SpiNet: A Deep Neural Network for Schatten p-norm Regularized Medical Image Reconstruction
Aditya Rastogi and Dr. Phaeendra K Yalavarthy
Department of Computational and Data Sciences
Model based deep learning architectures for solving inverse problems consists of two parts, a deep learning based denoiser and an iterative data consistency solver. The former has either L2 norm or L1 norm enforced on it, which are convex and can be easily minimized. This work proposes a method to enforce any p norm on the noise prior where 0 < p ≤ 2. This is achieved by using Majorization Minimisation algorithm, which upper bounds the cost function with a convex function, thus can be easily minimised. The proposed SpiNet has the capability to work for a fixed p or it can learn p based on the data. The network was tested for solving the inverse problem of reconstructing magnetic resonance (MR) images from undersampled k space data and the results were compared with a popular model based deep learning architecture MoDL which enforces L2 norm along with other compressive sensing based algorithms. This comparison between MoDL and proposed SpiNet was performed for undersampling rates (R) of 2×, 4×, 6×, 8×, 12×, 16× and 20×. Multiple figures of merit such as PSNR, SSIM and NRMSE were utilized in this comparison. A two tailed t-test was performed for all undersampling rates and for all metrices for proving the superior performance of proposed SpiNet compared to MoDL. For training and testing, the same dataset that was utilized in MoDL implementation was deployed. We also validated our algorithm on chest and dyanmic breast MRI data.

Learning non linear mappings from data with applications to priority based clustering, adaptive prediction, and detection
Amrutha Machireddy
Department of Electronic Systems Engineering
With the drive for handling big data applications, algorithms to extract and understand the underlying relations within the data have gained momentum. In the unsupervised learning framework, we formulate a systematic theory for clustering data with different priority over the data attributes. We also formulate a potential function based on a spatio-temporal metric and create hierarchical vector quantization feature maps by embedding memory structures across the feature maps to learn the spatio temporal correlations in the data across clusters. In the supervised learning framework, we propose an architecture for prediction and classification ofvideo sequences based on an adaptive pooling layer along with detection of out of distribution classes. We also propose a learning algorithm towards the encoding of linear block codes over binary fields.

Adaptive Sample Selection for Robust Learning under Label Noise
Deep Patel and Prof. P.S. Sastry
Department of Electrical Engineering
Deep Neural Networks have been shown to be susceptible to overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A prominent class of algorithms relies on sample selection strategies. For example, many algorithms use the ‘small loss’ trick wherein a fraction of samples with loss values below a certain threshold are selected for training. These algorithms are sensitive to such thresholds which often depend on (typically unknown) label noise rates, and it’s difficult to fix or learn these thresholds. We propose a data-dependent, adaptive sample selection strategy that relies only on batch statistics of given mini-batch for robustness against label noise. The algorithm doesn’t have any additional hyperparameters, doesn’t need information on noise rates, and doesn’t need access to extra data with clean labels. We empirically demonstrate the effectiveness of our algorithm on benchmark datasets.

Independent Sets in Semi-random Hypergraphs
Yash Khanna, Anand Louis and Rameesh Paul
Department of Computer Science and Automation
In this work, we study the independent set problem on hypergraphs in a natural semi-random family of instances. Our semi-random model is inspired by the Feige Kilian model [FK01] This popular model has also been studied in the works of [FK01, Ste17, MMT20] etc. McKenzie, Mehta, and Trevisan [MMT20] gave algorithms for computing independent sets in such a semi-random family of graphs. The algorithms by McKenzie et al. [MMT20] are based on rounding a “crude-SDP”. We generalize their results and techniques to hypergraphs for an analogous family of hypergraph instances. Our algorithms are based on rounding the “crude-SDP” of McKenzie et al. [MMT20], augmented with “Lasserre/SoS like” hierarchy of constraints. Analogous to the results of McKenzie et al. [MMT20], we study the ranges of input parameters where we can recover the planted independent set or a large independent set.

A provably optimal algorithm for finding fair communities
Shubham Gupta and Ambedkar Dukkipati
Department of Computer Science and Automation

ASR in Low-resource Languages Using Domain Adaptation Approaches
Anoop C S, Prathosh A P, A G Ramakrishnan
Department of Electrical Engineering
Building an automatic speech recognition (ASR) system from scratch requires a large amount of annotated data. For many of the languages in the world, there are not enough annotated data to train an ASR. However, there are cases where the low-resource language shares a common acoustic space with a high resource language, which has enough annotated data to build an ASR. In such cases, weshow that the domain independent acoustic models learnt from the high-resource language through deep domain adaptation approaches can perform well in the ASR task on the low-resource language. The assumption is that the low-resource language has limited audio data for domain training and good enough text data to build a reasonable language model. We use the specific example of Hindi in the source domain and Sanskrit in the target domain. The results suggest that domain adaptation approaches can help in the accelerated development of ASR systems for low-resource languages.

Speaker Diarization for conversational speech using graph clustering
Prachi Singh
Department of Electrical Engineering
Speaker diarization is the task of automatic segmentation of the given audio recording into regions corresponding to different speakers. It is an important step in information extraction from conversational speech. The applications range from rich speech transcription to analysing turn-taking behavior in clinical diagnosis. The state-of-the-art approach involves extraction of short segment features called as speaker embeddings followed by computation of pairwise similarity scores which are then used to perform clustering using similarity scores. In this talk, In this talk, I will discuss various challenges and popular clustering approaches in this field, followed by our recent work on representation learning and graph based path integral clustering (PIC). The representation learning is based on principles of self-supervised learning. It uses the cluster targets from PIC and the clustering step is performed on embeddings learned from the self-supervised deep model. Our results on telephone and meeting dataset shows improvements over the baseline approach.

Neural PLDA Modeling for End-to-End Automatic Speaker Verification
Shreyas Ramoji
Department of Electrical Engineering
Given an enrollment recording of a particular target speaker, automatic speaker verification (ASV) is the problem of determining whether a test speech segment is spoken by the enrolled target speaker or not. The state-of-the-art methods used for ASV involve an embedding extractor that takes a speech segment of arbitrary duration and transforms it into an embedding vector of fixed dimensions. As the embedding extractors are typically trained with classification objective, a separate back-end model is trained to determine if a pair of speech segment embeddings belong to the same speaker or a different speaker. In this talk, I will briefly discuss the popular approaches for Embedding extraction and back- end modeling, followed by our recent work on Neural Probabilistic Linear Discriminant Analysis (NPLDA) back-end using a verification cost function. I will then discuss a simple extension to train the embedding extractor and the NPLDA in an end-to-end framework

Simultaneous Identification of Gulf Stream and Rings from Concurrent Satellite Images of Sea Surface Temperature and Height Images
Devyani Lambhate, Deepak N. Subramani
Department of Computational and Data Sciences
Accurate digitization of synoptic ocean features is crucial for climate studies and the operational forecasting of ocean and coupled ocean-atmosphere systems. For operational regional models of the north Atlantic, skilled human operators visualize and extract the Gulf Stream and Rings (warm and cold eddies) through a timeconsuming manual process. To automate this task, we develop a dynamics-inspired deep learning system(W-Net) that extracts the Gulf Stream and Rings from concurrent satellite images of sea surface temperature (SST) and sea surface height (SSH). Our approach’s novelty is that the above extraction task is posed as a multi label semantic image segmentation problem solved by developing and applying a deep convolutional neural network with two parallel Encoder-Decoder networks (one branchfor SST and the other for SSH). We obtain 82.7% raw test accuracy for Gulf Stream and more than 70% raw eddy detection accuracy. Detailed ablation studies and an examination of both SST and SSH parts of the network is also performed.

Functional Connectivity based EEG Biometrics
Pradeep Kumar G
Department of Electrical Engineering
Resting state brain electrical activity has evidence of unique neural signatures motivating investigation of the discriminative information for person identification systems. In this talk, methods are discussed to extract features from high density electroencephalogram (EEG) for potential use in biometric applications. Functional connectivity (FC) metrics and graph based (GB) metrics derived from FC are used as features in a support vector machine classifier. 184 subjects pooled from 3 different datasets obtained using different protocols and acquisition systems are used to investigate the performance of FC and GB metrics. Independent datasets are assessed for obtaining a consistent feature set and frequency band. An identification accuracy of 97.4% is obtained using phase locking value based measures extracted from the gamma oscillations of the EEG data. Increase in classification accuracy with higher frequency bands indicates the presence of unique neural signatures in those bands.

Reliable sleep stage classification method using multiple EEG features and RUSBoost technique
Ritika Jain
Department of Electrical Engineering
Sleep is extremely crucial for our physical and mental health, and deprivation or poor quality of sleep can lead to numerous health problems. Sleep patterns can also be used to diagnose neurological disorders like dementia, Alzheimer’s etc. Therefore, it is important to analyze the sleep signals accurately. Traditionally, sleep scoring is done by the sleep experts by visual inspection of overnight polysomnography (PSG) data. However, this manual scoring suffers from issues like subjectivity, high cost, time-consuming and inter-rater variability. An automatic sleep scoring system can be useful to tackle these problems. This work proposes a reliable sleep scoring method by using a fusion of multiple EEG features and an ensemble classifier called random undersampling with boosting technique (RUSBoost). The results achieved with a single channel EEG are comparable or better than the state-of-the-art methods in the literature for both two-class (sleep-awake) and multi-class classifications, on three different publicly available sleep databases. The proposed method also provided promising results on the unseen test subjects using both 50%- holdout as well as 10-fold cross-validation approach, emphasizing the reliability of the method.

