The 50 most-cited articles in Electronics Letters, 2024

Abstract The introduction of YOLOv9, the latest version of the you only look once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer‐assisted diagnosis to help radiologists and surgeons to interpret X‐ray images. Specifically, this paper trained the model on the GRAZPEDWRI‐DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50–95 of the current state‐of‐the‐art model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9‐Fracture‐Detection .

Abstract The introduction of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement, have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth‐intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With an increase in channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel compared to an ideal classical channel. This novel attempt at constructing a quantum communication‐based image transmission system indicates the potential of the approach to be applied to satisfy the ever‐increasing demands of high‐quality media transmission applications.

Abstract Telecommunications operators are tasked with enhancing service quality, reducing operational costs, and preserving customer privacy. This study presents an innovative application of large language models (LLMs) integrated with the LangChain technology framework, aimed at revolutionizing customer service in the telecom sector. The LangChain framework features a Knowledge Organizing Module and a Knowledge Search Module, both designed to refine customer support operations. The research develops an LLM‐based approach to improve the segmentation and organization of knowledge bases, tailored for the telecommunications industry. This approach ensures seamless integration with existing LLMs while preserving distinct knowledge domains, crucial for search accuracy. Additionally, the framework includes an advanced information security protocol with a robust filtering system that effectively excludes sensitive data from the model's outputs, enhancing data security. Empirical findings indicate that the ChatGLM2‐6B+LangChain model outperforms the baseline ChatGLM2, demonstrating…

Abstract This paper proposes a technique named self‐packaged metal integrated suspended line (MISL) to design a high Q factor bandpass filter (BPF). This unique construction simplifies the traditional suspended stripline and substrate integrated suspended line configuration and creates a loss‐reduced structure, allowing for the implementation of high Q ‐factor circuits. To demonstrate the advantages of the MISL platform, the shorted 3D metal stepped impedance resonators are first investigated using a miniaturized prototype based on this novel platform. Subsequently, a third‐order BPF based on MISL technology is designed, fabricated, and characterized. The fabricated BPF exhibits a minimum insertion loss (IL) of 0.31 dB, with centre frequencies of 5.88 GHz and 3‐dB fractional bandwidths of 19.8%, respectively.

Abstract The line of sight (LoS) probability is a key factor for the channel modeling of air‐to‐ground (A2G) communication. However, the existing LoS probability models do not account for the effects of airframe shadowing (AS) and building density, which can cause serious link obstruction and performance loss due to the six‐dimensional (6D) mobility and self‐body of unmanned aerial vehicle (UAV). In this article, a new LoS probability model is proposed that considers the AS and building density for different UAV heights. Adding to this, the AS is derived in terms of UAV framework and 6D mobility. Next, the machine learning (ML) based graph neural network (GNN) method is developed to learn the features and structure of the urban environment and predict the LoS probability. Then, the GNN model is trained and evaluated based on the ray tracing (RT) data to establish the relationship between model parameters and UAV heights under the building density and AS factors. The interpretation and explanation of the proposed GNN model and prediction are also discussed in this article. It is…

Abstract This study enhances the traditional sparrow search algorithm (SSA) to address its weaknesses, such as poor convergence rates and precision due to a tendency to fall into local optima. The improved version, OCSSA, integrates the Osprey optimization algorithm (OOA) and Cauchy mutation. Logistic chaotic mapping is used for initial population generation to increase genetic diversity. OOA boosts global exploration in the producer phase, and Cauchy mutation in the scroungers phase disrupts suboptimal solutions, enhancing the algorithm's ability to avoid local optima. OCSSA's performance, validated through ten benchmark functions and fault diagnosis optimization tasks, significantly improves convergence speed and accuracy, proving its effectiveness in complex optimization challenges.

