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X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
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TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220601T170000
DTEND;TZID=Asia/Kolkata:20220601T180000
DTSTAMP:20260614T021102
CREATED:20220530T233759Z
LAST-MODIFIED:20220531T224824Z
UID:239758-1654102800-1654106400@ee.iisc.ac.in
SUMMARY:Lecture by Dr. Ayush Bhandari @ 11.30am
DESCRIPTION:Title: Digital Acquisition via Modulo Folding: Revisiting the Legacy of Shannon-Nyquist\, Prony\, Schoenberg\, Pisarenko and Radon \nDate and time: June 1\, 2022; 11.30 AM\nVenue: Multimedia Classroom\, Electrical Engineering Department\, IISc \nCoffee will be served during the talk. \nAbstract: Digital data capture is the backbone of all modern day systems and “Digital Revolution” has been aptly termed as the Third Industrial Revolution. Underpinning the digital representation is the Shannon-Nyquist sampling theorem and more recent developments such as compressive sensing approaches. The fact that there is a physical limit to which sensors can measure amplitudes poses a fundamental bottleneck when it comes to leveraging the performance guaranteed by recovery algorithms. In practice\, whenever a physical signal exceeds the maximum recordable range\, the sensor saturates\, resulting in permanent information loss. Examples include (a) dosimeter saturation during the Chernobyl reactor accident\, reporting radiation levels far lower than the true value\, and (b) loss of visual cues in self-driving cars coming out of a tunnel (due to sudden exposure to light). \nTo reconcile this gap between theory and practice\, we introduce a computational sensing approach—the Unlimited Sensing framework (USF)—that is based on a co-design of hardware and algorithms. On the hardware front\, our work is based on a radically different analog-to-digital converter (ADC) design\, which allows for the ADCs to produce modulo or folded samples. On the algorithms front\, we develop new\, mathematically guaranteed recovery strategies. \nIn the first part of this talk\, we prove a sampling theorem akin to the Shannon-Nyquist criterion. Despite the non-linearity in the sensing pipeline\, the sampling rate only depends on the signal’s bandwidth. Our theory is complemented with a stable recovery algorithm. Beyond the theoretical results\, we also present a hardware demo that shows the modulo ADC in action. \nBuilding on the basic sampling theory result\, we consider certain variations on the theme. This includes different signal classes (e.g. smooth\, sparse and parametric functions) as well as sampling architectures\, such as One-Bit and Event-Triggered sampling. Moving further\, we reinterpret the USF as a generalized linear model that motivates a new class of inverse problems. We conclude this talk by presenting a research overview in the context of single-shot high-dynamic-range (HDR) imaging\, sensor array processing and HDR computed tomography based on the modulo Radon transform. \nAbout the speaker:  Ayush Bhandari received the Ph.D. degree from Massachusetts Institute of Technology (MIT)\, Cambridge\, MA\, USA\, in 2018\, for his work on computational sensing and imaging which is being shaped as a forthcoming\, co-authored book Computational Imaging in MIT Press. He is currently a faculty member with the Department of Electrical and Electronic Engineering\, Imperial College London\, U. K. He has held research positions at INRIA (Rennes)\, France\, Nanyang Technological University\, Singapore\, the Chinese University of Hong Kong and Ecole Polytechnique Fédérale de Lausanne (EPFL)\, Switzerland among other institutes. He was appointed the August–Wilhelm Scheer Visiting Professor (Department of Mathematics)\, in 2019 by the Technical University of Munich. \nHe has been a tutorial speaker at various venues including the ACM Siggraph (2014\,2015) and the IEEE ICCV (2015) and he was the keynote speaker at the Intl. Workshop on Compressed Sensing applied to Radar\, Multimodal Sensing and Imaging (CoSeRa)\, 2018. Some aspects of his work have led to new sensing and imaging modalities which have been widely covered in press and media (e.g. BBC news). Applied aspects of his research have led to more than 10 US patents. His scientific contributions have led to numerous prizes\, most recently\, the Best Paper Award at IEEE ICCP 2020 (Intl. Conf. on Computational Photography) and the Best Student Paper Award (senior co-author) at IEEE ICASSP 2019 (Intl. Conf. on Acoustics\, Speech and Signal Processing). In 2020\, his doctoral work was awarded the Best PhD Dissertation Award from the IEEE Signal Processing Society. In 2021\, he received the President’s Medal for Outstanding Early Career Researcher at Imperial College London. \nHost: Prof. Chandra Sekhar Seelamantula (EE)