Automatic speech recognition for speech conversations
Srikanth Raj Chetupalli, Sriram Ganapathy
Department of Electrical Engineering
The transcription of long-form conversations between multiple speakers is challenging, due to the speaker variability, unknown segment end-points and the conversational language. Further, the speakers may speak simultaneously, leading to overlapped speech in single channel recordings. A “rich transcription” is often desired for downstream applications in speech analytics, recording archival, audio indexing, etc. In this talk, we discuss a novel approach for the tran- scription of speech conversations with natural speaker overlap, from single channel recordings. We propose a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system with speaker activity assisted acoustic model (AM). An end-to-end neural network system is used for speaker diarization, and the acoustic model is trained to output speaker specific senones. Further, we propose enhancements to language model using conversation context. We illustrate the benefits of the proposed multi-speaker ASR system for speech conversations using natural telephone conversations from Switchboard corpus.

CDW induced phase transition in layered heterostructures
Mehak Mahajan and Kausik Majumdar
Department of Electrical Communication Engineering
1T-TaS2 is a unique layered material exhibiting strong charge density wave (CDW) driven resistance switching that can be controlled by temperature, an external electric field as well as optical pulses. However, such resistivity switching effects are often weak, and cannot be modulated by an external gate voltage – limiting their widespread usage. Using a back-gated 1T-TaS2/2H-MoS2 heterojunction,we achieve a gate-controlled resistivity switching during the nearly-commensurate (NC) to incommensurate (IC) phase transition of TaS2, and the switching ratio is enhanced by a factor of approximately 14.5 compared to the standalone TaS2 control. This will boost a plethora of device applications that exploit these phase transitions, such as ultra-broadband photodetection, negative differential conductance, fast oscillator and threshold switching in neuromorphic circuits. Interestingly, by simply changing the geomertry we demonstrate a negative differential resistor (NDR) in an asymmetrically designed 1T-TaS2/2H-MoS2 T-junction using an electrically driven CDW phase transition.

Excitons in flatlands
Sarthak Das, Kausik Majumdar
Department of Electrical Communication Engineering
An exciton is a bound state of an electron and an electron hole which are attracted to each other by the electrostatic Coulomb force. When the dimensionality of the crystals reduced to atomic scale, the binding energy of the excitons enhance dramatically due reduced dielectric screening and increased quantum confinement. The larger binding energy in these low dimensional semiconductors persist even in multilayers compared to conventional semiconductors. In fact, in a multilayer system the excitons can form an even-odd type alternating pair and retain the 2d properties. In a bilayer system the excitons can be completely confined to a single layer (intralayer exciton) or the electron and hole can be in different layer (interlayer exciton). With an application of vertical electric field, the intralayer excitons can be converted to interlayer and the corresponding exciton oscillator strength can be significantly modulated. Further, by changing the carrier concentration the oscillator strength of excitons can also be tuned corresponding to the trions

Theory of nonvolatile resistive switching in monolayer molybdenum disulfide with passive electrodes
Sanchali Mitra, Arnab Kabiraj, Santanu Mahapatra
Department of Electronic Systems Engineering
Resistive-memory devices promise to revolutionize modern computer architecture eliminating the data- shuttling bottleneck between memory and processing unit.Recent years have seen a surge of experimental demonstrations of 2D material-based metal-insulator-metal devices. However, the fundamental mechanism of switching has remained elusive. Here, we conduct reactive molecular dynamics simulations for sulfur vacancy inhabited monolayer molybdenum disulfide-based device to gain insight into such phenomena. We observe that with the application of a suitable electric field, at the vacancy positions, the sulfur atom from the other plane pops and gets arrested in the plane of the molybdenum atoms. Rigorous first-principles calculations surprisingly reveal localized metallic states (virtual filament) and stronger chemical bonding for this new atomic arrangement, explaining nonvolatile resistive switching. We further observe that localized Joule heating plays a crucial role in restoring the original position of popped sulfur atom. The proposed theory may provide useful guidelines for designing high-performance resistive-memory-based computing architecture.

Learning Based Classification of Magnetism in 2D materia
Tripti Jain and Prof. Santanu Mahapatra
Department of Electronic System Engineering
Predicting magnetism in two-dimensional materials with fundamental information about the crystal is a challenging task in the field of material science. So far, the magnetism in two-dimensional materials is being predicted computationally. With the popularity of machine learning algorithms in predicting material properties with accuracy close to ab initio calculations and computational speed order of magnitude faster, we have tried to use machine learning for predicting magnetism in two-dimensional materials. With a very small database of these materials, alongwith lots of boundary cases and sparsity in data, make it a difficult task for the algorithms to build a classifier that can classify such imbalanced overlapped datasets. We have tried exploring the available crystal graph network for predicting magnetism. Here, we use basic information of crystal structure like atomic and bond features to design a model that can predict magnetism in such materials with good accuracy

An Evaluation of Methods to Port Legacy Code to SGX Enclaves
Kripa Shanker, Arun Joseph, Vinod Ganapathy
Department of Computer Science and Automation
The Intel Security Guard Extensions (SGX) architecture enables the abstraction of enclaved execution, using which an application can protect its code and data from powerful adversaries, including system software that executes with the highest processor privilege. While the Intel SGX architecture exports an ISA withlow-level instructions that enable applications to create enclaves, the task of writing applications using this ISA has been left to the software community. We consider the problem of porting legacy applications to SGX enclaves. In the approximately four years to date since the Intel SGX became commercially available, the community has developed three different models to port applications to enclaves—the library OS, the library wrapper, and the instruction wrapper models. In this paper, we conduct an empirical evaluation of the merits and costs of each model. We report on our attempt to port a handful of real-world application benchmarks (including OpenSSL, Memcached, a Web server and a Python interpreter) to SGX enclaves using prototypes that embody each of the above models. Our evaluation focuses on the merits and costs of each of these models from the perspective of the effort required to port code under each of these models, the effort to re-engineer an application to work with enclaves, the security offered by each model, and the runtime performance of the applications under these models.

Non-malleable Codes
Sruthi Sekar
Department of Computer Science and Automation and Department of Mathematics
Non-malleable codes are coding schemes that help in securing cryptographic protocols against a class of attacks called as “related-key attacks”, which allow the adversary to tamper with the secret key (on which the security of the protocol relies) and observe additional input-output behaviour of the protocol on the tampered key, leading to a security breach. Non-malleable codes, informally, give a guarantee that, under a tampering attack, the recovered tampered message is either same as the original message, or is independent of it. In this talk, I will be formally defining these primitives and further talk about my work towards building such codes for a strong adversarial setting with optimal rate. Furthermore, I would also talk about some applications of non-malleable codes to other interesting cryptographic primitives

Heap Aware Symbolic Execution
Ajinkya Rajput, K.Gopinath
Department of Computer Science and Automation
In this paper, we show that dynamic symbolilc execution, when coupled with a well-defined model of the allocator behavior, can be effective in detecting heap based vulnerabilities. Specifically, we model the behavior of heap memory allocator to avoid states that will not occur in a concrete execution and define precise conditions that represent heap based vulnerabilities by extracting metadata from the allocator. We develop a tool named RADAR that uses these techniques in tandem on ptmalloc memory allocator. RADAR effectively detects vulnerabilities in a set of micro-benchmarks and real-world projects fetched from github.

Scaling Blockchains Using Coding Theory and Verifiable Computing
Nilesh Rathi
Department of Computer Science and Automations
The issue of scalability has been restricting blockchains from their widespread adoption. In this talk, we will focus on blockchain scaling solutions inspired by coding theory. We first consider SeF, a blockchain archiving architecture using LT codes to reduce storage constraints by 1000x. SeF enables full nodes to store a small number of encoded blocks or droplets instead of a complete blockchain. While other rate-less codes utilizing two encoding levels are proven better than LT codes, we investigate their suitability in the proposed architecture. We propose and simulate three techniques about how to incorporate these coding strategies. The other work we examine is PolyShard, which introduces the notion of codedsharding, making sharding resilient to an adaptive adversary. However innovative, PolyShard requires decoding RS codes over large fields, making it computationally intensive and less practical. We propose replacing the decoding phase with verifiable computing, reducing the bottleneck, and making architecture practical for light verification functions.

MPC for small population with applications to Privacy-Preserving Machine Learning
Ajith Suresh
Department of Computer Science and Automation
Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy preserving Machine Learning (PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards adopting the Secure Outsourced Computation (SOC) paradigm due to the heavy computation. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay per use basis. In this work, we propose a robust PPML framework using secure multi party computation (MPC) for a range of ML algorithms in the SOC setting that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. We demonstrate our framework’s practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting.