Abstract Monitoring nocturnal animals in the field is an important task in ecological research and wildlife conservation, but the complexity of nocturnal images and low light conditions make it difficult to cope with traditional image processing methods. To address this problem, researchers have introduced infrared cameras to improve the accuracy of nocturnal animal behaviour observations. Object detection in nighttime images captured by infrared cameras faces several challenges, including low image quality, animal scale variations, occlusion, and pose changes. This study proposes the YOLOv8‐night model, which effectively overcomes these challenges by introducing a channel attention mechanism in YOLOv8. The model is more focused on capturing animal‐related features by dynamically adjusting the channel weights, which improves the saliency of key features and increases the accuracy rate in complex backgrounds. The main contribution of this study is the introduction of the channel attention mechanism into the YOLOv8 framework to create a YOLOv8‐night model suitable for object detection…

Abstract Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental…

Abstract High‐power applications with low‐voltage (LV) dc loads, for example, fast charging stations for electric vehicles (EVs), are typically supplied from the medium‐voltage (MV) grid. Aiming for low volume, MVac‐LVdc solid‐state transformers (SSTs) that provide galvanic separation with medium‐frequency transformers (MFTs) are thus considered, which are conventionally realized as two‐stage systems that consist of an ac‐dc power‐factor‐correction (PFC) rectifier and an isolated dc‐dc converter. This letter extends a new single‐stage isolated bidirectional PFC rectifier concept to MV levels, resulting in an SST that performs MVac‐LVdc conversion in a single converter stage and, unlike many modular SST concepts, employs only a single MFT. The SST's primary side operates modular‐multilevel‐converter (MMC) bridge legs, whose input voltages contain large ac components, in the quasi‐two‐level mode to minimize the cell capacitors. The secondary side employs a standard LV three‐phase rectifier, and a dual‐active‐bridge‐(DAB)‐like modulation strategy allows power flow regulation and…

Abstract The integration of renewable energy into power grid planning poses significant challenges. In this investigation, planning strategies across eight key indicators encompassing economic, reliability, environmental, and adaptability are assessed. Leveraging the analytical hierarchy process alongside the entropy weight method, subjective and objective weights are ascertained and comprehensive weights with parsimonious discriminative data are synthesized. Scores for planning strategies are then derived utilizing these weights. To illustrate and validate the efficacy of the proposed methodology, it was applied to four schemes within the IEEE‐6 node system, incorporating wind and photovoltaic power plants.

Abstract The authors report a ductile dicing process for manufacturing optical‐quality facets in a multi‐layered silicon nitride platform without the need for polishing. A surface roughness (Sa) of 1.5 nm was achieved. This technique was extended to fabricate ridge waveguides, and the results and characterization are reported.

Abstract High Resolution Range Profiles (HRRPs) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non‐cooperative circumstances. Currently, deep learning based models treat HRRPs as sequences, which may lead to ignorance of the internal relationship of range cells. This letter proposes HRRPGraphNet, a novel graph‐theoretic approach, whose primary innovation is the use of the graph‐theory of HRRP which models the spatial relationships among range cells through a range cell amplitude‐based node vector and a range‐relative adjacency matrix, enabling efficient extraction of both local and global features in noneuclidean space. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet's superior accuracy and robustness compared with existing methods, particularly in limited training sample condition. This underscores the potential of…

Abstract One of the most important challenges in an autonomous and robotics system is the path planning in which the system finds the optimal path from start point to goal point. The traditional path planning algorithms may have large memory requirements which scale with the size and resolution of the configuration space. To address these challenges, this paper introduces a novel path planning algorithm that combines Particle Swarm Optimization and Artificial Potential Field in the form of a path planning algorithm for mobile robots. The biological and physical concepts from Particle Swarm Optimization and Artificial Potential Field algorithms are combined to yield an algorithm which minimizes instances of getting stuck in local minima and generates a smooth but feasible path. The developed method requires memory which scales only with the number of particles and the time taken to reach the goal. This results in a memory‐efficient solution that generates smooth and feasible paths for mobile robots navigating in a 2D space.

Abstract There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time‐consuming and prone to error, especially at scale. There has thus been significant interest in automatic PCB component detection, particularly leveraging deep learning. While deep neural networks are able to perform such detection with greater accuracy, these networks typically require high computational resources, limiting their feasibility in real‐world use cases, which often involve high‐volume and high‐throughput detection with constrained edge computing resource availability. To bridge this gap between performance and resource requirements, PCBDet, an attention condenser network design that provides state‐of‐the‐art inference throughput while achieving superior PCB component detection performance compared to other state‐of‐the‐art efficient architecture designs, is introduced. Experimental results show that PCBDet can achieve up to 2× inference speed‐up on an ARM Cortex A72 processor when compared to an EfficientNet‐based design while achieving…

Abstract Wireless quantum optical communications over multipath channels with uncorrelated scattering still seem to be an open issue, although classical optical transmission was already considered, however, neglecting Bose–Einstein distributed thermal noise photons, leading to results that cannot be applied here. A viable way forward is proposed.

