Zobrazeno 1 - 10
of 51 160
pro vyhledávání: '"RASHEED, A."'
Autor:
Rasheed, Zeeshan, Sami, Malik Abdul, Rasku, Jussi, Kemell, Kai-Kristian, Zhang, Zheying, Harjamaki, Janne, Siddeeq, Shahbaz, Lahti, Sami, Herda, Tomas, Nurminen, Mikko, Lavesson, Niklas, de Cerqueira, Jose Siqueira, Hasan, Toufique, Khan, Ayman, Hasan, Mahade, Saari, Mika, Rantanen, Petri, Soini, Jari, Abrahamsson, Pekka
Present-day software development faces three major challenges: complexity, time consumption, and high costs. Developing large software systems often requires battalions of teams and considerable time for meetings, which end without any action, result
Externí odkaz:
http://arxiv.org/abs/2411.08507
This paper explores the development and practical application of a predictive digital twin specifically designed for condition monitoring, using advanced mathematical models and thermal imaging techniques. Our work presents a comprehensive approach t
Externí odkaz:
http://arxiv.org/abs/2411.05887
Autor:
Menges, Daniel, Rasheed, Adil
Autonomous surface vessels (ASVs) are becoming increasingly significant in enhancing the safety and sustainability of maritime operations. To ensure the reliability of modern control algorithms utilized in these vessels, digital twins (DTs) provide a
Externí odkaz:
http://arxiv.org/abs/2411.03465
Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is cru
Externí odkaz:
http://arxiv.org/abs/2410.02956
Autor:
Duan, Minxuan, Qian, Yinlong, Zhao, Lingyi, Zhou, Zihao, Rasheed, Zeeshan, Yu, Rose, Shafique, Khurram
Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integr
Externí odkaz:
http://arxiv.org/abs/2410.01011
This paper addresses the challenge of classifying and assigning programming tasks to experts, a process that typically requires significant effort, time, and cost. To tackle this issue, a novel dataset containing a total of 4,112 programming tasks wa
Externí odkaz:
http://arxiv.org/abs/2409.20189
Autor:
Fehrentz, Maximilian, Azampour, Mohammad Farid, Dorent, Reuben, Rasheed, Hassan, Galvin, Colin, Golby, Alexandra, Wells, William M., Frisken, Sarah, Navab, Nassir, Haouchine, Nazim
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure pre
Externí odkaz:
http://arxiv.org/abs/2409.11983
Autor:
Rasheed, Hassan, Dorent, Reuben, Fehrentz, Maximilian, Kapur, Tina, Wells III, William M., Golby, Alexandra, Frisken, Sarah, Schnabel, Julia A., Haouchine, Nazim
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intra
Externí odkaz:
http://arxiv.org/abs/2409.08169
This paper presents a nested tracking framework for analyzing cycles in 2D force networks within granular materials. These materials are composed of interacting particles, whose interactions are described by a force network. Understanding the cycles
Externí odkaz:
http://arxiv.org/abs/2409.06476
Autor:
Stanford, Chris, Adari, Suman, Liao, Xishun, He, Yueshuai, Jiang, Qinhua, Kuai, Chenchen, Ma, Jiaqi, Tung, Emmanuel, Qian, Yinlong, Zhao, Lingyi, Zhou, Zihao, Rasheed, Zeeshan, Shafique, Khurram
Collecting real-world mobility data is challenging. It is often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomalies in large-scale data is nearly impossible, as it demands meticulous
Externí odkaz:
http://arxiv.org/abs/2409.03024