Electronics

Kwak, Sihoon;Kong, Jae-Ik;Min, Moonsik

doi: 10.3390/electronics13203973pmid: N/A

This study introduces distance metrics for quantized-channel-based precoding in multiuser multiantenna systems, aiming to enhance spectral efficiency in dense cellular networks. Traditional metrics, such as the chordal distance, face limitations when dealing with scenarios involving limited feedback and multiple receive antennas. We address these challenges by developing distance measures that more accurately reflect network conditions, including the impact of intercell interference. Our distance measures are specifically designed to approximate the instantaneous rate of each user by estimating the unknown components during the quantization stage. This approach enables the associated users to efficiently estimate their achievable rates during the quantization process. Our distance measures are specifically designed for block diagonalization precoding, a method known for its computational efficiency and strong performance in multi-user multiple-input and multiple-output systems. The proposed metrics outperform conventional distance measures, particularly in environments where feedback resources are constrained, as is often the case in 5G and emerging 6G networks. The enhancements are especially significant in dense cellular networks, where accurate channel state information is critical for maintaining high spectral efficiency. Our findings suggest that these new distance measures offer a robust solution for improving the performance of limited-feedback-based precoding in cellular networks.

Fan, Yuanshuang;Chen, Qiurui;Wu, Sihan;Xiao, Jing;Wang, Zhihui

doi: 10.3390/electronics13203974pmid: N/A

A non-contact slip ring is proposed in this paper. The bidirectional simultaneous wireless power and data transfer (BD-SWPDT) technology is utilized to transfer power and data bidirectionally. A bidirectional constant-voltage LC hybrid compensation topology is proposed, which utilizes the LC series parallel structure to have different equivalent models at different frequencies. By using different operating frequencies for forward and reverse power transfer, the system’s forward and reverse transfer can be equivalent to different constant-voltage output compensation topologies. The resonant parameters of the system are designed to achieve consistent voltage gain for forward and reverse power transfer. And based on this topology, a data carrier injection method is designed to achieve high Signal Noise Ratio (SNR) simultaneous data transfer. To improve the flexibility of non-contact slip ring installation, a caliper-type coupling structure is proposed. Finally, the feasibility of the proposed method is verified through experiments, achieving a forward and reverse output power of 200 W and half duplex communication with a data rate of 19.2 kbps.

Huang, Fei;Wang, Zhiwen;Zheng, Yu;Wang, Qi;Hao, Bingsen;Xiang, Yangkai

doi: 10.3390/electronics13203975pmid: N/A

To address the issue of low accuracy in the segmentation of dynamic objects using semantic segmentation networks, a dual-branch dynamic object segmentation network has been proposed, which is based on the fusion of spatiotemporal information. First, an appearance–motion feature fusion module is designed, which characterizes the motion information of objects by introducing a residual graph. This module combines a co-attention mechanism and a motion correction method to enhance the extraction of appearance features for dynamic objects. Furthermore, to mitigate boundary blurring and misclassification issues when 2D semantic information is projected back into 3D point clouds, a majority voting strategy based on time-series point cloud information has been proposed. This approach aims to overcome the limitations of post-processing in single-frame point clouds. By doing this, this method can significantly enhance the accuracy of segmenting moving objects in practical scenarios. Test results from the semantic KITTI public dataset demonstrate that our improved method outperforms mainstream dynamic object segmentation networks like LMNet and MotionSeg3D. Specifically, it achieves an Intersection over Union (IoU) of 72.19%, representing an improvement of 9.68% and 4.86% compared to LMNet and MotionSeg3D, respectively. The proposed method, with its precise algorithm, has practical applications in autonomous driving perception.

