BEGIN:VCALENDAR VERSION:2.0 PRODID:-//EE - ECPv5.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:EE X-ORIGINAL-URL:https://ee.iisc.ac.in X-WR-CALDESC:Events for EE BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD TZOFFSETFROM:+0530 TZOFFSETTO:+0530 TZNAME:IST DTSTART:20210101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20211124T163000 DTEND;TZID=Asia/Kolkata:20211124T173000 DTSTAMP:20260404T185935 CREATED:20211122T233509Z LAST-MODIFIED:20211122T233826Z UID:239276-1637771400-1637775000@ee.iisc.ac.in SUMMARY:PhD Thesis Defense of Mr. Jitendra Kumar Dhiman DESCRIPTION:Date and Time: November 24\, 2021\,  11 AM. \nClick here to join the meeting \nTitle of the thesis: Spectrotemporal Processing of Speech Signals Using the Riesz Transform \nExaminer: Prof. S. R. Mahadeva Prasanna\, IIT Dharwad and IIT Guwahati \nAbstract: Speech signals have time-varying spectra. Spectrograms have served as a useful tool for the visualization and analysis of speech signals in the joint time-frequency plane. In this thesis\, we consider 2-D analysis of speech spectrograms. We consider a spectrotemporal patch and model it as a 2-D amplitude-modulated and frequency-modulated (AM-FM) sinusoid. Demodulation of the spectrogram yields the 2-D AM and FM components\, which correspond to the slowly varying vocal-tract envelope and the excitation\, respectively. For solving the demodulation problem\, we rely on the complex Riesz transform\, which is a 2-D extension of the 1-D Hilbert transform. The demodulation viewpoint brings forth many interesting properties of the speech signal. The spectrotemporal carrier helps us identify the regions that are coherent and those that are not. Based on this idea\, we introduce the coherencegram corresponding to a given spectrogram. The temporal evolution of the pitch harmonics can also be characterized by the orientation at each time-frequency coordinate\, resulting in the orientationgram. We show that these features collectively enable solutions for the important problems of voiced/unvoiced segmentation\, aperiodicity estimation\, periodic/aperiodic signal separation\, and pitch tracking. We compare the performance of the proposed methods with benchmark methods. The spectrotemporal amplitude characterizes the time-varying magnitude response of the vocal-tract filter. We show how the formants and their bandwidths manifest in the spectrotemporal amplitude. It turns out that the formant bandwidths are mildly overestimated\, which are perceptible when one performs speech synthesis using the estimated parameters. We propose a method for correcting the formant bandwidths\, which also restores the speech quality. Finally\, we use the curated spectrotemporal amplitude\, pitch\, aperiodicity\, and voiced/unvoiced decisions for the task of speech reconstruction in a spectral synthesis model and a neural vocoder\, namely\, WaveNet. We show that conditioning WaveNet on the spectrotemporal features results in high-quality speech synthesis. The quality of the synthesized speech is assessed using both objective and subjective measures. \nWe rely on the Perceptual Evaluation of Speech Quality (PESQ) measure and standard Mean Opinion Score (MOS) test for objective and subjective evaluation\, respectively. The performance of the proposed parameters is evaluated in a vocoder framework that uses the spectral synthesis model for speech reconstruction. The objective evaluation shows that the performance of the Riesz transform-based speech parameters is on par with the baseline systems. Using the spectral synthesis model\, we report an average PESQ score in the range from 2.30 to 3.45 over a total of 200 speech waveforms taken from the CMU-ARCTIC database comprising both male and female speakers. In comparison\, WaveNet-based speech reconstruction gave an average PESQ score of 3.65. \nSubjective evaluation was carried out through listening tests conducted in an acoustic test chamber on volunteers in the age group of 21 to 30. The average MOS score was 4.30 when the Riesz transform-based features were used in WaveNet for speech reconstruction\, which was also comparable with the baseline systems: STRAIGHT and WORLD. Both objective and subjective evaluations also showed that the quality of reconstructed speech waveforms was superior with the proposed features in a WaveNet vocoder than in the spectral synthesis model. \n An audio demonstration is available at the GitHub link: http://jitendradhiman.github.io/Demo \nBiography of Jitendra Kumar Dhiman: Jitendra Kumar Dhiman received his B.Tech. degree in Electronics and Telecommunication Engineering from the Institution of Electronics and Telecommunication Engineering\, Delhi\, India\, in 2010\, and M.Tech. degree in Signal Processing from Indian Institute of Technology Hyderabad in 2013. Subsequently\, he joined as a project assistant in Spectrum Lab (EE Department\, IISc) and worked on prosody modification of speech signals\, and then as a PhD student working on spectrotemporal models for speech processing. His research interests include speech and audio signal processing and machine learning. He will soon be joining Samsung Research and Innovation\, Bangalore (SRIB) as Chief Engineer. URL:https://ee.iisc.ac.in/event/phd-thesis-defense-of-mr-jitendra-kumar-dhiman/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20211207T203000 DTEND;TZID=Asia/Kolkata:20211207T213000 DTSTAMP:20260404T185935 CREATED:20211130T223747Z LAST-MODIFIED:20211207T044204Z UID:239361-1638909000-1638912600@ee.iisc.ac.in SUMMARY:EE PhD Thesis defense of Rejesh N A @3pm DESCRIPTION:Guide: Prof.. Muthuvel Arigovindan \nTitle: Novel Regularized Image Reconstruction Methods for Sparse Photoacoustic Tomography \nExaminers: Prof. Michael Liebling\, IDIAP Switzerland\, and Prof. Hari Varma\, IIT Bombay \nDate and Time: 7th December 2021 (Tuesday): 3:00 pm – 5:30 pm \nVenue : Online through Microsoft Teams. Click here to join the meeting \nSummary: Among all tissue imaging modalities\, photo-acoustic tomography (PAT)\, has been getting increasing attention in the recent past due to the fact that it has high contrast\, high penetrability\, and has the capability of retrieving high resolution. By using the combination of optical absorption and acoustic wave propagation\, PAT has been able to image tissues at relatively large depths with high resolution compared to purely optical modalities. Upon shining with a laser pulse\, the substance under investigation absorbs optical energy and undergoes thermoelastic expansion; as a result\, the spatial distribution of the concentration of the substance gets translated into the distribution of pressure-rise. This initial pressure rise travels outwards as ultrasound waves which are collected by ultrasound transducers placed at the boundary. From the ultrasound signal measured by the transducers as a function of time\, a PAT reconstruction method recovers an estimate of the initial pressure-rise by solving the associated inverse problem. The inverse problem is however challenging. It is challenging because the image has to be recovered for the entire cross-sectional plane\, whereas the samples of the acoustics pressure are available only from the points lying in the periphery of the imaging specimen where the transducers are located. In this thesis\, we make contributions in two widely used types of reconstructions methods known as the time-reversal method\, and the model-based method. \nWe summarize our contributions in three parts in the following. \nIn the first part\, we develop an improved model-based method. Model-based reconstruction methods in PAT express the measured pressure samples as a linear transformation on the initial pressure-rise and perform a regularized reconstruction. Model-based methods yield superior image quality even in the situation where measured data size is small. We propose a model-based image reconstruction method for PAT involving a novel form of regularization and demonstrate its ability to recover good quality images from datasets of significantly reduced size. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images\, high intensities and high second-order derivatives are jointly sparse. This regularization is combined with a data fidelity cost\, and the required image is obtained as the minimizer of this cost. As this regularization is non-convex\, the efficiency of the minimization method is crucial in obtaining artefact-free reconstructions. We develop a custom minimization method for efficiently handling this non-convex minimization problem. Further\, as non-convex minimization requires a large number of iterations and the PAT forward model in the data-fidelity term has to be applied in the iterations\, we propose a computational structure for efficient implementation of the forward model with reduced memory requirements. We evaluate the proposed method on both simulated and real measured data sets and compare them with a recent reconstruction method that is based on well-known total variation regularization. \nAppropriate tuning of the regularization weight\, λ\, plays a crucial role in determining the quality of reconstructed images in PAT. To make any regularization method practicable\, we need to have a way to determine the λ from the measured data. Unfortunately\, an appropriately tuned value of the regularization weight varies significantly with the variation in the noise level\, as well as\, with the variation in the high-resolution contents of the image\, in a way that has not been well understood. In the part of the work described above\, we did not address this problem as the focus has been to demonstrate the suitability of the intensity-augmented regularization for PAT image recovery; in the experimental demonstration\, we determined the required regularization weight by using the models that generated data. In the second part of the thesis\, we develop a semi-automatic method for determining the regularization weight from measured data. As a first step\, we introduce a relative smoothness constraint with a parameter; this parameter computationally maps into the actual regularization parameter\, but its tuning does not vary significantly with variation in the noise level\, as well as with the variation in the high-resolution contents of the image. Next\, we construct an algorithm that integrates the task of determining this mapping along with obtaining the reconstruction. Finally\, we demonstrate experimentally that we can run this algorithm with a nominal value of the relative smoothness parameter—a value independent of the noise level and the structure of the underlying image—to obtain good quality reconstructions. We compare the structural similarity (SSIM) scores of reconstructions obtained this way to that of reconstructions in which the regularization weight was determined using the models themselves; we show that the SSIM scores are comparable. This means that\, from a practical point of view\, our work solves the problem of determining the required regularization weight from measured images. \nIn the first two parts\, we assumed that the forward model that measures the signal from the target object be ideal. In particular\, we assumed that the excitation pulse and transducers’ impulse response are Dirac deltas. We focused only on the non-ideality of the transducer configuration\, i.e.\, we handled the case where the transducer locations do not densely sample the detection surface as required by the well-known back-projection method to work. Both excitation pulse and transducer impulse response have a finite width\, and this leads to some distortions in the reconstructed image. In the last part of the thesis\, we propose a pre-processing method for correcting the distortions in the context of using time-reversal methods which are similar to the back-projection method. To this end\, we formulate the broadening of the PA signals as a convolution between the impulse response of the system and the input excitation pulse. A deconvolution method using Tikhonov regularization is proposed to correct the PA signals before applying the time-reversal method. This resulted in improved resolution in the reconstructed images. A two-level deconvolution with the Tikhonov regularization method is also proposed to remove the blurring caused by the finite bandwidth of transducers and by the broad excitation pulses. We evaluate the usefulness of our method using numerical simulations and demonstrate that the reconstructed images from the deconvolved PA signals remain unaffected by the change in pulse widths or pulse shapes\, as well as by the limited bandwidth of the ultrasound detectors. \n—————————— \nALL ARE CORDIALLY INVITED URL:https://ee.iisc.ac.in/event/ee-phd-thesis-defense-of-rejesh-n-a-3pm/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20211214T203000 DTEND;TZID=Asia/Kolkata:20211214T213000 DTSTAMP:20260404T185935 CREATED:20211116T224237Z LAST-MODIFIED:20211207T044124Z UID:239261-1639513800-1639517400@ee.iisc.ac.in SUMMARY:PhD Colloquium of Asha Radhakrishnan @3pm DESCRIPTION:Title of the Thesis: Protection of Transmission and Distribution Networks in Presence of Converter – Interfaced Renewable Generators \nAdvisor: Dr. Sarasij Das \nDegree Registered: PhD (Eng.) \nClick here to join the meeting \nAbstract: Power generation from conventional sources like fossil fuels is widely being supplemented by renewable power sources. An increasing degree of penetration of renewable generators is observed in transmission and distribution levels. Power electronic converters are commonly used as interfaces to control the power output of these generators. Fast control and complex dynamics of the power electronic interfaces pose significant challenges to the legacy protection schemes employed in the transmission and distribution networks. This research focuses on addressing the impact of the wide penetration of CIGs on selected protection schemes in transmission and distribution networks. \nDistance protection is one of the most popular methods employed for the primary protection of transmission lines. Mho elements are used extensively in distance protection\, which employs positive sequence memory-polarization (PSMP) technique to give the most reliable performance during close-in faults. The use of memory voltage for polarization leads to the dynamic expansion of the mho circle in distance relays. The dynamic expansion of the mho characteristics increases resistive coverage in the case of synchronous generators. In this research work\, the performance of PSMP mho elements in the presence of CIGs has been studied. Simulations have been performed on the benchmark IEEE-39 bus system with a connected Vdc – Q control-based PV plant. Performances during Zone 1 forward faults and Zone 3 reverse faults have been studied. The results indicate the possibility of a significant reduction in the resistive coverage of the PSMP mho distance element during forward faults and loss of dependability during reverse faults. A novel solution to the observed problem has been proposed and validated using PSCAD simulations on the IEEE 39-bus system. The proposed solution is found to effectively achieve a predictable performance of PSMP mho relays in the presence of PV generators as well as synchronous generators. \nBack-up protection of transmission lines is important to be in place to ensure dependable protection during failure of primary protection. Breaker failure protection (BFP) is an important backup protection. It is employed to take appropriate action to clear a fault when the breaker that is normally expected to clear the fault fails to do so for any reason. Fault current contribution of CIGs is usually comparable with load currents. Low fault current contribution by utility-scale CIGs may lead to significant loss of security of BFP because of the existing practice of using lower setting for 50BF overcurrent element. This research work proposes a voltage-dependent adaptive setting of 50BF element to enhance the security of BFP schemes while maintaining dependability. Use of voltage helps in differentiating loads from fault situations. In traditional power systems\, CT subsidence current is known to delay the reset of BFP schemes. The impact of low fault contribution by CIGs on the reset time of BFP has been studied. Mathematical expression for CT subsidence current\, which influences the reset time\, has been derived. It is observed that the BFP reset may not be delayed if the fault current seen by the breaker is low due to the presence of CIG. The performance of the proposed 50BF setting and the findings on the subsidence current are supported using PSCAD simulations. \nThe Rate of Change of Frequency (RoCoF) relays are employed to arrest frequency collapse of a grid in the