URL:https://ee.iisc.ac.in/event/lecture-by-dr-ayush-bhandari-11-30am/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220602T150000
DTEND;TZID=Asia/Kolkata:20220602T163000
DTSTAMP:20260614T021102
CREATED:20220505T035934Z
LAST-MODIFIED:20220601T013004Z
UID:239718-1654182000-1654187400@ee.iisc.ac.in
SUMMARY:Talk by Prof Sukumar Brahma @ 9.30am
DESCRIPTION:Dear all\,\n\nWe are starting “TCE Lecture Series on Power Systems” at the EE Dept of IISc with the funding support from Tata Consulting Engineers Ltd. In this regard\, we cordially invite you to the inaugural talk to be given by Prof Sukumar Brahma\, FIEEE of Clemson University\, USA on 2nd June\, 2022 from 10 am IST at the MMCR of EE Dept. The talk title\, abstract and the brief bio of the speaker are given below. This talk can also be attended online. The link is given in the attached poster.\n\n——————————————————————————————-\nTitle: Challenges to Power System Protection in Presence of Renewables\n————————————————————————————-\n\nAbstract: Power system protection has been conceived and refined through decades of innovation and experience. However\, the underlying assumptions behind all protection design and implementation have been that the faulted power system behaves as a linear system\, and load currents can be neglected compared to fault currents. These assumptions are under scrutiny as more and more renewables connect at transmission and distribution levels. Renewable generation like solar and wind connect through power converters. The response of the renewable generation to faults depends largely on the converter controls. The controls restrict the fault currents to values comparable to load currents\, and can also control the power factor of the fault current. Such response creates problems with various protection functions\, and system analysis that underpin the design of some of the protection functions. This lecture will describe the challenges in detail for both transmission and distribution systems\, including microgrids\, and offer insight into some potential solutions.\n————————————————————————————————–\nSpeaker Bio: Sukumar Brahma received his Bachelor of Engineering from Gujarat University in 1989\, Master of Technology from Indian Institute of Technology\, Bombay in 1997\, and PhD in from Clemson University in 2003; all in Electrical Engineering. He joined Clemson university as the Dominion Energy Distinguished Professor of Power Engineering in August 2018. He also serves as the director of the industry-funded Clemson University Electric Power Research Association (CUEPRA). Before joining Clemson he was William Kersting Endowed Chair Professor at New Mexico State University\, USA. Dr. Brahma has chaired IEEE Power and Energy Society’s Power and Energy Education Committee\, Life Long Learning Subcommittee and Distribution System Analysis Subcommittee. He is a member of the Power System Relaying and Control Committee (PSRCC)\, where he has been contributing to and leading working groups that produce reports\, guides and standards in the area of power system protection. He has been an editor for IEEE Transactions on Power Delivery\, and served as Guest Editor-in-Chief for the Special Issue on Frontiers of Power System Protection for the journal. His research\, widely published and funded by the National Science Foundation\, US Department of Energy\, utilities\, and other government agencies has focused on different aspects of power system modeling\, analysis\, and protection. Fundamentally\, it spans across diverse areas of electrical engineering and computer science\, integrating signal processing\, machine learning\, control and communication to holistically approach the emerging problems in the power and energy domain. Current research\, funded by the US Department of Energy\, investigates and addresses protection and fault location issues in integration of renewables with power systems and develops new paradigms in protection of smart grid\, at both transmission and distribution levels.\n\nDr. Brahma is a Distinguished Lecturer of the IEEE. He was elected IEEE Fellow “for contributions to power system protection with distributed and renewable generation”.
