This article investigates the event-triggered leader-follower consensus control problem for linear continuous-time multiagent systems (MASs). A new consensus protocol and an event-triggered communication (ETC) strategy based on a closed-loop state estimator are designed. The closed-looped state estimator renders us more accurate state estimations, therefore the triggering times can be decreased while maintaining control performance. We show that the consensus of MASs can be achieved by employing the proposed control scheme. In addition, the Zeno-phenomena can be excluded. As a practical application, our method is successfully applied to the vehicle platoon control. The effectiveness is further verified by simulations conducted on the Prescan platform.

This article is devoted to the bipartite tracking consensus for multiagent systems with control input delay and nonlinear dynamics under directed signed graph. Based on the relative information of the agents' neighbors, a distributed control protocol is designed to make the systems achieve bipartite consensus in the presence of a single leader. For the sake of dealing with the nonlinear function, the reduction transformation is applied. Via Lyapunov stability theory, the sufficient conditions which guarantee the multiagent system reach bipartite consensus. And finally, the simulations are given to verify the effectiveness of the results in the paper.

Rotated Bounding Boxes (RBBs) for oriented object detection are labor-intensive and time-consuming to annotate. Single-point supervision offers a cost-effective alternative but suffers from insufficient size and orientation information, leading existing methods to rely heavily on complex priors and fixed refinement stages. In this paper, we propose DP2PNet (Diffusion-Point-to-Polygon Network), the first diffusion model-based framework for single-point supervised oriented object detection. DP2PNet features three key innovations: (1) A multi-scale consistent noise generator that replaces manual or external model priors with Gaussian noise, reducing dependency on domain-specific information; (2) A Noise Cross-Constraint module based on multi-instance learning, which selects optimal noise point bags by fusing receptive field matching and object coverage; (3) A Semantic Key Point Aggregator that aggregates noise points via graph convolution to form semantic key points, from which pseudo-RBBs are generated using convex hulls. DP2PNet supports dynamic adjustment of refinement stages without retraining, enabling flexible accuracy optimization. Extensive experiments on DOTA-v1.0 and DIOR-R datasets demonstrate that DP2PNet achieves 53.82% and 53.61% mAP50, respectively, comparable to methods relying on complex priors. It also exhibits strong noise robustness and cross-dataset generalization.