event of sudden loss of major generation. With large-scale CIGs replacing the synchronous generators\, the primary frequency response of the system is often affected due to the decrease in the system inertia. The rate of change of frequency at the inception of an event is observed to increase for a system with high penetration of CIGs. The settings of RoCoF relays are therefore required to be revised to account for the faster dynamics of CIGs. With varying degree of CIG penetrations\, the settings may change further. This work proposes a new method to detect the system changes by considering voltage as the parameter. The proposed method has been validated using PSCAD simulations performed on the IEEE39-bus system. Different degrees of CIG penetrations have been considered to test the performance of the proposed method. \nPenetration of Distributed Generations (DGs) has made traditional distribution protection schemes mostly ineffective. Sophisticated protection schemes cannot be implemented using fuses\, reclosers and Miniature Circuit Breakers (MCBs). Economics limits the use of protective relays in distribution systems. Voltage based protection is often not economical for distribution systems. Smart Meters (SMs) are available at various load points in a distribution system. SMs are equipped with measuring\, calculation\, and communication capabilities. This work proposes the utilization of SMs in distribution system protection. The possible applications of SMs in high impedance faults (HIF)\, overcurrent\, reverse power\, series arcing\, and under-voltage protection of distribution systems are identified in this work. Voltage-based protection can also be implemented using SMs. This work also proposes a voltage-based HIF location method. The fault signature of HIF is significant at the SMs which are nearer to the fault. The proposed method uses the SMs to compute an index to capture the fault signature. The HIF is then located in a zone defined by SMs adjacent to the SM with the highest index. The performance of the proposed method has been evaluated considering electric arc furnaces\, DGs and power electronics-interfaced loads. The effectiveness of the proposed SM-based protection has been demonstrated by simulating a European low voltage test feeder comprising of 906 buses in PSCAD. The proposed algorithm has also been implemented on a commercial smart energy meter to demonstrate its feasibility. \nALL ARE CORDIALLY INVITED \n  URL:https://ee.iisc.ac.in/event/phd-colloquium-ee-by-asha-radhakrishnan/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20211216T210000 DTEND;TZID=Asia/Kolkata:20211216T223000 DTSTAMP:20260404T185935 CREATED:20211207T044035Z LAST-MODIFIED:20211207T044405Z UID:239398-1639688400-1639693800@ee.iisc.ac.in SUMMARY:MTech(Research) Colloquium of Paawan Kirankumar Dubal @3.30pm DESCRIPTION:Guide: Dr. Sarasij Das \nTitle: Cyber Attack Resilient Breaker Failure Protection Scheme Using Wide Area measurements \nName of the Student: Paawan Kirankumar Dubal \nDate and Time: 16th December\, 2021\, 3:30 PM \nclick here for  the Meeting Link \nAbstract: Breaker Failure Protection (BFP) is a backup protection that comes into play when a circuit breaker fails to isolate the fault. If the circuit breaker fails to clear the fault\, the BFP scheme commands other required breakers to isolate the fault. BFP schemes are usually incorporated in microprocessor-based digital relays. Commonly employed BFP schemes use overcurrent element (50BF) and Breaker Failure Initiation (BFI) signals as inputs. The BFI signal is issued to the BFP relays from other digital relays. Line current sensed via current transformer is fed to the BFP relay for the overcurrent element (50BF). When both 50BF and BFI are high\, it waits for a specified time for the primary protection to operate. The 50BF element is usually set much lower than the rated load currents. So\, it will be high during the normal loading conditions. A cyber-attack can be launched by issuing false BFI or blocking a legitimate BFI signal to the BFP relay. Operation of BFP scheme usually leads to disconnection of a larger amount of loads. As a result\, mal-operation of BFP schemes can cause major disturbances in power systems. There is a need to make the BFP schemes resilient to cyber-attacks for reliable operation of power systems. Currently\, there is a lack of literature on the cyber-attack resilient BFP schemes. \nHence\, this thesis proposes a Wide-Area Measurement-Based Cyber-Resilient Breaker Failure Protection Scheme. The scope of the work is to develop an algorithm that will ascertain if the BFI received by the BFP relay is genuine. Blocking a legitimate BFI will cause the backup protection to operate and clear the fault. The proposition assumes that the BFP relay is not compromised in any manner. However\, a fake BFI can be issued by other digital relays\, which may cause unwanted BFP operations. In the proposed algorithm\, when the BFI is received. The BFP relay will communicate the receipt of BFI to the Phasor Data Concentrator (PDC). The proposed algorithm will run at the PDC\, which has access to the time-stamped measurements of the adjacent substations and the substation that triggered the algorithm. The decision of the proposed algorithm is communicated back to the BFP relay\, which will allow the tripping if the BFI is genuine. Hence\, we also propose modifications in the BF scheme in the BFP relay to incorporate the algorithm’s decision in issuing the final trip. The proposal running at PDC is a two-layer algorithm. The first layer randomly samples the bus voltages at the adjacent substations considering different groups of digital relays. The relay which has issued the BFI may be compromised. It makes relays of the same make and family more susceptible to a cyber-attack exploiting the same vulnerabilities. Hence we propose grouping of relays by their make and relay families. The first layer is meant to determine if there is a fault in the vicinity of the BFP relay that issued the trigger. The second layer provides discrimination between fault and cyber-attack by measuring the impedance observed at the two ends of the perceived-faulted line. Since the proposed solution is computationally lightweight\, it adheres to the timing requirement of the BFP. The proposition requires healthy communication between the PMUs and the PDC. Nevertheless\, the proposed method is fail-safe. It will resort to the conventional BFP scheme in case of loss of communication with the PDC. The proposed solution mitigates n number of cyber-attacks in a no-fault scenario. Additionally\, the proposed solution can detect one cyber-attack if the attacker times the cyber-attack during a fault condition. PSCAD simulations were performed to validate the proposition on IEEE 118 bus system. Furthermore\, the hardware was developed emulating the PMU-PDC communication as per IEEE C37.118-2 standard\, and the execution time of the proposal was verified to ensure adherence to the timing requirement of the BFP. \nALL ARE CORDIALLY INVITED URL:https://ee.iisc.ac.in/event/mtechresearch-colloquium-of-paawan-kirankumar-dubal-3-30pm/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20211217T203000 DTEND;TZID=Asia/Kolkata:20211217T220000 DTSTAMP:20260404T185935 CREATED:20211216T020558Z LAST-MODIFIED:20211216T020726Z UID:239421-1639773000-1639778400@ee.iisc.ac.in SUMMARY:PhD Thesis Colloquium of Kiran Kumar Challa @ 3pm DESCRIPTION:Title: Estimation of Synchronous Generator Parameters from Digital Relay Records and Development of an Experimental Testbed for Validation \nFaculty Adviser: Dr. Gurunath Gurrala. \nDate : 17th December 2021 \nTime: 3pm – 4.30pm \nMode: Hybrid mode\, both Teams and Physical \nVenue: MMCR\, 1st floor C-wing\, EE Department\, IISc \nTeams Link: Click here to join the meeting. \nAbstract: The development of dynamic power system components models became increasingly important in the modern grids dominated by high penetration of renewables because of the increased dependency of planning and operational decisions on dynamic simulation studies. The parameters of synchronous machines and associated control models play significant role in the overall model of the grid\, which need to be updated regularly by the utilities. So\, the parameters of the power plants are calibrated/estimated either using off-line testing or online measurements