Anubha Gupta

EDBT 2026 Demo / reviewers in the wild / expert

dblp:95/491 · DBLP profile ↗

← Back

49ranked-venue papers

6as first author

18since 2021 · last 2026

0000-0002-7752-1926ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 25 · 4 first-author · 8 since 2021Artificial intelligence and machine learning · 11 · 8 since 2021Applied, interdisciplinary, general and emerging computing · 11 · 2 first-author · 3 since 2021Databases, data management, data science and information retrieval · 5 · 1 first-authorComputer networks · 3Human-computer interaction and ubiquitous computing · 3 · 2 since 2021

Year	Publication	Venue	Position
2026	MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation abstract While multilingual large language models (LLMs) perform well on high-level tasks like translation and question answering, their ability to handle grammatical gender and morphological agreement remains underexplored.In morphologically rich languages, gender influences verb conjugation, pronouns, and even first-person constructions with explicit and implicit mentions to gender.We thus introduce MORPHOGEN a morphologically grounded largescale benchmark dataset for evaluating genderaware generation in three typologically diverse grammatically gendered languages i.e.French, Arabic and Hindi.The core task, GENFORM, requires models to rewrite a first-person sentence in the opposite gender while preserving its meaning and structure.We construct a highquality synthetic dataset spanning French, Arabic, and Hindi, and benchmark 15 popular multilingual LLMs (2B-70B) on their ability to perform this transformation.Our results reveal gaps and interesting insights into the handling of morphological gender in current models.MORPHOGEN offers a focused diagnostic lens for gender-aware language modeling and lays the groundwork for future research on inclusive and morphology-sensitive NLP. Mehul Agarwal, Aditya Aggarwal, Arnav Goel, Medha Hira, Anubha Gupta	ACL (1)	5
2026	Smooth or Jarring? Evaluating Video Transitions with TransiSense and VT-Bench abstract With the rapid advancement of state-of-the-art AI tools and diffusion-based models for scene interpolation, qualitative analysis has become increasingly time-consuming and tedious. Here, we address this challenge by proposing a novel metric, TransiSense, which offers an improvement over current benchmarks in the field. Existing evaluation methods primarily focus on isolated aspects of video quality, failing to comprehensively assess the overall transition between an initial and a final frame. Our proposed metric overcomes these limitations by providing a more holistic evaluation, serving as a valuable tool for researchers developing video generation models. Additionally, we introduce a new dataset, VT-Bench, designed to facilitate the testing and benchmarking of future models and metrics. The dataset can be accessed directly at https://huggingface.co/datasets/Abhirup04/VT-Bench . Abhirup Das, Nishant Singh, Anubha Gupta	ICPR (13)	3
2026	Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages V. Divya Sharma, Anubha Gupta	LREC	3
2025	IndicSynth: A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages abstract Recent advances in synthetic speech generation technology have facilitated the generation of high-quality synthetic (fake) speech that emulates human voices. These technologies pose a threat of misuse for identity theft and the spread of misinformation. Consequently, the misuse of such powerful technologies necessitates the development of robust and generalizable audio deepfake detection (ADD) and anti-spoofing models. However, such models are often linguistically biased. Consequently, the models trained on datasets in one language exhibit a low accuracy when evaluated on out-of-domain languages. Such biases reduce the usability of these models and highlight the urgent need for multilingual synthetic speech datasets for bias mitigation research. However, most available datasets are in English or Chinese. The dearth of multilingual synthetic datasets hinders multilingual ADD and anti-spoofing research. Furthermore, the problem intensifies in countries with rich linguistic diversity, such as India. Therefore, we introduce IndicSynth, which contains 4,000 hours of synthetic speech from 989 target speakers, including 456 females and 533 males for 12 low-resourced Indian languages. The dataset includes rich metadata covering gender details and target speaker identifiers. Experimental results demonstrate that IndicSynth is a valuable contribution to multilingual ADD and anti-spoofing research. The dataset can be accessed from https://github.com/vdivyas/IndicSynth. V. Divya Sharma, Vijval Ekbote, Anubha Gupta	ACL (1)	3
