To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed. However, existing datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photorealistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper, we introduce an end-to-end simulation pipeline designed to generate photorealistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging Process, closely aligning with real-world image capture methods. Based on this pipeline, we present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions, as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-worldfoggy images.

We propose a contaminant detection methodology suitable for robotic automation, which is able to not only locate the source(s) of the contaminant but also estimate its intensity in an environment that is allowed to evolve over both space and time. The essential idea is to flexibly model the contaminant field surface nonlinearly via radial basis functions and to utilize basic notions from the statistical design of experiments concerning optimal placement of observations in order to make incremental decisions about robot movements. Algorithms are presented for determining such movements and the subsequent collection of measurements in three different cases corresponding to different modes of spatio-temporal evolution. The result is an iterative scheme that gradually locates the peaks (sources), as well as the entire contaminant surface. The performance of the method is assessed through simulations from known surfaces. Theoretical issues concerning convergence of parameter estimates in a multiple robots scenario are examined. The method can accommodate measurement noise and does not rely on surface gradient information.

This paper develops three novel hybrid stabilization techniques addressing fast energy equipartition for cyber-physical network systems, establishes an optimization-based network topology design framework to achieve cascading resilience and efficiency of geospatially distributed physical networks, and discusses the future application of the proposed approach to power network systems. Thus, the main contributions of this paper are three-fold. First, we present three hybrid distributed stabilization architectures for cyber-physical network systems to achieve the robust performance of geospatial physical networks by mimicking the dynamic behavior of thermodynamic systems. The proposed stabilization architectures are constructed in such a way that each stabilizer has a one-directional energy transfer from a plant to itself, and exchanges energy with its neighboring stabilizers. Second, to balance resilience to cascading failures and efficiency of energy flow in geospatially distributed physical networks, we propose an entropy metric-based multiobjective optimization framework for network topology design to characterize this resilience-efficiency trade-off design in networks. Moreover, we propose a novel cascade-connectivity swarm optimization algorithm which combines swarm intelligence and graph theory together to solve this multiobjective optimization problem. Finally, we apply our hybrid stabilization techniques and topology design algorithms to power network systems, and simulation studies are carried out to show the efficacy of the proposed approach.

In this paper, the optimal cognitive network reconfiguration problem is considered in the presence of jamming interference and delayed signals in a game play scenario. In most previous literature, only one jammer is investigated. However, in practical applications, multiple jammers occur frequently. Moreover, the delay of signals cannot be neglected in the context of network communication, which may result in an unstable system. Therefore, we formulate the optimization problem for network reconfiguration with multiple jammer's interference and delayed signals. To solve the optimization problem numerically, two types of swarm optimization algorithms are proposed. In the first approach, the dynamic objective method is used via bilevel programming while in the second approach, a hybrid multiagent swarm optimization algorithm is applied. Some illustration results are also provided in the paper.