Výsledky vyhledávání - "Silva, Óscar"

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Improving the Region of Attraction of a Multi-rotor UAV by Estimating Unknown Disturbances

Autor: Atapattu, Sachithra, De Silva, Oscar, Wanasinghe, Thumeera R, Mann, George K I, Gosine, Raymond G

This study presents a machine learning-aided approach to accurately estimate the region of attraction (ROA) of a multi-rotor unmanned aerial vehicle (UAV) controlled using a linear quadratic regulator (LQR) controller. Conventional ROA estimation app

Externí odkaz: http://arxiv.org/abs/2409.00257

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A Generative Adversarial Network-based Method for LiDAR-Assisted Radar Image Enhancement

Autor: Thilakanayake, Thakshila, De Silva, Oscar, Wanasinghe, Thumeera R., Mann, George K., Jayasiri, Awantha

This paper presents a generative adversarial network (GAN) based approach for radar image enhancement. Although radar sensors remain robust for operations under adverse weather conditions, their application in autonomous vehicles (AVs) is commonly li

Externí odkaz: http://arxiv.org/abs/2409.00196

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LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype

Autor: Shankar, Vivek, Yang, Xiaoli, Krishna, Vrishab, Tan, Brent, Silva, Oscar, Rojansky, Rebecca, Ng, Andrew, Valvert, Fabiola, Briercheck, Edward, Weinstock, David, Natkunam, Yasodha, Fernandez-Pol, Sebastian, Rajpurkar, Pranav

The accurate classification of lymphoma subtypes using hematoxylin and eosin (H&E)-stained tissue is complicated by the wide range of morphological features these cancers can exhibit. We present LymphoML - an interpretable machine learning method tha

Externí odkaz: http://arxiv.org/abs/2311.09574

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MUN-FRL: A Visual Inertial LiDAR Dataset for Aerial Autonomous Navigation and Mapping

Autor: Thalagala, Ravindu G., Gunawardena, Sahan M., De Silva, Oscar, Jayasiri, Awantha, Gubbels, Arthur, Mann, George K. I, Gosine, Raymond G.

This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset captured using a multi-sensor payload to promote the global navigation satellite system (GNSS)-denied navigation research. The dataset features flight distances ranging from 30

Externí odkaz: http://arxiv.org/abs/2310.08435

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