Anamorphic Depth Embedding based Light-Weight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images
Naveen Paluru
Department of Computational and Data Sciences
Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experimentsshowed that the proposed method could provide good Dice similarity scores for abnormal and normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures, such as ENet, LEDNet, UNet++, SegNet, Attention UNet, and DeepLabV3+. The proposed Anam-Net was also deployed on embedded systems, such as Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile-based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms.

Towards Deep Robust Deconvolution of Low-Dose Perfusion Computed Tomography
Arindam Dutta, Phaneendra K. Yalavarthy
Department of Computational and Data Sciences
Computed Tomography Perfusion (CTP) has established itself as an economic, fast and reliable clinical modality for diagnosis of cerebrovascular diseases such as acute ischemia and vasospasm. To obtain the necessary perfusion maps for accurate diagnosis, it is mandatory to subject the patient to high radiation dosage, which in turn have several pernicious biological side effects. Thus, obtaining high-quality perfusion maps even at low radiation dosage remains an interesting and convoluted challenge. To combat the same, we propose a novel deep learning based end-to-end framework, to produce high quality Cerebral Blood Flow (CBF) maps from low-dose raw CTP data. The proposed model is able to perform the deconvolution without explicit information on the Arterial Input Function (AIF) and isn’t susceptible to varying levels of noise. Our experiments and results validate the superiority of our framework over existing state of the art algorithms.

Wavelet Design in a Learning Framework
Dhruv Jawali
National Mathematics Initiative & Department of Electrical Engineering
The wavelet transform has proven to be a highly successful data representation tool, and is used in several signal and image processing applications, such as compression, denoising, singularity detection, etc. The problem of wavelet design has traditionally been approached from an analytical perspective, using tools from harmonic analysis to construct continuous-domain functions satisfying the admissibility criterion. In this talk, a learning based approach to wavelet design is introduced, which provides a coherent computational framework for addressing the design problem. Perfect reconstruction filterbanks are viewed as convolutional autoencoders, and wavelets are designed by training these filterbank autoencoders. The designed wavelets are data-independent, which precludes the need for customized datasets. In fact, high-dimensional Gaussian data vectors can be used for training, and it will be shown that a near-zero training loss implies that the learnt filters satisfy the perfect reconstruction property with very high probability. Properties of a wavelet such as orthogonality, compact support, smoothness, symmetry, and vanishing moments are incorporated by designing the autoencoder architecture appropriately, with a suitable regularization term added to the mean-squared error cost when needed. The proposed approach not only recovers the well known Daubechies family of orthogonal wavelets and the Cohen-Daubechies-Feauveau family of symmetric biorthogonal wavelets, but also learns wavelets outside these families.

Appearance Consensus Driven Self-Supervised Human Mesh Recovery
Jogendra Nath Kundu
Department of Computational and Data Sciences
We present a self-supervised human mesh recovery framework to infer human pose and shape from monocular images in the absence of any paired supervision. Almost all the avalble approaches aims to regress a parametric human model by supervising on datasets with 2D landmark annotations. Different from these, we propose a novel appearance consensus driven self-supervised objective. To effectively disentangle the foreground (FG) human we rely on image pairs depicting the same person (consistent FG) in varied pose and background (BG) which are obtained from unlabeled wild videos. The proposed FG appearance consistency objective makes use of a novel, differentiable color-recovery module to obtain vertex colors without the need for any appearance network, via efficient realization of color-picking and reflectional symmetry. Furthermore, the resulting colored mesh prediction opens up the usage of our framework for a variety of appearance-related tasks beyond pose and shape estimation.

Nonconvex Analysis-Sparse Model — Moreau Envelope Regularization and Projected Proximal Gradient Descent Optimization
Praveen Kumar Pokala
Department of Electrical Engineering
We consider the problem of nonconvex analysis-sparse recovery in which the signal is assumed to be sparse in a highly redundant analysis operator. Standard non-convex 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 motivated us to develop two alternatives – Moreau envelope regularization and projected transformation for solving the problem under con- sideration. First, we consider the Moreau envelope counterpart to nonconvex penalty defined in the analysis setting as a sparsity enforcing prior and develop algorithms for optimizing the corre- sponding cost. Second, we employ a projection transform that maps the analysis-sparse recovery problem into an equivalent constrained synthesis-sparse formulation, which can be optimized by the projected proximal algorithm. Finally, we demonstrate the efficacy of the proposed techniques in comparison with benchmark techniques in a real application, namely, 2-D magnetic resonance imaging (MRI) reconstruction considering shift-invariant discrete wavelet transform (SIDWT) as the redundant analysis operator.

Zero-shot Sketch-based Image Retrieva
Titir Dutta
Department of Electrical Engineering
Sketch-based image retrieval (SBIR) addresses the problem of ranking images in a database according to their relevance to a given hand-drawn sketch-query. It is an important research area, though earlier SBIR protocol assumed that the query always belongs to one of the training categories. Recently an evolved version, Zero shot SBIR (ZS-SBIR), has emerged; which breaks down this restriction and thus the query may belong to categories, either seen or unseen to the model. The goal of my doctoral research is addressing different challenges, i.e. the assumption of paired sketch-image training data, attribute-augmented query etc. associated with ZSSBIR. The focus of this talk is to discuss one such challenge, the effect of imbalance in training data. Towards this goal, we proposed an adaptive-margin based regularizer which can be seamlessly incorporated with any existing ZS-SBIR algorithm to improve its performance under imbalanced data. We discuss relevant experiments to demonstrate its effectiveness.

Siamese-SR: A Siamese Super-Resolution model for boosting resolution of Digital Rock images for improved petrophysical property estimation
Utkarsh Gupta
Department of Computational and Data Sciences
The Digital Rock workflow is an emerging framework utilizing advances in imaging technologies and state-of-the-art image processing algorithms to construct digital models of reservoir rocks, which are used to run physics simulations to calculate several petrophysical properties of interest. The accuracy of the digital rock workflow relies crucially on the resolution of the acquired images which is currently limited by the hardware of the micro-CT scanning technology. Super-resolutionmethods can effectively address this limitation by digitally boosting the resolution of images acquired using micro-CT scanners. We have proposed a novel deep learning based super-resolution model called Siamese-SR to improve the resolution of Digital Rock images whilst retaining the texture and providing optimal denoising. The Siamese-SR model consists of a generator which is adversarially trained with a Relativistic and a Siamese Discriminator utilizing Materials In Context (MINC) loss estimator. We also propose to move away from image-based metrics (SSIM & PSNR) to physics based quantification metrics which are based on running subsequent steps in the workflow mainly calculation of image based porosity and running Mercury Injection Capillary Pressure (MICP) simulations for calculation of accurate petrophysical parameters of interest.

Revealing Disocclusions in Temporal View Synthesis for Frame Rate Upsampling
Vijayalakshmi Kanchana, Nagabhushan Somraj, Suraj Yadwad, and Rajiv Soundararajan
Department of Electrical Communication Engineering
Suppose a user is exploring a virtual environment on a head mounted display device. Given the past view of a rendered video frame and the most recent head position, our goal is to directly predict a future video frame without having to graphically render it. We refer to the above problem as egomotion-aware temporal view synthesis. The biggest challenge in such view synthesis through warping is the disocclusion of the background. For infilling them, we introduce the idea of infilling vectors which point from disoccluded regions to known regions in the synthesized view. Tapping the correlation between camera motion in the past frames, we design a learning framework with temporal guidance to predict these infilling vectors. Our extensive experiments on a large scale dataset that we build for evaluating temporal view synthesis, in addition to the SceneNet RGB-D dataset, demonstrate that our infilling vector prediction approach achieves superior quantitative and qualitative infilling performance compared to other approaches in literature.

Sparsity Driven Latent Space Sampling for Generative Prior based Compressive Sensing
Vinayak Killedar
Department of Electrical Engineering
We address the problem of recovering signals from compressed measurements based on generative priors. Recently, generative model based compressive sensing (GMCS) methods have shown superior performance over traditional compressive sensing (CS) techniques in recovering signals from fewer measurements. However, it is possible to further improve the performance of GMCS by introducing controlled sparsity in the latent-space. We propose a proximal meta-learning (PML) algorithm to enforce sparsity in the latent space while training. Sparsity naturally divides the latent space and leads to a union-of-submanifolds model in the solution space. The overall framework is named as sparsity driven latent space sampling (SDLSS). In addition, we derive the sample complexity bounds for the proposed model. Furthermore, we demonstrate the efficacy of the proposed framework over the state-of-the-art techniques with application to CS on standard datasets such as MNIST and CIFAR-10. Our findings show that the proposed approach improves the accuracy and aids in faster recovery compared to GMCS.

There is More to a Spectrogram Than Meets the Eye!
Jitendra Kumar Dhiman
Department of Electrical Engineering
Developing methods for accurate speech analysis has a direct impact on applications such as speech synthesis, speaker recognition, speech recognition, and voice morphing, etc. A widely used tool to analyze speech is the spectrogram, which represents the time-varying spectral content of the speech signal. We model a spectrotemporal patch of the spectrogram by using 2-D amplitude-modulated and frequency-modulated (AM-FM) sinusoids and solve the demodulation problem in 2-D using a novel tool, namely, the Riesz transform. The AM and FM componentscorrespond to the vocal tract smooth envelope and excitation signal. We demonstrate the impact of the Riesz transform technique for applications such as vocal tract filter estimation, voiced/unvoiced separation, pitch tracking, and aperiodicity estimation. The effectiveness of the new representation and speech parameters is shown for the task of speech reconstruction using WaveNet, a neural vocoder.

