSEPs) already reported in literature.
13. Identification of new TSEPs that can be obtained from the measured gate-emitter voltage during switching delay times.

ALL ARE WELCOME

PhD Thesis Defense of Mr. Siddarth Asokan
2023-09-15 09:30 - 11:00 | MMCR, Hall C 241, 1st floor, EE department
Name of the Candidate: Mr. Siddarth Asokan
Ph.D. Supervisor: Prof. Chandra Sekhar Seelamantula (EE)

External Examiner: Prof. Santanu Chaudhury (Director, IIT Jodhpur; Professor, IIT Delhi)

Title of the Thesis: On the Optimality of Generative Adversarial Networks — A Variational Perspective

Date & Time: September 15, 2023; 9.30 AM (Coffee will be served during the defense)
Venue: Multimedia Classroom (MMCR), Department of Electrical Engineering, IIScAbstract:Generative adversarial networks are a popular generative modeling framework, where the task is to learn the underlying distribution of data. GANs comprise a min-max game between two neural networks, the generator and the discriminator. The generator transforms noise, typically Gaussian distributed, into a desired output, typically images. The discriminator learns to distinguish between the target samples and the generator output. The objective is to learn the optimal generator — one that can generate samples that perfectly confuse the discriminator. GANs are trained to either minimize a divergence function or an integral probability metric (IPM) between the data and generator distributions. Common divergences include the Jensen-Shannon divergence in the standard GAN (SGAN), the chi-squared divergence in least-squares GAN (LSGAN) and f-divergences in f-GANs. Popular IPMs include the Wasserstein-2 metric or the Sobolev metric. The choice of the IPM results in a constraint class over which the discriminator is optimized, such as Lipschitz-1 functions in Wasserstein GAN (WGAN) or functions with bounded energy in their gradients as in the case of Sobolev GAN. While GANs excel at generating realistic images, their optimization is not well understood. This thesis focuses on understanding the optimality of GANs, viewed from the perspective of Variational Calculus. The thesis is organized into three parts.In Part-I, we consider the functional analysis of the discriminator in various GAN formulations. In f-GANs, the functional optimization of the loss coincides with pointwise optimization as reported in the literature. We extend the analysis to novel GAN losses via a new contrastive-learning framework called Rumi-GAN, in which the target data is split into positive and negative classes. We design novel GAN losses that allow for the generator to learn the positive class while the discriminator is trained on both classes. For the WGAN IPM, we propose a novel variant of the gradient-norm penalty, and show by means of Euler-Lagrange analysis, that the optimal discriminator solves the Poisson partial differential equation (PDE). We solve the PDE via Fourier-series approximations and involving radial basis function (RBF) expansions. We extend the approach to image generation by means of latent-space matching in Wasserstein autoencoders (WAE). We also present generalizations to higher-order gradient penalties for the LSGAN and WGAN losses, and show that the optimal discriminator can be implemented by means of a polyharmonic spline interpolator, giving rise to the name PolyGANs. PolyGANs, implemented by means of an RBF discriminator whose weights and centers are evaluated in closed-form, results in superior convergence of the generator.In Part-II, we tackle the issue of choosing the input distribution of the generator. We introduce Spider GANs, a generalization of image-to-image translation GANs, wherein providing the generator with data coming from a closely related/“friendly neighborhood” source dataset accelerates and stabilizes training, even in scenarios where there is no visual similarity between the source and target datasets. Spider GANs can be cascaded, resulting in state-of-the-art performance when trained with StyleGAN architectures on small, high-resolution datasets, in merely one-fifth of the training time. To identify “friendly neighbors” of a target dataset, we propose the “signed Inception distance” (SID), which employs the PolyGAN discriminator to quantify the proximity between datasets.In Part-III, we extend the analysis performed in Part-I to GAN generators. In divergence-minimizing GANs, the optimal generator matches the gradient of its push-forward distribution with the gradient of the data distribution (known as the score), linking GANs to score-based Langevin diffusion. In IPM-GANs, the optimal generator performs flow-matching on the gradient-field of the discriminator, thereby deriving an equivalence between the score-matching and flow-matching frameworks. We present implementations of flow-matching GANs, and develop an active-contour-based technique to train the generator in SnakeGANs. Finally, we leverage the gradient field of the discriminator to evolve particles in a Langevin-flow setting, and show that the proposed discriminator-guided Langevin diffusion accelerates baseline score-matching diffusion without the need for noise conditioning.
Biography of the Candidate: Siddarth Asokan received a Bachelor of Engineering (B.E.) degree in 2017 with a specialization in Electronics and Communication Engineering from M.S. Ramaiah Institute of Technology, Bangalore. During 2016–2017, he worked in Robert Bosch Centre for Cyber-Physical Systems (RBCCPS) as a Project Intern on the Smart Cities Project. Subsequently, he joined RBCCPS as a direct PhD student in 2017 working under the guidance of Prof. Chandra Sekhar Seelamantula, and has since been with the Spectrum Lab, Department of Electrical Engineering. He received the Microsoft Research Fellowship in 2018, the Qualcomm Innovation Fellowship in 2019, 2021, 2022, and 2023 and the RBCCPS PhD Fellowship in 2020 and 2021. He is also a recipient of the Best Presenter Award at the AI/ML track of the IISc EECS Symposium 2023, and has been selected to present his PhD research at the Doctoral Consortium at the British Machine Vision Conference, 2023. His research interests are in signal processing, image processing and machine learning, focusing on building mathematical foundations of generative learning frameworks.
All are invited.

