Outputs (3144)

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.

Multi-Tier Security Framework for Blockchain-enabled Supply Chain Systems (2025)
Presentation / Conference Contribution
Alrewetae, A., & Aujla, G. (2025, June). Multi-Tier Security Framework for Blockchain-enabled Supply Chain Systems. Presented at IEEE International Conference on Communications, Montreal, Canada

Leveraging Digital Twins for Anomaly Detection and Adaptive Healing in Software-defined IoT (2025)
Presentation / Conference Contribution
Singh, A., Aujla, G., Jindal, A., Sun, H., & Aslam, N. (2025, June). Leveraging Digital Twins for Anomaly Detection and Adaptive Healing in Software-defined IoT. Presented at IEEE International Conference on Communications, Montreal, Canada

PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction (2025)
Journal Article
Zhou, K., Shum, H. P. H., Li, F. W. B., Zhang, X., & Liang, X. (in press). PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction. IEEE Transactions on Image Processing,

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*.

Using Fixed and Mobile Eye Tracking to Understand How Visitors View Art in a Museum: A Study at the Bowes Museum, County Durham, UK (2025)
Presentation / Conference Contribution
Warwick, C., Beresford, A., Casteau, S., Shum, H. P. H., Smith, D., & Zhang, F. X. (2025, July). Using Fixed and Mobile Eye Tracking to Understand How Visitors View Art in a Museum: A Study at the Bowes Museum, County Durham, UK. Paper presented at 2025 ADHO Digital Humanities Conference, Lisbon, Portugal

HotReRAM: A Performance-Power-Thermal Simulation Framework for ReRAM based Caches (2025)
Journal Article
Chakraborty, S., Bunnam, T., Arunruerk, J., Agarwal, S., Yu, S., Shafik, R., & Sjalander, M. (online). HotReRAM: A Performance-Power-Thermal Simulation Framework for ReRAM based Caches. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, https://doi.org/10.1109/TCAD.2025.3546855

This paper proposes a comprehensive thermal modeling and simulation framework, HotReRAM, for resistive RAM (ReRAM)-based caches that is verified against a memristor circuit-level model. The simulation is driven by power traces based on cache accesses... Read More about HotReRAM: A Performance-Power-Thermal Simulation Framework for ReRAM based Caches.

Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation (2025)
Journal Article
Wang, Y., Li, M., Liu, J., Leng, Z., Li, F. W. B., Zhang, Z., & Liang, X. (online). Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation. International Journal of Computer Vision, https://doi.org/10.1007/s11263-025-02392-9

We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships specified in text due to: (1) lack of effective text parsing for detailed... Read More about Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation.

Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation (2025)
Journal Article
Alsayed, A., Bana, F., Arvin, F., Quinn, M. K., & Nabawy, M. R. A. (2025). Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation. Aerospace, 12(3), Article 189. https://doi.org/10.3390/aerospace12030189

This study examines the application of low-cost 1D LiDAR sensors in drone-based stockpile volume estimation, with a focus on indoor environments. Three approaches were experimentally investigated: (i) a multi-drone system equipped with static, downwa... Read More about Experimental Evaluation of Multi- and Single-Drone Systems with 1D LiDAR Sensors for Stockpile Volume Estimation.