Vital signs monitoring using FMCW radar
Deepchand Meshineni
Department of Electrical Engineering
In this talk, the discussion is on non-contact vital signs monitoring using a frequency modulated continuous wave (FMCW) radar. Here, we analyse phase of the Intermediate Frequency signal (IF) in detail. Any human or clutters which is exposed to FMCW radar signal is able to reflect the chirp, the reflected chirp goes to mixer to produce IF signal. This paper deals with extracting vital signs such asbreathing rate, heart rate from a subject who sits in front of radar irrespective of the chest orientation towards the radar. Phase of the IF signal is processed to obtain the spectrum in desired range-bin. The spectrum is further subjected to filtering and threshold as will be detailed. Random body movements are detected and those samples are discarded for calculation of heart-rate. We also classify the subjects based on breathe apnea using avaialble data set.

Resource Allocation with Limited Feedback in Underlay D2D Networks
Bala Venkata Ramulu Gorantla
Department of Electrical Communication Engineering
Device to device (D2D) communication enables direct communication between the devices. In underlay D2D, D2D users reuse the subchannels assigned to cellular users (CUs), which leads to interference between them. We explore the problem of allocating multiple D2D pairs per subchannel and the resulting trade off between the spatial reuse and inter-D2D interference. We study the practical partial and statistical channel state information (CSI) models in which the D2D user only knows the statistics of inter D2D and inter cell interferences. We propose a limited feedback scheme, where the D2D user computes its rate with a reliability guarantee despite the limited CSI and feeds it back to the base station. We propose interference-aware resource allocation algorithms that allocate multiple D2D pairs per subchannel to maximize D2D sum rate while guaranteeing minimum rate for the CUs. The proposed algorithms provably achieve at least one third and half of the optimal sum rate. Numerically, they achieve near-optimal performance.

Design of k-space trajectories to reduce scan time for an MRI system
Shubham Sharma, K.V.S. Hari
Department of Electrical Communication Engineering
MRI is an essential non-invasive medical imaging modality. However, it is limited by long scan times. The sampling in MRI happens in the 2D/3D Fourier domain called as the k-space. It is traversed along continuous paths called trajectories. One of the more recent ways to reduce scan time in MRI is using compressed sensing (CS) methods which allow random undersampling of the k-space. However, this does not satisfy system constraints of MRI. Here, the design of feasible trajectories to traverse the k-space is discussed. We propose a generalized framework that encompasses projection-based methods to generate feasible trajectories. This framework allows to construct feasible trajectories from both random and structured initial trajectories, e.g., based on the traveling salesman problem (TSP). It is observed that the proposed TSP-based and random-like trajectories provide better reconstruction performance than the state-of-the art methods with a shorter scan time. In particular, the SIP-random method improves reconstruction performance with a 26% reduction in scan time

Probabilistic Forwarding with Coding for Network-wide Broadcast
Vinay Kumar B.R.
Department of Electrical Communication Engineering
Broadcasting information is crucial in ad-hoc networks such as Internet of Things and Wireless Sensor Networks which have no centralized infrastructure. Mechanisms such as flooding are not suitable as they involve redundant transmissions. In our work, we propose and analyze a completely distributed, low complexity, energy-efficient broadcast algorithm for these networks. We consider a network with a distinguished source node that has data packets to broadcast. It encodes these data packets into n coded packets in such a way that, any node receiving at least k out of the n coded packets can retrieve the original data packets. The source transmits the n coded packets to its one-hop neighbours. Every other node in the network follows a probabilistic forwarding protocol: it forwards a previously unreceived packet to all its neighbours with probability p and does nothing with probability 1 − p. Our primary interest is to analyze this mechanism on random geometric graphs (RGGs) which are used to model deployments of ad-hoc networks. We find that with a judicious choice of n and the forwarding probability p, we can significantly reduce the expected total number of transmissions needed to broadcast data from the source. Moreover, while this holds for other well-connected graphs (grids, lattices etc.), it is not true for trees. We provide theoretical justifications for the same

Second order Rate Bounds for a Block Fading Channel Powered by an Energy Harvesting Transmitter
Deekshith P K and Vinod Sharma
Department of ECE
In this talk, we illustrate how second-order lower and upper bounds can be derived for a point to point complex Gaussian fading channel with an energy harvesting transmitter, and fading gains available at the transmitter and the receiver. A standard approach to derive such results for a channel with finite states makes use of the method of types, especially in the derivation of upper bound. Such an approach is not feasible in our case as the number of states is uncountable. Further, a dynamic power constraint on the codeword symbols, imposed by the energy harvesting transmitter, has to be met. We illustrate how strong approximation principle turns in handy in deriving bounds in such a scenario, especially while deriving upper bounds. As a special case, we obtain corresponding results for no state information at the transmitter and full state information at the receiver.

MCS selection algorithms for URLLC in Multi-connectivity
Govindu Saikesava
Department of Electrical Communication Engineering
Ultra-reliable and low-latency communications, is a new field in 5G, requires very high reliability and very low latency. It will deliver services like factory automation, telesurgery and autonomous driving. Multi-connectivity, in which multiple base stations (BSs) send the same information to the receiver, is crucial for improving reliability. In addition, to satisfy the latency requirement, URLLC data must be scheduled immediately, preempting conventional traffic. We propose an algorithm that selects one or more BSs and their modulation and coding schemes (MCSs) to minimize the degradation of the broadband data while meeting the reliability requirements of URLLC.We then generalize our approach to account for the time-varying nature of the wireless channel, which causes the periodic channel quality feedback reports to become outdated. For this scenario, we propose a novel outage probability-based definition of reliability and a new algorithm to determine the BSs and their MCSs.

Streaming Codes for Reliable, Low-Latency Communication
Vinayak Ramkumar
Department of Electrical Communication Engineering
Reliable, low-latency communication is key to many envisaged next-generation communication applications such as assisted driving, augmented reality and telesurgery. Streaming codes represent an approach to forward error-correction at the packet level in which a dropped or lost packet is viewed as an erased code symbol and where the codes are designed to be operated under a decoding-delay constraint.This talk will focus on our recent constructions of streaming code. These constructions are based on careful embedding of code symbols belonging to a scalar block code within the packet stream. Different embedding approaches and attendant constructions will be presented and compared.

Design of Reduced Feedback Scheme, User Scheduling, and MAC protocols for Full-Duplex Communication Systems
Rama Kiran and Prof. Neelesh B. Mehta
Department of Electrical Communication Engineering
Full-duplex (FD) promises to double the data rates by enabling simultaneous transmission and reception over the same frequency band. Enabling FD in cellular networks creates inter- user interference between uplink and downlink. We propose a novel user-pair scheduling and mode selection algorithm (UPSMA) that maximizes sum spectral efficiency, and we characterize its asymptotic behaviour. We also propose a reduced feedback scheme for it, in which each user feeds back a limited number of inter-user interferences. We then study FD wireless local area networks (WLANs). We propose a medium access control (MAC) protocol called asymmetric FD-MAC (AFD-MAC). AFD-MAC intelligently uses hidden users to exploit FD. It uses the random back-off counter-based channel contention mechanism of 802.11 and introduces two new signals to provide as many FD transmission and reception opportunities to the AP as possible.

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning
Ramkumar Raghu, Mahadesh Panju, Vinod Sharma, Vaneet Aggarwal
Department of Electrical Communication Engineering
Multicasting in wireless systems exploits the redundancy in user requests in a Content Centric Networks. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. Model-based approaches for power control and scheduling are not scalable to large state space or changing system dynamics. Here, we propose a constrained Deep RL approach to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learnt for reasonably large systems via this approach. Further we use multi-timescale stochastic optimization to maintain the average power. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queueing strategy along with power control. We demonstrate scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed approach can be used in general large state-space dynamical systems with multiple objectives and may be of independent in

Medium refractive index contrast sub-wavelength dielectric structures for nonlinear optics and sensing application
Sruti Hemachandran Menon
Department of Electrical Communication Engineering
Dielectric sub-wavelength structures are of increased interest for enhancing light matter interaction, owing to its capability to mould the field within or aroundthe structure. CMOS compatible materials like silicon nitride (SiN), silicon dioxide (SiO2), having a wide transparency window ranging from visible to mid-infrared wavelengths make suitable grating materials. They, however, lack sufficient scattering efficiency due to its weak refractive index contrast with respect to air. In this work, we study one-dimensional SiN based guided mode resonant (GMR) structures for enhancing the fluorescence from Rhodamine B dye using polarization sensitive dual mode resonances making it conducive for sensitive imaging of bio-samples with low analyte concentration and for improving the nonlinear signals from amorphous silicon overlayers at lower power inputs

Design and Development of a Dual Functional Intubation Catheter for Chronic Airway Management
Alekya B and Hardik J. Pandya
Department of Electronic Systems Engineering
In this work, we report the design and development of an intubation catheter for grading stenosis in the pediatric upper airway. Severely constricted airways often warrant continuous monitoring as resistance to flow increases to fourth power for every one-degree reduction in tracheal patency. The complexities and impediments with conventional diagnostic tools such as misclassification on the degree of narrowing and long radiation exposure make them sub- optimal for emergent diagnosis. We propose an intraoperative diagnostic tool integrated with an array of flow sensorsand tactile sensors mounted at the distal end of the manoeuvrable catheter. The flow sensors generate localised flow patterns across various segments of the tracheobronchial tree with unique peaks corresponding to the site of obstruction. While the flow patterns allow for locating stenosis, the tactile sensors can determine the target tissue stiffness. Quantitative evaluation of alteration in the airway column biomechanics facilitates targeted diagnosis and expedites on-site decision making.