Cancelled [Talk] Prof Neetesh Saxena Cardiff University UK
2023-08-25 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Cancelled *

Due to some unavoidable circumstances, we are cancelling the talk by Prof Neetesh Saxena of Cardiff University scheduled today. Sorry for any inconvenience this may cause.

Title: Cyber-physical Smart Grid Situational Awareness

Speaker:

Prof Neetesh Saxena
Senior Lecturer
School of Computer Science and Informatics
Cardiff University, UK

Date: 25th August 2023, 3 pm

Venue: MMCR, EE Dept, IISc

Abstract:
In recent years, the impact evaluation of cyber-physical security of the smart grid has become highly notable and extremely important and critical research direction due to several recent cyber-attacks attempts in different countries. The smart grid is vulnerable to cyber-attacks due to its integration with communication and control technologies. The cyber-attacks can affect operations and decision-making at the energy management system or effectively destroy the critical components, even shut down the power operations and can disrupt service for its customers. Inaccurate information leads to triggering inappropriate actions by the operators. The cyber-attacks either can directly target a power component of the smart grid system or can be triggered through the communication network to the power system. In this talk, I will discuss cyber security issues with a case study to explore the cyber-physical situational awareness.

Biography:
Prof Neetesh is a Senior Lecturer with the School of Computer Science and Informatics at Cardiff University, UK having 16+ years of professional experience. Previously, he had affiliations with the Bournemouth University (UK), Georgia Institute of Technology (USA), Stony Brook University (USA) and SUNY Korea. He was a DAAD Scholar at Bonn-Aachen International Center for Information Technology (B-IT), Rheinische-Friedrich-Wilhelms Universität, Bonn (Germany) and was also a TCS Research Scholar. His current research interests include cyber security and critical infrastructure security, including cyber-physical system security: smart grid, V2G and cellular communication networks.

EE PhD Colloquium on Imaging Inverse problems
2023-08-23 11:00 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
Title : Improved Derivative based Regularizations for Imaging Inverse problems
Student : Manu GhulyaniAdvisor : Prof. Muthuvel Arigovindan
Date and Time: 23.08.2023 (Wednesday), 11 am.
Venue : MMCR, Department of Electrical Engineering
Abstract:
Images undergo degradation during the capturing process due to physical limitations inherent to the capturing devices. Addressing this degradation and recovering high-quality images constitute the image recovery problem, a crucial concern with diverse applications across various fields such as biology, astronomy, and medicine. The enhancement of captured image resolution significantly influences these disciplines. Examples of this problem include tasks like reconstructing computed tomography images, magnetic resonance imaging, image deconvolution, and microscopic image reconstruction.
Image recovery is frequently approached using regularization techniques, with derivative-based regularizations being popular due to their ability to exploit image smoothness, yielding interpretable results devoid of introduced artifacts. Total Variation regularization (TV), proposed by Rudin, Osher, and Fatemi, is a seminal approach for image recovery. TV involves the norm of the image’s gradient, aggregated over all pixel locations. As TV encourages minimal values in the derivative norm, it leads to piece-wise constant solutions, resulting in what is known as the “staircase effect.” To mitigate this effect, the Hessian Schatten norm regularization (HSN) employs second-order derivatives, represented by the pth norm of eigenvalues in the image hessian vector, summed across all pixels. HSN demonstrates superior structure-preserving properties compared to TV. However, HSN solutions tend to be overly smoothed. To address this, we introduce a non-convex shrinkage penalty applied to the Hessian’s eigenvalues, deviating from the convex lp norm. While the analytical form of this penalty was unknown, we derived the algorithm using proximal operations. We established that the proposed regularization adhered to restricted proximal regularity, ensuring the algorithm’s convergence. The images recovered by this regularization were sharper than the convex counterparts.
In the subsequent work, we extend the concept of the Hessian-Schatten norm. By encompassing Schatten norms of the Hessian and introducing a smoothness constraint, we broaden the scope of Hessian Schatten norm. The resulting regularization can be derived as a Lagrange dual of the Hessian Schatten norm, akin to the total generalized variation. Furthermore, we present an efficient variable splitting scheme for solving image restoration challenges.
Total Generalized Variation (TGV) represents an important generalization of Total Variation. TGV involves multiple orders of derivatives, with higher-order TGV leading to improved recovered image quality. This enhancement has been validated through numerical experiments in image denoising. Consequently, a demand arises for an algorithm capable of solving TGV for any order. While various methods address TGV regularization, many are confined to second-order TGV, and only a few explore orders greater than three for image recovery with TGV regularization. To our knowledge, no algorithm resolves image recovery challenges employing TGV regularization for orders exceeding three under a general forward model. This challenge arises from the intricate nature of TGV representation. We surmount this obstacle by presenting two simple matrix based representations of TGV: the direct and compact forms. We prove the equivalence of both forms with the original TGV definition. Leveraging the compact representation, we propose a generalized ADMM-based algorithm to solve TGV regularization for any order.
ALL ARE WECOME.