URL:https://ee.iisc.ac.in/event/talk-by-prof-sukumar-brahma-10am/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220602T193000
DTEND;TZID=Asia/Kolkata:20220602T203000
DTSTAMP:20260614T021102
CREATED:20220530T234721Z
LAST-MODIFIED:20220530T234721Z
UID:239762-1654198200-1654201800@ee.iisc.ac.in
SUMMARY:Lecture by Harmeet Singh @ 2pm
DESCRIPTION:Title: Technology Advancements in Buck Converters \nTime: 2 June 2022\, 2:00 pm \nVenue: MMCR EE \nAbstract: The D-CAP™ ( “Direct connection to the output CAPacitor”) mode control in power converters provides many attractive features\, such as ease of use with no loop compensation\, minimum external components\, and fast transient response which reduces output capacitance and high efficiency.  In the presentation\, I would talk about the advancement in the variable frequency control modes of buck converters\, especially TI’s DCAP architecture.  I would also talk about how the integration of input decoupling and boot capacitors in the IC helps to solve electromagnetic interference (EMI). I will discuss how TI has been able to reduce the size of external differential and common mode EMI filters with the integration of the Active EMI filter (AEF) inside the IC.  Lastly\, I will briefly touch upon the advancement w.r.t to IC package development. \nSpeaker’s Bio: Harmeet Singh is a Principal Analog Field Application Engineer at Texas Instruments\, India. He is a member of\, the Group Technical staff. He is responsible for the growth of Analog revenue in Grid infrastructure in India. Before joining TI\,  he worked as an R&D manager in Samtel\, Ghaziabad\,  handling the power division of the Plasma Display department.  Prior to that\, he worked as Joint Manager R&D in PUNCOM\, heading the SMPS division and responsible for the design of AC-DC\, DC-DC SMPS and 48V telecom power plants for various telecom equipment. He holds a Bachelor’s degree in Electronics and Telecommunications Engineering from Punjab Engineering College (PEC)\, Chandigarh. \nAll are welcome.
URL:https://ee.iisc.ac.in/event/lecture-by-harmeet-singh-2pm/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220606T200000
DTEND;TZID=Asia/Kolkata:20220606T210000
DTSTAMP:20260614T021102
CREATED:20220527T040603Z
LAST-MODIFIED:20220527T040603Z
UID:239752-1654545600-1654549200@ee.iisc.ac.in
SUMMARY:PhD Thesis Colloquium of Jerrin Thomas Panachakel
DESCRIPTION:Title of the thesis: Machine Learning for Decoding Imagined words and Altered State of Consciousness from EEG\nDate and Time:            June 6\, 2022 (Monday)  2.30 PM\n\nMicrosoft Teams meeting link:\nhttps://teams.microsoft.com/l/meetup-join/19%3ameeting_YzRlNjZhN2ItNzYzMC00MmNhLWE5ZmUtY2NhY2U0ODBhNzIz%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%2213e2f8ed-a5d8-408f-b912-af1da693f745%22%7d\n\n\n\n\n\n\n\n\n\n\n\n\n\nAbstract: The thesis explores several architectures for accurately decoding the cognitive activity from EEG recorded during speech imagery and Rajayoga meditation. The major contributions of the thesis are listed below:\n\n\n\n\n\n\n\n\n\n\n\n\n\nNeural Correlates of Phonological Category in Speech Imagery EEG\n\n\nWe have shown that neural correlates of phonological categories exist in the EEG recorded during speech imagery. These correlates lead to significant differences in the mean phase coherence (MPC) values of the EEG across several cortical regions.\nWe have also shown that MPC values can be used for accurately classifying the EEG recorded during speech imagery based on the phonological category of the prompts. The proposed architecture for this task has an accuracy of 84.9%.\n\n\n\nDecoding Imagined Words from EEG\n\n\nOne of the challenges in designing systems for decoding imagined words from EEG is the limited availability of data. We have presented three architectures for decoding imagined words from EEG. All three architectures alleviate this problem of limited availability of data.\nThe transfer learning-based architecture employs MPC and magnitude-squared coherence values along with a ResNet50-based classifier. This architecture achieves an accuracy of 92.8% on a publicly available EEG dataset in classifying speech imagery.\n\nClassification of Altered State of Consciousness from Resting State\n\n\nWe have presented three architectures for classifying the altered state of consciousness during Rajayoga meditation from the resting state.\nBoth CSP-LDA-LSTM (common spatial pattern-linear discriminant analysis-long short-term memory) and SVD-DNN (singular value decomposition-deep neural network) architectures are able to capture subject-invariant features.\nThe best intra-subject accuracy obtained is 98.2% and the best inter-subject accuracy is 96.4%.\n\n\nPublications from the Thesis: \n\n\nJournals\n\n\nPanachakel\, Jerrin Thomas\, and Ramakrishnan Angarai Ganesan. “Decoding Imagined Speech From EEG Using Transfer Learning.” IEEE Access 9 (2021): 135371-135383.\nPanachakel\, Jerrin Thomas\, and Angarai Ganesan Ramakrishnan. “Decoding covert speech from EEG – a comprehensive review.” Frontiers in Neuroscience (2021): 392.