A portable MEMS-based platform for the electrothermal phenotyping of breast biopsy tissue: towards a system for tumour delineation
Anil Vishnu G K, Annapoorni Rangarajanb and Hardik J. Pandya
Centre for BioSystems Science and Engineering
We report a portable diagnostic platform integrated with MEMS-based biochips for rapid phenotyping of breast biopsy tissues using electro-thermal modalities. The biochips fabricated on a silicon substrate using microfabrication techniques measure the bulk resistivity (ρB), surface resistivity (ρS), and thermal conductivity (k) of the samples. Measurements were performed from deparaffinized and formalinfixed tumour and adjacent normal breast biopsy samples from N = 8 patients. For formalin fixed samples, the mean ρB for tumour reported a statistically significant fold change of 4.42 ( p = 0. 014) when the tissue was heated from 25 degree to 37 degree, while for normal the fold change was 3.47. The mean ρS measurements also reported similar trend. The normal tissue reported a mean k of 0.563 ± 0.028 W.m−1.K−1, while tumour reported significantly lower k of 0.309 ± 0.02 W.m−1.K−1. These observations, after validation with fresh tissues, could potentially be used for rapid tumour delineation.

Design and Development of a Micro-Force Sensor for Ablation Catheters
V S N Sitaramgupta V and Hardik J. Pandya
Department of Electronic Systems Engineering
In this work, we present simulation, fabrication, and characterization of a micro force sensor that has the potential to integrate with the ablation catheters for real-time measurement of catheter tip contact forces. FEM analysis was used to optimize the sensor design. The sensor was charac- terized using a custom-built micro-indentation setup and automated using LabVIEW. The sensor characteristics such as linearity, sensitivity, and hysteresis were evaluated. The sensor showed a linear response with R2–value of 0.995 for the force ranging from 0 –1.2 N. The sensitivity and hys- teresis were found to be 108 ±11WN−1 and 5% respectively. The fabricated sensor was integrated into a customized catheter tip and tested on the porcine heart tissues for evaluating the tip contact forces

Design and development of Opto-Thermo-Acoustic (OTA) based measurement techniques to aid breast cancer diagnosis
Uttam M. Pal and Hardik J. Pandya
Department of Electronic Systems Engineering
Breast cancer is among the leading cause of mortality among women world wide. Histopathology that accounts for the microscopic evaluation of thin slices of breast biopsy tissue under a microscope to study cell structure and morphology is considered the gold standard. We design and develop a system that combines the optical, thermal, and acoustic modalities to estimate the bulk tissue optical properties (reduced scattering and absorption coefficient), thermal properties (thermal co nductivity and specific heat), and acoustic (attenuation coefficient). The additional information gauged through these measurements can potentially aid the pathologist in confirming the breast cancer diagnosis. The measurements were performed on formalin-fixed (FF) and deparaffinized (DP) breast biopsy tissues from a total of N = 24 subjects.

Towards unique device identification using Physically Unclonable Functions
Girish Vaidya and Dr. T.V. Prabhakar
Department of Electronic Systems Engineering
Uniqueness of device-ID is critical to ensure trustworthiness of the data reported by the device. This work presents a novel device-specific identifier - IoT-ID, based on Physically Unclonable Functions (PUFs) that exploits variations in thesemiconductor manufacturing process. We design novel PUFs for Commercially Off the Shelf (COTS) components such as clock oscillators and ADC, to derive IoT-ID for a device. Hitherto, system component PUFs are either invasive or rely on additional dedicated hardware circuitry to create a unique fingerprint. A highlight of our PUFs is doing away with special hardware. IoT-ID is non-invasive and can be invoked using simple software APIs running on COTS components. During our evaluation, IoT-ID has demonstrated 100% accuracy in uniquely identifying 50 devices in our deployment. We show the scalability of IoT-ID with the help of numerical analysis on 1000s of IoT devices.

Estimation of stochastic responses due to multiple variabilities in electromagnetic finite element modelling
Aakash Khochare
Department of Computational and Data Sciences
Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used for urban applications like traffic monitoring and construction surveys. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. Here, we propose a novel Mission Scheduling Problem (MSP) for co-scheduling the flight route to visit and record video at waypoints, and their subsequent on-board analysis, for a fleet of drones. The schedule maximizes the utility from the activities, while meeting activity deadlines, and the energy and computing constraints. We provide two efficient heuristic algorithms, JSC and VRC, to obtain fast, sub-optimal solutions. Our detailed evaluation of these algorithms using real drone benchmarks show the utility-runtime trade-offs of the 3 schedulers under diverse workloads.

Optimal Link Capacity Selection and Edge Server Placement using Column Generation
Devyani Gupta and Joy Kuri
Department of Electronic Systems Engineering
When an end user wants to subscribe to a gaming service, he expects the network to respond quickly and provide good Quality of Service and Quality of Experience. Any network has few Data Centers (DC), and they are placed deep inside the core network. Therefore, it becomes challenging to provide a service to a user which requires high bandwidth and very low latency. Placing servers near the user and on the edge of the core network can help overcome this challenge. Thus, the question of optimal placement of these servers arises. Also, to reduce the total cost incurred, it is essential to activate only those links which connect users to the DC and assign appropriate capacities to these links. We present the Network Design Problem of optimal server placement and link capacity selection. The problem formulated is a Large Integer Linear Program and we provide exact and fast solution by using the Column Generation (CG) Technique.

Automatically finding bugs in web applications
Geetam Chawla
Department of CSA
Most modern web applications are based on the Model view controller design pattern. It is desirable to detect bugs automatically in controllers based on specifications given by developers. We propose an approach for this problem. The core step of our approach is to automatically extract a formal model of a controller using program analysis techniques. The model is based on relational algebra, and represents the database accesses and queries performed by the controller. We then feedthis model to a pre-existing constraint solver along with the user’s specification. We have implemented our approach as a tool. We evaluated our tool on a set of 56 specifications. The tool found 34 of these to be satisfied; of the rest, upon manual analysis, we found that two were genuinely violated, while the remaining 20 ‘unsatisfied’ warnings were actually false positives. Apart from verification purposes our model can also aid in developer understanding.

Hardware Accelerator For Particle Transport Simulation
Ramakant Joshi, Kuruvilla Varghese
Department of Electronic Systems Engineering
Simulating Particle Transport through matter lies at the heart of Particle Physics applications, including Nuclear and High Energy Physics, extending to medical physics for treatment planning and radiation dose calculation. Our current work investigates the principle of particle transport simulation, identifying major computational elements involved and developing an efficient architecture for accelerating the computation on hardware. The ultimate goal is to implement the system on a Field Programmable Gate Array (FPGA). For a non-physicist, it is cumbersome to develop such a system from scratch. We show how we can leverage the power of High-Level Synthesis Tools to directly convert existing C/C++ Code for Particle Transport to hardware implementation, highlighting the benefits and downsides of such an approach. The final System Architecture is based on the Xilinx Vitis tool targeted to the Xilinx Alveo U250 FPGA card. The execution model for the same will also be discussed

Optimal Algorithms for Range Searching over Multi-Armed Bandits
Siddharth Barman, Ramakrishnan Krishnamurthy and Saladi Rahul
Department of Computer Science and Automation
This paper studies a multi-armed bandit (MAB) version of the range-searching problem. In its basic form, range searching considers as input a set of points (on the real line) and a collection of (real) intervals. Here, with each specified point, we have an associated weight, and the problem objective is to find a maximum-weight point within every given interval. The current work addresses range searching with stochastic weights: each point corresponds to an arm (that admits sample access) and the point’s weight is the (unknown) mean of the underlying distribution. In this MAB setup, we develop sample efficient algorithms that find, with high probability, near-optimal arms within the given intervals, i.e., we obtain PAC (probably approximately correct) guarantees. We also provide an algorithm for a generalization wherein the weight of each point is a multi-dimensional vector. The sample complexities of our algorithms depend, in particular, on the size of the optimal hitting set of the given intervals. Finally, we establish lower bounds proving that the obtained sample complexities are essentially tight. Our results highlight the significance of geometric constructs—specifically, hitting sets—in our MAB setting.