Wang, Renliang;Liu, Feng;Bai, Yanhui

doi: 10.3390/electronics13203976pmid: N/A

Software defect prediction datasets often suffer from issues such as class imbalance, noise, and class overlap, making it difficult for classifiers to identify instances of defects. In response, researchers have proposed various techniques to mitigate the impact of these issues on classifier performance. Oversampling is a widely used method to address class imbalance. However, in addition to inherent noise and class overlap in the datasets themselves, oversampling methods can introduce new noise and class overlap while addressing class imbalance. To tackle these challenges, we propose a software defect prediction method called AS-KDENN, which simultaneously improves the effects of class imbalance, noise, and class overlap on classification models. AS-KDENN first performs oversampling using the Adaptive Synthetic Sampling Method (ADASYN), followed by our proposed KDENN method to address noise and class overlap. Unlike traditional methods, KDENN takes into account both the distance and local density information of overlapping samples, allowing for a more reasonable elimination of noise and instances of overlapping. To demonstrate the effectiveness of the AS-KDENN method, we conducted extensive experiments on 19 publicly available software defect prediction datasets. Compared to four commonly used oversampling techniques that also address class overlap or noise, the AS-KDENN method effectively alleviates issues of class imbalance, noise, and class overlap, subsequently improving the performance of the classifier models.

Alamri, Maram;Ykhlef, Mourad

doi: 10.3390/electronics13203978pmid: N/A

For financial institutions, credit card fraud detection is a critical activity where the accuracy and efficiency of detection models are important. Traditional methods often use standard feature selection techniques that may ignore refined patterns in transaction data. This paper presents a new approach that combines feature aggregation with Exhaustive Feature Selection (EFS) to enhance the performance of credit card fraud detection models. Through feature aggregation, higher-order characteristics are created to capture complex relationships within the data, then find the most relevant features by evaluating all possible subsets of features systemically using EFS. Our method was tested using a public credit card fraud dataset, PaySim. Four popular learning classifiers—random forest (RF), decision tree (DT), logistic regression (LR), and deep neural network (DNN)—are used with balanced datasets to evaluate the techniques. The findings show a large improvement in detection accuracy, F1 score, and AUPRC compared to other approaches. Specifically, our method had improved F1 score, precision, and recall measures, which underlines its ability to handle fraudulent transactions’ nuances more effectively as compared to other approaches. This article provides an overall analysis of this method’s impact on model performance, giving some insights for future studies regarding fraud detection and related fields.

Kim, Jinyeol;Kim, Raehyeong;Oh, Jongwon;Lee, Seung Eun

doi: 10.3390/electronics13203979pmid: N/A

WebAssembly (WASM) has emerged as a novel standard aimed at enhancing the performance of web applications, developed to complement traditional JavaScript. By offering a platform-independent binary code format, WASM facilitates rapid and efficient execution within web browsers. This attribute is particularly advantageous for tasks demanding significant computational power. However, in resource-constrained environments such as embedded systems, the processing speed and memory requirements of WASM become prominent drawbacks. To address these challenges, this paper introduces the design and implementation of a hardware accelerator specifically for WASM. The proposed WASM accelerator achieves up to a 142-fold increase in computation speed for the selected algorithms compared to embedded systems. This advancement significantly enhances the execution efficiency and real-time processing capabilities of WASM in embedded systems. The paper analyzes the fundamentals of WebAssembly and provides a comprehensive description of the architecture of the accelerator designed to optimize WASM execution. Also, this paper includes the implementation details and the evaluation process, validating the utility and effectiveness of this methodology. This research makes a critical contribution to extending the applicability of WASM in embedded systems, offering a strategic direction for future technological advancements that ensure efficient execution of WASM in resource-limited environments.

Bokolo, Biodoumoye George;Liu, Qingzhong

doi: 10.3390/electronics13203980pmid: N/A

Depression detection through social media analysis has emerged as a promising approach for early intervention and mental health support. This study evaluates the performance of various machine learning and transformer models in identifying depressive content from tweets on X. Utilizing the Sentiment140 and the Suicide-Watch dataset, we built several models which include logistic regression, Bernoulli Naive Bayes, Random Forest, and transformer models such as RoBERTa, DeBERTa, DistilBERT, and SqueezeBERT to detect this content. Our findings indicate that transformer models outperform traditional machine learning algorithms, with RoBERTa and DeBERTa, when predicting depression and suicide rates. This performance is attributed to the transformers’ ability to capture contextual nuances in language. On the other hand, logistic regression models outperform transformers in another dataset with more accurate information. This is attributed to the traditional model’s ability to understand simple patterns especially when the classes are straighforward. We employed a comprehensive cross-validation approach to ensure robustness, with transformers demonstrating higher stability and reliability across splits. Despite limitations like dataset scope and computational constraints, the findings contribute significantly to mental health monitoring and suggest promising directions for future research and real-world applications in early depression detection and mental health screening tools. The various models used performed outstandingly.

