Electronics Letters最新文献

Today's society is a high-tech information society. The way of information transmission is constantly improving, from manual transmission to wired network transmission, from wired network to wireless network transmission. With the network increasingly becoming the necessary means and tools for the rapid development of all walks of life, the importance of network security is beyond doubt. In the environment of increasing informatization level, the importance of information, network and communication security is also increasing. Modern society relies more and more on computer communication networks. Once the network communication is insecure, all kinds of resources of our personal information will be intercepted and copied, including property passwords or ID card numbers, which will threaten our property security, and even affect national economic development, social stability, and national defence security. However, we cannot deny the convenience and rapidity brought by informatization. Therefore, in view of the impact, risks and existing problems of information, network and communication security, this topic aims to bring relevant researchers from the industry and academia together for discussion. This special issue focuses on research ideas and experimental studies related to “Information, Network, and Communications Security”, covering the development and design of security policies and algorithms in the context of the Internet of Things (IoT), blockchain, and wireless communication networks.

In this Special Issue, we have received 10 papers, all of which underwent peer review. Of the 10 originally submitted papers, 3 have been accepted and 2 have been ‘rejected’, that is, they did not meet the Electronics Letters criteria for rapid publication. Thus, the overall submissions were of high quality, which marks the success of this Special Issue.

The three papers that were finally accepted can be classified into three main categories according to their research scenarios, that is, blockchain, IoT, and wireless communication networks. The first category of papers focuses on intrusion detection systems in blockchain, and the research aims to further improve the security of multiple industries such as finance, healthcare, and cybersecurity. The second category of papers focuses on the vulnerability of devices in the Internet of Things to cyberattacks such as remote infiltration, and the research can further ensure the security of sensitive data of governments, enterprises, and individuals. The third category of papers addresses the vulnerability of wireless communication networks to strong interference that affects the BER and thus threatens communication security. A brief presentation of each of the paper in this special issue is given as follows:

Abubakar et al. address the problem of high false alarm rates in intrusion detection systems due to insufficient training data and improper threshold selection and propose a blockchain

The high spatial search complexity of the direct positioning method in passive positioning systems leads to long positioning time and high computational resource consumption. In response to this issue, this article proposes a fast localization scheme based on composite convolutional neural networks (CCNN), which can effectively explore the correlation between the position of the radiation source and the characteristics of the received signal. CCNN is a 20-layer composite network based on fully convolutional network layer, which is composed of convolutional layers, batch normalization (BN) layers, and ReLU activation function layers with unidirectional connections. Then, CCNNs are adjusted and trained for positioning single and multiple radiation sources, respectively. Simulation results show that the computational time of the proposed method can be reduced by nearly 98% compared with the direct positioning scheme. Meanwhile, about 71.2% of positioning error's reduction is achieved.

As a potential solution to improve wireless security, specific emitter identification is a lightweight access authentication technology. However, the existed deep learning-based specific emitter identification methods are highly dependent on the training sample size, leading to serious overfitting problem when the training samples are inadequate, which obstructs their practical applications. To address this issue, an innovative data augmentation method to effectively expand the sample size is proposed. In this design, after data preprocessing, a random integration based data augmentation is applied to integrate several initial samples and generate new samples. Furthermore, compared with the existed methods, chaotic sequences are utilized to randomly set the integration weight of each initial sample, and thus enhancing the diversity of augmented samples. The superiority of the proposed chaotic integration-based data augmentation method in accuracy, generalization ability and robustness is validated by the hardware implementation on digital mobile radio portable radios.

This letter presents a novel millimetre-wave (mm-wave) on-chip substrate integrated waveguide (SIW) filtering crossover using 0.25 µm GaAs pHEMT technology. The design methodology of the proposed crossover is thoroughly illustrated. The proposed filtering crossover employs a dual-mode cavity with TE₁₀₂ and TE₂₀₁ degenerate mode resonances at the intersection, leveraging the degenerate modes for in-band resonance and inter-channel isolation. Additionally, four TE₁₀₁ mode resonant half-mode SIW cavities are coupled around the dual-mode cavity to achieve two third-order bandpass response channels and reduce the overall size. A prototype is designed, analysed, and fabricated to validate the proposed approach, with measured results showing good agreement with simulations. The presented on-chip SIW filtering crossover offers promising potential for mm-wave applications, demonstrating the effectiveness of the design methodology and GaAs pHEMT technology integration.

