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:20220101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Asia/Kolkata:20220209T153000 DTEND;TZID=Asia/Kolkata:20220209T170000 DTSTAMP:20260615T231857 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:20260615T231857 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