Zobrazeno 1 - 10
of 9 119
pro vyhledávání: '"Elango, A."'
This paper presents an architecture for Ultrasound Beamforming using Synthetic Transmit Aperture with Low Complexity and High SNR for medical imaging. Synthetic Transmit Aperture is a novel approach in ultrasound imaging system by which frame rate an
Externí odkaz:
http://arxiv.org/abs/2407.10242
Autor:
Elango, Venmugil
Publikováno v:
2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Portland, OR, USA, 2021, pp. 1025-1034
Training a deep neural network (DNN) requires substantial computational and memory requirements. It is common to use multiple devices to train a DNN to reduce the overall training time. There are several choices to parallelize each layer in a DNN. Ex
Externí odkaz:
http://arxiv.org/abs/2407.04001
This paper introduces a generalized mean-based C^1-smooth robustness measure over discrete-time signals (D-GMSR) for signal temporal logic (STL) specifications. In conjunction with its C1-smoothness, D-GMSR is proven to be both sound and complete. Fu
Externí odkaz:
http://arxiv.org/abs/2405.10996
We introduce a GPU-accelerated Monte Carlo framework for nonconvex, free-final-time trajectory optimization problems. This framework makes use of the prox-linear method, which belongs to the larger family of sequential convex programming (SCP) algori
Externí odkaz:
http://arxiv.org/abs/2404.18034
We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluation of the running cost, without compromising computational efficiency.
Externí odkaz:
http://arxiv.org/abs/2405.00061
Autor:
Elango, Purnanand, Luo, Dayou, Kamath, Abhinav G., Uzun, Samet, Kim, Taewan, Açıkmeşe, Behçet
We present successive convexification, a real-time-capable solution method for nonconvex trajectory optimization, with continuous-time constraint satisfaction and guaranteed convergence, that only requires first-order information. The proposed framew
Externí odkaz:
http://arxiv.org/abs/2404.16826
The purpose of this note is to highlight and address inaccuracies in the convergence guarantees of SCvx, a nonconvex trajectory optimization algorithm proposed by Mao et al. (arXiv:1804.06539), and make connections to relevant prior work. Specificall
Externí odkaz:
http://arxiv.org/abs/2403.00733
Autor:
Rouhani, Bita Darvish, Zhao, Ritchie, More, Ankit, Hall, Mathew, Khodamoradi, Alireza, Deng, Summer, Choudhary, Dhruv, Cornea, Marius, Dellinger, Eric, Denolf, Kristof, Dusan, Stosic, Elango, Venmugil, Golub, Maximilian, Heinecke, Alexander, James-Roxby, Phil, Jani, Dharmesh, Kolhe, Gaurav, Langhammer, Martin, Li, Ada, Melnick, Levi, Mesmakhosroshahi, Maral, Rodriguez, Andres, Schulte, Michael, Shafipour, Rasoul, Shao, Lei, Siu, Michael, Dubey, Pradeep, Micikevicius, Paulius, Naumov, Maxim, Verrilli, Colin, Wittig, Ralph, Burger, Doug, Chung, Eric
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and int
Externí odkaz:
http://arxiv.org/abs/2310.10537
Autor:
B Elango, Marcin Kozak
Publikováno v:
International Journal of Information Science and Management, Vol 22, Iss 4, Pp 79-90 (2024)
The present study aims to analyze the acceptance times of scientific papers presenting bibliometric analyses of COVID-19 literature published in the first phase of the pandemic. We collected the data from the Web of Science of Clarivate Analytics. Th
Externí odkaz:
https://doaj.org/article/9a270461457c4d93bcfe3a0beb822a9a
This paper presents a funnel synthesis algorithm for computing controlled invariant sets and feedback control gains around a given nominal trajectory for dynamical systems with locally Lipschitz nonlinearities and bounded disturbances. The resulting
Externí odkaz:
http://arxiv.org/abs/2303.10504