Frontiers of Information Technology & Electronic Engineering最新文献

We investigate the resource allocation problem of a cell-free massive multiple-input multiple-output system under the condition of colluding eavesdropping by multiple passive eavesdroppers. To address the problem of limited pilot resources, a scheme is proposed to allocate the pilot with the minimum pollution to users based on access point selection and optimize the pilot transmission power to improve the accuracy of channel estimation. Aiming at the secure transmission problem under a colluding eavesdropping environment by multiple passive eavesdroppers, based on the local partial zero-forcing precoding scheme, a transmission power optimization scheme is formulated to maximize the system’s minimum security spectral efficiency. Simulation results show that the proposed scheme can effectively reduce channel estimation error and improve system security.

In this study we present the design and realization of a tunable dual band wireless power transfer (TDB-WPT) coupled resonator system. The frequency response of the tunable band can be controlled using a surface-mounted varactor. The transmitter (Tx) and the receiver (Rx) circuits are symmetric. The top layer contains a feed line with an impedance of 50 Ω. Two identical half rings defected ground structures (HR-DGSs) are loaded on the bottom using a varactor diode. We propose a solution for restricted WPT systems working at a single band application according to the operating frequency. The effects of geometry, orientation, relative distance, and misalignments on the coupling coefficients were studied. To validate the simulation results, the proposed TDB-WPT system was fabricated and tested. The system occupied a space of 40 mm×40 mm. It can deliver power to the receiver with an average coupling efficiency of 98% at the tuned band from 817 to 1018 MHz and an efficiency of 95% at a fixed band of 1.6 GHz at a significant transmission distance of 22 mm. The results of the measurements accorded well with those of an equivalent model and the simulation.

With the development of satellite miniaturization and remote sensing, the establishment of microsatellite constellations is an inevitable trend. Due to their limited size, weight, and power, spaceborne storage systems with excellent scalability, performance, and reliability are still one of the technical bottlenecks of remote sensing microsatellites. Based on the commercial off-the-shelf field-programmable gate array and memory devices, a spaceborne advanced storage system (SASS) is proposed in this paper. This work provides a dynamic programming, queue scheduling multiple-input multiple-output cache technique and a high-speed, high-reliability NAND flash controller for multiple microsatellite payload data. Experimental results show that SASS has outstanding scalability with a maximum write rate of 2429 Mb/s and preserves at least 78.53% of the performance when a single NAND flash fails. The scheduling technique effectively shortens the data scheduling time, and the data remapping method of the NAND flash controller can reduce the retention error by at least 50.73% and the program disturbance error by at least 37.80%.

Controller area networks (CANs), as one of the widely used fieldbuses in the industry, have been extended to the automation field with strict standards for safety and reliability. In practice, factors such as fatigue and insulation wear of the cables can cause intermittent connection (IC) faults to occur frequently in the CAN, which will affect the dynamic behavior and the safety of the system. Hence, quantitatively evaluating the performance of the CAN under the influence of IC faults is crucial to real-time health monitoring of the system. In this paper, a novel methodology is proposed for real-time quantitative evaluation of CAN availability when considering IC faults, with the system availability parameter being calculated based on the network state transition model. First, the causal relationship between IC fault and network error response is constructed, based on which the IC fault arrival rate is estimated. Second, the states of the network considering IC faults are analyzed, and the deterministic and stochastic Petri net (DSPN) model is applied to describe the transition relationship of the states. Then, the parameters of the DSPN model are determined and the availability of the system is calculated based on the probability distribution and physical meaning of markings in the DSPN model. A testbed is constructed and case studies are conducted to verify the proposed methodology under various experimental setups. Experimental results show that the estimation results obtained using the proposed method agree well with the actual values.

