Magnetic particle imaging technology, a novel medical imaging technology, possesses rapid imaging, high penetration depth, and is free from ionizing radiation. However, the system point spread function causes imaging blurring, which can be further exacerbated by external environmental interferences. Although hardware improvements and system optimization can mitigate blurring, these approaches are often expensive and time-consuming, particularly for low-field imaging in large-scale systems. This article proposes a Fast Context-aware Saliency-enhanced Deblurring Network, FCS-edNET, to solve the challenging issue by deblurring the reconstructed images. The network introduces the Multi-scale Global module to enhance the multi-scale feature perception ability. The Multi-scale Denoising Prior algorithm, which employs a low-frequency filter operator to restrict image noise and offers priors for each layer of subnetworks, is designed to improve the model robustness. Finally, proposing a Multi-level Joint loss optimizes model parameters to promote model convergence speed and space distribution simulation capability. Extensive experiments on multiple public and private datasets demonstrate that FCS-edNET outperforms the state-of-the-art methods in MPI image deblurring efficiently, suggesting its potential to support future research toward clinical imaging applications. The code is available at https://github.com/ydz1118/FCS-edNET.

High-altitude airships (HAS) and unmanned aerial vehicles (UAVs) equipped with Multiaccess Edge Computing (MEC) servers have emerged as promising aerial MEC nodes for providing task offloading (TO) services to intelligent mobile devices (IMDs) in post-disaster scenarios. HAS offers robust computing and energy resources, while UAVs provide flexible, low-altitude coverage for rapid deployment. However, direct task offloading from IMDs to HAS often leads to task failures due to high transmission delays. UAVs with limited onboard resources require to minimize resource waste. Additionally, IMDs in sparse areas face insufficient TO services due to unfair UAV coverage. This paper defines these challenges as a joint optimization problem involving TO, RA, and UAV coverage fairness. It proposes a cooperative aerial Multiaccess Edge Computing (AMEC) framework integrating HAS and UAVs to address the issue. Within this framework, a hybrid TO scheme is first developed to mitigate the high transmission delay between IMDs and HAS. Second, a Distance, Resource, Urgency-based Decision Mechanism (DRUDM) is designed to enhance the accuracy of UAVs in selecting target IMDs for TO services. Third, a Coverage Fairness Guarantee (CFG) strategy is proposed to optimize UAV flight trajectories, ensuring IMDs in sparse areas receive fair TO services. Finally, the joint optimization problem is modeled as a Multi-Agent Partially Observable Markov Decision Process (MA-POMDP), and a DRUDM–CFG algorithm is presented to efficiently solve this complex non convex optimization problem. Experimental results demonstrate that the proposed algorithm outperforms other compared algorithms in task completion rate and average delay, benefiting from the DRUDM mechanism. Meanwhile, the CFG strategy effectively improves TO service fairness for IMDs in sparse areas.

In the Internet of Autonomous Vehicles (IoAV), task offloading is crucial for managing tasks that require extensive computing power to guarantee vehicle safety under different weather scenarios. However, extreme weather events can lead to infrastructure damage and network disruptions, significantly increasing the computational demands of autonomous vehicles. These vehicles require additional computing resources to navigate complex road conditions and risks, all while facing a high degree of uncertainty, such as fluctuations in vehicle resource utilization and task workloads. To address these challenges, a new and lightweight task offloading decision framework, named 2FDP-BRL, has been first proposed in this paper. This framework not only considers the fast response time required for autonomous driving, but also considers the resource shortage and offloading uncertainty caused by extreme weather. Therefore, we introduce the dynamic pricing idea and the Interval Type-2 Fuzzy Inference System (IT2FIS) utilizing broad reinforcement learning to deal with various dynamic uncertainties in the IoAV under extreme weather. For the authenticity of experimental results, we utilize the VISSIM platform to collect experimental data and conduct simulations. Moreover, to accurately simulate extreme weather scenarios, we also account for the variability of infrastructure and road elements, including reduced transmission rates and decreased efficiency in executing tasks. Furthermore, to enhance the realism of the simulation, we incorporate historical weather data from NOAA for Shenyang in 2024 to model dynamic uncertainties under extreme weather conditions and conduct comparative experimental analyses focusing on task completion rates. Finally, the proposed framework was implemented on both a local setup and the Huawei Atlas 200I DK A2 device, illustrating its efficacy design.

Person re-identification (Re-ID) aims to match individuals across different cameras, a task complicated by variations in camera positions, resolutions, and lighting conditions. While supervised training improves Re-ID model accuracy, it requires significant annotation efforts. Unsupervised domain adaptation (UDA) methods address this by leveraging unlabeled target domain data but often fail to fully utilize multiple source domains and are constrained by computational resources. This article introduces a lightweight multi-source domain adaptation method for person Re-ID that combines meta-learning with pseudo-label-based UDA. By employing Self-paced Meta-Learning (SpML) and style enhancement techniques, the model learns domain-invariant knowledge from easy to difficult source domains, enhancing pseudo-label quality during adaptation. Our approach, based on an omni-scale feature extraction network using deep separable convolution, combines global and partial feature branches to capture richer pedestrian features. Experiments on public and real-world datasets demonstrate that our method achieves competitive performance with significantly fewer parameters and Floating Point Operations (FLOPs) compared to state-of-the-art models, proving its effectiveness and practicality.