Abstract Promising synaptic behaviour has been exhibited by memristors based on natural organic materials. Such memristor‐based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non‐volatile storage capability, and bio/Complementary Metal‐Oxide‐Semiconductor (CMOS) compatibility. Here, a 256‐level honey memristor‐based neuromorphic system is experimentally evaluated for image recognition. In detail, first, 256‐level honey memristors are manufactured and tested based on in‐house technology; next, the non‐linear characteristics and inherent variation of honey memristor devices, which lead to imprecise weight updates and limit the inference accuracy, are investigated. Experimental results indicate that the inference accuracy of the 256‐level honey memristor‐based neuromorphic system is greater than 88% without cycle‐to‐cycle variations and 87% with cycle‐to‐cycle variations for different optimization algorithms. The overall performance of optimization algorithms with and without variation is compared in terms of…

Abstract 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…

Abstract A wearable energy harvester (EH) incorporating a face mask with a thermoelectric generator is demonstrated. The function of this device is to generate electrical power from the heat produced by the human body, particularly breath, with the specific aim of powering wearable sensor applications. A prototype was built using a commercially available N95 face mask, a thermoelectric generator, and a heatsink. The performance of this EH device was assessed using experimental and numerical methodologies. The experimentally tested power output of the prototype was found to be ≈100 µW, with a corresponding power density of ≈30 µW/cm 3 , for a temperature difference of 7°C.

Abstract A novel bidirectional broadside slot antenna array is proposed in this letter. The outstanding merits of this antenna array lie in the minimum width and the highest gain‐to‐area ratio (G/A) compared to previous works. The ingenious combination of the radiation mode and the transmission mode of slots is adopted, eliminating the design of the feed network. Thus, a simple and compact configuration is obtained. Moreover, two metal plates are added vertically at the sides of the slot array, with a more homogeneous radiation aperture and a higher G/A. With the 3D structure, a compact volume of the antenna array is ensured. For performance evaluation, our proof‐of‐concept design is fabricated with a cuboid dimension of 1.94 λ 0 × 0.12 λ 0 × 0.12 λ 0 , where λ 0 denotes the wavelength in free space at the centre frequency. An impedance bandwidth of 2.38 to 2.49 GHz is achieved, with a maximum gain of 9.7 dBi and a higher G/A of 39.5. The advantages including compact size and high G/A are paving the path for the potential application of communications in long and narrow spaces.

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…

Abstract Leveraging the ultra‐wideband advantages of the terahertz band, Integrated sensing and communication (ISAC) facilitates high‐precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g. radar cross‐section and micro‐Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analysing the power angular delay profiles with and without human target, the changes in quantity and power of multipath components (MPCs) are observed. Then, a parameter called power control factor is proposed to evaluate the human target impact on pathloss, thereby modifying the existing…

Abstract This paper presents experimental verification results for a finite‐sized array of a simple non‐reciprocal metasurface elements. The metasurface elements consists of ferrite loaded metal patches. Experimental results demonstrate nonreciprocity, achieving isolation exceeding 20 dB at 6.4 GHz in a 2×2 array. Additionally, these findings are compared with simulation results for a 4×4 array to validate its practicality in a finite array setting.

Abstract This letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting‐edge SLAM method, LIO‐SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature‐independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state‐of‐the‐art 3D SLAM technique. The convergence methods, namely “dynamic weight assignment” and “winner takes all”, are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method…

Abstract Occluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID .

Abstract Most previous works in the field of nonlinear memristive chaotic systems mainly focus on non‐material memristor while seldom on material memristor. In this letter, the model and the corresponding parameter identification of the Knowm memristor is presented. The chaotic circuit based on the Knowm memristor is designed. Equilibrium points and stability of this Knowm‐memristor‐based chaotic system is analyzed. Nonlinear dynamic behavior is presented by the bifurcation diagram and Lyapunov exponents. Finally, the hardware circuit of this Knowm‐memristor‐based chaotic system is designed and the experimental results are presented for confirmation.