[Talk]: Prof Ramakrishna Gokaraju University of Saskatchewan, Canada
2023-08-18 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Talk Title: Future Clean Power and Energy Systems: Co-generation with New Nuclear Based Small Modular Reactors (SMRs) and Renewable Energy for Electricity and Energy Applications

Speaker:

Prof Ramakrishna Gokaraju, PhD, PEng
Professor, Department of Electrical & Computer Engineering
Associate Dean, Graduate Studies & Special Projects, College of Engineering
University of Saskatchewan, Canada

Date: 18th August 2023, 4 pm

Venue: MMCR, EE Dept, IISc

Abstract:
Small modular reactors (SMRs)—a fast-emerging nuclear power plant technology—and renewables hold significant promise for the development of future clean energy systems. They are suitable for large grids as well as remote load centers and offer load following and frequency response capabilities. This talk will first provide a background of this technology and recent developments related to this technology. Followed by this it will describe a dynamic model of an integral pressurized water reactor (iPWR)-type SMR and studies assessing the contribution of the reactor to the electrical side dynamics. SMRs with their faster response rates, along with intermittent renewable energy sources (RESs) could be effectively used to develop sustainable clean energy systems. The talk will also describe a dynamic simulation model developed in our lab showing how SMRs along with renewable energy could be used for electricity and district heating.

Biography:
Prof. 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, Canada, in 1996 and 2000, respectively. He worked with IBM Toronto Lab from 2000-‘22. Prof. Gokaraju joined the Electrical & Computer Engineering Department, University of Saskatchewan in 2003 and is a professor in the department of electrical & computer engineering. He is also currently the Associate Dean of Graduate Studies and Special Projects in the College of Engineering. His current research works are on power system protection, smart grids and nuclear-based “SMRs” for power and energy applications. He currently serves the Natural Sciences and Engineering Council of Canada (NSERC) Evaluation Group in Electrical and Computer Engineering. His research is funded by the NSERC Discovery Grant and the Canadian Nuclear Safety Commission (CNSC).

[Talk] Prof Nando Ochoa of University of Melbourne 17th Aug 4 PM MMCR EE
2023-08-17 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Title: “Smart Meter-Driven Approaches for PV-Rich Low Voltage Network Modelling, Operation and Planning”

Speaker: Prof Nando Ochoa Pizzali

Affiliation: University of Melbourne, Australia

Venue: MMCR, EE Dept

Date: 17th Aug 2023

Time: 4:00 PM

Abstract: Residential solar PV is installed behind the meter of mainly single-phase customers connected to three-phase low voltage (LV) feeders (e.g., 400V line-to-line). This means that for distribution companies to adequately quantify the impacts from reverse power flows due to excess solar PV generation, the corresponding electrical models are required. These models are critical when calculating voltages given the non-linear and unbalance nature of LV feeders. However, the task of producing electrical models of thousands of LV feeders is already a significant challenge for distribution companies around the world, which, in turn, makes the operation and planning of PV-rich LV networks even more challenging. It is in this context that the exploitation of historical smart meter data can not only help distribution companies with their modelling tasks but also provide radical alternatives to how they operate and plan future PV-rich LV networks.

This talk presents and discusses the findings of three advanced smart meter-driven approaches using realistic case studies from Victoria, Australia. The first enhances LV models. Using simplified three-phase voltage drop equations and multiple linear regression, it is able to estimate three-phase and single-phase line impedances which, in turn, allows for the quick and accurate calculation of customer voltages for operational purposes. The second, a more radical approach, goes model-free. It demonstrates that neural networks can be trained to capture the physics of three-phase LV feeders with dozens of single-phase customers; making it possible to have fast and accurate voltage calculations. The last one, from a planning perspective, also demonstrates that regression techniques and data from early solar PV penetrations can be used to quickly estimate the hosting capacity of LV networks without the need for complex and detailed network studies.

Bio: Prof Nando is a Professor of Smart Grids and Power Systems at The University of Melbourne, Australia, and Chief Scientist & Co-Founder at VoltMind. He is an IEEE PES Distinguished Lecturer, an Editorial Board Member of the IEEE Power and Energy Magazine, and an IEEE Senior Member. Previously, from 2011 to 2021, he was full and part-time with The University of Manchester, UK. From 2007 to 2010 he was a Research Fellow in Energy Systems at the University of Edinburgh, UK. In 2010, he also undertook an industrial secondment with the Edinburgh-based company Psymetrix Ltd (part of GE). He hold a Bachelor’s degree in Mechanical and Electrical Engineering received from UNI (Peru), and a Research MSc and a PhD in Electrical Power Engineering, both received from UNESP Ilha Solteira (Brazil).