from phasor measurement units (PMU) or digital fault recorders (DFR). Development of individual generator models is feasible only if the PMU/DFR data is available for each generator in a power plant. Otherwise\, they can provide only aggregate model of a generating plant as PMU/DFRs are usually placed in substations. Digital protective relay (DPR) records are available for individual generators in any generating plant. This thesis explores the possibilities of utilizing DPR records of individual generators for parameter estimation. About 200 relay records have been collected from a hydro plant and a thermal plant in Karnataka. It is found that most of the records contain at the most 3 seconds data. Existing methods of parameter estimation using PMU/DFR data failed to work with the short duration records. There is no prior work reported in the literature which uses short relay records for parameter estimation of the synchronous generators. Constrained iterated unscented Kalman filter (CIUKF) and enhanced scattered search (eSS) algorithms are proposed for the parameter estimation using DPR records in this thesis. The parameters of a turbo alternator and its excitation system (210 MW) are estimated from the relay records collected using the proposed algorithms and the results are found be accurate. This is a first of its kind effort in the literature to the best of our knowledge. It is also found that the relay records should contain pre-fault data\, during fault data and some post-fault data for accurate estimation. However\, from the collected records only a small percentage of the records are found to be useful. To generate realistic data in the laboratory an experimental test bed development\, replicating the field implementation aspects of the digital relays\, is proposed in this thesis. A realistic scaled-down generalized substation model for translational research in smart grids is developed\, which can be configured to operate in 7 widely used substation bus bar schemes with prevalent current transformer (CT) configurations. All the potential transformers (PT) and CT measurements\, circuit breaker (CB)\, isolator and earth switch status signals are made available to configure any protection strategy like bus-bar protection\, unit protection schemes\, etc. precisely the same way they get implemented in the field. A systematic procedure for the development of an experimental scaled-down frequency-dependent transmission line model of a 230 kV transmission line is proposed. A lumped parameter frequency dependent transmission line model using modal transformation is derived for a 230 kV transmission line and scaled-down to 220 V. Clarke and inverse Clarke transformations are implemented using specially designed 1-phase transformers. The inductances of the scaled-down model are realized using amorphous cores. A new algorithm is proposed to fit a reduced-order R-L equivalent circuit to the frequency response of the modal impedances of the transmission lines. A close enough fitting is achieved with lesser number of passive elements using the proposed method compared to the widely used vector fitting algorithm. This kind of physical realization of a frequency dependent power transmission line model in the laboratory is first of its kind effort in the literature to the best of our knowledge. \nNote: Know how generated from the implementation of the generalized substation panels and transmission line models has been licensed to MCore Technologies Pvt Ltd\, Bangalore for commercialization. \nAcknowledgements: This work is supported by Fund for Improvement of Science and Technology (FIST) program\, DST\, India\, No.SR/FST/ETII-063/2015 (C) and (G) under the project “Smart Energy Systems Infrastructure – Hybrid Test Bed”. Acknowledge partial funding support from Robert Bosch Centre for Cyber Physical Systems (RBCCPS)\, IISc. Also acknowledge the Tata Trust Travel Grant. URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-kiran-kumar-challa-3pm/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20211223T153000 DTEND;TZID=Asia/Kolkata:20211223T163000 DTSTAMP:20260404T185935 CREATED:20211221T011203Z LAST-MODIFIED:20211221T011411Z UID:239439-1640273400-1640277000@ee.iisc.ac.in SUMMARY:PhD Thesis Colloquium of Shamibrota Kishore Roy DESCRIPTION:Title: Characterization and Modelling of Switching Dynamics of SiC MOSFETs \nName of the Advisor: Dr Kaushik Basu \nDate and Time: 23rd December 2021\, 10:00 AM \nPlace: Offline (MMCR EE\,) online: Meeting Link: Click here to join the meeting \nAbstract: Silicon Carbide (SiC) MOSFET is a wide bandgap (WBG) power device commercially available in the voltage range of 600-1700V. With superior switching\, conduction\, and thermal performance\, it is in close competition with the state-of-the-art Si IGBTs in this voltage range. \nIn power electronic converters\, semiconductor devices operate as switches. They can be turned on or off using a control signal. Unlike ideal switches\, practical devices require a finite amount of time to transit between on and off states. This is termed as switching transient. Non-zero finite product of voltage and current during switching transient results in switching loss. Characterization and modelling of switching dynamics help gain insight into the switching process and estimate switching loss. Estimated loss can be used to determine switching frequency and selection of power devices. Also\, switching dynamics is strongly impacted by the device and circuit parasitics. Insight into the switching process helps in the proper design of gate driver and power circuit layout. \nThe switching transient of SiC MOSFET is fast compared to its Si counterpart\, resulting in reduced switching loss. However\, it excites device and circuit parasitics that may lead to prolonged oscillations\, spurious turn on\, high device stress\, EMI-related issues\, Etc. The nonlinearity of the device characteristics and impact of circuit parasitics makes the switching transient of SiC MOSFET more involved than its Si counterpart. So\, the characterization and modelling of switching dynamics of SiC MOSFET are essential. \nExperimental\, simulation and analytical approaches are used to study the switching dynamics and estimate switching loss. The experimental approach is inaccurate\, requires expensive measurement set-ups\, and is not suitable for the early stages of power converter design. The behavioural modelling approach is a widely used simulation-based approach (i.e.\, Spice simulation) where the circuit-based model of the device is used along with lumped parameter model of the external circuit. These models are simple and can capture the switching transient with sufficient accuracy. However\, it does not provide insight into the switching process\, and applying it for a large set of devices and operating conditions will be time-consuming. The analytical model belongs to another class of switching transient models. It is based on the simplified approximate solution of a set of coupled non-linear differential equations obtained from the behavioral model and results in analytical closed-form solutions or reduced order coupled non-linear equations. This model is computationally efficient and can be implemented easily in freely available programming platforms such as C or Python. Also\, the parameters required for analytical models can be obtained from the device datasheet. This modelling approach is beneficial at the early stages of the converter design when switching loss and junction temperature need to be evaluated over several operating points for many available devices from different manufacturers. This work focuses on developing analytical models. \nThe first part of the work proposes an analytical model to capture the switching dynamics (turn on and off) of SiC MOSFET. In the existing literature\, simplified modelling of channel current and device capacitances were used\, resulting in underestimating switching transition time and loss. On the contrary\, the proposed approach considers the detailed non-linear model of channel current and device capacitances along with circuit parasitics. It accurately estimates transition time\, switching loss\, (dv/dt)\, (di/dt)\, and transient over-voltage. \nIn soft switched converters (i.e.