2025	CardioRiskNet: Attention-based CVAE-enabled GCN for Risk Prediction in STEMI abstract Cardiovascular diseases (CVDs) are a major cause of death worldwide, taking almost 18 million lives each year. ST Elevation Myocardial Infarction (STEMI) is one of the highest contributors to the same. The immediate 30-day period post-STEMI is critical in judging long-term patient outcomes. Thus, there is a need for an accurate risk predictor to guide clinical interventions immediately after STEMI. In this paper, we propose CardioRiskNet, a post-STEMI 30-day mortality predictor based on Graph Convolutional Networks, designed to adapt to different populations with the relational nature of graph-based models. To address class imbalance, we propose a data synthesis method for CVD data by introducing a self-attention mechanism in a Conditional Variational Autoencoder. To demonstrate robustness, the model has been tested on three datasets including two publicly available datasets. CardioRiskNet shows better performance compared to the state-of-the-art methods. Posthoc interpretability analysis also suggests that CardioRiskNet offers promising advancements in data-driven risk assessment, providing clinicians with a precise tool for patient management. Akshat Gupta, Anubha Gupta, Manu Kumar Shetty, Dixit Goyal, Girish M. P, Mohit D. Gupta	ICASSP	2
2025	PP-CNN: probabilistic pooling CNN for enhanced image classification Narendra Kumar Mishra, Pushpendra Singh 0002, Anubha Gupta, Shiv Dutt Joshi	Neural Comput. Appl.	3
2025	Differentiating presence in virtual reality using physiological signals abstract Advancements in wearable technologies have made the use of physiological signals, such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV), more prevalent for detecting changes in the autonomic nervous system within virtual reality (VR). However, the challenge lies in utilizing these signals to objectively detect presence in VR, which typically relies on self-reports that can be inherently biased. This paper addresses this issue and presents a study ( N =26) that investigates the effect that different levels of presence has on physiological responses in VR. A neutral VR environment was created that incorporated three levels of presence (high, medium and low) that were invoked by tuning different parameters. Participants wore a wrist-worn wearable device that captured their physiological signals whilst they experienced each of these environments. Results indicated that tonic and phasic components of the EDA signal were significant in differentiating between the levels. Two novel features, constructed using both the phasic and tonic components of EDA, successfully differentiated between presence levels. Analysis of the HRV data illustrated a significant difference between the low and medium levels using the ratio between low frequency to high frequency. • Validation of the design of three different levels of presence in a VR environment. • Development of two novel features (cf1, cf2) significantly differentiated between levels of presence. • Poincare maps illustrate the variability in the data between different presence levels. Shuvodeep Saha, Chelsea Dobbins, Anubha Gupta, Arindam Dey 0001	Pervasive Mob. Comput.	3
2024	Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning Arnav Goel, Medha Hira, Anubha Gupta	INTERSPEECH	3
2023	Why is the Winner the Best? abstract International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein	CVPR	29
2023	SegPC-2021: A challenge & dataset on segmentation of Multiple Myeloma plasma cells from microscopic images Anubha Gupta, Shiv Gehlot, Shubham Goswami, Sachin Motwani, Álvaro García-Faura, Dejan Stepec, Tomaz Martincic, Reza Azad, Dorit Merhof, Afshin Bozorgpour, Babak Azad, Alaa Sulaiman, Deepanshu Pandey, Pradyumna Gupta, Sumit Bhattacharya, Aman Sinha 0002, Xinyun Qiu, Yoonbeom Park, Dae-Hong Lee, Joon Sik Park, KwangYeol Lee, Jaehyung Ye	Medical Image Anal.	1