\n\nConferences\n\n\n\nPanachakel\, Jerrin Thomas\, et al. “Can we identify the category of imagined phoneme from EEG?.” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE\, 2021.\nPanachakel\, Jerrin Thomas\, and Ramakrishnan Angarai Ganesan. “Classification of phonological categories in imagined speech using phase synchronization measure.” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE\, 2021.\nPanachakel\, Jerrin Thomas\, Ramakrishnan Angarai Ganesan\, and T. V. Ananthapadmanabha. “Decoding imagined speech using wavelet features and deep neural networks.” 2019 IEEE 16th India Council International Conference (INDICON). IEEE\, 2019.\nPanachakel\, Jerrin Thomas\, Ramakrishnan Angarai Ganesan\, and T. V. Ananthapadmanabha. “Common Spatial Pattern Based Data Augmentation Technique for Decoding Imagined Speech.” 2021 IEEE International Conference on Electronics\, Computing and Communication Technologies (CONECCT). IEEE\, 2021.\nPanachakel\, Jerrin Thomas\, et al. “Binary classification of meditative state from the resting state using EEG.” 2021 IEEE 18th India Council International Conference (INDICON). IEEE\, 2021.\nPanachakel\, Jerrin Thomas\, et al. “Automated classification of EEG into meditation and non-meditation epochs using common spatial pattern\, linear discriminant analysis\, and LSTM.” TENCON 2021-2021 IEEE Region 10 Conference (TENCON). IEEE\, 2021.\n\n\nALL ARE WELCOME!
URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-jerrin-thomas-panachakel/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220607T163000
DTEND;TZID=Asia/Kolkata:20220607T173000
DTSTAMP:20260614T021102
CREATED:20220530T235139Z
LAST-MODIFIED:20220530T235139Z
UID:239764-1654619400-1654623000@ee.iisc.ac.in
SUMMARY:PhD Thesis Colloquium of Pravin Nair @11am
DESCRIPTION:Date: June 7\, 2022. \nTime: 11-12 am. \nVenue: MS Teams (online). \nLink: https://tinyurl.com/2p8pwa3c \nTitle: Provably convergent algorithms for denoiser driven image regularization. \nAbstract: Some fundamental reconstruction tasks in image processing can be posed as an inverse problem where we are required to invert a given forward model. For example\, in deblurring and superresolution\, the ground-truth image needs to be estimated from blurred or low-resolution images\, whereas in CT and MR imaging\, we need to reconstruct a high-resolution image from few linear measurements. Such inverse problems are invariably ill-posed—they exhibit non-unique solutions\, and the process of direct inversion is unstable. Some kind of image model (or prior) on the ground-truth is required to regularize the inversion process. For example\, a classical solution involves minimizing f+g\, where the loss term f is derived from the forward models and the regularizer g is used to constrain the search space. The challenge is to come up with a formula for g that can yield high-fidelity reconstructions. This has been the center of research activity in image reconstruction for the last two decades. \n“Regularization using denoising” is a recent breakthrough in which a powerful denoiser is used for regularization purpose\, instead of having to specify some hand-crafted g (however the loss f is used). This was empirically shown to yield significantly better results than staple f+g minimization. In fact\, the results are generally comparable and often superior to state-of-the-art deep learning methods. In this thesis\, we study two such popular models for image regularization—Plug-and-Play (PnP) and Regularization by Denoising (RED). In particular\, we focus on the convergence aspect of these iterative algorithms which is not well understood even for simple denoisers. This is important since lack of convergence guarantee can result in spurious reconstructions in imaging applications. The contributions of this thesis in this regard are as follows: \n(1) We show that for a class of non-symmetric linear denoisers that includes kernel denoisers such as nonlocal means\, one can associate a convex regularizer $g$ with the denoiser. More precisely\, we show that any such linear denoiser can be expressed as the proximal operator of convex function\, provided we work with a non-standard inner product (not the Euclidean inner product). A direct implication of this observation is that (a simple variant of) the PnP algorithm based on this linear denoiser amounts to solving an optimization problem of the form f+g\, though it was not originally conceived this way. Consequently\, if f is convex\, both objective and iterate convergence are guaranteed for the PnP algorithm. Apart from the convergence guarantee that it brings in\, we go on to show that this observation has algorithmic value as well. For example\, in the case of linear inverse problems such as superresolution\, deblurring and inpainting (where f is quadratic)\, we can reduce the problem of minimizing f+g to a linear system. In particular\, we show how using Krylov solvers we can solve this system efficiently in just few iterations. Surprisingly\, the reconstructions are found to be comparable with state-of-the-art deep learning methods. To the best of our knowledge\, the possibility of achieving near state-of-the-art image reconstructions using a linear solver has not been demonstrated before. \n(2) In general\, state-of-the-art PnP and RED algorithms work with trained CNN denoisers such as DnCNN. Unlike linear denoisers\, it is difficult to place PnP and RED algorithms within an optimization framework for CNN denoisers. Nonetheless\, we can still try understanding the convergence of iterates\, i.e.