\, DAB)\, hard turn on is avoided by the converter operation\, and the switching loss is solely dictated by the turn off loss. The addition of external capacitance across the device prolongs the voltage rise period and reduces overlap between voltage and current during turn off transient. This is termed as zero voltage switching (ZVS). However\, the selection of external capacitance is not straightforward. A large external capacitance reduces switching loss\, (dv/dt)\, (di/dt)\, and transient over-voltage but may also result in higher dead-time loss and reduced switching frequency. Also\, this may lead to partial soft switching for light load conditions if the dead-time is not sufficient. In this work\, an analytical model to capture capacitor-assisted turn-off switching transient is also presented where the detailed non-linear modelling of the SiC MOSFET is used. This leads to a better estimation of switching transition time\, actual loss\, (dv/dt)\, (di/dt)\, and transient over voltage. Also\, a step-by-step design procedure of the optimal external snubber capacitor was proposed. It ensures the soft-switching condition is satisfied\, and the maximum (dv/dt) rate is within a predefined limit for a specified DC bus voltage and range of load currents. This procedure also helps in the selection of proper dead-time to avoid partial soft-switching conditions. \nDouble pulse test (DPT) based experimental measurement is used to first validate the behavioural model. Then\, the behavioural model is used to verify the correctness of the proposed analytical models. This indirect verification approach is necessary as it is not possible to measure the actual switching loss directly from the experimental measurements. Two 1.2-kV SiC MOSFETs of different current ratings are used for validation. It has been observed that there can be a significant difference between the experimentally measured switching loss and actual loss\, and the difference is more prominent for low external gate resistances. Also\, the turn off loss of SiC MOSFET is small compared to the turn-on loss. \nFast switching transient of SiC MOSFET is significantly impacted by circuit parasitics. Circuit parasitic inductances are dependent on both device package (device lead\, wire bond etc.) and circuit layout (PCB layout)\, whereas circuit parasitic capacitances are contributed solely by the circuit layout. Proposed switching transient models require circuit parasitics as input\, and the values are not usually available in the device datasheet. Measurement is the only way to accurately estimate some device package-dependent circuit parasitics when the internal package geometry is unknown. In this context\, a set of simple measurement techniques are proposed to determine important circuit parasitics necessary for switching dynamics study. The accuracy of the proposed technique is verified through behavioural simulation\, and experimental results of the hard turn off and capacitor assisted soft turn off dynamics of SiC MOSFET over a range of operating conditions for two 1.2-kV discrete SiC MOSFET of different current ratings and two different PCB layouts. Measured circuit parasitic when used in switching transient model\, correctly predicted both hard turn-off and capacitor assisted soft turn off switching dynamics over a wide range of operating conditions. \nAn interactive software based on the proposed analytical model is also developed in Python environment. The developed software takes device parameters and circuit parasitics as input and estimates transition time\, switching loss\, (dv/dt)\, (di/dt) and transient over-voltage as a function of load current. \nALL ARE CORDIALLY INVITED URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-shamibrota-kishore-roy/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220104T150000 DTEND;TZID=Asia/Kolkata:20220104T170000 DTSTAMP:20260404T185935 CREATED:20220103T014957Z LAST-MODIFIED:20220103T015245Z UID:239455-1641308400-1641315600@ee.iisc.ac.in SUMMARY:PhD Thesis Defence of Katam Nishanth @ 9:30 am DESCRIPTION:Research Supervisor: Prof. BS Rajanikanth\nTitle of the thesis: Plasma catalysis of diesel exhaust using industrial wastes: a study on NOX and THC removal\nTime and Date: 9.30 AM\, 4th January 2022 (Tuesday)\nVenue: MS Teams Link \nAbstract: Air pollution\, caused by large scale consumption of fossil fuels such as diesel\, has been the leading cause of several adverse environmental effects such as global warming\, higher acidity in rainwater\, lower yield of agriculture production and several health issues. Diesel has been the primary and inevitable fuel source of energy worldwide\, in both stationary power supplies and automobile applications. Several developing countries like India continue to rely heavily on usage of diesel fueled machinery and automobiles\, which has resulted in high soot\, particulate and hazardous gas emissions. The prominent gaseous pollutants of concern are the oxides of nitrogen (NOX) and total hydrocarbon content (THC) present in the diesel exhaust. Though efficient systems have been discovered for reducing soot and particulate emissions\, treatment techniques for removal of gaseous pollutants are yet to reach a similar level of progress. Therefore\, research efforts aimed at identifying treatment techniques for curbing hazardous gaseous pollutants are a welcoming step towards addressing the pertinent issue of air pollution. \nThe gaseous pollutants emitted from the diesel engine can be reduced by applying control strategies at the level of engine design (p= re-combustion) or as an aftertreatment technique of the exhaust stream (post-combustion). Although the pre-combustion control strategies are limited by the possible engine design modifications\, the post-combustion approach allows for greater flexibility and scope by utilizing a variety of plasma discharges\, catalysts and adsorbents. One such post-combustion strategy which involves treatment of NOX/THC using non-thermal plasma (NTP) generated from dielectric barrier discharge (DBD)\, has yielded promising results at the laboratory level. Non-thermal plasma produces an oxidative environment containing several charged species\, which include energetic electrons\, excited species\, ions\, and radicals\, at atmospheric pressure and ambient temperature conditions. Diesel exhaust exposed to such a non-thermal plasma environment has been found to cause the formation of higher oxides of nitrogen and oxidized hydrocarbon intermediates\, which necessitates exposing them further to adsorbents or catalysts for effective removal of the harmful pollutants. In recent years\, a treatment technique which involves filling a plasma reactor with catalytic materials that enhance reactions in the presence of plasma\, referred to as plasma catalysis\, has given promising results at laboratory level in terms of pollutant removal efficiency\, on par with conventional thermal catalysis. The highly reactive environment produced by the interaction between reactive species in the plasma and the surface of the catalytic material can facilitate reactions that usually occur only at high temperatures in conventional (thermal) catalysis. The literature on plasma catalysis for several gas treatment applications reveals the utilization of expensive\, commercially available catalytic materials. The expensive rare metals used in such catalysts and the need for replacement due to choking of the catalytic material\, makes their usage an economically non-viable option. It is at this juncture that the utilization of freely available industrial wastes as potential catalysts appears to be an economically feasible alternative. Such environmentally safe and inexpensive treatment techniques for NOX/THC abatement are a desirable and welcoming option for exhaust treatment in the long run. \nIn the current work\, gaseous pollutants from a stationary diesel engine exhaust were exposed to an electrical discharge plasma in a reactor packed with pellets made from industrial wastes\, in a carefully controlled laboratory condition. Oxides of nitrogen and the total hydrocarbons are the two components of the diesel exhaust that were studied as the gaseous pollutants. The pellets were made from solid industrial wastes such as foundry sand\, fly ash\, red mud\, oyster shells\, bagasse\, and mulberry residue. The plasma was either volume discharge type or surface discharge type during the study. The thesis then progresses with a study of the results of NOX and THC removal through plasma catalysis and performing qualitative analysis of experiments to ascertain the dominance of plasma catalysis over other pollutant removal processes\, such as plasma-cascaded adsorption and plasma-only treatment. \nIt was observed that among the solid industry wastes studied\, red mud showed better NOX and THC removal efficiencies compared to the other industrial waste pellets. Further\, plasma catalysis showed moderate to significant increase in NOX and THC removal when compared to the plasma-cascaded and plasma-only methods\, for all the pellets studied. This approach of using industrial waste pellets for plasma catalysis of diesel exhaust is the first of its kind in the NTP fraternity. The results will be presented in detail along with the possible reaction pathways associated with conversion or removal of NOX/THC under plasma catalysis.