2023	A novel PRFB decomposition for non-stationary time-series and image analysis Pushpendra Singh 0002, Amit Singhal 0002, Binish Fatimah, Anubha Gupta	Signal Process.	4
2022	Effects of interacting with facial expressions and controllers in different virtual environments on presence, usability, affect, and neurophysiological signals Arindam Dey 0001, Amit Barde, Ekansh Sareen, Chelsea Dobbins, Aaron Goh, Anubha Gupta, Mark Billinghurst	Int. J. Hum. Comput. Stud.	8
2022	ARCANE-ROG: Algorithm for reconstruction of cancer evolution from single-cell data using robust graph learning Akanksha Farswan, Anubha Gupta	J. Biomed. Informatics	3
2021	An Improved Data Driven Dynamic SIRD Model for Predictive Monitoring of COVID-19 abstract COVID-19 pandemic spreaded across the world in early 2020. It forced many countries to impose lockdown to pre-vent surge in the number of infected cases. There has been a huge impact on social and economic activities worldwide. In this work, we carry out the functional modeling of COVID-19 infection trends using two models: the Gaussian mixture model (GMM) and the composite logistic growth model (CLGM). Unlike the traditional SIRD models that use numerical data fitting, we utilize the best data-fitted curves employing GMM and/or CLGM to construct the Susceptible-Infected-Recovered-Dead (SIRD) pandemic model. Further, we derive the explicit expressions of time-varying parameters of the SIRD model unlike most works that consider static parameters without any closed form solution. The proposed parameterized dynamic SIRD model is generically applicable to any pandemic, can capture the day-to-day dynamics of the pandemic and can assist the governing bodies in devising efficient action plans to deal with the prevailing pandemic. Pushpendra Singh 0002, Amit Singhal 0002, Binish Fatimah, Anubha Gupta	ICASSP	4
2021	DURAS: Deep Unfolded Radar Sensing Using Doppler Focusing abstract Sub-Nyquist sampling is used in modern high-resolution pulse-Doppler radar systems to reduce system resources and improve resolution. Xampling with Doppler focusing is utilized to implement these sub-Nyquist radar systems. Signal recovery involves iterative optimization requiring large computational time that may be prohibitive in real applications. In this paper, we propose Deep Unfolded Radar Sensing (DURAS), a model-based deep learning architecture to address this problem. We utilize the recently introduced complex LISTA (C-LISTA) with recurrent neural network units and complex soft-thresholding to handle the complex-valued measurement signals. We propose a partial Doppler focusing (PDF) framework with ensembling of multiple PDF measurement vectors via a convolutional neural network (CNN). This CNN followed by a complex cardioid activation function is added to the front end of the C-LISTA architecture. Thus, DURAS is a hybrid architecture of partial Doppler focusing, CNN, and C-LISTA that provides considerably improved performance compared to existing methods on target detection in radar systems. Pranav Goyal, Satish Mulleti, Anubha Gupta, Yonina C. Eldar	ICASSP	3
2021	A comparative study on inter-brain synchrony in real and virtual environments using hyperscanning Ihshan Gumilar, Ekansh Sareen, Reed Bell, Augustus Stone, Ashkan F. Hayati, Jingwen Mao, Amit Barde, Anubha Gupta, Arindam Dey 0001, Gun A. Lee, Mark Billinghurst	Comput. Graph.	8
2021	InPHYNet: Leveraging attention-based multitask recurrent networks for multi-label physics text classification Vishaal Udandarao, Anubha Gupta, Tanmoy Chakraborty 0002	Knowl. Based Syst.	3
2021	A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis Shiv Gehlot, Anubha Gupta	Medical Image Anal.	2
2020	EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation abstract Accurate nuclei identification is an important step in diagnosis of several diseases. The problem is complex due to heterogeneity in structure, color, and texture among the different categories of cells. The problem is further complicated due to overlapped/clustered nuclei. To address these challenges, we propose an Encoder-Decoder based Convolutional Neural Network (CNN) with Nested-Feature Concatenation (EDNFC-Net) for automatic nuclei segmentation. The feature concatenation cell (FCC) of the EDNFC-Net is made up of two stacks of convolutional filters combined with non-linearity, followed by a concatenation of features. Apart from intra-FCC feature concatenation, a mechanism is also provided for inter-FCC feature concatenation. This arrangement leads to better feature flow and feature-reusability. Similarly, direct feature flow is provided between the encoder and decoder module that preserves the context information. A new loss function with better-penalizing capability is also proposed that helps in the better background and foreground separation. Qualitative and quantitative results are provided on two datasets to validate the proposed architecture and loss function. Shiv Gehlot, Anubha Gupta	ICASSP	2
