ELECTRONICS LETTERS 是国际知名的同行评审快速传播期刊，每两周发表一次简短的原创研究论文。其广泛的跨学科范围涵盖了所有电子工程相关领域的最新发展，包括通信、生物医学、光学和设备技术。 ELECTRONICS LETTERS 还通过特刊和采访进一步深入了解一些最新发展。SCOPEAS 处于该领域最前沿的期刊，ELECTRONICS LETTERS 发表的论文涵盖电子和电气工程的所有主题。该期刊的主要主题如下：天线和传播生物医学和仿生技术、信号处理和应用控制工程电磁学：理论、材料和器件电子电路和系统图像、视频和视觉处理和应用信息、计算和通信仪器和测量微波技术光学通信光子学和光电功率电子、能源和可持续发展雷达、声纳和导航半导体技术信号处理MIMO

Electronics Letters is a well-known international peer-reviewed rapid publication journal that publishes concise original research papers every two weeks. It covers the latest developments across all areas of electronic engineering, including communications, biomedical, optics, and device technology. The journal further explores some of the latest advancements through special issues and interviews. As one of the leading journals in the field, Electronics Letters publishes papers on all topics related to electronic and electrical engineering. The main topics include: antennas and propagation, biomedical and bionic technologies, signal processing and applications, control engineering, electromagnetics (theory, materials, and devices), electronic circuits and systems, image, video and visual processing and applications, information, computing and communications, instrumentation and measurement, microwave technology, optical communications, photonics and optoelectronics, power electronics, energy and sustainability, radar, sonar and navigation, semiconductor technology, signal processing, and MIMO.

《Electronics Letters》是国际知名的同行评审快速传播期刊，每两周发表一次简短的原创研究论文。期刊涵盖了电子工程相关领域的最新发展，包括通信、生物医学、光学和设备技术。通过特刊和采访，期刊进一步深入探讨一些最新发展。《Electronics Letters》是该领域最前沿的期刊，发表的论文涉及电子和电气工程的所有主题。主要主题包括：天线与传播、生物医学与仿生技术、信号处理与应用、控制工程、电磁学（理论、材料与器件）、电子电路与系统、图像、视频与视觉处理与应用、信息、计算与通信、仪器与测量、微波技术、光学通信、光子学与光电、功率电子、能源与可持续发展、雷达、声纳与导航、半导体技术、信号处理和MIMO。

Electronics Letters

The most basic and generic configuration, which consists of a unidirectional coupling between a ring resonator and a waveguide, is considered. The fundamental working equations required to describe the associated power transfer are derived and the application of this geometry to a variety of optical phenomena is discussed. These phenomena include 'add/dropping' of optical beams, add/drop filtering and optical power switching.

A. Yariv

Universal relations for coupling of optical power between microresonators and dielectric waveguides

A novel N-type MOS (NMOS) only Schmitt trigger with voltage booster (NST-VB) circuit is presented. The proposed NST-VB circuit uses NMOS transistors in both pull-up and pull-down networks to reduce the effect of negative bias temperature instability (NBTI) on the circuit. The proposed circuit is less affected by both inter-die and intra-die process variations in consequence of NMOS only structure. Owing to NBTI, the increase in delay for the proposed NST-VB circuit is only 0.47% as compared with 7.2% for conventional Schmitt trigger after the stress time of three years. For the viability of the proposed circuit figure of merit (FOM) is used as a performance metric and it is found that the proposed circuit has and improved FOM as compared with the conventional Schmitt trigger and NMOS inverter, respectively.

A.P. Shah

N. Yadav

A. Beohar

S.K. Vishvakarma

NMOS only Schmitt trigger circuit for NBTI resilient CMOS circuits

The limitations of present automatic and adaptive equalisers stem from the use of feedforward transversal filters. These drawbacks may be obviated by using a feedback transversal filter, the inverse filter, but this is only suitable for limited use since it can be an unstable circuit. It is shown that modulo arithmetic may be used in the inverse filter to eliminate completely the possibility of instability, and a very simple automatic or adaptive equalisation system is presented. Some interesting properties of the modulo inverse filter are included.

M. Tomlinson

Electron. Lett.

