Underwater object detection (UOD) is crucial for monitoring marine ecosystems, underwater robotics, environmental protection, and autonomous underwater vehicles (AUVs). Despite progress, many models struggle under real-world conditions due to poor visibility, dynamic lighting, and domain shifts. Traditional methods like Faster R-CNN are computationally expensive, while YOLO-based models suffer in challenging underwater scenarios. The scarcity of large-scale annotated datasets further limits model generalization. To address these challenges, we introduce UOD-SZTU-2025, a new dataset of 3,133 high-quality underwater images, sourced primarily from video platforms. The dataset is used in EFCWM (Enhanced Feature Correction and Weighting Module) to extract and refine a feature material library for detection targets. We propose EFCWM-Mamba-YOLO, a lightweight, real-time detection model designed to enhance feature representation and adapt to diverse underwater environments. The EFCWM module incorporates domain adaptation for improved robustness. Additionally, a two-stage training strategy first trains on a source domain and fine-tunes with limited target domain samples to enhance generalization. Experiments show our approach surpasses existing lightweight UOD models in accuracy, real-time performance, and robustness. Our dataset, model, and benchmark establish a strong foundation for future UOD research. The dataset for EFCWM-Mamba-YOLO is available at https://github.com/wojiaosun/UOD-SZTU-2025.

UAV gesture recognition, a novel human-computer interaction form, offers an intuitive approach to controlling UAVs in various environments. However, there is a lack of comprehensive datasets for AI-powered UAV gesture recognition. This paper contributes in several ways: (i) We introduce MD-UHGRD, a unique UAV static gesture dataset with 20, 000 images and annotations, collected from a diverse group of participants in different environmental conditions. This dataset is expected to bridge a significant gap in UAV gesture recognition algorithms. (ii) We propose SA-YOLO, a multifunctional UAV gesture recognition method that not only enables gesture recognition but also includes face and pedestrian tracking, optimizing UAV control in complex scenarios. SA-YOLO incorporates the Spatial Asymptotic Feature Pyramid Network (SAFPN), Scale Pyramid Pooling with Cross Stage Partial Networks Convolution (SPPCSPC), and Space-to-Depth Convolution (SPD-Conv). (iii) Extensive evaluation of SAYOLO on MD-UHGRD establishes it as a benchmark in this domain. Our method demonstrates high accuracy, processing speed, and a compact model size, achieving a 93.2% mean Average Precision (mAP) with 10.3 million parameters and 48 frames per second (FPS). Among competing models, SA-YOLO not only achieves the highest mAP but also maintains a balance in model size and FPS. The database and code are available at: https://github.com/ijcnn2024/SA-YOLO.

The advancement of machine vision systems necessitates efficient and accurate signal reconstruction methods to enhance real-time perception and decision-making capabilities. This paper introduces a Generalized Backtracking Regularization Adaptive Matching Pursuit (GBRAMP) algorithm, designed to reconstruct signals within machine vision systems using compressed sensing techniques. The GBRAMP algorithm improves upon existing methods by incorporating regularization for enhanced atom selection and a backtracking approach to accurately estimate sparsity, addressing the limitations of traditional convex optimization, greedy, and Bayesian reconstruction algorithms. The paper provides a comparative analysis of the GBRAMP algorithm against other prominent reconstruction techniques. Experimental results validate the GBRAMP algorithm's improved performance in terms of both reconstruction accuracy and computational speed, making it a competitive solution for the next generation of machine vision systems.

In this article, we propose a controller that can coordinate a swarm of robots to cover a region persistently by forming a mobile sensor network. Therefore, the robot swarm can monitor a large area of interest (AOI) over a long time. The performance of the swarm can achieve high flexibility and coverage efficiency with swarm intelligence. This method is inspired by the behavior of large predators, such as lions that use liquid markers containing pheromones to mark their territories and indicate their status. Via interactions based on these clues, the ecosystem can achieve a dynamic balance. The controller inspired by this phenomenon consists of two layers, which are a region divider and a path planner. The region divider evenly splits the area into random shapes according to a pheromone clue and adapts to dynamic changes of the swarm size. Then, the path planner generates a closed patrol path for each robot. Like a random search scheme or an ant pheromone-inspired scheme, the proposed controller can adapt to the dynamic change of swarm size providing high efficiency of region coverage. The effectiveness of this controller is proved by modeling the life cycle of a robot swarm as a finite state machine. It is also verified with simulations and experiments with unmanned ground vehicles (UGVs).

Avionics Full Duplex Switched Ethernet is a safety critical network in which a redundancy management mechanism is employed to enhance the reliability of the network. However, as stated in the ARINC664-P7 standard, there still exists a potential problem, which may fail redundant transmissions due to sequence inversion in the redundant channels. In this paper, we explore this phenomenon and provide its mathematical analysis. It is revealed that the variable jitter and the transmission latency difference between two successive frames are the two main sources of sequence inversion. Thus, two methods are proposed and investigated to mitigate the effects of jitter pessimism, which can eliminate the potential risk. A case study is carried out and the obtained results confirm the validity and applicability of the developed approaches.

Avionics Full Duplex Switched Ethernet (AFDX) is a standard proposed to implement deterministic networks by providing predictable performance guarantees. The determinism is enforced through the concept of Virtual Link, which defines a logical unidirectional connection between end systems. Although an upper bounded end-to-end delay can be obtained using analysis based on, e.g., network calculus, frame arrival uncertainty in destination End-System is a source of nondeterminism that introduces a problem with respect to real-time fault detection. In this paper, a mechanism based on frame insertion is proposed to enhance the determinism of frame arrival within AFDX networks. In order to mitigate network load increase due to frame insertion, a Sub-Virtual Link aggregation strategy, formulated as a multiobjective optimization problem, is introduced. In addition, a brute force algorithm, a greedy algorithm, and a greedy algorithm with preprocessing have been developed to find solutions to the optimization problem. Experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.