Han, Yoonsang;Kim, Inseo;Kim, Jinsung;Moon, Gordon Euhyun

doi: 10.3390/electronics13203981pmid: N/A

Sparse matrix–matrix multiplication (SpMM) is essential for deep learning models and scientific computing. Recently, Tensor Cores (TCs) on GPUs, originally designed for dense matrix multiplication with mixed precision, have gained prominence. However, utilizing TCs for SpMM is challenging due to irregular memory access patterns and a varying number of non-zero elements in a sparse matrix. To improve data locality, previous studies have proposed reordering sparse matrices before multiplication, but this adds computational overhead. In this paper, we propose Tensor Core-Adapted SpMM (TCA-SpMM), which leverages TCs without requiring matrix reordering and uses the compressed sparse row (CSR) format. To optimize TC usage, the SpMM algorithm’s dot product operation is transformed into a blocked matrix–matrix multiplication. Addressing load imbalance and minimizing data movement are critical to optimizing the SpMM kernel. Our TCA-SpMM dynamically allocates thread blocks to process multiple rows simultaneously and efficiently uses shared memory to reduce data movement. Performance results on sparse matrices from the Deep Learning Matrix Collection public dataset demonstrate that TCA-SpMM achieves up to 29.58× speedup over state-of-the-art SpMM implementations optimized with TCs.

Li, Shaohang;Yao, Yuan;Cheng, Xiaohe;Yu, Junsheng

doi: 10.3390/electronics13203982pmid: N/A

In this paper, the design of a dual-band filter based on the band gap waveguide (BGW) is presented. In the low-frequency band, the TE201 mode rectangular waveguide cavity resonator was used to design the bandpass filter, which significantly reduces the impact of the high-frequency transmission line (TL). In the high-frequency band, a TE101 mode cavity resonator based on the gap waveguide (GW) structure was used to design the high-frequency band filter. A lower insertion loss can be achieved with the use of all-metal structure. A dual-band filter prototype was fabricated to verify its performance. According to the measurement results, the insertion loss is less than 1.3 dB and the return loss is better than 14 dB in the frequency range of 5.92–6.06 GHz; and the insertion loss is less than 1.77 dB and the return loss is better than 15 dB in the frequency range of 80.6–86.2 GHz. The frequency ratio is as large as 13.9, and because the high-frequency band filter is embedded in the cavity resonator of the low-frequency band filter, it saves space to a certain extent and realizes the integrated design of the dual-band filter, which is of great significance for the improvement of the performance of the dual-band communication system in higher-frequency bands.

Posielek, Tobias

doi: 10.3390/electronics13203983pmid: N/A

The development and validation of lateral control strategies for railway running gears with independently rotating driven wheels (IRDWs) are an active research area due to their potential to enhance straight-track centering, curve steering performance, and reduce noise and wheel–rail wear. This paper focuses on the practical application of theoretical models to a 1:5 scaled test rig developed by the German Aerospace Center (DLR), addressing the challenges posed by unmodeled phenomena such as hysteresis, varying damping and parameter identification. The theoretical model from prior work is adapted based on empirical measurements from the test rig, incorporating the varying open-loop stability of the front and rear wheel carriers, hysteresis effects, and other dynamic properties typically neglected in literature. A transparent procedure for identifying dynamic parameters is developed, validated through closed- and open-loop measurements. The refined model informs the design and tuning of a cascaded PI and PD controller, enhancing system stabilization by compensating for hysteresis and damping variations. The proposed approach demonstrates improved robustness and performance in controlling the lateral displacement of IRDWs, contributing to the advancement of safety-critical railway technologies.