New automatic equaliser employing modulo arithmetic

Although traditionally associated with defence and security domains, radar sensing has attracted significant interest in recent years in healthcare applications. These include the monitoring of vital signs such as respiration, heartbeat, and blood pressure, analysis of gait and mobility levels, classification of human activities to promptly detect critical events such as falls, as well as the evaluation of fitness and reactivity levels. The attractiveness of radar against alternative technologies such as wearable sensors or cameras lies in its contactless capabilities, whereby people do not need to wear, carry, or interact with any additional device, and plain images of people and private environments are not recorded. In this letter, we discuss some of the most recent achievements and outstanding research challenges related to radar applications in healthcare and present some results from our work at the University of Glasgow, including a dataset of radar signatures of human activities that are openly shared with the wider community.

Dr Francesco Fioranelli

Dr Syed Aziz Shah

Haobo Li1

Aman Shrestha

Dr Shufan Yang

Dr Julien Le Kernec

Radar sensing for healthcare

Abstract Robots with telepresence capabilities are typically employed for tasks where human presence is not feasible due to geography, safety risks like fire or radiation exposure, or other factors like any epidemic disease. Time delay is a significant consideration in controlling a telepresence robot. This study proposes a deep learning‐based approach to compensate for the delay by predicting the behaviour of the teleoperator. The authors integrate a recurrent neural network (RNN) based on the Long Short‐Term Memory (LSTM) architecture with the reinforcement learning‐based Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method predicts the teleoperator's angular and linear controlling commands by using data gathered by embedded sensors on the specially designed and built telepresence robot. Simulations and experiments assess the operation of the proposed technique in Gazebo simulation and MATLAB with robot operating system (ROS) integration, which shows 2.3% better response in the presence of static.

Fawad Naseer

Muhammad Nasir Khan

Akhtar Rasool

Nafees Ayub

A novel approach to compensate delay in communication by predicting teleoperator behaviour using deep learning and reinforcement learning to control telepresence robot

一种通过使用深度学习和强化学习预测远程操作员行为来补偿通信延迟并控制远程呈现机器人的新方法

Abstract The direction‐of‐arrival (DoA) estimation methods based on coprime array (CA) largely rely on having a sufficient number of snapshots. However, under single‐snapshot conditions typical of vehicle‐mounted imaging radar, CA‐based DoA estimation often suffers from ambiguity leakage, resulting in significant degradation in performance. This paper thoroughly analyses the mechanism of ambiguity leakage in CA by re‐deriving the covariance matrix model under single‐snapshot conditions. To suppress the ambiguity leakage, a triple extended coprime array composed of three pairwise coprime sub‐arrays enhanced coprime constraint is designed. Building on this design, a DoA estimation algorithm based on an iterative adaptive approach is proposed. By leveraging the power spectrum distribution from iterative adaptive approach, a reduced‐noise signal is constructed by retaining only the high‐energy components. Next, the noise subspace using a multi‐stage Wiener filter, and finally, achieve super‐resolution DoA estimation through multiple signal classification spectrum analysis.

Tianyao Long

Lei Huang

Qiang Li

Wei Wang

Triple extended coprime array for single‐snapshot DoA estimation

单快照方向估计的三重扩展互质阵列

Abstract This paper presents a network traffic anomaly detection model based on feature grouping and multiple autoencoders (multi‐AEs) integration. This model comprises four modules: feature grouping module, feature learning module, AUC and optimal threshold calculation module, and anomaly detection application module. In the feature grouping module, multiple group features are constructed by selecting the different features according to their attributes and variances. In the feature learning module, the group features of normal traffic are learned based on multi‐AEs. In the AUC and optimal threshold calculation module, the AUC of each AE is calculated according to the ROC curve of the verification data, and the optimal thresholds for each AE are determined using the Youden index. In the anomaly detection application module, the AEs that participated in fusion are selected and their weights are calculated by analysing AUC value, and the scores of unknown traffic in each AE are evaluated considering both the reconstruction error distribution and the optimal threshold. Finally, the anomaly detection result can be obtained by the fusion of these multiple scores. Through validation on the UNSW‐NB15 and CICIDS2017 datasets, the accuracy of the proposed model is improved by 12.04% and 10.52%, respectively, compared to the baseline model.