\, do these algorithms stabilize eventually. Again\, for convex loss f\, we show that this question can be resolved using the theory of monotone operators\, namely\, that nonexpansivity of the denoiser is sufficient for iterate convergence of PnP and RED. Using numerical examples\, we show that existing CNN denoisers are not nonexpansive and can cause PnP and RED algorithms to diverge. The question is can we train nonexpansive denoisers? Unfortunately\, this is computationally challenging—simply checking nonexpansivity of a CNN is known to be intractable. As a result\, existing algorithms for training nonexpansive CNNs either cannot guarantee nonexpansivity or are computation intensive. We show that this problem can be solved by moving away from CNN denoisers to unfolded deep denoisers. In particular\, we are able to construct unfolded networks that are efficiently trainable and come with convergence guarantees for PnP and RED\, and whose regularization capacity can be matched with CNN denoisers. \nWe will discuss our findings in greater detail during the colloquium and present numerical results to validate of our theoretical findings.
URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-pravin-nair-11am/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220608T203000
DTEND;TZID=Asia/Kolkata:20220608T213000
DTSTAMP:20260614T021102
CREATED:20220531T050415Z
LAST-MODIFIED:20220531T050635Z
UID:239766-1654720200-1654723800@ee.iisc.ac.in
SUMMARY:Lecture by Mr. Sidhu Sridhara Rao @3pm
DESCRIPTION:Click here for Online Teams Meeting link. \nVenue: MMCR\, EE\, IISc\, Bangalore / Hybrid mode \nTime: 8 June 2022\, 3 pm \nTitle: Demystifying Cybersecurity Program \nSpeaker: Mr. Sidhu Sridhara Rao\, Head of Information Security\, Well Fargo. \nAbstract: “What comes to one’s mind when we say the word ‘cybersecurity’?\nVulnerabilities\, Breaches\, incident response\, hackers\, hacktivists\, nation state actors\, cyber criminals etc.\nWith the rapid digitalization of the global economic activity during the pandemic\, the world is even more connected than it was 2 years ago. And the benefits of digitalization to organizations will continue to drive this journey. However\, as the world is more and more interconnected\, the risks of that digital partnerships become even more important for resiliency of the organization’s business. There is no single organization in the world that is not dependent of extended enterprise partner ecosystem to run their business to serve their clients. As such the attack surface of the organizations continue to grow due to digitalization. \nThere is lot to cybersecurity than vulnerabilities\, breaches\, incident response. The purpose of this session is to throw some light on to all the elements that constitutes a comprehensive cybersecurity program\, what type of skill sets are needed where and how the global economy can benefit from it. This session will cover industry agnostic generic cybersecurity framework that is easy to understand. This framework also has linkages to various regulatory frameworks around security and privacy. \nThe audience can be from broad spectrum of disciplines. It’s not necessary that they have core computer science background.” \nBio: \nSidhu leads the Information and Cyber Security Services Group at Wells Fargo India reporting into the Global Chief Information Security Officer. Prior to joining Wells Fargo\, Sidhu served as “Risk Management Fellow (Banking and Securities)” at Deloitte. As Regional Leader of the Finance Risk Transformation Services practice of the firm\, Sidhu led global M&A projects in the financial services sector. He holds multiple certifications in IT Risk Management and IT Governance. Sidhu is a regular keynote speaker\, panel member and moderator on cyber security\, innovation and financial services matters in India and abroad. He has been invited by global media organizations like The Economist\, Economic Times\, Financial Times\, Information Security Media Group\, Indian agencies like ASSOCHAM\, FICCI\, and global premier business Institutions like the IIMs and ISB for his expertise in the industry. Sidhu was awarded Editor’s Choice CISO of the year in 2018. Sidhu takes personal interest in coaching and mentoring women in middle management levels across Asia\, Europe and the Americas. \n  \nRequest everyone to attend the event.
