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TZID:Asia/Kolkata
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DTSTART:20230101T000000
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DTSTART;TZID=Asia/Kolkata:20230915T093000
DTEND;TZID=Asia/Kolkata:20230915T110000
DTSTAMP:20260528T075055
CREATED:20230906T053622Z
LAST-MODIFIED:20230908T103500Z
UID:241024-1694770200-1694775600@ee.iisc.ac.in
SUMMARY:PhD Thesis Defense of Mr. Siddarth Asokan
DESCRIPTION:Name of the Candidate: Mr. Siddarth Asokan\nPh.D. Supervisor: Prof. Chandra Sekhar Seelamantula (EE)\n \nExternal Examiner: Prof. Santanu Chaudhury (Director\, IIT Jodhpur; Professor\, IIT Delhi)\n\nTitle of the Thesis: On the Optimality of Generative Adversarial Networks — A Variational Perspective\n\nDate & Time: September 15\, 2023; 9.30 AM (Coffee will be served during the defense)\nVenue: 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.\nBiography 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.\nAll are invited.
URL:https://ee.iisc.ac.in/event/phd-thesis-defense-of-mr-siddarth-asokan/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230927T160000
DTEND;TZID=Asia/Kolkata:20230927T170000
DTSTAMP:20260528T075055
CREATED:20230926T041251Z
LAST-MODIFIED:20230926T041251Z
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SUMMARY:PhD Thesis Colloquium - Subhas Chandra Das\, EE (ERP)
DESCRIPTION:Title: Experimental Investigations on Switching Behaviour of Traction-Grade IGBTs over Wide Operating Conditions\n\nSubhas Chandra Das (PhD -ERP) \n\nSupervisor: Prof. G. Narayanan\nDate and Time: 27 Sep 2023 (Wednesday)\, 4 pm – 5 pm\nVenue: MMCR\, EE Department (Hybrid mode)\n\nTeams meeting link:\n\nAbstract\n\n\nInsulated 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. \nThe 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. \nIn 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: \n1. Generation of experimental data on switching behavior of IGBTs over wide range of operating conditions as mentioned above. \n2. The experimental data\, which are generated\, complement the technical information available in device datasheets. \n3. 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. \n4. 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. \n5. 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. \n6. 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. \n7. Experimental study of reverse recovery characteristics of anti-parallel diode of IGBTs with varying DC link voltage\, load current and junction temperatures. \n8. 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. \n9. 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. \n10. Correlation of the various turn-on and turn-off switching parameters with junction temperatures based on the experimental data generated. \n11. Study of the consistency of the above correlations across different traction-grade devices of comparable ratings and different makes. \n12. Critical review of various thermo-sensitive electrical parameters (TSEPs) already reported in literature. \n13. Identification of new TSEPs that can be obtained from the measured gate-emitter voltage during switching delay times. \n\n\nALL ARE WELCOME
URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-subhas-chandra-das-ee-erp/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
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DTSTART;TZID=Asia/Kolkata:20230929T100000
DTEND;TZID=Asia/Kolkata:20230929T110000
DTSTAMP:20260528T075055
CREATED:20230925T092359Z
LAST-MODIFIED:20230925T092359Z
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SUMMARY:[EE Thesis Defense] - "Speech Dereverberation Using Autoregressive Models of Sub-band Envelopes"\, Anurenjan\,
DESCRIPTION:Title Dereverberation of Speech Using Autoregressive Models of Sub-band Envelopes. \nSpeaker Anurenjan P. R.  \nFaculty Advisor: Dr. Sriram Ganapathy \nExaminer : Prof. Umesh S. (IITM) \nAbstract \nThe 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  \nThe 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. \nThis 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. \nIn 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.  \nThe 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.     \nAcknowledgement This work was partly supported by project grants from Samsung Research India\, Bangalore and the College of Engineering\, Trivandrum\, Kerala.  \nBio 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.  \n——- \nAll are invited. Coffee/Tea will be served before the talk. 
URL:https://ee.iisc.ac.in/event/ee-thesis-defense-speech-dereverberation-using-autoregressive-models-of-sub-band-envelopes-anurenjan/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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DTSTART;TZID=Asia/Kolkata:20230929T160000
DTEND;TZID=Asia/Kolkata:20230929T173000
DTSTAMP:20260528T075055
CREATED:20230913T053937Z
LAST-MODIFIED:20230918T040821Z
UID:241060-1696003200-1696008600@ee.iisc.ac.in
SUMMARY:[EE Faculty Colloquium] Overview of Research on Pulse Width Modulation\, Motor Control and Electrical Machines
DESCRIPTION:Title: Overview of Research on Pulse Width Modulation\, Motor Control and Electrical Machines at EE\, IISc \nSpeaker: Prof. G. Narayanan\, Professor\, Electrical Engineering\, IISc \nVenue: MMCR\, EE & Online Teams link \nTime: 4pm\, 29 September 2023 \nAbstract:\nThe 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: \n\nSpace vector based PWM for motor drives and four-quadrant voltage-source converters\nOffline optimized PWM for induction motor drives\nHigh power converters for motor drives and active front-end converters\nPower-electronic control of induction motor and its variants\nEffect of inverter dead-time and its compensation\nInduction motor drive-based emulation of wind turbine\nPower electronic based emulation of electrical machines\n\nRecent and on-going activities on the following with be discussed briefly: \n\nPermanent-magnet-free high-performance motors based on reluctance principle\nHigh-speed switched reluctance motors and contact-free electromagnetic bearings\nMulti-disciplinary initiatives involving multiple departments\, multiple institutions / organizations across technical domains\n\nSpeaker’s Bio:\nG. 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.\n\n* All are welcome * \n—————————————-
URL:https://ee.iisc.ac.in/event/ee-faculty-colloquium-overview-of-research-on-pulse-width-modulation-motor-control-and-electrical-machines/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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