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.

Phishing detection methods are used to protect Internet users from leaking private information to phishing websites. However, the passive phishing detection method, which is widely used and based on blacklists, has limitations on timeliness and defense against zero-day phishing attacks and active phishing detection models with single-feature can be easily targeted by attackers. It is necessary to design and establish an active phishing detection model with high timeliness and strong adaptability. We propose a phishing detection method based on multi-feature extraction and deep learning technology. The model is constructed of a multilayer perceptron (MLP) for self-defined feature, a convolutional neural network (CNN) for image feature, a recurrent neural network (RNN) for text feature to extract feature vectors, and a classification network to fuse features and make the judgement. Our model’s accuracy achieves 0.9775 and recall reaches up to 0.9901. Experiment results of our model prove superior performance to those of other classification algorithms, demonstrating our model’s ability to deal with complex and changeable phishing detection tasks at this stage.

Precise time synchronization is essential for 5G systems, data centers, industrial automation systems, military fields, and more. Although the precision of IEEE 1588 PTP can achieve sub-hundred-nanosecond accuracy, it works only when being deployed hop-by-hop within a LAN with limited range. Hop-by-hop deployment leads to high deployment costs and makes it inapplicable over the Internet. In this paper, we propose the multi-hop precision time protocol (M-PTP), a high-precision and low-cost time synchronization protocol, which does not require hop-by-hop deployment, and no special functions need to be added to the network relay devices such as routers and switches. M-PTP leverages two key ideas. First, to mitigate the "asymmetry in forward delay and reverse delay" problem, SVM-based delay estimation is used to calculate the distribution of positive and negative random delays, then L-estimator is leveraged to estimate the time offset. Second, based on time offset, M-PTP exploits loop effect optimization among nodes. We implemented the protocol and tested its performance on variance hops under different traffic conditions and CPU loads. The experimental results show that M-PTP can achieve a precision of 11.61ns at 5 hops, which is approximately 3 times the precision of HUYGENS and approximately 30 times the precision of PTP.

A radial basis function (RBF) neural network control algorithm can effectively improve the robotic manipulators' performance against a large amount of uncertainty. The adaptive law can be derived by using the Lyapunov method so that the stability of robotic manipulator control system and the weight self-adaptive convergence of RBF neural networks will be guaranteed. Meanwhile, system fluctuations and even overshot phenomenon under every start-up process, which are caused by the system's convergence from the given nonoptimal initial weight value to the optimal weight value, can be avoided by using memristors to remember the optimal weight after the system's first operation. According to the above analysis, this correspondence paper designs a kind of RBF neural network control algorithm based on spintronic memristors, and then analyzes its theoretical derivation process and core design idea. Finally, the system simulation model, which uses a two-link robotic manipulator as control object, is built to prove the algorithm's validity and feasibility. Simulation results show that the proposed algorithm can satisfy the effect of presupposition.