URL:https://ee.iisc.ac.in/event/lecture-by-mr-sidhu-sridhara-rao-3pm/
LOCATION:EE\, MMCR
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220624T163000
DTEND;TZID=Asia/Kolkata:20220624T173000
DTSTAMP:20260614T021102
CREATED:20220624T013054Z
LAST-MODIFIED:20220624T013315Z
UID:239809-1656088200-1656091800@ee.iisc.ac.in
SUMMARY:PhD Thesis Colloquium of Unni V S @11am
DESCRIPTION:Date: June 24\, 2022.Time: 11am Venue: MS Teams (online).Link: https://tinyurl.com/yckf6en2 \nTitle: Efficient and Convergent Algorithms for High-Fidelity Hyperspectral Image Fusion.Abstract: Hyperspectral (HS) imaging refers to acquiring images with hundreds of bands corresponding to different wavelengths of light. HS imaging has a wide range of applications such as remote sensing\, industrial inspection\, environmental monitoring\, etc. A fundamental consideration with multiband sensors is that the amount of incident energy is limited and this creates an intrinsic tradeoff between spatial resolution and the number of bands—current optical sensors can either generate images with high resolution but a small number of bands or images with a large number of bands but reduced resolution. For example\, HS images have hundreds of bands but low spatial resolution\, whereas the opposite is true for multispectral (MS) images. An extreme case is a panchromatic (PAN) image with very high spatial resolution but just a single band. Image fusion refers to techniques where multiband images with high spatial resolution are synthetically generated using image processing algorithms. It includes pansharpening (MS+PAN)\, hyperspectral sharpening (HS+PAN)\, and HS-MS fusion (HS+MS). Reconstructing a fused image from the observed images is ill-posed and needs regularization. Diverse regularization methods have been proposed over the years for general imaging problems\, many of which perform very well for fusion. This includes vector total variation\, sparsity and dictionary-based penalties\, generalized Gaussian- and GMM-based priors\, etc. This thesis proposes novel regularization models and algorithms that can outperform state-of-the-art image fusion techniques. We can broadly group these into two classes—explicit and implicit regularization.Explicit regularization refers to the design of hand-crafted penalty functions that impose desirable properties (e.g.\, smoothness) on the reconstruction; this is used along with the observed data for fusion. We propose a convex regularizer that is motivated by nonlocal patch-based methods for image restoration. Our regularizer accounts for long-distance correlations in hyperspectral images\, considers patch variation for capturing texture information\, and uses the higher resolution image for guiding the fusion process. Unlike local pixel-based methods\, where variations along just horizontal and vertical directions are penalized\, we use a wider search window in terms of nonlocality and directionality. This is shown to yield state-of-the-art results. The catch is that the resulting optimization problem is non-differentiable and we cannot use simple gradient-based algorithms. However\, we show that by expressing patch variation as filtering operations and judiciously splitting the original variables and introducing latent variables\, we develop a provably convergent iterative algorithm\, where the subproblems can be solved efficiently using FFT-based convolution and soft-thresholding.In the implicit approach\, we rely on a recent paradigm known as plug-and-play (PnP) regularization\, where powerful off-the-shelf denoisers are used for regularization purposes. While this has been shown to give state-of-the-art results for general restoration tasks\, it has not so much been explored for fusion. In fact\, we faced few technical challenges in applying PnP for hyperspectral fusion. Firstly\, existing denoisers are slow when applied to multiband images and we need to apply such denoisers several times with the PnP framework. Secondly\, convergence is generally not guaranteed for PnP regularization since the mechanism is ad-hoc. Along with efficiency and good denoising performance\, we need to come up with a denoiser with specific properties that can guarantee convergence. We proposed a couple of approaches to solve this problem. In the first approach\, we have developed a high-dimensional kernel denoiser with low cost yet good denoising performance\, which can guarantee PnP convergence. The overall algorithm is fast and competitive with state-of-the-art methods. In the second approach\, we leverage the power of deep learning to develop a trained patch denoiser which has a couple of advantages over conventional end-to-end learning: (1) Unlike end-to-end networks which require excessive ground-truth data for training\, we can be trained the denoiser from patches extracted from the observed images. For example\, in HS+MS fusion\, the MS image captures the same scene and has the same spatial resolution as the target image. We train the denoiser by sampling clean patches from the MS image and corrupting them with noise.