Tracking an AR(1) Process with limited communication per unit time
Rooji Jinan, Parimal Parag and Himanshu Tyagi
Department of Robert Bosch Centre for Cyberphysical Systems
Samples from a high-dimensional AR[1] process are quantized and sent over a communication channel of finite capacity. The receiver seeks to form an estimate of the process in real-time. We consider a time-slotted communication model in slow-sampling regime where multiple communication slots occur between two sampling instants. We propose a successive update scheme which uses communication between sampling instants to refine estimates of the latest sample. We study the following question: Is it better to form refined estimates and send them over multiple communication slots, making the receiver wait more for an update, or to be fast but loose and send new information in every communication opportunity? We show that the fast but loose successive update scheme with spherical codes is universally optimal asymptotically for large dimension.

Translating Natural Language Instructions to Computer Programs for Robot Manipulation
Sagar Gubbi Venkatesh, Raviteja Upadrashta, and Bharadwaj Amrutur
Department of Electrical Communication Engineering
It is highly desirable for robots that work alongside humans to be able to understand instructions in natural language. Existing language conditioned imitation learning models directly predict the actuator commands from the image observation and the instruction text. Rather than directly predicting actuator commands, we propose translating the natural language instruction to a Python function which queries the scene by accessing the output of the object detector and controls the robot to perform the specified task. This enables the use of non-differentiable modules such as a constraint solver when computing commands to the robot. Moreover, the labels in this setup are significantly more informative computer programs that capture the intent of the expert rather than teleoperated demonstrations. We show that the proposed method performs better than training a neural network to directly predict the robot actions

Latency Redundancy Tradeoff in Distributed Read-Write Systems
Saraswathy Ramanathan, Parimal Parag and Vikram Srinivasan
Department of Electrical Communication Engineering
Data is replicated and stored redundantly over multiple servers for availability in distributed databases. We focus on databases with frequent reads and writes, where both read and write latencies are important. This is in contrast to databases designed primarily for either read or write applications. Redundancy has contrasting effects on read and write latency. Read latency can be reduced by potential parallel access from multiple servers, whereas write latency increases as a larger number of replicas have to be updated. We quantify this tradeoff between read and write latency as a function of redundancy, and provide a closed-form approximation when the request arrival is Poisson and the service is memoryless. We empirically show that this approximation is tight across all ranges of system parameters. Thus, we provide guidelines for redundancy selection in distributed databases

Process with limited communication per unit timeInfluence of soil’s electrical parameters on lightning stroke-current evolution
Rupam Pal
Department of Electrical Engineering
The lightning return stroke forms one of the severest natural sources of electromagnetic inter- ference for systems, both in the air and soil. Several physical fields govern this complex physical phenomenon, and most of the engineering applications resort to much simplifications. Several pertinent aspects are somewhat unclear, and one such important aspect is the influence of soil’s electrical properties on the stroke current evolution. This is investigated in the present work in a theoretical framework using the ‘Self-consistent return stroke’ model. The ‘Finite difference time domain’ (FDTD) method is suitably adopted for the modelling. The developed FDTD formulation is then used to investigate and ascertain the role of soil’s electrical properties on the stroke current evolution. For the first time, it is shown that the soil’s electrical conductivity has some noticeable influence on the stroke current magnitude.

Characterization and Modelling of Switching Dynamics of SiC MOSFE
Shamibrota Kishore Roy, Kaushik Basu
Department of Electrical Engineering
SiC MOSFET is a wide bandgap (WBG) power device and 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. Fast switching transient of SiC MOSFET reduces switching loss but may induce prolonged oscillations, spurious turn on, high device stress and EMI related issues, etc. The nonlinearity of the device characteristics and impact of circuit parasitic makes the switching transient of SiC MOSFET different from its Si counterpart. So,modelling the switching dynamics and estimate the switching loss of SiC MOSFET is essential. Analytical models are derived through the simplification of complex switching dynamics. Estimation using this type of model is computationally efficient and can be easily implemented with freely available programming platforms such as C or Python. This approach is beneficial at the early stage of the converter design when switching loss and junction temperature need to be evaluated over several operating points for many available devices from different vendors. Analytical models require parameters given in the datasheet. This talk presents an analytical model to capture the switching dynamics of SiC MOSFET. A detailed non-linear model of the device along with circuit parasitic is considered. It results in accurate estimation of transition time, switching loss, (dv/dt), (di/dt), and transient over-voltage. Also, an analytical model to capture capacitor-assisted turn-off switching transient will be presented. It helps design the optimal value of the external snubber capacitor for capacitor-assisted zero voltage switching (ZVS) converter. In this talk, we will present simple measurement techniques to determine important circuit parasitic that impacts switching dynamics, such as the common-source inductance. We will also demonstrate software developed using Python based on the presented analytical model.

Generalized Source emulation approach on micro-alternator
Tanmay Mishra
Department of Electrical Engineering
This talk presents an approach to emulate different power system sources (different synchronous generators with excitation systems). A testbed consists of a micro-alternator having an open-loop H-bridge chopper as an exciter has been developed. To replicate a large machine’s dynamics, a digital time constant regulator has been incorporated to increase the d-axis transient time constant of the micro alternator. The mathematical models of standard IEEE excitation systems are usedas references, and their behaviors have been emulated. The proposed excitation emulation approach is tested under fault and step change in reference terminal voltage. The experimental results of a Single Machine infinite bus with these excitation systems have been compared with simulation for validity. This experimental platform is essential to test the different control and protection prototypes for validating the technologies. Further, a dynamic inversion-based non-linear control approach is presented to replicate different synchronous generators’ behavior on a micro-alternator. This approach is validated in simulation under different test cases.

Design Of Active Magnetic Bearing
Kamisetti N V Prasad
Department of Electrical Engineering
Bearings are crucial parts in any rotating machine, such as a motor, generator, or turbine. Majority (> 50%) of the reported faults in electrical machines are related to bearing life and maintenance. These problems are particularly pronounced in high-speed rotating machines. Active magnetic bearing (AMB) is a contactless bearing, which supports the rotor of a high-speed electric machine or turbine by controlling the magnetic forces produced by a set of electromagnets positioned around the rotor. AMBs are increasingly used in high-speed aerospace, industrial, and energy applications. Due to its contact-less and lubrication-free operation, AMBs are suitable for harsh operating conditions (e.g., extreme temperatures, extreme pressures, corrosive environment) as well as for clean environments (e.g., food processingand pharmaceutical industries). This presentation discusses the operating principle of AMB, its components, and a system-level overview of AMB. The state-of-the-art differential-mode current control of AMB and the resulting force-current relationship is explained. The main specifications of an AMB, namely, load capacity and force slew rate, are discussed. The existing design procedure of AMB is based on linearized magnetic circuit analysis, which cannot account for the saturation in iron. An improved design procedure is presented, which accounts for magnetic saturation and ensures that the force generated is proportional to the control current up to the desired load capacity. The improved design procedure can achieve both desired load capacity and linear characteristics while balancing the compactness requirement. The improved design also achieves the desired force slew rate, faster dynamic response, and enhances the stability of the AMB.

An Algorithm for Fitting Passive Equivalent Circuits for Lumped Parameter Frequency Dependent Transmission Line models
Kiran Kumar Challa
Department of Electrical Engineering
Accurately fitting rational functions to the frequency response of modal impedances is very crucial for including frequency dependency in lumped parameter models of transmission lines. Vector fitting is widely used for fitting rational functions to the frequency response of modal impedancesand then an R-L equivalent circuit is obtained to get lumped parameter transmission line model.A single-step method based on the properties of Foster equivalent circuit is proposed in this paper to directly fit an R-L equivalent circuit to the frequency response of modal impedances. A close enough fitting is achieved using the proposed method with less number of passive elements. The proposed line model is validated by studying the switching transients in 400 kV, 765 kV and 1200 kV transmission lines and comparing the results with the constant parameter cascaded π-model and the Marti’s Model in EMTP-RV. The prototype of an experimental scaled-down 230 kV line is developed using amorphous core inductors and the Clarke’s transformation using 1-φ transformers.

Impact Of Inverter Based Generation On Transmission Protection
Meenu Jayamohan, Dr. Sarasij Das
Department of Electrical Engineering
The generation mix in the electric power grids around the world is undergoing a significant change from synchronous machines to inverter based resources (IBR). As a result, it is essential for the conventional system to adapt to these changes to bring reliability to the bulk power system(BPS). The increasing penetration of IBR has affected the conventional protection schemes. While synchronous machines were capable of providing sufficient amount of fault currents, IBR inverter controls reduce the fault current levels and short circuit strength. This talk presents a detailed review on the issues faced by conventional protection schemes in presence of an IBR.

Switched Capacitor Converters for High Power Applications
Nakul Narayanan K and Prof. L Umanand
Department of Electronic Systems Engineering
The switched capacitor (SC) converters are more compact and have higher power density when compared with the conventional switched inductor converters. Due to this, SC converter is being considered for high-power DC-DC applications like offshore HVDC, electric vehicles and grid integration. A new SC converter topology is presented with minimum capacitor volt-ampere rating and minimum voltage rating for active switches. The converter is then modified with multiple resonant inductors to achieve zero-current switching, reducing the switching losses considerably. Two variants of the proposed SC converter topology are further discussed with a reduced number of resonant inductors.

