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Outputs (125)

BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction (2025)
Journal Article
Li, R., Katsigiannis, S., Kim, T.-K., & Shum, H. P. H. (2025). BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14566 - 14580. https://doi.org/10.1109/TNNLS.2025.3545268

Trajectory prediction allows better decision-making in applications of autonomous vehicles (AVs) or surveillance by predicting the short-term future movement of traffic agents. It is classified into pedestrian or heterogeneous trajectory prediction.... Read More about BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction.

Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions (2025)
Presentation / Conference Contribution
Pan, H., Wang, H., Arvin, F., & Hu, J. (2025, July). Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions. Presented at 2025 IFAC Symposium on Robotics, Paris, France

This paper addresses the shape formation problem for large-scale robotic swarms by proposing an optimization-based cooperative navigation method. First, the physical space is partitioned into multiple disjoint bins, and the stochastic evolution of ro... Read More about Decentralized Autonomous Navigation of Large-Scale Robotic Swarms with Control Barrier Functions.

Reconfigurable routing in data center networks (2025)
Journal Article
Stewart, I., & Kutner, D. (2025). Reconfigurable routing in data center networks. Theoretical Computer Science, 1038, Article 115154. https://doi.org/10.1016/j.tcs.2025.115154

A hybrid network is a static (electronic) network that is augmented with optical switches. The Reconfigurable Routing Problem (RRP) in hybrid networks is the problem of finding settings for the optical switches augmenting a static network so as to ac... Read More about Reconfigurable routing in data center networks.

Automated Artificial Intelligence Framework for Anomaly Detection in Healthcare SD-IoT Networks (2025)
Presentation / Conference Contribution
Algamdi, H., Aujla, G. S., Singh, A., Jindal, A., & Trehan, A. (2024, December). Automated Artificial Intelligence Framework for Anomaly Detection in Healthcare SD-IoT Networks. Presented at GLOBECOM 2024 - 2024 IEEE Global Communications Conference, Cape Town, South Africa

In healthcare IoT networks, network anomalies can disrupt the flow of reliable data, potentially compromising healthcare data's security and integrity. To address this challenge, several anomaly detection methods have been developed using artificial... Read More about Automated Artificial Intelligence Framework for Anomaly Detection in Healthcare SD-IoT Networks.

Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control (2025)
Presentation / Conference Contribution
Chen, D., Hu, J., Zhang, H., & Chen, B. (2025, June). Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control. Presented at 2025 European Control Conference (ECC), Thessaloniki, Greece

Traffic signal control is essential for managing urban traffic, reducing congestion, and minimizing environmental impact by optimizing both vehicular and pedestrian flow. This paper investigates the application of Reinforcement Learning (RL) in traff... Read More about Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control.

Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow* (2025)
Presentation / Conference Contribution
Nan, F., Li, F., Wang, Z., Tam, G. K. L., Jiang, Z., DongZheng, D., & Yang, B. (2025, April). Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*. Presented at ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India

Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, resulting in incomplete information that complicates motion es... Read More about Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*.