Abstract In Wireless Sensor Networks (WSN), there are three conflicting important issues, including extensive coverage, low node redundancy, and energy consumption for secondary deployment. To balance network coverage and longevity, a WSN coverage model with three objectives is developed. A novel multi‐strategy gold rush optimizer is proposed for the coverage optimization problem in WSN. First, the fusion search method becomes extensive and flexible by combining the migration strategy with the Levy flight mechanism and joining a follower group. Then, the dynamic opposite learning strategy is used to increase population diversity in the panning stage. In addition, a multi‐player collaborative strategy is utilized to improve search ability of the algorithm in the collaboration stage. Finally, the random differential mutation method is used to try to assist the algorithm in escaping local optima. The simulation results show that this algorithm compared with other algorithms performs better on all three objectives considered.

Abstract To reduce the occurrence of information loss and distortion in image style transfer, a method is proposed for researching and designing image style transfer technology based on multi‐scale convolutional neural networks (CNNs) feature fusion. Initially, the VGG19 model is designed for coarse and fine‐scale networks to achieve multi‐scale CNN feature extraction of target image information. Subsequently, while setting the corresponding feature loss function, an additional least‐squares penalty parameter is introduced to balance the optimal total loss function. Finally, leveraging the characteristics of stochastic gradient descent iteration, image features are fused and reconstructed to obtain better style transfer images. Experimental evaluations utilize peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), information entropy (IE), and mean squared error (MSE) as metrics for assessing the transferred images, comparing them with three typical image style transfer methods. Results demonstrate that the proposed method achieves optimal performance across all…

Abstract This letter fully studies the coloured point cloud quality assessment in augmented reality (AR) environment through subjective test. Firstly, a point cloud dataset, named point cloud quality dataset‐AR, including ten reference point clouds and their 90 distorted versions is presented, which were generated using the reference software of video‐based point cloud compression across various combinations of geometry and texture quantization parameters. Then, the impact of geometry and texture distortions on perceived quality of point clouds in AR environment was discussed in detail. Moreover, the performance of existing objective point cloud quality assessment metrics on the proposed dataset is evaluated. The subjective dataset including the values of mean opinion score were released to public.

Abstract This letter presents a comprehensive analysis of the radiation behaviour exhibited by nine distinct on‐wafer radio‐freqeuncy (RF) probes used for device characterization in the mm‐wave bands of integrated circuits and antennas. The tested probes are sourced from two different manufacturers and feature various probe pitches. All probes are intended for use with the WR‐3.4 waveguide band ranging from 220 to 330 GHz. To the best of the authors' knowledge, such a detailed examination of RF probes operating in this band with respect to their radiation behaviour has not been demonstrated before.

Abstract M. Fossorier proposed how to determine the necessary and sufficient conditions for the existence of cycles in the Tanner graph of quasi‐cyclic LDPC (QC‐LDPC) codes, which has been widely investigated in the study of LDPC codes. This paper presents some new necessary and sufficient conditions for the existence of cycles with arbitrary lengths and proposes a simple and novel method for counting cycles of QC‐LDPC codes based on the improved conditions. Numerical results show that, compared with the existing methods, the presented method is effective and feasible and can enumerate cycles of QC‐LDPC codes in a cyclic‐shift way.

Abstract Satellite communication has rapidly advanced, from navigation to broadband Internet and direct mobile connectivity. Driven by low Earth orbit satellites, these networks are merging with terrestrial mobile networks, forming integrated space–air–ground systems. However, the growing scale of satellite constellations has intensified intra‐system interference, particularly among ground cells utilizing co‐frequency beams. A mitigation method based on switching array elements is proposed, optimizing their state by considering beamwidth and maximum sidelobe level. Simulations show that the proposed algorithm effectively reduces beam coverage widening and sidelobes, mitigating the interference between co‐frequency beams.

Abstract Path planning is the key part in the process of transportation conducted by logistics robots, and there often exist some problems with it. The path designed is not always smooth enough and its search efficiency is low, for example. As a common global path planning algorithm, A‐star is based on the traditional algorithm, which is unable to solve the problem of uneven path in the movement of logistics robots. Through improving the heuristic function of the traditional A‐star algorithm, weighing the heuristic function dynamically, removing the redundant points of the traditional star algorithm path with Floyd algorithm, and setting a safe distance to prevent the logistics robot from collision at the same time, the path is finally curved to be more appropriate to the movement path of the logistics robot. The MATLAB simulation of A‐star algorithm before and after the improvement shows that the turning points of the advanced A‐star algorithm reduced 61.5% on average compared to the traditional algorithm. The path length decreased 2.4% and the traversing points reduced 58.5%. At…