In the Internet of Autonomous Vehicles (IoAV), task offloading is a method to address computationally intensive tasks and ensure the safe operation of vehicles. However, under extreme weather conditions, the number of these tasks significantly increases, posing higher risks and challenges. Therefore, to mitigate risks and ensure the safe operation of vehicles, it is crucial to make quick and effective decisions during the task offloading process. Currently, most methods in this domain utilize Deep Reinforcement Learning (DRL). However, the large number of parameters in deep networks results in the characteristics of long decision time and large consumption of computational resources. In order to solve this problem, this paper proposes a task offloading scheme named Dynamic Pricing Driven Double Broad Reinforcement Learning (DP-DBRL), which utilizes Double Broad Reinforcement Learning (DBRL) to reduce model memory consumption and decision time. Additionally, it considers the high-speed mobility and resource variability to devise a more efficient dynamic pricing scheme that minimizes the overall delay in task processing for vehicles. To validate the proposed scheme, we conduct simulations using the VISSIM platform, meanwhile, we simulate task offloading scenarios under extreme weather conditions by randomly reducing factors such as the transmission rate and task execution efficiency of infrastructure and vehicles on the road. Finally, we deployed the proposed scheme both locally and on the Huawei Atlas 500 device to demonstrate its effectiveness and lightweight nature.

Time delay has always been one of the main factors affecting the application performance of neural network (NN) systems, and dynamic performance research of NNs with time delays has been the focus of many scholars in recent years. This article enquires into the exponentially synchronous problem of switched delayed NNs with time delay in the leakage term. Adopting an unusual form from a common switched system, the switching modes of the switched delayed NNs system in this article are dependent on time delays. In the first place, the master, slave, and error NNs models are reconstructed into the switched form by introducing the switched delay idea. Then with the help of the admissible edge-dependent average dwell time (AED-ADT) method and delay-dependent switching adjustment indicators, a novel set of generalized delay-mode-dependent multiple Lyapunov-Krasovskii functionals (MLKFs) is built for analyzing the cases where a state-feedback controller exists and does not exist in the model, and where parts of LKFs may increase during the period when the corresponding subsystems are activated. For these cases, several effective exponential synchronization criteria and switching laws are presented accordingly. At last, the verification of the theoretical results is shown through a few examples.

Autonomous driving has brought about a growing interest in enhancing traffic efficiency and ensuring road safety. One of the fundamental functions of autonomous driving technology is lane-keeping, which has become a popular research topic in autonomous driving. However, current deep reinforcement learning (DRL)-based algorithms used for solving lane-keeping problems have limitations, such as low sample utilization and high time cost in complex scenarios. To address this, we propose a lane-keeping transfer Double Deep Q Network (LK-TDDQN) framework that leverages transfer learning (TL). Our framework enables autonomous vehicles to perform lane-keeping tasks in similar scenarios, transferring knowledge from a single-lane rural road scenario to a two-lane racing scenario. The effectiveness of the proposed LK-TDDQN was demonstrated in several simulation experiments in OpenAI Gym. These simulations demonstrate that our approach can enhance decision-making efficiency by 22% and reduce the time cost of autonomous vehicles by 11%, ensuring the safety of autonomous driving while alleviating the burden on drivers.

Neural networks (NNs) have been deeply studied due to their wide applicability. Since time delays are unavoidable in reality, it is basic and crucial for all applications based on NNs to guarantee system stability under the influence of mixed time delays. To better exploit the variation information of time delay, we introduce the switching idea and approaches into mixed time-delay NNs to solve the stability problem. First, the considered mixed time-delay NNs are modeled as the switched NNs by dividing the two classes of time delays, discrete and distributed time delays, into some variable intervals and combining these intervals as new switching modes. With the help of mode-dependent average dwell-time switching, Lyapunov theory, and mathematical techniques, several exponential stability criteria on the modeled switched systems containing different modes are obtained. Moreover, via introducing the mathematical condition of the unstable subsystem in the switching system, a less conservativeness condition on the exponential stability of the modeled NNs is proposed. We perform three examples for testifying the validity of the proposed methods over existing ones.

This article analyzes the exponentially stable problem of neural networks (NNs) with two additive time-varying delay components. Disparate from the previous solutions on this similar model, switching ideas, that divide the time-varying delay intervals and treat the small intervals as switching signals, are introduced to transfer the studied problem into a switching problem. Besides, delay-dependent switching adjustment indicators are proposed to construct a novel set of augmented multiple Lyapunov-Krasovskii functionals (LKFs) that not only satisfy the switching condition but also make the suitable delay-dependent integral items be in the each corresponding LKF based on each switching mode. Combined with some switching techniques, some less conservativeness stability criteria with different numbers of switching modes are obtained. In the end, two simulation examples are performed to demonstrate the effectiveness and efficiency of the presented methods comparing other available ones.