Xianliang Jiang

Agricultural pests pose a persistent threat to crop yield and quality, making accurate and efficient detection essential for modern crop protection. Although trap-based systems are widely used for automatic pest monitoring, pest detection in captured images remains challenging due to tiny pest bodies, clustered distributions, diverse capture states, and inter-class similarity. To address these challenges, we propose PestFG-RTDETR, a detector with three key components: frequency-guided adaptive feature interaction, dynamic upsampling, and a lightweight backbone. We also introduce a biologically inspired Copy-Paste augmentation strategy to enrich minority classes and generate realistic dense pest layouts on the dataset. When evaluated at an input resolution of 416 × 416 pixels, PestFG-RTDETR achieves an mAP@0.5 of 61.64% and an mAP@0.5:0.95 of 37.27%, outperforming RT-DETR-R18 by 4.67 and 6.08 percentage points, respectively, while using 1.55M fewer parameters. Extensive comparisons with representative two-stage, one-stage, and transformer-based detectors show that PestFG-RTDETR achieves competitive performance, indicating the effectiveness of the proposed design. Independent training and evaluation on a self-collected rice pest dataset further indicate that the proposed architecture can be applied to another trap-based pest detection scenario.

Traditional congestion control algorithms struggle to maintain the consistent and satisfactory data transmission performance over time-varying networking condition. Simultaneously, as video traffic becomes dominant, the loose coupling between the DASH framework and TCP congestion control results in the un-matched bandwidth usage, thereby limiting video streaming performance. To address these issues, this paper proposes a receiver-driven congestion control framework named Nuwa. Nuwa deploys the congestion avoidance phase at the receiver-side, utilizing one-way queueing delay detection to monitor network congestion and setting specific target delays for different applications. Experimental results demonstrate that, in most cases, with appropriate parameter configuration, Nuwa can improve the throughput of TCP flows 4% to 15.4% and reduce average queueing delay by 6.9% to 29.4%. Furthermore, we also introduce the use of reinforcement learning to dynamically adjust Nuwa's key parameter , enhancing Nuwa's adaptability to the unpredictable environment.

Blood pressure is a critical indicator of human health, making convenient and easy-to-use monitoring methods a significant research focus. Existing blood pressure monitoring techniques often rely on additional sensors or devices, imposing both usage and economic burdens on users. To tackle this problem, we propose MetaBP, a blood pressure monitoring system that leverages the camera on smart devices. The basic idea is to record a video when the fingertip covers the camera to capture the tiny skin color changes caused by the heartbeat and extract pulse signals from the video frames. Furthermore, MetaBP employs a meta-learning approach, enabling the model to quickly adapt to the blood pressure monitoring task with minimal data and develop personalized models. By leveraging meta-learning, the model can efficiently learn the unique features of an individual’s physiological signals, providing accurate and personalized blood pressure estimation even with limited training samples. To obtain accurate measurements, we use a variational mode decomposition (VMD) method to reduce signal noise, ensuring that the pulse signals are both reliable and precise. To evaluate the robustness of MetaBP, we conduct experiments with 30 participants and implement MetaBP on commercial devices with camera parameters. The results demonstrate that MetaBP can accurately estimate systolic and diastolic blood pressure, with mean errors of 1.37 and 0.82 mmHg and standard deviations of 7.39 and 5.83 mmHg. Furthermore, our results demonstrate the feasibility of using widely available smart device cameras for reliable blood pressure monitoring, offering a practical solution for continuous health tracking.

Falls are a significant cause of injury in older people, and in the home elevator environment, the consequences of a fall can be more severe due to the confined and narrow space. This paper focuses on the home elevator scenario. First, we construct a fall detection dataset based on infrared array sensors, considering non-human factors such as pets in a home environment. Secondly, we propose a background filtering method based on Otsu’s method to eliminate the interference of non-human factors in the background. In addition, we introduce a new and more generalized single-frame fall detection model, ISFDNet, which shows higher fall detection precision than traditional models and achieves a precision rate of 96.64%. Finally, we adopt a single-frame detection approach combined with a sliding window-based fall detection event triggering mechanism, which ensures fall detection accuracy, reduces memory requirements, and improves inference speed; the method obtained a 100% precision rate of fall event detection.