Do deep networks see the way we do?
Georgin Jacob, Pramod R.T., Harish Katti, and S. P. Arun
Department of Electrical Communication Engineering
Deep neural networks have recently revolutionized computer vision with their impressive perfor- mance on vision tasks. Their object representations have been found to match neural representations in the ventral pathway. But do deep neural networks see the way we do? This is an important question because it will elucidate the conditions and computations under which perceptual phe- nomena might arise in neural networks optimized for object classification. Here, we uncover the qualitative similarities and differences between brains and deep networks by comparing the representations of human perception eliciting objects. The main findings are:(1) Perceptual phenomena like Thatcher effect, Mirror confusion, and Weber’s law emerge when deep networks are trained for object recognition; (2) Perceptual phenomena like 3D shape processing, surface invariance, and the global advantage are absent. These results show us when we can consider deep networks good models of vision and how deep networks can be improved.

Deep Learning Methods For Audio EEG Analysis
Jaswanth Reddy Katthi, Sriram Ganapathy
Department of Electrical Engineering
Brain activations capturing techniques like electroencephalogram (EEG) capture signals not related to the stimuli, which distort the stimulus-response analysis, and their effect becomes more evident for naturalistic stimuli. To reduce the intersubject redundancies, the EEG responses from multiple subjects can be normalized.Linear methods are the most prominent methods for single-trial analysis. But, they assume a simplistic linear transfer function. In this talk, we discuss a deep learning framework for audio-EEG data for intra-subject and inter-subject analyses. This boosts the signals common across the subjects and improves the intra-subject analysis for each subject. Experiments performed on naturalistic speech and music stimuli listening datasets show that the deep methods obtain better representations than the linear methods, and that the results are statistically significant. As an extension work, we compare the linear and deep methods for the task of speech reconstruction from the EEG recordings.

Decoding Imagined Speech from EEG using Transfer Learning
Jerrin Thomas Panachakel
Department of Electrical Engineering
In this talk, we present a transfer learning based approach for decoding imagined speech from electroencephalogram (EEG). Rather than extracting features separately from individual EEG channels, features are extracted simultaneously from multiple EEG channels. This helps in capturing the information transfer between the cortical regions. To alleviate the problem of lack of enough data for training deep networks, sliding window based data augmentation is performed. Mean phase coherence (MPC) and magnitude-squared coherence (MSC), two popular measures used in EEG connectivity analysis are used for extracting the features. These features are compactly arranged, exploiting their symmetry, to obtain a three-dimensional “image-like” representation. A deep network with ResNet50 as the base model is used for classifying the imagined prompts. The proposed method is tested on a publicly available EEG dataset recorded during speech imagery. The accuracies obtained are comparable to the state-of-the-art methods, especially in decoding prompts of different complexities

Design and Developement of a Headband for Cortical Response Extraction
Rathin K. Joshi and Hardik J. Pandya
Department of Electronic Systems Engineering
Hearing Deficit is the most prevalent chronic neonatal sensory deficit. Considering adverse repercussions and congenital deafness statistics, early identification and subsequent intervention significantly save a newborn from lifelong deficiency. Brainstem Evoked Response Audiometry (BERA) and Otoacoustic Emissions (OAE) are the existing methods for Neonatal Hearing Screening (NHS). BERA and OAE do not scan the complete auditory system. Additionally, the lack of clinicians, expensive equipment, specialized hospitals with audiometry tools, and patient follow-up are the challenges for the NHS in India. We developed a quick, non-invasive, cost effective, objective headband to assess the complete auditory pathway. The cortical auditory evoked response provides important information about the functionality of subsequent elements of the auditory pathway, making it a comprehensive signature to evaluate the entire auditory pathway. We obtained a cortical response (Mismatch Negativity) from n=3 young adults. We aim to replicate a similar headband for Neonatal Hearing Screening.

Best influential spreaders identification using network global structural properties
Amrita Namtirtha, Yogesh Simmhan
Department of Computational and Data Science
Influential spreaders are the crucial nodes in a complex network that can act as a controller in case of epidemic spreading or maximizer for information propagation. We have noticed that each individual network holds different connectivity structures; complete, incomplete, or in-between based on their components and density. These affect the accuracy of existing indexing methods in the identification of the best influential spreaders. Thus, no single indexing strategy is sufficient for all varieties of network connectivity structures. This article proposes a new indexing method Network Global Structure-based Centrality (ngsc), which intelligently combines existing kshell and sum of neighbors’ degree methods with knowledge of the network’s global structural properties, the giant component, average degree, and percolation threshold. The experimental results show the proposed method yields a better spreading performance over a large variety of network connectivity structures and correlates well with the SIR model used as ground truth.

Modelling Electrical Transport in Biological Tissues
Tushar Sakorikar, Anil Vishnu GK, Hardik J. Pandya
Department of Electronic Systems Engineering Centre for BioSystems Science and Engineering
Electrical transport in disordered systems such as biological tissues is characterized by scaling laws which provide a quantifiable description of the system. In this work, two modes of transport viz. temperature and frequency dependent electrical transport, are studied using formalin-fixed breast biopsy tissues. Experiments were performed using an in-house electronic platform integrated with a device that has on-chip electrodes and a microheater. Temperature dependent direct current (DC) transport studies were modelled under the realm of general effective medium theory, with critical temperature (Tc) as a model fit parameter showing higher value for adjacent normal compared to tumour tissue. Frequency dependent alternating current (AC) transport studies were modelled using a universal scaling law, with onset frequency fc as a model fit parameter showing higher values for adjacent normal samples compared to tumour indicating higher disorder in tumour. We use these model fit parameters to delineate normal from tumour breast biopsy tissues.

Sensitivity Analysis of ANNs for Fluid Flow Simulation
Subodh Joshi, Sashikumaar Ganesan
Department of Computational and Data Sciences
Numerical simulation of convection dominated flows requires numerical schemes which are high-order accurate and stable. A numerical scheme can be stabilized by adding the right amount of artificial dissipation to the flux residual. Our ongoing work focuses on developing a strategy in which the traditional finite element methods are augmented with Artificial Neural Networks (ANN) to enhance their stability and efficiency. However, selection of hyper-parameters of the ANN is a non-trivial task. In this work, we try to address this issue by carrying out a systematic global sensitivity analysis of ANN hyper-parameters using a technique called Analysis of Variance (ANOVA) based on four performance metrics. This analysis highlights the effect of different hyper-parameters on the performance of the ANN as well as helps us identify the network architectures better suited for our application

Estimation of stochastic responses due to multiple variabilities in electromagnetic finite element modelling Fleet Missions
B N Abhijith
Department of Electrical Communication Engineering
A practical electromagnetic system involves several uncertainties like material properties, surface defects, orientation of radiating systems etc. These deviationswhich are random and distributed all over the domain cannot be taken into account from the model specifications in a standard FEM simulation. This work formulates a way to predict the variation of the electromagnetic system response with respect to the random variation of the input parameters (material and geometric uncertainties) in a full wave 3D edge element FEM, for electromagnetic problems. A spec- tral stochastic finite element method (SSFEM) is implemented in 3D full wave edge element based EM problems successfully for multiple material variations. The method works well for microwave circuit involving waveguides or microstrip lines. This is then used to formulate geometric variation where the mesh coordinates are varied as random parameters. This method is found to be faster than the generally used stochastic collocation and Monte Carlo methods.

Inverse Problems in 3D Full-Wave Electromagnetics
Harikiran Muniganti
Department of Electrical Communication Engineering
This talk addresses inverse problems specific to the area of electromagnetics, arising in three different scenarios. The first problem is 3-D quantitative imaging primarily targeted towards bio-medical applications. Two approaches are proposed for solving the first problem, a multilevel methodology approach and Machine Learning classification followed by optimization (ML-OPT) approach. The second problem is in the domain of high-speed circuits and is focused on synthesis of transmission line physical parameters given the desired electrical parameters like characteristic impedance and propagation constant. A forward solver is used to train Neural network for several different configurations for analysis and an optimization algorithm is used for synthesis. The third problem is focused on finding the source of radiation in an electronic system e.g. an automotive ECU, given the measured field at the antenna in the radiated emissions setup. A method based on Huygens box is proposed to quantify the radiation from cable and DUT at each frequency. Some part of the presented work is used via technology-transfer at Simyog Technology Pvt. Ltd., an IISc incubated startup, to develop a simulation software called Compliance scope which allows the hardware designer to predict the EMI/EMC performance of electronics modules from an early design stage.

Comparative Analysis of Topological Structures
Raghavendra G S
Department of Computer Science and Automation
Scientific processes often result in scalar fields, whose comprehensive analysis leads to a better understanding of the underlying phenomena. The feature rich nature of the scalar fields demands for a summarized representation which has been addressed by topological structures like persistence diagrams, merge/contour trees, etc. Many such processes result in a set of fields which may be related temporally, be part of an ensemble, or unrelated. Thus it also requires methods to compare them meaningfully. We develop two global measures, for merge trees, both based on tree edit distances. One, based on the assumption that they are ordered rooted trees, and the second, called mted, a metric, assumes unordered rooted trees. Then, we develop a local comparison measure lmted, also a metric, to compare hierarchical substructures of scalar fields. We propose dynamic programming algorithms for all these measures along with intuitive cost models and applications to highlight their utility.