Abstract Visual defect recognition techniques based on deep learning models are crucial for modern industrial quality inspection. The backbone, serving as the primary feature extraction component of the defect recognition model, has not been thoroughly exploited. High‐performance vision transformer (ViT) is less adopted due to high computational complexity and limitations of computational resources and storage hardware in industrial scenarios. This paper presents LSA‐Former, a lightweight transformer architectural backbone that integrates the benefits of convolution and ViT. LSA‐Former proposes a novel self‐attention with linear computational complexity, enabling it to capture local and global semantic features with fewer parameters. LSA‐Former is pre‐trained on ImageNet‐1K and surpasses state‐of‐the‐art methods. LSA‐Former is employed as the backbone for various detectors, evaluated specifically on the PCB defect detection task. The proposed method reduces at least 18M parameters and exceeds the baseline by more than 2.2 mAP.

Abstract Because of the intermittency and randomness of wind power generation, constructing an accurate wind power generation forecasting model is of great necessity for stable operation and optimal scheduling of modern power systems. Considering the unsatisfied performance of the single learner model and the diverse learning abilities of different machine learning algorithms, XGBoost model, KNN algorithm, SVM algorithm, and CNN‐BiLSTM‐Attention neural network are integrated via blending ensemble architecture to construct the multi‐model fusion short‐term wind power forecasting model. Pearson correlation analysis is applied to reveal the interrelation between meteorological factors and wind power. Additionally, the training samples of base learners are reconstructed for ensuring all data can be utilized. The advantages of each learner are combined co‐ordinately via blending ensemble learning framework. Prediction results of ensemble learning model and single learner model are compared in the same scenario. Simulation results indicate that the ensemble learning model can effectively…

Abstract Renewable energy‐powered DC microgrids have emerged as a sustainable alternative for standalone power systems in remote locations, which were traditionally reliant on diesel generators (DIG) only. To ensure power quality and reliability, energy storage systems (ESS) and demand‐side management (DSM) techniques are employed, addressing the intermittent nature of renewable energy sources (RES). This manuscript presents a novel multi‐objective optimisation framework to determine the equipment sizing, depth of discharge (DoD) of ESS, and share of controllable loads contributing to DSM in a standalone DC microgrid incorporated with RES as a primary energy source and a backup DIG. The proposed optimisation strategy utilises genetic algorithm with the objectives of minimizing lifecycle cost and carbon footprint. A novel battery energy storage system (BESS) management criterion is introduced, which accounts for battery degradation in the lifecycle cost calculation. The minimum allowable DoD of the BESS is considered a decision variable in the optimisation problem to assess the…

Abstract The authors report a novel structure to improve the high‐frequency performance of nitrogen‐polar GaN deep‐recess high electron mobility transistors (HEMTs) wherein a timed plasma etch was used to tailor the profile of the gate dielectric. A favourable trade‐off between gate fringing capacitance and transconductance was established, resulting in an improved current gain cutoff frequency times gate length ( f t ·L G ) figure of merit compared to standard Schottky‐gate and metal‐insulator‐semiconductor (MIS) HEMTs. The etched gate dielectric MISHEMT demonstrated a maximum f t ·L G of 15.0 GHz·µm for an L G of 100 nm.

Abstract In recent years, Orthogonal Time Frequency Space Modulation (OTFS) has gained popularity in integrated sensing and communications (ISAC) system due to its robustness against Doppler offset and delay changes. Traditional pilot‐based methods for accurate channel parameter estimation are complex and struggle with rapidly changing channel conditions. In this letter, a deep encode‐decode network (DED‐Net) is proposed. It uses DL to automatically learn and eliminate channel interference from OTFS signals. The framework employs a deep encoding and decoding network, similar to a filter, learning complex signal features to effectively remove interference. Our experiments demonstrate DED‐Net's ability to eliminate interference in OTFS modulation signals, offering an alternative to pilot‐based methods and showcasing DL's potential for ISAC systems.