Yang Zhou

Haoyang Zeng

Zhourong Zheng

Wei Zhang

Network traffic anomaly detection model based on feature grouping and multi‐autoencoders integration

基于特征分组和多自动编码器集成的网络流量异常检测模型

Abstract This letter, based on 3D slotline structures (SLSs), proposes a dual‐polarization frequency selective rasorber (FSR) with a high selectivity and wide transmission passband. The broadband transmission and absorption paths are created by etching three independent SLSs onto the 3D periodic printed circuit boards (PCBs). These include an open‐circuit slotline that is loaded with both a metal strip and an LC structure at its centre, as well as two short‐circuit slotlines that are loaded with lumped resistors. With this design, two transmission zeros (TZs) are introduced to achieve a fast transition between the passband and absorption bands. Through analysing surface currents and electric field distributions, the working principle is elucidated. The proposed FSR is designed, manufactured, and measured. Simulation and experimental results demonstrate that the presented FSR exhibits broadband characteristics, high selectivity, and angular stability.

Yuchen Yan

Weiwei Wu

Yuhong Ma

Jingjian Huang

Naichang Yuan

Dual‐polarization 3D FSR with a high selectivity and wide transmission passband

双极化三维FSR具有高选择性和宽传输通带

Abstract The exponential rise of electric vehicles (EVs) is transforming the global automobile industry, driving a shift towards greater cleanliness and environmental sustainability. EV charging stations (EVCSs) play a pivotal role in this massive transition towards EVs, where accurate forecasting of EVCS demand is crucial for seamlessly integrating EVs into existing power grids. Most of the existing research mainly concentrates on univariate forecasting, neglecting the multiple factors influencing EVCS demand. Hence, this study offers a comparative analysis of different algorithms for univariate forecasting and multivariate forecasting, where the multivariate scheme incorporates metadata such as charging time, greenhouse gas savings, and gasoline savings. The experimental results indicate the superiority of the multivariate scheme over the univariate forecasting. For multivariate forecasting, the gated recurrent unit (GRU) has outperformed other models such as categorical boosting (Catboost), recurrent neural network (RNN), long short‐term memory (LSTM), extreme gradient boosting (XGBoost), random forest, convolutional neural network (CNN), CNN + LSTM, and LSTM + LSTM. The results of this study emphasize the significance of using the GRU model for multivariate forecasting with metadata during normal and noisy scenarios to yield more reliable and accurate predictions. This approach enhances decision‐making, policy development, and efficient grid integration in the growing EV sector.

Najmul Alam

M. A. Rahman

Md. Rashidul Islam

M. J. Hossain

Machine learning‐based multivariate forecasting of electric vehicle charging station demand

基于机器学习的电动汽车充电站需求多变量预测

Abstract This paper investigates using deep reinforcement learning (DRL) methods for optimizing trustworthy federated learning models, with a focus on integrated sensing and communication in practical wireless sensing scenarios. Challenges include computational disparities among edge sensing nodes, network transmission differences, and the non‐independent and identically distributed (non‐IID) nature of local training datasets. As the number of edge sensing nodes increases, the likelihood of encountering untrusted nodes also rises, further limiting the performance of traditional federated learning aggregation algorithms. To address these issues, the paper proposes a DRL‐based strategy aimed at optimizing the node selection process in federated learning environments. This strategy intelligently selects nodes for global aggregation, improving overall model performance and efficiency by addressing computational and communication differences among nodes and the non‐IID nature of data.

Hao Zhang

Yi Jing

Wenhui Xu

Ronghui Zhang

Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems

基于深度强化学习的ISAC系统可信无线感知模型优化

Abbreviation	Electron. Lett.
Journal Impact	0.56
Quartiles(Global)	ENGINEERING, ELECTRICAL & ELECTRONIC(Q4)

ISSN	0013-5194, 1350-911X
h-index	159

Publication Information	Publisher: John Wiley & Sons Inc.，Publishing cycle: Semimonthly，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 1965，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 77.70%
Journal Fees	$2540Details
Average review cycle	网友分享经验：平均3.4个月
Average recruitment ratio	网友分享经验：50%

Electronics Letters

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