https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICDE2024_Online_Federated_Learning_on_Distributed_Unknown_Data_Using_UAVs.pdf
Abstract—Along with the advance of low-altitude economy, a variety of applications based on Unmanned Aerial Vehicles (UAVs) have been developed to accomplish diverse tasks. In this paper, we focus on the scenario of multiple UAVs performing Federated Learning (FL) tasks. Specifically, a group of UAVs is scheduled to repeatedly visit some Points of Interest (PoIs), collect the data produced ...
https://cis-linux1.temple.edu/~jiewu/research/publications/Publication_files/ICPADS2024.pdf
Abstract—Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned Aerial Vehicle (UAV) networks, named DDFL, which features an innovative ...
https://cis.temple.edu/~pwang/5603-AI/Project/2024F/SitongTie/CIS5603_project.pdf
1 Project Overview This project aims to design and implement a real-time target detection and au-tomatic tracking system based on deep learning models and automatic control. The system uses computer vision technology to detect and automatically track targets in a gaming environment, and combines control theory to achieve precise mouse operations. The project involves the deployment of deep ...
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https://sites.temple.edu/trail/files/2021/11/XieXinDamesIROS2021.pdf
Zhanteng Xie, Pujie Xin, and Philip Dames Abstract—This paper proposes a novel neural network-based control policy to enable a mobile robot to navigate safety through environments filled with both static obstacles, such as tables and chairs, and dense crowds of pedestrians. The network architecture uses early fusion to combine a short history of lidar data with kinematic data about nearby ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/MM_2025_UAV_CR.pdf
Abstract In UAV applications, dense haze severely obscures small ground-level objects, hindering the recovery of fine details. Existing visible-only dehazing methods struggle with such dense occlusions, while infrared imaging lacks color and fine texture information. To ad-dress these limitations, we propose the Haze Distribution-aware Cross-modal Fusion Network (HDCFN). HDCFN features two key ...
https://igem.temple.edu/products/software/mega
iGEM researchers are pursuing a variety of interdisciplinary research and discovery projects
https://cis.temple.edu/~pwang/5603-AI/Lecture/05-Reasoning-uncertain.htm
Between everyday reasoning and mathematical reasoning, one of the differences is the uncertainty of various types. In the study of AI, the representing and processing of uncertainty has been an active field for decades, with many approaches influenced by the theories in mathematics, logic, psychology, decision theory, game theory, economics, etc.