Abstract Here, a novel design of a microstructured quad‐band terahertz metamaterial absorber is presented consisting of a copper‐based merged circular and square split ring resonator‐integrated over a lead glass substrate intended to detect changes in the surrounding medium's refractive index for gas sensing applications through THz spectroscopy. Outstanding performance is shown by the suggested absorber, which achieves a maximum absorption rate of 99.4%, 98.7%, 99.2%, and 99.4%, at resonance frequencies of 8.055 (), 8.423 (), 9.257 (), and 9.507 () THz, respectively. The quality factors (Q‐factors) attained are 201, 129, 298, and 271 for these four absorption bands. The obtained sensitivity values at these four distinct bands are 3898, 2927, 4719, and 4102 GHz per refractive index unit with high figure of merits (FoM) of 97, 95, 152, and 117, respectively, throughout a range of refractive indices from n = 1.00 to n = 1.05 (with a step size of 0.005). The study's novelty lies in its novel design and remarkable ability to excel in gas sensing with high sensitivity and high FoM across…

Abstract Sampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi‐domain uniform sampling method (MDU‐sampling) for large‐scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the…

Abstract In scenarios with constrained physical aperture sizes, aiming to enhance the resolution and accuracy of Direction of Arrival (DOA) estimation, this paper proposes a novel approach that integrates a moving synthetic virtual array with Sparse Bayesian Learning (SBL) for DOA estimation. Initially, a virtual array is constructed based on the motion characteristics of the target. Subsequently, the SBL method is employed to estimate the DOA of the target. Simulation experiments validate the effectiveness of this approach, demonstrating comparable DOA estimation performance to synthetic aperture methods with larger aperture sizes, even in situations with limited aperture expansion. Furthermore, under constant virtual aperture expansion, this method surpasses non‐SBL methods regarding DOA resolution.

Abstract Deep learning has demonstrated notable success in mechanical signal processing with a large amount labelled data. However, the systems of the Jiaolong deep‐sea submersible prone to malfunction are typically diverse, due to the high complexity of its structure and operational environment. Consequently, this diversity gives rise to variations in the types of sensor signals and their associated data distributions that require analysis. Unsupervised domain adaptation (UDA) uses transferable knowledge derived from the source domain and applies it to an unlabelled target domain in order to improve the reusability of pre‐existing models and data. Inspired by the diffusion models that have the robust capabilities to transform data distributions across a large gap, we propose a novel diffusion‐based unsupervised domain adaptation (diffusion‐UDA) model, which leverages contrastive learning to alleviate the challenges of cross‐domain analysis for fault diagnosis within different systems of the deep‐sea submersible. Experimental results show the proposed method achieves…

Abstract Light‐field (LF) images offer the potential to improve feature capture in live scenes from multiple perspectives, and also generate additional normal vectors for performing super‐resolution (SR) image processing. With the benefit of machine learning, established AI‐based deep CNN models for LF image SR often individualize the models for various resolutions. However, the rigidity of these approaches for actual LF applications stems from the considerable diversity in angular resolution among LF instruments. Therefore, an advanced neural network proposal is required to utilize a CNN‐based model for super‐resolving LF images with different resolutions obtained from provided features. In this work, a preprocessing to calculate the depth channel from given LF information is first presented, and then a multiple‐decouple and fusion module is introduced to integrate the VGGreNet for the LF image SR, which collects global‐to‐local information according to the CNN kernel size and dynamically constructs each view through a global view module. Besides, the generated features are…

Abstract In this letter, we address the problem of designing the joint waveform of radar detection, wireless communication and jamming. In the current most powerful filter‐bank multi‐carrier comb spectrum framework, a parameter‐agile time–frequency structure between symbols is elaborated to improve the uncertainty of waveform generation architecture. Furthermore, a novel composite modulation strategy that combines intra‐symbol chaos‐based radar frequency modulation and inter‐symbol chaos‐based phase modulation is proposed for a substantial jamming performance improvement. Simulation results verify that the proposed joint waveform produces a superior jamming performance while satisfying the requirements of radar detection and wireless communication.

Abstract The integrated communication and jamming system is proposed by exploiting the diversity gain and flexible deployment of unmanned aerial vehicle (UAV)‐based distributed multiple input multiple output technology, which is expected to gain higher spectral efficiency and system scalability. This paper designs transmit beamforming of multiple UAVs so that they can simultaneously impose jamming signal to jamming targets and communication signal to communication users at the same frequency. The aim is to maximize the total jamming effect of all jamming targets while achieving a certain communication rate of communication users by optimizing the transmit beamforming matrices. Two cases are considered in which the UAV swarm works in clock synchronization and clock asynchronization. Specifically, the problem is formulated into a non‐convex optimization in the synchronous case. A near optimal solution is derived by decoupling the beamforming matrix into multi‐user interference suppression matrix and single‐user beamforming matrix. In the asynchronous case, the transmitter–receiver…