2020	MSR-Hardi: Accelerated Reconstruction of Hardi Data Using Multiple Sparsity Regularizers abstract Brain neural connectivity patterns are increasingly analyzed with diffusion magnetic resonance imaging (dMRI) via the estimation of local fiber-tract orientations. High angular resolution diffusion imaging (HARDI), a variant of dMRI, is known to produce better representation of fiber orientations than the traditionally used diffusion tensor imaging (DTI). However, it requires a large number of samples leading to longer scan times. In this paper, we propose a new method, namely, MSR-HARDI, for the accelerated reconstruction of HARDI data using multiple sparsity regularizers in the k - q space. Combination of regularizers is observed to provide improved reconstructions as compared to individual regularizers. The proposed method is also observed to provide better reconstruction than the existing state-of-the-art methods in terms of the normalized mean squared error. Ashutosh Vaish, Anubha Gupta, Ajit Rajwade 0001	ICIP	2
2020	SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis Shiv Gehlot, Anubha Gupta	Medical Image Anal.	2
2020	GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images Anubha Gupta, Rahul Duggal, Shiv Gehlot, Anvit Mangal, Nisarg Thakkar, Devprakash Satpathy	Medical Image Anal.	1
2019	EDUQA: Educational Domain Question Answering System Using Conceptual Network Mapping abstract Most of the existing question answering models can be largely compiled into two categories: i) open domain question answering models that answer generic questions and use large-scale knowledge base along with the targeted web-corpus retrieval and ii) closed domain question answering models that address focused questioning area and use complex deep learning models. Both the above models derive answers through textual comprehension methods. Due to their inability to capture the pedagogical meaning of textual content, these models are not appropriately suited to the educational field for pedagogy. In this paper, we propose an on-the-fly conceptual network model that incorporates educational semantics. The proposed model preserves correlations between conceptual entities by applying intelligent indexing algorithms on the concept network so as to improve answer generation. This model can be utilized for building interactive conversational agents for aiding classroom learning. Nikhil Sachdeva, Raj Kamal Yadav, Vishaal Udandarao, Vrinda Mittal, Anubha Gupta, Abhinav Mathur	ICASSP	6
2019	TV-DCT: Method to Impute Gene Expression Data Using DCT Based Sparsity and Total Variation Denoising abstract Most of the bioinformatics tools used in the analysis of gene expression data require complete data matrices. Missing values in data can adversely influence the downstream analysis for diagnostics and treatment. Several methods to impute missing values in gene data have been developed. However, most of these work at high levels of observability. In this paper, we have proposed a novel 2-stage method, namely, TV-DCT for imputing incomplete gene expression matrices using Total Variation denoising and Discrete Cosine Transform Domain Sparsity (TV-DCT) that achieves smaller imputation errors, consistently, at all levels of observability. The proposed method has been compared with three state-of-the-art matrix completion methods on three different cancer datasets and is observed to perform better. The validation of imputed data has been demonstrated on the application of classification. Akanksha Farswan, Anubha Gupta	ICASSP	2
