Biometric footstep recognition, based on a person’s unique pressure patterns under their feet during walking, is an emerging field with growing applications in security and safety. However, progress in this area has been limited by the lack of large, diverse datasets necessary to address critical challenges such as generalization to new users and robustness to shifts in factors like footwear or walking speed. The recent release of the UNB StepUP-P150 dataset, the largest and most comprehensive collection of high-resolution footstep pressure recordings to date, opens new opportunities for addressing these challenges through deep learning. To mark this milestone, the First International StepUP Competition for Biometric Footstep Recognition was launched. Competitors were tasked with developing robust recognition models using the StepUP-P150 dataset that were then evaluated on a separate, dedicated test set designed to assess verification performance under challenging variations, given limited and relatively homogeneous reference data. The competition attracted global participation, with 23 registered teams from academia and industry. The top-performing team, Saeid UCC, achieved the best equal error rate (EER) of 10.77% using a generative reward machine (GRM) optimization strategy. Overall, the competition showcased strong solutions, but persistent challenges in generalizing to unfamiliar footwear highlight a critical area for future work.

Person re-identification (ReID) across aerial and ground vantage points has become crucial for large-scale surveillance and public safety applications. Although significant progress has been made in ground-only scenarios, bridging the aerial-ground domain gap remains a formidable challenge due to extreme viewpoint differences, scale variations, and occlusions. Building upon the achievements of the AG-ReID 2023 Challenge, this paper introduces the AG-VPReID 2025 Challenge—the first large-scale video-based competition focused on high-altitude (80–120 m) aerial-ground person ReID. Constructed on the new AG-VPReID dataset with 3,027 identities, over 13,500 tracklets, and approximately 3.7 million frames captured from UAVs, CCTV, and wearable cameras, the challenge featured four international teams. These teams developed solutions ranging from multi-stream architectures to transformer-based temporal reasoning and physics-informed modeling. The leading approach, X-TFCLIP from UAM, attained 72.28% Rank-1 accuracy in the aerial-to-ground ReID setting and 70.77% in the ground-to-aerial ReID setting, surpassing existing baselines while highlighting the dataset’s complexity. For additional details, please refer to the official website at https://agvpreid25.github.io.

We present the deployment procedure of a private 4G-LTE network with standard User Equipment in two different scenarios using OpenAirInterface and Magma core networks. Our lessons learned from deploying the segregated end-to-end cellular network testbed, comparison of connection performance in two scenarios, challenges of connecting smartphones to the network, and comparison among the possible use-cases with each scenario are the highlighted subjects provided in this paper.

Leveraging the path diversity in heterogeneous wireless networks by Multi-path TCP (MPTCP) not only depends on end-user's decisions but also on other competitors who are looking for maximizing their benefits. The incomplete information repeated Bayesian game is proposed to enhance the MPTCP throughput in a resource-shared wireless network context. Mobile nodes establish the initial path in the first stage by choosing the best opening sub-flows connection. Moreover, by receiving feedbacks regarding the preference of other opponents the repeated Bayesian game in the second stage improves the achievable throughput via selecting the best combination of networks. The numerical and simulation results demonstrate that the proposed algorithm could at least achieve (17%-24%) more throughput than WiFi in the initial path selection and (11%-14%) more throughput than MPTCP.