Reconfigurable intelligent surface (RIS), equipped with a large number of small, low-cost, and re-configurable elements, is envisioned as a potential enabler for the upcoming 5G-Advanced and 6G networks. Here, based on a flexibly tunable and readily programmable RIS, the capability of beamforming toward arbitrary desired directions and coverage enhancement are investigated and experimentally demonstrated. The theory of RIS-based beamforming is introduced, the simulated beamforming radiation patterns are provided, and an over-the-air radiated testing platform is designed for characterizing RIS beamforming performance. The RIS beam steering radiation performance tests are conducted, and the radiation patterns for different directions are extracted and analyzed. In addition, the indoor field trials on the RIS performance evaluation of enhancing coverage are reported. The field trials for multiple RIS-deployed scenarios, including RIS mirror placement, RIS non-mirror placement, and non-RIS assisted scenarios, are conducted, and the channel characteristics for those scenarios are extracted and modelled. Significant improvements in overcoming path loss and shadow fading in typical coverage holes can be observed. The proposed testing method and measurement results may provide some insights into the design and optimization of RIS-aided wireless communications.

In recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high-frequency cyber-attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature-based IDS detects threats. Secondly, anomaly-based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K-Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning-based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%.

A 4-tap time-domain decision feedback equalizer (TD-DFE) is presented to implement a multi-tap DFE in a matched DQ (data)-DQS (strobe) tree architecture. Traditionally, matched architecture holds an advantage in terms of power noise immunity, but it suffers from low-speed performance due to the unavailability of decision feedback equalizer (DFE) applications. By adopting the proposed TD-DFE, both high-speed operation and power noise immunity can be achieved within the matched architecture. An 8-DQ parallel receiver with the proposed 4-tap TD-DFE, designed in 28 nm CMOS, achieves a data rate of 20 Gb/s with 0.6 UI eye-opening even with 215 mV power fluctuations.

A novel enlarging fractional bandwidth (FBW) technique for acoustic-wave-lumped-element resonator (AWLR)-based bandpass filters is presented. The new technique is based on a series of matching inductors that replace the parallel or series acoustic wave (AW) resonators of AW filters to beak the bandwidth constraint of the ladder-type structure. In addition, the transmission response analysis of the proposed AWLR-based filter is provided. A prototype of the AWLR-based filter is fabricated and tested to validate the proposed FBW widening technique. The measured insertion loss, FBW, and out-of-band rejection are 1.5 dB, 1.63k_t² (k_t² is the electromechanical coupling coefficient of the AWR), and 38 dB, respectively.

Point cloud data is a large collection of high dimensional 3D points with 3D coordinates and attributes, which has been one of the mainstream representations for emerging 3D applications, such as virtual reality, autonomous vehicles, and robotics. Due to the large-scale unstructured high-dimensional nature of point clouds, point cloud processing, transmitting and analysing has been challenging issues in multimedia signal processing and communication. Deep learning is a powerful tool to learn statistical knowledge from massive data. Advances in artificial intelligence, especially deep learning models are offering new opportunities for point cloud processing, compression and analysis. This special issue aims at promoting cutting-edge research on deep learning-based point cloud processing, including object detection, segmentation, registration, compression, and visual quality assessment.

The purpose of epilepsy detection is to determine whether epilepsy has occurred by analysing the patient's electroencephalogram (EEG) signals. Compared to traditional methods, epilepsy detection methods based on deep learning have achieved significant improvements in detection accuracy. However, when the number of training samples is limited, the model's detection performance often significantly declines. To address this issue, here a sample enhancement method based on electroencephalogram signal channel swapping is proposed. This method generates new electroencephalogram samples by exchanging electroencephalogram sequences from different channels, thereby expanding the training set and improving epilepsy detection accuracy in few-shot scenarios. Experiments using the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset show that for training sets with 100, 500, and 1000 samples, detection accuracy improves from 0.6797 to 0.7789, 0.6952 to 0.8210, and 0.7273 to 0.8517, respectively. Compared to the sliding window method, the proposed method demonstrates higher accuracy in extreme low sample sizes. Combining both methods can further enhances detection performance, showing an improvement of approximately 8% across various configurations.