2019	Rethinking Teaching Practices for Signal Processing Education abstract There are three fundamental components of teaching a course: course content plan, delivery, and assessment. While teaching methodologies have evolved vis-a`-vis all the above components, signal processing domain has itself evolved significantly over the past few years. It would not be wrong to say that the signal processing domain has redefined itself in the past few years and has also evolved rapidly creating a need to devise innovative teaching practices. Learning habits of students have also become diverse and are considerably different compared to those of students of the late 20th century. In this paper, we present the changing landscape of signal processing theory and discuss some of the teaching practices that may prove more helpful in this fast-changing era of technology. Anubha Gupta, Akanksha Farswan	ICASSP	1
2019	TS-MC: Two Stage Matrix Completion Algorithm for Wireless Sensor Networks abstract Wireless sensor network (WSN) data is prone to huge losses and corruption. Hence, the existing matrix completion algorithms experience high estimation errors in such scenarios. Therefore, a robust matrix completion algorithm is required for WSN data to meet the above challenges. This paper proposes a robust "two stage matrix completion (TS-MC)" algorithm to recover data from missing and corrupted values. The proposed TS-MC algorithm consists of two stages. For the first stage, two different methods have been proposed for recovering the incomplete data that exploit the double DCT sparsity as WSN data varies smoothly in both time and spatial domain. In the second stage, the recovered data of the first stage is de-noised in the matrix factorization framework, wherein the rank of the data is estimated from the data recovered from the first stage. Simulations are performed on two real datasets of Intel Lab and Data Sensing Lab. Results demonstrate that the proposed TS-MC algorithm achieves high accuracy even when 90% of the data is missing. Neha Jain 0002, Anubha Gupta, Vivek Ashok Bohara	ICASSP	2
2019	Group-fused multivariate regression modeling for group-level brain networks Priya Aggarwal, Anubha Gupta	Neurocomputing	2
2019	Multivariate graph learning for detecting aberrant connectivity of dynamic brain networks in autism Priya Aggarwal, Anubha Gupta	Medical Image Anal.	2
2018	Statistical Learning of Rational Wavelet Transform for Natural Images abstract Motivated with the concept of transform learning and the utility of rational wavelet transform in audio and speech processing, this paper proposes Rational Wavelet Transform Learning in the Statistical sense (RWLS) for natural images. The proposed RWLS design is carried out via lifting framework and is shown to have a closed form solution. The efficacy of the learned transform is demonstrated in the application of compressed sensing (CS) based reconstruction. The learned RWLS is observed to perform better than the existing standard dyadic wavelet transforms. Naushad Ansari, Anubha Gupta	ICASSP	2
2018	An Iterative Transmitted Reference UWB Receiver for Joint ToA and Data Symbols Estimation abstract In impulse radio ultra-wideband (IR-UWB) literature, simple transmitted reference (TR) autocorrelation receiver has been analyzed for data symbol detection. However, TR receiver is neither energy efficient nor data transmission rate efficient due to transmission of two pulses per data symbol. In this paper, we propose an iterative TR (ITR) UWB receiver for the joint time of arrival (ToA) and data symbol estimation. The proposed ITR receiver uses only single reference pulse for a burst of data symbols. The ITR receiver estimates a new reference pulse from the burst of data symbols' pulses to further enhance the signal-to-noise ratio (SNR) of UWB system. Hence, the proposed ITR receiver has high energy efficiency and data rate transmission efficiency with improved SNR as compared to a TR receiver. Further, the proposed ITR receiver is implemented at sub-Nyquist rate to overcome the high sampling rate analog-to- digital converter. The ITR receiver's ToA and data symbol estimation performance is analyzed for the binary phase shift keying modulated UWB system in standard IEEE 802.15.4a channels in the presence of additive white Gaussian noise. Sanjeev Sharma 0001, Vimal Bhatia, Anubha Gupta	ICC	3