Abstract Energy information is vulnerable to malicious denial of service (DoS) attacks due to the diversity and openness of the smart grid environment. In order to cope with the above challenges, this paper first proposes to adopt Markov hopping model to describe the random packet loss of measurement due to DoS attacks. Then, based on Holt two‐parameter exponential smoothing and untraced Kalman filtering techniques, a one‐step predictive value compensation method for measurement data loss is proposed, and an improved dynamic untraced particle filtering algorithm based on data fusion compensation strategy is designed. Finally, an IEEE‐30 bus system is used to simulate the proposed dynamic state estimation method, which proves that the proposed method can effectively resist DoS attack.

Abstract Compared with the traditional two‐step localization methods, the direct position determination (DPD) method is more robust at low signal‐to‐noise ratio. However, to guarantee the optimal location result, the computational complexity of DPD with grid search is too high, especially for moving targets. Therefore, a new DPD method is proposed for moving targets with low computational complexity. First, using a proposed cost function, the position information is obtained from the received array signals directly, which enhances the accuracy of localization. Second, utilizing the method of successional difference the velocity is extracted, which avoid the multi‐dimensional grid search and reduces the computational complexity. Simulation results demonstrate that the proposed method reduces the computational complexity greatly and outperforms the conventional methods.

Abstract This article presents a novel control approach using a two‐degree‐of‐freedom (2‐DOF) Smith predictor structure for double integrating plus time delay plants. The PD and PID controllers, integral components of the Smith predictor scheme, serve distinct roles in the control process. The PD controller is responsible for set‐point tracking, while both PD and PID controllers contribute to the rejection of load disturbances. Each controller is designed using the pole placement technique, with an appropriate tuning parameter established to balance performance and robustness effectively. A low‐pass filter is incorporated to improve the response of the set‐point signal by attenuating high‐frequency noise. The proposed method's effectiveness is compared with Skogestad internal model control, a well‐known PID tuning method, to benchmark performance improvements and demonstrate the advantages of the 2‐DOF Smith predictor approach. Simulation and comparison analysis demonstrates that the proposed controller setting is suitable for industrial applications due to its quick set‐point…

Abstract This letter presents experimental angularly resolved measurements and a model framework for characterizing continuous wideband reflection coefficients at sub‐terahertz frequencies between 92 and 110 GHz. Surfaces that appear “smooth” but with internal features comparable to the wavelength have shown to cause frequency selective scattering. An n th‐degree polynomial regression model is employed to quantify non‐linear multi‐path scattering that cannot be described by a best‐fit line, with least‐squares regression applied to find the best‐fitting polynomial and hence the coefficients. The obtained coefficients are then validated against the delay domain statistics of the propagation channel, demonstrating the proposed model's good agreement with measurements and its efficiency in reproducing angle‐dependent reflection coefficients for use in ray‐tracing tools.

Abstract Retinopathy of prematurity (ROP) mainly occurs in premature infants with low birth weight, and it is the leading cause of childhood blindness. Early and accurate ROP diagnosis is imperative for appropriate treatment. However, less research concentrates on early‐stage ROP diagnosis based on limited‐labelled images in an imbalanced dataset. To address the dilemma, this study proposed a novel self‐supervised network, MOCO‐MIM, for early ROP grading. The proposed classification network was evaluated on a total of 553 labelled fundus images from 89 preterm infants. The trained network achieved a test accuracy of 98.29% and an AUC score of 97.6% for three stages of grading. The adopted method is verified that the proposed method can be detected early stages of ROP more efficiently and grade the severity more accurately based on limited‐labelled fundus images, which is superior to the existing state‐of‐the‐art methods.

Abstract The model‐based sensorless control of permanent magnet synchronous motor (PMSM) relies on the motor parameters. In practice, the considerable uncertainty of inductance and the difference between the apparent inductance and the incremental inductance exists. To overcome it, an online apparent inductance identification method for PMSM sensorless control is proposed. First, an initial incremental inductance table (IIT) is obtained offline. Then, an asymmetric high‐frequency square wave injection method is proposed to identify the incremental inductance at the operation point, and an adaptive minimum adjustment algorithm is proposed to adjust IIT automatically. Finally, the apparent inductance is calculated using the adjusted IIT. Based on it, the complete PMSM sensorless control is designed. The experimental results indicate this method is feasible and effective.