Zobrazeno 1 - 10
of 19 265
pro vyhledávání: '"Ganesan, P"'
Autor:
Anandh, Thivin, Ghose, Divij, Jain, Himanshu, Sunkad, Pratham, Ganesan, Sashikumaar, John, Volker
This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. First, a term in the spirit of a SUPG
Externí odkaz:
http://arxiv.org/abs/2411.09329
Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this cloud-only approach underutilizes edge computational resources and req
Externí odkaz:
http://arxiv.org/abs/2411.05961
Autor:
S, Vatchala, C, Yogesh, Govindarajan, Yeshwanth, M, Krithik Raja, Ganesan, Vishal Pramav Amirtha, A, Aashish Vinod, Ramesh, Dharun
In this study, we introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Utilizing a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Netwo
Externí odkaz:
http://arxiv.org/abs/2411.02112
Autor:
Lei, Hongbin, Zhang, Qian, Xie, Hongqiang, Meng, Congsen, Peng, Zhaoyang, Liu, Jinlei, Bai, Guangru, Ganesan, Adarsh, Zhao, Zengxiu
The mechanical analog of optical frequency combs, phononic frequency combs (PFCs), has recently been demonstrated in mechanical resonators via nonlinear coupling among multiple phonon modes. However, for exciting phononic combs in molecules, the requ
Externí odkaz:
http://arxiv.org/abs/2409.19607
Wildlife monitoring via camera traps has become an essential tool in ecology, but the deployment of machine learning models for on-device animal classification faces significant challenges due to domain shifts and resource constraints. This paper int
Externí odkaz:
http://arxiv.org/abs/2409.07796
Autor:
T V, Arunima Dev, K, Anagha P., C. V, Midhun, Musthafa, M. M, T, Vafiya Thaslim T., Akbar, Shaima, B, Swapna, Thomas, Nicemon, Joseph, Antony, Ganesan, S.
The capability of standard Geant4 PhysicsLists to address the fragmentation of $^{12}$C$-^{12}$C was assessed through a comparative analysis with experimental cross sections reported by Divay et al. and Dudouet et al. The standard PhysicsLists were f
Externí odkaz:
http://arxiv.org/abs/2409.07090
Autor:
Anandh, Thivin, Ghose, Divij, Tyagi, Ankit, Gupta, Abhineet, Sarkar, Suranjan, Ganesan, Sashikumaar
Physics-informed neural networks (PINNs) are able to solve partial differential equations (PDEs) by incorporating the residuals of the PDEs into their loss functions. Variational Physics-Informed Neural Networks (VPINNs) and hp-VPINNs use the variati
Externí odkaz:
http://arxiv.org/abs/2409.04143
3D sensing is a fundamental task for Autonomous Vehicles. Its deployment often relies on aligned RGB cameras and LiDAR. Despite meticulous synchronization and calibration, systematic misalignment persists in LiDAR projected depthmap. This is due to t
Externí odkaz:
http://arxiv.org/abs/2407.19154
Autor:
Mohan, Rishi Kesav, Kanmani, Risheek Rakshit Sukumar, Ganesan, Krishna Anandan, Ramasubramanian, Nisha
In the era of big data, conventional RDBMS models have become impractical for handling colossal workloads. Consequently, NoSQL databases have emerged as the preferred storage solutions for executing processing-intensive Online Analytical Processing (
Externí odkaz:
http://arxiv.org/abs/2405.17731
Aluminium monofluoride (AlF) is a promising candidate for laser cooling and the production of dense ultracold molecular gases, thanks to its relatively high chemical stability and diagonal Frank-Condon factors. In this study, we examine the interacti
Externí odkaz:
http://arxiv.org/abs/2405.03276