2018	U-Segnet: Fully Convolutional Neural Network Based Automated Brain Tissue Segmentation Tool abstract Automated brain tissue segmentation into white matter (WM), gray matter (GM), and cerebro-spinal fluid (CSF) from magnetic resonance images (MRI) is helpful in the diagnosis of neuro-disorders such as epilepsy, Alzheimer's, multiple sclerosis, etc. However, thin GM structures at the periphery of cortex and smooth transitions on tissue boundaries such as between GM and WM, or WM and CSF pose difficulty in building a reliable segmentation tool. This paper proposes a Fully Convolutional Neural Network (FCN) tool, that is a hybrid of two widely used deep learning segmentation architectures SegNet and U-Net, for improved brain tissue segmentation. We propose a skip connection inspired from U-Net, in the SegNet architetcure, to incorporate fine multiscale information for better tissue boundary identification. We show that the proposed U-SegNet architecture, improves segmentation performance, as measured by average dice ratio, to 89.74% on the widely used IBSR dataset consisting of T-1 weighted MRI volumes of 18 subjects. Pulkit Kumar, Pravin Nagar, Chetan Arora 0001, Anubha Gupta	ICIP	4
2018	Poster: Sparse Signal Recovery and Energy Harvesting for Potential 5G Applications abstract In this paper, a new protocol for simultaneous wireless information and power transfer (SWIPT) has been proposed, which randomly switches the signal for information decoding (ID) and energy harvesting (EH). However, the information decoder recovers the complete data by utilizing the sparse behaviour of the real world signal, thus not compromising the system performance, unlike the conventional SWIPT protocols such as time switching and power splitting. Neha Jain 0002, Vivek Ashok Bohara, Anubha Gupta	MobiCom	3
2017	Joint Estimation of ToA and Data Symbols in UWB Communication in Presence of Impulsive Interference abstract Ultra-wide band (UWB) ranging requires precise estimation of time-of-arrival (ToA) of first path (FP) signal. In UWB communication, estimation of ToA is challenging in the presence of impulsive interference and multipath. Generally in literature, ToA is estimated using threshold crossing techniques. Although threshold based ToA methods are simple, optimal threshold value is hard to determine in practice. In this paper, we propose joint estimation of ToA and data symbols by exploiting the cluster-sparsity of the received UWB signal in the presence of impulsive interference. The proposed receiver structure enhances the signal-to- noise ratio at the receiver output by mitigating the impulsive interference and barring inter-clusters noise accumulation. The proposed method is also free from any threshold, training, and optimization process. Robustness of the proposed algorithm is validated for standardized IEEE 802.15.4a channel models in the presence of both additive white Gaussian noise and impulsive interference. Sanjeev Sharma 0001, Anubha Gupta, Vimal Bhatia	GLOBECOM	2
2017	SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging Rahul Duggal, Anubha Gupta, Pramit Mallick	MICCAI (3)	2
2017	A Simple Modified Peak Detection Based UWB Receiver for WSN and IoT Applications abstract Ultra-wide band (UWB) communication is a viable solution for Wireless Sensor Network (WSN) and Internet of Things (IoT) due to low cost and low power requirement. However, UWB transceiver design is more complex due to large bandwidth and precise synchronization requirement. In this paper, we propose a simple peak detection based non-coherent UWB receiver, suitable for low data rate WSN and IoT based applications. The proposed receiver divides each data symbol frame duration into smaller multiple time windows. In each time window, peak of received signal is detected independently using threshold comparison. The transmitted signal is detected in a frame by employing decisions on all multiple time windows. From simulation, it is observed that the proposed receiver outperforms existing non-coherent receivers. The performance analysis of the proposed receiver is carried out by using time hopping pulse position modulation (TH-PPM) UWB signal in additive white Gaussian noise (AWGN), multipath communication using the existing IEEE 802.15.4a standard. Sanjeev Sharma 0001, Anubha Gupta, Vimal Bhatia	VTC Spring	2
2017	Multivariate brain network graph identification in functional MRI Priya Aggarwal, Anubha Gupta, Ajay Garg	Medical Image Anal.	2