Information-centric satellite networks play a crucial role in remote sensing applications, particularly in the transmission of remote sensing images. However, the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands. Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content. In this paper, we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks, specifically focusing on the transmission of remote sensing images. Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time, effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion. We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction. To address these challenges, we leverage federated reinforcement learning techniques. Additionally, we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images. Through software-based simulations using a low Earth orbit satellite constellation, we validate the effectiveness of our proposed strategy, achieving a significant 17% reduction in the average delivery delay. This paper offers valuable insights into efficient content delivery in satellite networks, specifically targeting the transmission of remote sensing images, and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning networks. However, after embedding watermark signals by convolution, the feature fusion efficiency of convolution is relatively low; this can easily lead to distortion in the embedded image. When distortion occurs in medical images, especially in diffusion tensor images (DTIs), the clinical value of the DTI is lost. To address this issue, a robust watermarking algorithm for DTIs implemented by fusing convolution with a Transformer is proposed to ensure the robustness of the watermark and the consistency of sampling distance, which enhances the quality of the reconstructed image of the watermarked DTIs after embedding the watermark signals. In the watermark-embedding network, T1-weighted (T1w) images are used as prior knowledge. The correlation between T1w images and the original DTI is proposed to calculate the most significant features from the T1w images by using the Transformer mechanism. The maximum of the correlation is used as the most significant feature weight to improve the quality of the reconstructed DTI. In the watermark extraction network, the most significant watermark features from the watermarked DTI are adequately learned by the Transformer to robustly extract the watermark signals from the watermark features. Experimental results show that the average peak signal-to-noise ratio of the watermarked DTI reaches 50.47 dB, the diffusion characteristics such as mean diffusivity and fractional anisotropy remain unchanged, and the main axis deflection angle α_AC is close to 1. Our proposed algorithm can effectively protect the copyright of the DTI and barely affects the clinical diagnosis.

Electronic healthcare systems can offer convenience but face the risk of data forgery and information leakage. To solve these issues, we propose an identity-based searchable attribute signcryption in lattice for a blockchain-based medical system (BCMS-LIDSASC). BCMS-LIDSASC achieves decentralization and anti-quantum security in the blockchain environment, and provides fine-grained access control and searchability. Furthermore, smart contracts are used to replace traditional trusted third parties, and the interplanetary file system (IPFS) is used for ciphertext storage to alleviate storage pressure on the blockchain. Compared to other schemes, BCMS-LIDSASC requires smaller key size and less storage, and has lower computation cost. It contributes to secure and efficient management of medical data and can protect patient privacy and ensure the integrity of electronic healthcare systems.

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model, and has been widely adopted in detecting network attacks. However, existing methods cannot achieve desirable performance on dynamic network traffic streams because (1) their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and (2) their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on streams. To address these issues, we propose an active tree based model, adaptive and augmented active prior-knowledge forest (A³PF), for anomaly detection on network traffic streams. A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal traffic. On one hand, to make the model adapt to the evolving stream, a novel adaptive query strategy is designed to sample informative instances from two aspects: the changes in dynamic data distribution and the uncertainty of anomalies. On the other hand, based on the similarity of instances in the neighborhood, we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances, which enables usage of the enormous unlabeled instances during model updating. Extensive experiments on two benchmarks, CIC-IDS2017 and UNSW-NB15, demonstrate that A³PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve (AUC-ROC) (20.9% and 21.5%) and the area under the precision-recall curve (AUC-PR) (44.6% and 64.1%).

Low-rank matrix decomposition with first-order total variation (TV) regularization exhibits excellent performance in exploration of image structure. Taking advantage of its excellent performance in image denoising, we apply it to improve the robustness of deep neural networks. However, although TV regularization can improve the robustness of the model, it reduces the accuracy of normal samples due to its over-smoothing. In our work, we develop a new low-rank matrix recovery model, called LRTGV, which incorporates total generalized variation (TGV) regularization into the reweighted low-rank matrix recovery model. In the proposed model, TGV is used to better reconstruct texture information without over-smoothing. The reweighted nuclear norm and L₁-norm can enhance the global structure information. Thus, the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the image. To solve the challenging optimal model issue, we propose an algorithm based on the alternating direction method of multipliers. Experimental results show that the proposed algorithm has a certain defense capability against black-box attacks, and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

Critical functionality and huge influence of the hot trend/topic page (HTP) in microblogging sites have driven the creation of a new kind of underground service called the bogus traffic service (BTS). BTS provides a kind of illegal service which hijacks the HTP by pushing the controlled topics into it for malicious customers with the goal of guiding public opinions. To hijack HTP, the agents of BTS maintain an army of black-market accounts called bogus traffic accounts (BTAs) and control BTAs to generate a burst of fake traffic by massively retweeting the tweets containing the customer desired topic (hashtag). Although this service has been extensively exploited by malicious customers, little has been done to understand it. In this paper, we conduct a systematic measurement study of the BTS. We first investigate and collect 125 BTS agents from a variety of sources and set up a honey pot account to capture BTAs from these agents. We then build a BTA detector that detects 162 218 BTAs from Weibo, the largest Chinese microblogging site, with a precision of 94.5%. We further use them as a bridge to uncover 296 916 topics that might be involved in bogus traffic. Finally, we uncover the operating mechanism from the perspectives of the attack cycle and the attack entity. The highlights of our findings include the temporal attack patterns and intelligent evasion tactics of the BTAs. These findings bring BTS into the spotlight. Our work will help in understanding and ultimately eliminating this threat.