\n******* URL:https://ee.iisc.ac.in/event/phd-thesis-defence-of-katam-nishanth/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220105T163000 DTEND;TZID=Asia/Kolkata:20220105T180000 DTSTAMP:20260404T185935 CREATED:20220104T012855Z LAST-MODIFIED:20220104T013652Z UID:239459-1641400200-1641405600@ee.iisc.ac.in SUMMARY:PhD Thesis Defense of Mr. Praveen Kumar Pokala @ 11am DESCRIPTION:Title: Robust Nonconvex Penalties for Solving Sparse Linear Inverse Problems and Applications to Computational Imaging\nThesis Supervisor: Prof. Chandra Sekhar Seelamantula\nExaminer: Prof. Suyash Awate\, IIT Bombay\nVenue: MS Teams (Click here to join the meeting)\nDate and time: January 5\, 2022; 11 AM onward \nAbstract: Sparse linear inverse problems require the solution to the l-0-regularized least-squares cost\, which is not computationally tractable. Approximate and computationally tractable solutions are obtained by employing convex/nonconvex relaxations of the l-0-pseudonorm. One such approximation is obtained by considering the l-1-norm\, which is a convex relaxation of the l-0-pseudonorm. However\, l-1 regularization is known to result in biased estimates due to over-relaxation of the l-0-pseudonorm but it comes with the advantage of convexity of the regularized least-squares cost. Several nonconvex approximations of the l-0 pseudonorm have been proposed to overcome the bias introduced by the l-1-norm and to ensure better sparsity. However\, certain aspects of nonconvex sparse regularization have not been explored. Some of these are as follows: \nNonconvex sparse priors have been explored in the synthesis-sparse framework\, but not in the analysis-sparse framework due to the unavailability of proximal operators in closed-form in the analysis setting. \nExisting nonconvex approaches attach the same regularization weights across all the components of a sparse vector and treat them as fixed hyperparameters. Considering different weights for the entries and adapting them iteratively is likely to result in a superior performance. \nPrior learning networks based on deep-unfolded architectures for solving nonconvex penalties have not been explored. \nThis thesis addresses the above aspects in three parts and considers applications to various computational imaging problems. \nPart-1: Nonconvex Analysis-sparse Recovery \nIn this part\, we solve the analysis-sparse recovery problem based on three regularization approaches: \nConvexity-preserving nonconvex regularization: We propose the analysis variants of the generalized Moreau envelope and generalized minimax concave penalty (GMCP) over a complex domain. Since the cost is a real-valued function defined over a complex domain\, it is nonholomorphic\, i.e.\, it does not satisfy Cauchy-Riemann (CR) conditions. To circumvent this problem\, we rely upon on Wirtinger calculus to derive the proximal operator for the analysis l-1 prior and develop an efficient optimization strategy employing projected proximal algorithms. The projection transform maps the analysis-sparse recovery problem into an equivalent constrained synthesis-sparse formulation. \nNonconvex sparse regularization: We consider the problem of nonconvex analysis sparse recovery in which the signal is assumed to be sparse in a redundant analysis operator. Standard nonconvex sparsity promoting priors do not have a proximal operator in closed-form under a redundant analysis operator and therefore\, proximal approaches cannot be applied directly. This led us to develop two alternatives — Moreau envelope regularization and projected transformation. \nGeneralized weighted l-1 regularization: We develop a generalized weighted l-1 regularization strategy\, which allows for efficient weight-update strategies for iteratively reweighted l-1-minimization under tight frames. Further\, we impose sufficient conditions on the weight function that leads to a reweighting strategy\, which follows the interpretation originally given by Candès et al.\, but is more efficient than theirs. Since the objective function is nonholomorphic\, we resort to Wirtinger calculus for deriving the update equations. We develop an algorithm called generalized iteratively reweighted soft-thresholding algorithm (GIRSTA) and its fast variant\, namely\, generalized fast iteratively reweighted soft-thresholding algorithm (GFIRSTA). We provide convergence guarantees for GIRSTA and empirical convergence results for GFIRSTA. \nWe demonstrate the efficacy of the proposed regularization strategies in comparison with the benchmark techniques considering compressive-sensing magnetic resonance image (CS-MRI) reconstruction under a redundant analysis operator\, more specifically\, shift-invariant discrete wavelet transform (SIDWT). \nPart-2: Weighted Minimax Concave p-pseudonorm Minimization \nIn this part\, we develop techniques for accurate low-rank plus sparse matrix decomposition (LSD) and low-rank matrix recovery. We proposed weighted minimax-concave penalty (WMCP) as the nonconvex regularizer and show that it admits a certain equivalent representation that is more amenable to weight adaptation. Similarly\, an equivalent representation to the weighted matrix gamma norm (WMGN) enables weight adaptation for the low-rank part. The optimization algorithms are based on the alternating direction method of multipliers. The optimization frameworks relying on the two penalties\, WMCP and WMGN\, coupled with a novel iterative weight-update strategy\, result in accurate low-rank plus sparse matrix decomposition and low-rank matrix recovery techniques. Further\, we derive an algorithm\, namely\, iteratively reweighted MGN (iReMaGaN) algorithm\, which has a superior low-rank matrix recovery performance. The proposed algorithms are shown to satisfy descent properties and convergence guarantees. On the applications front\, we consider the problems of foreground-background separation and image denoising. Simulations and validations on standard datasets show that the proposed techniques outperform the benchmark techniques. Next\, we extended the idea to obtain a generalized l-p-penalty\, namely\, minimax concave p-pseudonorm (MCpN) based on a novel p-Huber function as the sparsity promoting function\, and its weighted counterpart\, weighted MCpN (WMCpN) as a regularizer for solving the sparse linear inverse problem. WMCpN is a generalization of which several penalties\, namely\, l-1-norm\, minimax concave penalty (MCP)\, l-p penalty\, weighted l-1-norm\, and weighted l-p penalty become special cases. However\, MCpN and WMCpN regularizers do not have closed-form proximal operators\, which makes the optimization problem challenging. To overcome this hurdle\, we develop an equivalent representation that is more amenable to optimization and allows for an analytical weight-update strategy. MCpN is a special case of WMCpN where all the weights are fixed and equal. The optimization algorithms are based on the alternating direction method of multipliers. Considering the application of interferometric phase estimation\, we demonstrate that MCpN and WMCpN result in accurate interferometric phase estimation. Simulations and experimental validations on standard datasets show that the proposed techniques outperform the benchmark techniques. \nPart-3: Nonconvex Sparse Regularization and Deep-Unfolding \nIn the final part\, we transition from fixed analytical priors to data-driven priors. To begin with\, we develop a deep-unfolded architecture\, namely\, FirmNet\, for sparse recovery. FirmNet has two