2017	Image Reconstruction Using Matched Wavelet Estimated From Data Sensed Compressively Using Partial Canonical Identity Matrix abstract This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed images and are used for the reconstruction of the same. Matched wavelet can be easily designed if full image is available. Also compared with the standard wavelets as sparsifying bases, matched wavelet may provide better reconstruction results in compressive sensing (CS) application. Since in CS application, we have compressively sensed images instead of full images, existing methods of designing matched wavelets cannot be used. Thus, we propose a joint framework that estimates matched wavelets from compressively sensed images and also reconstructs full images. This paper has three significant contributions. First, a lifting-based, image-matched separable wavelet is designed from compressively sensed images and is also used to reconstruct the same. Second, a simple sensing matrix is employed to sample data at sub-Nyquist rate such that sensing and reconstruction time is reduced considerably. Third, a new multi-level L-Pyramid wavelet decomposition strategy is provided for separable wavelet implementation on images that leads to improved reconstruction performance. Compared with the CS-based reconstruction using standard wavelets with Gaussian sensing matrix and with existing wavelet decomposition strategy, the proposed methodology provides faster and better image reconstruction in CS application. Naushad Ansari, Anubha Gupta	IEEE Trans. Image Process.	2
2016	Joint Framework for Signal Reconstruction Using Matched Wavelet Estimated from Compressively Sensed Data abstract So far, no method exists in literature for the estimation of matched wavelet from compressively sensed data that can also be utilized at the same time for the efficient signal reconstruction of a compressively sensed signal. In this work, we address this problem. Naushad Ansari, Anubha Gupta	DCC	2
2016	Connection between DCT and Discrete-Time Fractional Brownian motion abstract A discrete-time fBm (dfBm) process BH(n) is a Gaussian, zero mean, non-stationary, statistically self similar random process with self-similarity index H (Hurst exponent). Signal modeling via these processes has been used in many engineering applications. In this paper, we have shown that the orthogonal matrix Q that diagonalizes the auto-covariance matrix of 1st order dfBm is close to the columns of DCT matrix. Anubha Gupta, Shiv Dutt Joshi	DCC	1
2016	Accelerated fMRI reconstruction using Matrix Completion with Sparse Recovery via Split Bregman Priya Aggarwal, Anubha Gupta	Neurocomputing	2
2015	Assessing the Impact of Virtual Labs: A Case Study with the Lab on Advanced VLSI abstract The laboratory is an indispensable component of learning in engineering education. In this paper, we examine the impact of Advanced VLSI Virtual Lab, which is a part of the Government of India's suite of Virtual Labs, in improving the understanding and learning of students at a small sized university in India. The Advanced VLSI Virtual Lab includes ten simulated interactive experiments in the area of design and application development. Over a hundred Virtual Labs have been proposed and built, but, so far, few have been subject to systematic investigation of their effectiveness in helping the student learn. Our work is one of the first efforts to statistically study the effectiveness of the Virtual Lab. To this end, we designed and conducted pre- and post-tests, and feedback surveys on the lab. The tests and the survey, on analysis, reveal that the lab is effective in enhancing student learning. Our results are encouraging to several teachers in India who are in the midst of using Virtual Labs at their colleges. Garima Ahuja, Anubha Gupta, Harsh Wardhan, Venkatesh Choppella	ICALT	2
2015	Design of signal-matched critically sampled FIR rational filterbank abstract Wavelet transform is used for efficient signal analysis in various applications. The traditional wavelet system is implemented using integer decimation factors, although frequency tiling offered by rational decimation may better adapt to signal characteristics. In this paper, we propose a design methodology for signal-matched filterbank (FB) with rational decimation factors that achieves perfect reconstruction with FIR filters. We have applied the proposed design on some real world signals. With the proposed design, we obtain a more compressible transform domain representation than the dyadic standard wavelet transforms. Anupriya Gogna, Gade Narayana Sri Harsha, Anubha Gupta	ICASSP	3
