Hierarchical Federated Learning (HFL) alleviates communication bottlenecks by organizing the system into multiple layers: client, intermediate aggregator, and server. Clients and aggregation layers form groups based on connection patterns, and the methods used for grouping and aggregation directly affect convergence performance. Currently, some studies have proposed grouping algorithms to address the non-independent and identically distributed (non-IID) characteristics of client data to improve performance. However, these methods do not account for network heterogeneity, such as clients using different quantization levels or adaptive quantization strategies to minimize communication overhead. Moreover, most methods rely on heuristics that blindly explore the combinatorial grouping space, incurring substantial computational overhead. In this paper, we conduct a rigorous convergence analysis and frame the dual challenges of heterogeneous data and quantization heterogeneity in HFL as the joint optimization of aggregation weights and grouping strategy. Specifically, we derive the optimal closed-form solution for the aggregation weights and propose an Alternating Optimization Hierarchical Optimal Weights (AO-HOW) algorithm to compute these weights. Building on this result, we propose DyGHFL—Dynamic Exclusion–Reallocation Grouping for Hierarchical Federated Learning, a structu-reaware and efficient greedy algorithm that reorganizes groups by maximizing structured gain and updating weight coefficients according to current system conditions. Experiments on multiple datasets show that DyGHFL consistently outperforms existing baselines, demonstrating its effectiveness in HFL.

Omnidirectional catadioptric systems are popular in robotic applications thanks to their large field of view. For 3D scene reconstruction in a single shot, usually two different catadioptric cameras are needed. More cameras may contribute to better reconstruction while larger mounting space and higher power cost are required. In this paper, a single camera multi-stereo catadioptric system with vertical and horizontal baseline structure is proposed. It features achieving multi-pair of central or non-central omnidirectional stereos in a compact manner. To make the 3D reconstruction process general and adaptive to various types of system configurations, a flexible calibration and reconstruction algorithm pipeline is presented. The algorithm features approximating the system into multiple central sub-cameras and carrying out the stereo matching in a spherical representation. In addition, an effective 3D point cloud fusion algorithm is proposed to optimize the reconstruction results from multiple stereo pairs. The experiment carried out with synthetic and real data verified the feasibility and effectiveness of our system.

Robust obstacle detection is an important task for unmanned ground vehide(UGV). Vegetation in off-road environments poses great challenges to this task. Usually, vegetation should not be considered as obstacles for off-road UGVs since they are soft and drivable. On the other hand, there are also possibilities that real obstacles exist in the vegetation, which makes the problem difficult. In this paper, a novel multi-spectrum data fusion based algorithm for partial occluded obstacle detection under complex vegetation environment is proposed. First a RGB and Near-infrared (NIR) multi-spectrum superpixel based segmentation strategy is employed to accurately segment the objects in the image. Obstacle candidate superpixels are then obtained through simple geometric computation in 3D laser data. Finally, the heterogeneous texture and 3D features are extracted from each candidate superpixel and fed in Support Vector Machine (SVM) to distinguish the real obstacles from vegetation. Experimental results on real data acquired from various vegetation environments demonstrate our success.

Passive sonar arrays are often used to find submerged vessels by measuring their radiated signal. Acoustic vector-sensor (AVS) arrays, which are normally towed by the ship at the ends of long cables, have been proven to be very effective for target direction of arrival (DOA) estimation. The flow noise, arising from the flow of water over the vector-sensor or its housing when the array is towed, is often a major source of interference to the towed array as it is usually directly coupled to the array sensing elements. In this paper, we obtain a closed-form expression for the spatial correlations of all the components of an AVS array in a flow-noise field. We apply the noise correlation model to DOA estimation using a uniform linear AVS array beamforming. The flow-noise impact on the beamforming capabilities is evaluated via numerical simulations.

Wakes induced by the motion of vessels may extend for tens of kilometers and exist for hours under certain conditions in the open sea. This provides a useful feature for long-range ship detection. The authors present a method for passively detecting a ship wake using measurements obtained by an airborne SQUID magnetic transducer that measures the first order gradients of the magnetic signature induced by the wake. Analytical formulas for wake magnetic gradients are derived to provide guidelines for using the airborne detector. They also derive bounds on the probability of wake detection for cross correlation and square-law detectors, which can be used to predict the expected performance. Numerical examples are used to demonstrate the applicability of their results.