Search Results - "Vedang, M."

Guaranteed Robust Performance of [Formula Omitted] Filters With Sparse and Low Precision Sensing by Deshpande, Vedang M, Bhattacharya, Raktim

“…The performance of estimation algorithms depends on the available sensors and their precisions. While higher precisions of sensors provide better estimation…”
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A unified framework to generate optimized compact finite difference schemes by Deshpande, Vedang M., Bhattacharya, Raktim, Donzis, Diego A.

“…A unified framework to derive optimized compact schemes for a uniform grid is presented. The optimized scheme coefficients are determined analytically by…”
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Guaranteed Robust Performance of \mathcal Filters With Sparse and Low Precision Sensing by Deshpande, Vedang M., Bhattacharya, Raktim

“…The performance of estimation algorithms depends on the available sensors and their precisions. While higher precisions of sensors provide better estimation…”
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Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles by Chinchilla, Raphael, Deshpande, Vedang M., Chakrabarty, Ankush, Laughman, Christopher R.

“…In order to improve the control performance of vapor compression cycles (VCCs), it is often necessary to construct accurate dynamical models of the underlying…”
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Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments by Deshpande, Vedang M., Bhattacharya, Raktim

“…In this paper the tracking problem of multi-agent systems, in a particular scenario where a segment of agents entering a sensing-denied environment or behaving…”
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Sparse Sensing and Optimal Precision: Robust H∞ Optimal Observer Design with Model Uncertainty by Deshpande, Vedang M., Bhattacharya, Raktim

“…We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in…”
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Constrained Smoothers for State Estimation of Vapor Compression Cycles by Deshpande, Vedang M., Laughman, Christopher R., Ma, Yingbo, Rackauckas, Chris

“…State estimators can be a powerful tool in the development of advanced controls and performance monitoring capabilities for vapor compression cycles, but the…”
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Surrogate Modeling of Dynamics From Sparse Data Using Maximum Entropy Basis Functions by Deshpande, Vedang M., Bhattacharya, Raktim

“…In this paper, we present a data driven approach for approximating dynamical systems. A system of governing equations is approximated using basis functions,…”
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Robust Kalman Filtering With Probabilistic Uncertainty in System Parameters by Kim, Sunsoo, Deshpande, Vedang M., Bhattacharya, Raktim

“…In this letter, we propose a robust Kalman filtering framework for systems with probabilistic uncertainty in system parameters. We consider two cases, namely…”
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Sparse Sensing and Optimal Precision: An Integrated Framework for H 2 /H ∞ Optimal Observer Design by Deshpande, Vedang M., Bhattacharya, Raktim

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Sparse Sensing and Optimal Precision: An Integrated Framework for H2/H∞ Optimal Observer Design by Deshpande, Vedang M., Bhattacharya, Raktim

“…In this letter, we simultaneously determine the optimal sensor precision and the observer gain, which achieves the specified accuracy in the state estimates…”
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Sensor Placement With Optimal Precision for Temperature Estimation of Battery Systems by Deshpande, Vedang M., Bhattacharya, Raktim, Subbarao, Kamesh

“…The temperature distribution in the battery significantly impacts the short-term and long-term performance of battery systems. Therefore, efficient, safe, and…”
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Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States by Deshpande, Vedang M., Chakrabarty, Ankush, Vinod, Abraham P., Laughman, Christopher R.

“…Physics-based computational models of vapor compression systems (VCSs) enable high-fidelity simulations but require high-dimensional state representations. The…”
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EP-1687: The current place of radiotherapy as treatment option for muscle-invasive bladder cancer by Rammant, E., Decaestecker, K., Lievens, Y., Dirix, P., Sargos, P., Vedang, M., Eapen, L., Christodouleas, J., Fonteyne, V.

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mathcal{H}_2/\mathcal{H}_\infty$ Optimal Control with Sparse Sensing and Actuation by Deshpande, Vedang M, Bhattacharya, Raktim

“…In this paper, we present novel convex optimization formulations for designing full-state and output-feedback controllers with sparse actuation that achieve…”
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Elective nodal dose of 60 Gy or 50 Gy in head and neck cancers: A matched pair analysis of outcomes and toxicity by Murthy, Vedang, MD, DNB, Gurram, Lavanya, MD, Kannan, Sadhana, MSc, Gandhi, Minakshi, MSc, Gupta, Tejpal, MD, DNB, Laskar, Sarbani Ghosh, MD, DNB, Budrukkar, Ashwini, MD, DNB, Agarwal, Jai Prakash, MD

“…Abstract Purpose The main objective of this study was to evaluate appropriate doses for elective nodal irradiation (ENI) in head and neck squamous cell…”
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Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing by Askari, Iman, Wang, Yebin, Deshpande, Vedang M., Fang, Huazhen

“…Safe and efficient motion planning is of fundamen-tal importance for autonomous vehicles. This paper investigates motion planning based on nonlinear model…”
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Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments by Deshpande, Vedang M, Bhattacharya, Raktim

“…In this paper the tracking problem of multi-agent systems, in a particular scenario where a segment of agents entering a sensing-denied environment or behaving…”
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Sensor Selection and Optimal Precision in $\mathcal{H}_2/\mathcal{H}_{\infty}$ Estimation Framework: Theory and Algorithms by Deshpande, Vedang M, Bhattacharya, Raktim

“…We consider the problem of sensor selection for designing observer and filter for continuous linear time invariant systems such that the sensor precisions are…”
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Sparse Sensing and Optimal Precision: Robust $\mathcal{H}_{\infty}$ Optimal Observer Design with Model Uncertainty by Deshpande, Vedang M, Bhattacharya, Raktim

“…We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in…”
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