The generalized homogenization-based protocol is proposed, which provides a novel direction for the challenging problem of finite-time consensus tracking (FTCT) control in singular multiagent systems (SMASs) in this paper. The proposed FTCT protocol is continuous and can be regarded as an upgrade of the distributed protocol for asymptotically stable analysis. Then, the homogeneity conditions of the closed-loop systems under the proposed protocol are derived, providing an important prerequisites for FTCT analysis. The FTCT problem for SMASs is successfully addressed by combining the construction of canonical homogeneous norm Lyapunov function and impulse free analysis. Finally, the simulation results are provided to validate the effectiveness of the proposed method.

The increasing demand for tasks and dynamically changing loads in the Low Earth Orbit (LEO) satellite networks creates significant challenges in terms of computing and routing. Currently, LEO satellites primarily offload tasks to ground stations or satellites within their line of sight, failing to fully utilize the computational resources of the entire network. In addition, existing routing algorithms fail to consider on-satellite loads and computational capacities, leading to bottlenecks in network routing as some satellites with limited processing capacity become overwhelmed. In this paper, the tasks generated by the source satellite can be offloaded to either satellites or ground stations while routing to the destination satellite. The offloading computation and routing decision problems are investigated to minimize the maximum delay. To solve this challenging problem, we first convert the optimization variables, encompassing both routing and computation offloading, into a form that depends solely on the latter, and model the problem as the Markov Decision Process (MDP). Subsequently, the problem is addressed using an algorithm based on Multi-Agent Proximal Policy Optimization (MAPPO), where multiple agents cooperatively determine routing and offloading computation strategies. Simulation results show that the proposed scheme achieves better delay performance.

In the rapidly evolving image processing domain, transformers have emerged as powerful tools, yet significant challenges are encountered when they are applied to underwater image enhancement, such as visual disparity and computational inefficiency. Transformers do not have a unique module to maintain their performance while reducing the number of parameters. This study addresses the gap in the literature by introducing the globally deformable selection transformer (GS-Transformer), which is a model designed to enhance global feature selection and pixel connectivity, thereby reducing the computational complexity of the model while maintaining the image processing effect. Our novel multiresolution encoder-decoder module explicitly incorporates global information, overcoming the limitations of traditional transformers, whereas the multilocal coherence preserving loss (MCPL) mechanism ensures content integrity and coherence. Compared with the latest transform-based underwater image algorithms, this method is 15 times faster and utilizes only 41.7% (or approximately a half less) of the number of parameters. The experimental results on the UIEB, EUVP, and Synthesize datasets reveal that GS-Transformer achieves state-of-the-art performance in underwater image enhancement, with a reduced parameter number and improved efficiency, representing a significant advancement in the field. Our research will promote the application of the transformer in scenarios with high real-time performance.

A novel post-processing method, online to offline (O2O), to improve the efficiency of shape retrieval is proposed in this paper. The essence of this proposed method is to move more work that requires a lot of computation to offline. Based on this approach, the O2O rerank the retrieval result online with the help of the offline analysis. The result of offline analysis can be reused indefinitely, regardless of the query shape, as long as the database is unchanged. Therefore, O2O is very efficient and suitable for real-time applications. We evaluated our method for shape retrieval and recognition on five databases including MPEG-7 CE-1 Part B, Tari 1000, Animals, Kimia 99, and Swedish Plant Leaf. Our experimental results show that as a post-processing algorithm, O2O provides highly efficient and effective shape retrieval.

An optimization technique named r/KPSO (Particle Swarm Optimization with r- and K-selection) was developed in this paper. In Ecology, two evolutionary “strategies” are termed, r-selection for those species that breed many “cheap” offspring and live in unstable environments and K-selection for those species that produce few “expensive” offspring and live in stable environments. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. r/KPSO selects r- and K-selection to produce the progenies in the iterative procedure according to the concerned particle’s fitness value. K-selection is performed for those particles (K-subswarm called in this paper) in high fitness, and K-subswarm only can produce few progenies but the progenies are nurtured delicately with much parent care. On the other hand, r-selection is performed for those particles (r-subswarm called) in relatively low fitness. And with little parent care, r-subswarm can produce a large number of progenies, the progenies have to compete with the r-subswarm for survival according to fitness and only the best ones can survive. In r/KPSO, the particles performed r-selection mainly explore the search space as possible as they can to find more potential solutions in large speed, and those particles performed K-selection keep the current optimum solutions and exploit the space as they can to find more ideal solutions. Combined the advantages of r-selection and K-selection, r/KPSO can converge in higher speed and higher precision.