SPDE-Net: Predict an optimal stabilization parameter for SUPG technique to solve Singularly Perturbed PDEs
Sangeeta Yadav, Prof. Sashikumaar Ganesan
Department of Computational and Data Sciences
Numerical techniques for solving Singularly Perturbed Differential Equations(SPDE) suffer low accuracy and high numerical instability in presence of interior and boundary layers. Stabilization techniques are often employed to reduce the spurious oscillations in the numerical solution. Such techniques are highly dependent on user chosen stabilization parameter. Here we propose SPDE-Net, a novel neural network training technique to predict the stabilization parameter. The prediction task is modeled as a regression problem and is solved using semi supervised learning. Global and local variants of stabilization parameter τ are demonstrated. Experiments on a bench-mark case of 1-dimensional convection diffusion equation show a reasonable performance as compared to the conventional supervised training. This makes the proposed technique eligible for extension to higher dimensions and other cases where the analytical formula for stabilization parameter is unknown, therefore making supervised learning impossible.

Novel p-type AlTiO Gate Oxide based e-mode Operation in AlGaN/GaN HEMTs: 600V Technology with Record High Performance
Sayak Dutta Gupta and Mayank Shrivastava
Department of Electronics Systems Engineering
AlGaN/GaN High Electron Mobility Transistors (HEMTs) are potential candidates for power applications. However, while enhancement-mode (e-mode) is preferred for power applications, AlGaN/GaN HEMTs are depletion mode devices. This work experimentally demonstrates e-mode AlGaN/GaN HEMTs by introducing a novel p-type high-κ AlxTi1−xO based ternary gate oxide. The threshold voltage (Vth) of the GaN HEMTs was found to be tuned by the concentration of Al in Al-Ti-O system, with increasing Al% resulting in a higher positive shift in Vth. Ternary oxide AlTiO was also demonstrated to be superior to regular binary p-oxides, like CuO and NiOx. Using the high-κ and p-type AlTiO, in conjunction with a thinner AlGaN barrier under gate, 600 V e-mode GaN HEMTs are demonstrated with superior ON-state performance (ION ∼ 400 mA/mm and RON = 8.9 Ω-mm) and gate control over channel (ION/IOFF = 107 , SS = 73 mV/dec and gate leakage < 200 nA/mm), beside improved safe operating area reliability

Unique Reliability Phenomena in AlGaN/GaN HEMTs
Ankit Soni and Mayank Shrivastava
Department of Electronics Systems Engineering
The Schottky Barrier Diode is a key component in any power electronic circuit or THz/mmW system. A comprehensive TCAD and experimental co-design strategies have been proposed for high power and THz SBD. The critical part of the SBD diode design involves modelling the non-idealities at the Schottky interface and designing physics based process experiments to fix these non-idealities. We have modelled the anode contact interface by accounting for these effects. In addition impact of recessed depth and nature of buffer traps are investigated. The repercussions of field plate design on other performance figures of merit parameters such as diode current collapse, reverse recovery time, reverse current overshoot, and electro-thermal behaviour is investigated. Using the systematic device design approach, we have experimentally demonstrated high power SBD with 15A forward current at 5.5V while having reverse blocking greater than 500V. The design metrics for THz SBD vastly differs from the one used for high power applications. It is imperative to account for the impact of associated device parasitic elements at high-frequency operating conditions. The planer, multi-finger SBD topology looks most promising due to the ease of integration and high cut-off frequencies demonstrated. We present the first report on the design and engineering of multi-finger THz SBD. The study investigates the design metrics of AlN/GaN-based multi-finger, lateral SBD and proposes guidelines to maximize THz operation performance.

Gallium Nitride Hetero-structure based Schottky Barrier Diodes for Power and RF Applications
Rajarshi Roy Chaudhuri and Mayank Shrivastava
Department of Electronics Systems Engineering
AlGaN/GaN High Electron Mobility Transistors (HEMTs) are potential candidates for power applications. However, they suffer from wide range of reliability challenges under different operating conditions. In this work, we explore - (1) dynamic on-resistance (RON) increase post OFF-state stress, (2) gate leakage (IG) current degradation during semi-ON state stress, and (3) OFF-state breakdown physics employing an electro-optic characterization setup and a well-calibrated TCAD simulation setup. Unique critical voltage (VCr) associated with dynamic RON effects was observed and attributed to carrier injection and trapping in Carbon doped GaN buffer governed by channel electric field. Similarly, unique VCr was noticed in semi-ON state beyond which IG degraded permanently. It was attributed to crack/ pit formation in buffer governed by thermo-elastic stress buildup owing to hot electron-buffer trap interaction. OFF-state breakdown in GaN HEMTs was found to be a strong function of surface/buffer trap concentration and device design parameters based on which device design guideline have been established.

Nonlinear Light Generation in Dielectric Metasurfaces
Jayanta Deka
Department of Electronics Systems Engineering

Electro-Thermal Stress Induced Material Reconfiguration & Device Perturbations in monolayer 2D-TMD Transistors
Department of Electronics Systems Engineering
Device and material reliability of 2-dimensional materials, especially CVD-grown MoS2, has remained unaddressed since 2011 when the first TMDC transistor was reported. For its potential application in next generation electronics, it is imperative to update our understanding of mechanisms through which MoS2 transistors’ performance degrades under long-term electrical stress. We report,for CVD-grown monolayer MoS2, the very first results on temporal degradation of material and device performance under electrical stress. Both low and high field regimes of operation are explored at different temperatures, gate bias and stress cycles. During low field operation, current is found to saturate after hundreds of seconds of operation with the current decay time constant being a function of temperature and stress cycle. Current saturation after several seconds during low field operation occurs when a thermal equilibrium is established. However, high field operation, especially at low temperature, leads to impact ionization assisted material and device degradation. It is found that high field operation at low temperature results in amorphization of the channel and is verified by device and Kelvin Probe Force Microscopy (KPFM) analyses. In general, a prolonged room temperature operation of CVD-grown MoS2 transistors lead to degraded gate control, higher OFF state current and negative shift in threshold voltage (VT). This is further verified, through micro-Raman and photoluminescence spectroscopy, which suggest that a steady state DC electrical stress leads to the formation of localized low resistance regions in the channel and a subsequent loss of transistor characteristics. Our findings unveil unique mechanism by which CVD MoS2 undergoes material degradation under electrical stress and subsequent breakdown of transistor behaviour. Such an understanding of material and device reliability helps in determining the safe operating regime from device as well as circuit perspective

Transmission Line Outage Identification - A Sequential Testing Framework
Chetan Kumar Kuraganti
Department of Electrical Communication Engineering
In this work, the problem of identifying transmission line failures in a power system is investigated. Identifying such failures in the shortest possible time is of importance due to cascading nature of these failures. With this motivation, a state estimation based sequential hypothesis testing procedure to localize the failed lines is proposed. The primary focus is on single line outages as these are more frequently occurring failures. The state estimation (SE) is performed using conventional power measurements (SCADA) and synchronized phasor measurement units (PMUs). The idea is that if there is an outage, then this information is embedded in the state estimation results, and this information can be utilized to infer the topology of the power system. The proposed framework involves a Kalman filter based state estimation followed by a generalized likelihood ratio testing procedure to locate the failed lines. This work considers both centralized and decentralized state estimation approaches. In a centralized approach, all the information from various parts of the power system is available to the system operator. However, in a decentralized approach, only limited information is considered to reduce the communication and computational overhead. The proposed algorithms are evaluated on the IEEE 14 and the IEEE 118 bus systems, and results show that all the high risk line failures were identified quickly. Furthermore, simulation results show that the use of PMUs enables accurate and quicker detection than conventional power measurements.

Scalable Asynchrony-Tolerant PDE Solver for Multi-GPU System
Vinod Jacob Matthew, Dr. Konduri Aditya
Department of Computational and Data Science
Data communication and synchronization between processing elements are likely pose a bottleneck in scalability of partial differential equation(PDE) solvers on future exascale machines. Recently, asynchrony-tolerant(AT) finite-difference schemes, which compute accurate solutions while relaxing data movement and synchronization, were developed using wider stencils in space and/or time to improve the scalability. As CPU-hardware struggles to keep pace with Moore’s law, GPUs are significantly contributing towards the compute capability of supercomputers. However, the GPUs, which possess high throughput, suffer high latency costs. In this work, we use AT schemes to develop an asynchronous solver for multi-GPUs and demonstrate their capability to hide the high latency costs. The code, implemented with CUDA+MPI, solves the compressible fluid-flow equations. Two algorithms —communication and synchronisation avoiding— were used to develop the solver. Comparison against a synchronous solver was performed to verify the accuracy and demonstrate the scalability. On a single node with 8 A100 GPUs a 2X speed-up was observed. Scalability study on a large number of nodes is currently under progress.