Quality of service (QoS) measuring in mobile broadband networks has become a significant issue as a result of the rising awareness of QoS. This paper examines the quality of 4G Internet access at different locations inside Vietnamese-German University campus (VGU),... Quality of service (QoS) measuring in mobile broadband networks has become a significant issue as a result of the rising awareness of QoS. This paper examines the quality of 4G Internet access at different locations inside Vietnamese-German University campus (VGU), in Vietnam, focusing on five key performance indicators (KPI) which are distinct by two mobile network operators (MNO) in Vietnam. To the best of our knowledge, there are many studies on evaluating quality of service (QoS) of mobile network signals, however, there is no extensive research on exploring 4G wireless mobile network at specific positions inside buildings. This paper provides radio frequency (RF) measurements and evaluation, with the experiments took place both indoors and outdoors and determines those operators performance based on stimulation results. Data was gathered from a walk test using QualiPoc installed in a smartphone and analyzed by a ROMES4 application. The study suggests that 4G signal quality is rather good but downloading speed is quite slow at ground level, and the higher building level the user goes, the worse the signal quality gets. Finally, the research group propose solution to enhance quality of the network quality inside VGU campus.

Real-time vehicle detection often requires trading off accuracy for speed. To validate a solution that excels on both fronts, we adopt fisheye imagery, a domain where extreme radial distortion and scale variation defeat standard detectors, as a rigorous testbed. Our... Real-time vehicle detection often requires trading off accuracy for speed. To validate a solution that excels on both fronts, we adopt fisheye imagery, a domain where extreme radial distortion and scale variation defeat standard detectors, as a rigorous testbed. Our pipeline comprises three key stages: (1) Multi-stage DETR Distillation, a four-phase knowledge transfer leveraging KD-DETR's fixed distillation queries with separate head- and feature-level stages to avoid gradient conflicts and ensure progressive learning; (2) Data-centric Enhancements, creating a diverse training pool via Co-DETR pseudo-labeling, CycleGAN-Turbo day-to-night style transfer, and object-level flash/blur augmentations; and (3) Adaptive Sample Mining, which dynamically upsamples complex examples to sharpen the model's focus. When paired with D-FINE-M, our method achieves an F1 score of 0.6318 at 145 FPS on the AI City Challenge 2024 Track 4 test set, and with D-FINE-N, it reaches 781 FPS with an F1-score of 0.5597, all measured on an RTX 4090. Evaluated on the challenging FishEye8K benchmark of the 9th AI City Challenge, our approach delivers strong accuracy while maintaining real-time FPS. By ignoring fisheye distortions and treating them as a domain-agnostic stress test, we demonstrate that this data-centric, multi-stage distillation framework generalizes seamlessly to standard vehicle and broader object detection tasks, offering a unified solution for high-precision, real-time vision. Our implemented code: https://github.com/NhatHuy1812/VGU_VLinsight_AICITY2025_Track4