2015	Joint Estimation of Hemodynamic Response Function and Voxel Activation in Functional MRI Data Priya Aggarwal, Anubha Gupta, Ajay Garg	MICCAI (1)	2
2015	A guard interval assisted OFDM symbol-based channel estimation for rapid time-varying scenarios in IEEE 802.lip abstract IEEE 802.11p standard is a wireless vehicular communication standard meant for outdoor applications. This standard suffers from the challenge of robust channel estimation due to rapid time-varying nature of the channel This paper proposes a novel scheme of channel estimation by utilizing the guard interval of every orthogonal frequency division multiplexing (OFDM) symbol. For a typical vehicular wireless communication where the channel fades quite rapidly, inter-symbol-interference (ISI) may not be as significant a problem as time varying nature of the channel due to Doppler effect. Hence, the proposed scheme utilizes the redundant space of guard interval (GI) (other than that required for cyclic prefix (CP) to combat ISI) to insert pseudo-random sequence (PRS)for channel estimation. A decision-directed time-domain least squares channel estimation method is proposed using the inserted PRS with CP. Simulation results show that the proposed scheme can considerably improve the bit error rate (BER) performance compared to the existing techniques. Priya Aggarwal, Anubha Gupta, Vivek Ashok Bohara	PIMRC	2
2011	Two-channel nonseparable wavelets statistically matched to 2-D images Anubha Gupta, Shiv Dutt Joshi	Signal Process.	1
2009	DCT Domain Message Embedding in Spread-Spectrum Steganography System abstract Spread-spectrum steganographic (SSIS) method offers high payload and robustness to additive noise in transmission channel but the visual quality of image is distorted and exact data recovery may not be satisfied. DCT-domain message hiding based steganographic techniques provide high image imperceptibility and exact data recovery in absence of noise. In this paper, we combined the best of SSIS and DCT-domain hiding to provide high image imperceptibility and robustness to noise. We demonstrate our proposed algorithm through experiments on additive noise and jpeg compression attacks in the transmitted channel. Neha Agrawal, Anubha Gupta	DCC	2
2009	R2D: Extracting Relational Structure from RDF Stores abstract The enthusiastic acceptance of Resource Description Framework (RDF) as a data model has given birth to a new data storage paradigm, namely, the RDF Graph model. The pool of modeling and visualization tools available for RDF stores is limited due to the technology being in its fledgling stage. The work presented in this paper, called R2D (RDF-to-Database) is an effort to make available, to RDF data stores, the abundance of relational tools that are currently in the market. This is done in the form of a JDBC wrapper around RDF Stores that presents a relational view of the stores and their data to the modeling and visualization tools. This paper presents key R2D functionalities and mapping constructs, procedures for every stage of R2D deployment, and sample results in the form of screenshots and performance graphs. Sunitha Ramanujam, Anubha Gupta, Latifur Khan, Steven Seida, Bhavani Thuraisingham	Web Intelligence	2
2009	Relationalizing RDF stores for tools reusability abstract The emergence of Semantic Web technologies and standards such as Resource Description Framework (RDF) has introduced novel data storage models such as the RDF Graph Model. In this paper, we present a research effort called R2D, which attempts to bridge the gap between RDF and RDBMS concepts by presenting a relational view of RDF data stores. Thus, R2D is essentially a relational wrapper around RDF stores that aims to make the variety of stable relational tools that are currently in the market available to RDF stores without data duplication and synchronization issues. Sunitha Ramanujam, Anubha Gupta, Latifur Khan, Steven Seida, Bhavani Thuraisingham	WWW	2
2005	A new method of estimating wavelet with desired features from a given signal Anubha Gupta, Shiv Dutt Joshi, Surendra Prasad	Signal Process.	1