(2) Compared to end-to-end learning\, where the training is done with a fixed forward model\, our method can be deployed for different forward models. This is possible thanks to the decoupling of the inversion (of the forward model) and denoising steps in PnP.We use the trained denoiser for PnP regularization and establish convergence of the PnP iterations under a technical assumption which we verify numerically. As far as the reconstruction quality is concerned\, our method outperforms state-of-the-art variational and deep learning fusion techniques.
URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-unni-v-s-11am/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220630T203000
DTEND;TZID=Asia/Kolkata:20220630T213000
DTSTAMP:20260614T021102
CREATED:20220630T014855Z
LAST-MODIFIED:20220630T015221Z
UID:239812-1656621000-1656624600@ee.iisc.ac.in
SUMMARY:PhD Colloquium of  Sounak Nandi
DESCRIPTION:Thesis Title: Experimental and Theoretical Investigations on High Voltage Polymeric Insulators \nResearch Supervisor: Dr Subba Reddy B \nDate and Time: Thursday 30th June 2022\, 3.00 pm \nVenue: Online. Click here to join the meeting \nAbstract: High Voltage Ceramic and glass Insulators are widely used by various transmission and distribution utilities for several decades across the globe. Recently composite or silicone rubber insulators have evolved and are now replacing ceramic/glass insulators due to their improved advantages\, however these Insulators suffer from degradation over a period in service. \nThe primary objective of the investigation relates to the performance of silicon rubber/polymer insulator under various climatic conditions\, both experimentally and comprehend theoretically. Experimental studies were conducted to understand the degradation of insulators under different climatic conditions which prevail in the Country. \nStudies on polymer insulators under sub-zero and under extreme high temperature conditions were attempted experimentally and their performance evaluated. During experimentation the leakage current was continuously monitored also material analysis which is very important factor and essential to correlate with the morphological changes of the insulator surface was studied. The experimental investigations demonstrate that there is a need to conduct multi-stress experimentation under specific climatic conditions before the Insulators are being installed in the field. \nThe next portion of the theses work deals with failure mechanism of Fibre Reinforced Plastic (FRP) Rod. Some portion of the work deals with mathematical analysis being extended to condition monitoring of dielectric surfaces and understanding the performance of FRP rods under high AC/DC voltages. Further\, experimental Investigations are performed on FRP Rods to analyse the behaviour witnessed like the field failures reported on Silicon rubber Insulators. \nCondition monitoring of dielectric surfaces is very important; hence it was felt necessary to analyse the field performance of transmission/distribution composite Insulators. To understand further a mathematical analysis based on Chaos has been evaluated for leakage current data and quantization of comparative degradation for a dielectric surface\, later Empirical Mode Decomposition is also used for understanding leakage current and implied degradation under minimal data condition. \nSubsequently\, Surface electric field of insulators exposed to HVDC is studied considering the temporal boundary conditions which may arise due to the capacitive-resistive transients\, some experimental investigations are also conducted to compare the simulated results. \nThe last portion of the thesis emphases on the study of bulk conductivity of polymer material. The Electric Field dependence of conductivity on the application of voltage and subsequent space charge distribution is attempted. \n—  All are Welcome — \n******
URL:https://ee.iisc.ac.in/event/phd-colloquium-of-sounak-nandi/
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