parameters — one that controls the noise variance\, and the other that allows for explicit sparsity control. We show that FirmNet is better than Learned-ISTA (LISTA) by at least three-fold in terms of the probability of error in support (PES)\, and about 2 to 4 dB higher reconstruction SNR. Further\, we solve the problem of reflectivity inversion\, which deals with estimating the subsurface structure from seismic data through FirmNet. As an application\, we consider the problem of seismic reflectivity inversion. We demonstrate the efficacy of FirmNet over the benchmark techniques for the reflectivity inversion problem by testing on synthetic 1-D seismic traces and 2-D wedge models. We also report validations on simulated 2-D Marmousi2 model and real data from the Penobscot 3D survey off the coast of Nova Scotia\, Canada. Next\, we propose convolutional FirmNet (ConFirmNet)\, which is an extension of the FirmNet approach to solve the problem of convolutional sparse coding. As an application\, we build a ConFirmNet based sparse autoencoder (ConFirmNet-SAE) and demonstrate suitability for image denoising and inpainting. Further\, we also show that training ConFirmNet-SAE with the Huber loss imparts robustness to outliers. ConFirmNet-SAE also proves to be robust to mismatch between training and test noise conditions than convolutional learned iterative soft-thresholding algorithm (CLISTA). Finally\, we propose a sparse recovery formulation that employs a nonuniform\, nonconvex synthesis sparse model comprising a combination of convex and nonconvex regularizers\, which results in accurate approximations of the l-0 pseudo-norm. The resulting iterative optimization employs proximal averaging. When unfolded\, the iterations give rise to a nonuniform sparse proximal average network (NuSPAN) that can be optimized in a data-driven fashion. We demonstrate the efficacy of NuSPAN also for solving the problem of seismic reflectivity inversion. \nBiography of the candidate: Praveen Kumar Pokala received his B.Tech. degree in Electronics and Telecommunication Engineering from Jawaharlal Nehru Technological University\, Hyderabad\, India\, in 2006 and M. Tech degree in Signal Processing from Indian Institute of Technology (IIT)\, Guwahati\, India\, in 2009. Subsequently\, he worked as an Assistant Professor in LPU university\, Jalandhar\, India and GITAM university\, Hyderabad\, India. He is currently pursuing Ph.D. in the Department of Electrical Engineering\, Indian Institute of Science\, Bangalore. His current research interests are machine learning\, deep learning\, and nonconvex optimization algorithms\, with applications to inverse problems in computational imaging. He is presently a Senior Lead Engineer at Qualcomm R&D\, Bangalore. \nAll are invited. \n  URL:https://ee.iisc.ac.in/event/phd-thesis-defense-of-mr-praveen-kumar-pokala-11am/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220209T153000 DTEND;TZID=Asia/Kolkata:20220209T170000 DTSTAMP:20260404T185935 CREATED:20220203T033158Z LAST-MODIFIED:20220412T222952Z UID:239553-1644420600-1644426000@ee.iisc.ac.in SUMMARY:PhD Thesis Colloquium of Ashiq Muhammed P E @ 10am DESCRIPTION:Date/Time:         09 Feb 2022\, at 10:00 to 11:30 \nTitle:                   Improved Understanding of Standing Waves in Single Layer Coil and Elegant Methods to Estimate Transformer Winding Parameters \nAbstract: Analyzing the effect of impulse voltages (like lightning\, switching and VFTOs) on transformer winding has occupied centerstage in core electrical engineering research for over a century. These investigations gather great significance and relevance as it eventually governs the design of insulation in the winding. Notwithstanding the colossal contribution this domain has witnessed from stalwarts in the past century\, a closer scrutiny surprisingly reveals that there still exists some grey areas that demands attention. Pursuing this line of thought\, the first part of this thesis aims to clearly describe what this grey area is\, and resolving it provides a deeper insight about fundamental understanding of surge response in transformer windings – with special emphasis on its standing wave phenomenon. Following this\, in the latter part\, elegant procedures are stitched together to determine a few electrical parameters of the transformer winding equivalent circuit – that have potential to help in assessing mechanical status of windings. Objectives of the thesis are – \n\nFormulate an analytical method to determine the exact shape of standing waves for all modes in a uniform single layer coil as a solution of its governing partial differential equation\nEstimate series capacitance of a uniform transformer winding from its measured driving point impedance\nDetermine effective air-core inductance of an iron-core uniform winding as a function of its axial length from measured driving point impedance\n\nFirst part of the thesis revisits a century-old classical theory of standing waves on uniform single layer coils. Accurate information about natural frequencies and shapes of the corresponding standing waves are essential for gaining a deeper understanding of the response of coils to impulse excitations. Analytical studies on coils have largely been based on the assumption that standing waves are sinusoids in both space and time. However\, this contradicts the results from numerical circuit analysis and practical measurements. So\, this thesis attempts to bridge this discrepancy by revisiting the classical standing wave phenomena in coils. It not only assesses the reason for the aforementioned inconsistency\, but also makes a contribution by analytically deriving the exact mode shape of standing waves for both neutral open/short conditions. For this\, the coil is modelled as a distributed network of elemental inductances and capacitances\, while an exponential function describes the spatial variation of mutual inductance between turns. Initially\, an elegant derivation of the governing partial differential equation (in terms of voltage as the variable instead of flux) for surge distribution is presented and to the best of our knowledge\, for the first time\, an analytical solution for the same has been found by the variable-separable method to find the complete solution (sum of time and spatial terms). Hyperbolic terms in the spatial part of the solution have always been neglected but are included here\, thus\, yielding the exact mode shapes. For verification\, both voltage and current standing waves computed from the analytical solution were plotted and compared with PSPICE simulation results on a 100-section ladder network representing a uniform single-layer coil. Then\, practical measurements were conducted on a tailor-made large-sized single layer coil with a length of 2.2 m\, diameter of 1 m and having 640 turns. It turns out that even in such simple single layer coils\, the shape of standing waves of all modes deviates considerably from being sinusoidal. It was further observed that this deviation depends on spatial variation of mutual inductance\, capacitive coupling\, and order of the standing waves. \nIn the second part\, an elegant method for determining the series capacitance (Cs) and air-core equivalent inductance of a uniform winding as a function of its axial length (termed as M0x in this thesis) of a uniform transformer winding\, from its measured DPI magnitude\, are discussed. Knowledge about the series capacitance of the winding is essential\, which along with shunt capacitance\, determines the initial impulse voltage distribution when a surge impinges on the winding. Unlike previously published approaches\, the proposed method does not involve any cumbersome and time-consuming curve-fitting or running of optimization/search algorithms. Neither does it require winding geometry data. The proposed procedure for finding series capacitance relies on a property that is observable in the driving point impedance function of a lossless winding with an open neutral condition\, viz.\, the ratio of the product of squares of open circuit natural frequencies to the product of squares of short circuit natural frequencies bears a particular relation to driving point impedance function coefficients. A simple procedure involving a deft manipulation and combination of a few well-known properties that correlate the roots of a polynomial to its coefficients are then utilized for determining series capacitance.  \nKnowledge about equivalent air-core inductance distribution as a function of its axial length (i.e.\, M0x) is useful for localizing a minor/incipient mechanical fault in the winding. A physically realizable empirical relationship to estimate M0x is initially proposed. The corresponding constants of the empirical relationship are then calculated from the measured driving point impedance. The proposed method requires three DPI measurements: one with neutral-end open and the other with neutral-end shorted. The third DPI is measured with a known external lumped capacitance connected between the neutral and ground. This method requires only the first few dominant natural frequencies observable in the first two of the DPIs. Feasibility of both proposed methods for estimating Cs and M0x was initially verified by simulation on an N-section ladder network and then by experiments on small-sized continuous-disk and interleaved-disk windings\, and thereafter on a large-sized 33 kV\, 3.5 MVA continuous-disk winding. Salient features of the proposed methods are – they are simple\, elegant and involve minimum post-processing after measuring the DPI. Given its inherent simplicity and their relevance\, the author is hopeful that industry will come forward to implement these procedures on an existing FRA measuring instruments – thus opening a new dimension to FRA measurements. \nALL ARE CORDIALLY INVITED \n* * * URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-ashiq-muhammed-p-e-10am/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220216T203000 DTEND;TZID=Asia/Kolkata:20220216T223000 DTSTAMP:20260404T185935 CREATED:20220216T001817Z LAST-MODIFIED:20220216T002011Z UID:239594-1645043400-1645050600@ee.iisc.ac.in SUMMARY:Ph.D. Thesis Defense of Sanjay Viswanath DESCRIPTION:Advisor: Prof.. Muthuvel Arigovindan\nTitle: Spatially Adaptive Regularization for Image Restoration\nThesis Examiners: Prof.  Suyash Awate\,  IIT Bombay\,   and  Prof. Ajit Rajwade\, IIT Bombay\nDefense Examiner:  Prof.  Ajit Rajwade\, IIT Bombay\nDate and Time: 16th February (Wednesday): 3:00 pm – 5:00 pm\nVenue: Microsoft Teams Live\nMicrosoft Teams meeting link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NjBkZTE1NmEtNzQ5Ny00NzJkLTllNTgtM2ViNWZiZDQzNzA4%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%22d7e91daa-7e70-4e9c-b565-b900dfd5b5b5%22%7d \n\n\n\n\n\n\n\n\n\nJoin conversation\nteams.microsoft.com\n\n\n\n\n\n\n Summary: Image restoration/reconstruction refers to the estimation of underlying image from measurements generated by imaging devices. This problem is generally ill-posed due to the fact that measurements are corrupted because of the physical limitations of the imaging device\, and the inherent noise involved in the measurement process. There are three main classes of methods in the current literature. The first class of methods are based on regularization framework that enforces an ad-hoc prior on the restored image. The second class of methods use regression-based learning paradigms\, where a training set of clean images and the corresponding distorted measurements are used to generate a trained prior. The third class of methods adopt trained priors similar to the ones utilized in second class of methods\, but within the regularization framework. This third class of methods\, the trained regularization methods\, are getting increasing attention because of their versatility as regularization methods\, while also encompassing natural priors obtained from training. However\, the need for training data can limit their applicability. In this thesis\, we propose spatially adaptive regularization methods where the adaptation information is retrieved from the measured data that undergoes reconstruction. Due to the adaptation\, the enforced prior is more natural than the existing regularization methods. At the same time\, our methods do not require training data. \nIn the first part\, we propose a novel regularization method that adaptively combines the well-known second order regularization\, called Hessian-Schatten (HSN) norm regularization\, and first order TV (TV-1) functionals with spatially varying weights. The relative weight involved in combining the first- and second-order terms becomes an image\, and this weight is determined through minimization of a composite cost function\, without user intervention. \nOur contributions in this part can be summarized as follows: \n• We construct a composite regularization functional containing two parts: (i) the first part is constructed as the sum of TV-1 and HSN with spatially varying relative weights; (ii) the second part is an additional regularization term for preventing rapid spurious variations in the relative weights. The total composite cost functional is convex with respect to either the required image or the relative weight\, but it is non-convex jointly. \n• We construct a block coordinate descent method involving minimizations w.r.t. the required image and the relative weight alternatively with the following structure: the minimization w.r.t. the required image is carried out using Alternating Direction Method of Multipliers (ADMM) \, and the minimization w.r.t. the relative weight is carried out as a single step exact minimization using a formula that we derive. \n• Since the total cost is non-convex\, the reconstruction results are highly dependent on the initialization for the block-coordinate descent method. We handle this problem using a multi-resolution approach\, where a series of coarse-to-fine reconstructions are performed by minimization of cost functionals defined through upsampling operators. Here\, minimization w.r.t. the relative weight and the required image is carried out alternatively\, as we progress from coarse to final resolution levels. At the final resolution level\, the above-mentioned block coordinate descent method is applied. \n• Note that the sub-problem of minimization w.r.t. to the required image involves spatially varying relative weights. Further\, this sub-minimization problem in the above-mentioned multi-resolution loop involves upsampling operators. Hence\, the original ADMM method proposed by Papafitsoros et al. turns out to be unsuitable. We propose an improved variable splitting method and computational formulas to handle this issue. \n• We prove that the overall block coordinate descent method converges to a local minimum of the total cost function using Zangwill’s convergence theorem. \nWe name our method as Combined Order Regularization with Optimal Spatial Adaptation (COROSA). We provide restoration examples involving deconvolution of TIRF images and reconstruction of Magnetic Resonance Imaging (MRI) images from under-sampled Fourier data. We demonstrate that COROSA outperforms existing regularization methods and selected deep learning methods. \nIn the second part\, we make COROSA more adaptive by replacing the HSN with a spatially varying weighted combination of Eigenvalues of the Hessian. This means that the resulting regularization will be in the form of a spatially varying weighted sum of three terms involving the gradient and two Eigenvalues of Hessian. This allows the functional to restore fine image structures through directional weighting\, in terms of the local Eigenvalues. We again adopt a BCD scheme that alternates between the spatially varying weight estimation and image computation\, as done in the first part. However\, both steps are more complex with the new form. The first task of weight estimation is more complex as it involves three terms. The second task of image computation is more complex\, because there is no known proximal operator for regularization involving unequally weighted Hessian Eigenvalues. We solve the first problem by constructing a novel iterative method\, and the second problem by deriving a novel proximal formula. Here too\, we adopt a multi-resolution approach to initialize the BCD method. We call our method the Hessian Combined Order Regularization with Optimal Spatial Adaptation (H-COROSA). We experimentally compare H-COROSA with well-known regularization methods and selected learning based methods for MRI reconstruction from under-sampled Fourier data. \nCompressive Sensing based methods have shown the advantage of l0-based sparsity enforcing functionals in restoration. For practical applications\, lp\, 0