Electronics Letters最新文献_第8页

FPGA Implementation of a Chopper Controller in PV Systems for IoT Applications 物联网应用光伏系统斩波控制器的FPGA实现

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-24 DOI: 10.1049/ell2.70364

Keyvan Barati, Abdolsamad Hamidi

{"title":"FPGA Implementation of a Chopper Controller in PV Systems for IoT Applications","authors":"Keyvan Barati, Abdolsamad Hamidi","doi":"10.1049/ell2.70364","DOIUrl":"10.1049/ell2.70364","url":null,"abstract":"This work presents the design and simulation of an Field-Programmable Gate Array (FPGA) based digital controller for a micro-power photovoltaic (PV) energy harvesting system tailored to Internet of Things (IoT) applications. The proposed system features a DC-DC boost converter with an H-bridge topology, designed to interface effectively with low-power PV sources and deliver regulated output to IoT loads. A constant voltage maximum power point tracking (CV-MPPT) algorithm is implemented to maintain optimal energy extraction under variable irradiance. The controller dynamically adjusts the chopping frequency, duty cycle, and output voltage. Leveraging the parallelism and reconfigurability of FPGAs, a high-speed digital control architecture is developed and synthesised on a Cyclone IV FPGA platform. Compared to conventional microcontroller or DSP-based solutions, this approach offers significantly improved response time, power efficiency, and scalability. The final implementation achieves a chip area of 0.92 mm2, a control loop delay of 106 ms, and an overall efficiency of 92.36%, demonstrating its advantages over existing designs in terms of compactness, performance, and energy efficiency.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of objects using their natural resonant complex frequencies under low signal-to-noise ratio conditions 在低信噪比条件下利用物体的自然共振复频率进行识别

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-24 DOI: 10.1049/ell2.70152

Yonggang Li, Haoyi Zhang, Yongjing Zhou, Weigang Zhu, Suqin Wu

引用次数: 0

Longitudinal and Feedforward Controller Design Based on Linear Matrix Inequalities for a Mini Aerial Vehicle 基于线性矩阵不等式的小型飞行器纵向前馈控制器设计

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-24 DOI: 10.1049/ell2.70365

Saeedreza Tofighi

{"title":"Longitudinal and Feedforward Controller Design Based on Linear Matrix Inequalities for a Mini Aerial Vehicle","authors":"Saeedreza Tofighi","doi":"10.1049/ell2.70365","DOIUrl":"10.1049/ell2.70365","url":null,"abstract":"This paper presents the design of a multi-input multi-output (MIMO) optimal proportional–integral–derivative (PID) controller and a feedforward controller to enhance disturbance rejection and reference tracking in a mini aerial vehicle (MAV). The feedback controller is developed using linear matrix inequality (LMI) constraints within an iterative framework for tuning the PID parameters. In addition, a feedforward control strategy is proposed to further improve performance in reference tracking and disturbance rejection. The feedforward controller is designed under LMI constraints to minimise the H∞ norm of the transfer function matrix relating disturbances (or set-points) to the output (error). This optimisation problem is challenging due to its non-convex and nonlinear nature. Therefore, the main contribution of this paper is to propose a method based on utilising frequency sampling techniques, iterative algorithms and convex optimisation formulation to guarantee optimal solutions for reference tracking and disturbance rejection within specified constraints, which is crucial for preventing irreversible damage in control systems and also enhances the manoeuvrability of the MAV.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CFIPC-YOLO for Concealed Object Detection in Millimetre Wave Images 基于CFIPC-YOLO的毫米波图像隐藏目标检测

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-22 DOI: 10.1049/ell2.70367

Huakun Zhang, Lin Guo, Deyue An, Odbal

引用次数: 0

Three-Dimensional Electrical Capacitance Tomography Based on a Novel Planar Array Capacitance Sensor 基于新型平面阵列电容传感器的三维电容层析成像

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-20 DOI: 10.1049/ell2.70340

Zhiheng Guo, Jiaming Ren

引用次数: 0

Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack 基于白盒攻击的信号检测网络信号对抗示例生成

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-15 DOI: 10.1049/ell2.70348

Dongyang Li, Linyuan Wang, Guangwei Xiong, Jinxian Peng, Dekui Ma, Bin Yan

{"title":"Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack","authors":"Dongyang Li, Linyuan Wang, Guangwei Xiong, Jinxian Peng, Dekui Ma, Bin Yan","doi":"10.1049/ell2.70348","DOIUrl":"10.1049/ell2.70348","url":null,"abstract":"With the development and application of deep learning in signal detection tasks, the vulnerability of neural networks to adversarial attacks has also become a security threat to signal detection networks. This paper defines a signal adversarial examples generation model for signal detection network from the perspective of adding perturbations to the signal. The model uses the inequality relationship of L2-norm between time domain and time-frequency domain to constrain the energy of signal perturbations. Building upon this model, we propose a method for generating signal adversarial examples utilizing gradient-based attacks and inverse short-time Fourier transform. The experimental results show that under the constraint of signal perturbation energy ratio less than 3<math>\u0000 <semantics>\u0000 <mo>%</mo>\u0000 <annotation>$%$</annotation>\u0000 </semantics></math>, our adversarial attack resulted in a 34.5<math>\u0000 <semantics>\u0000 <mo>%</mo>\u0000 <annotation>$%$</annotation>\u0000 </semantics></math> reduction in the mean average precision (mAP), a 31.6<math>\u0000 <semantics>\u0000 <mo>%</mo>\u0000 <annotation>$%$</annotation>\u0000 </semantics></math> reduction in recall, and a 37.5<math>\u0000 <semantics>\u0000 <mo>%</mo>\u0000 <annotation>$%$</annotation>\u0000 </semantics></math> reduction in precision of the signal detection network. Compared to random noise perturbation of equivalent intensity, our adversarial attack demonstrates a significant attack effect.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MOE-IR: Infrared Dim Small Target Detection Method With Mixture of Experts Feature Extraction 基于混合专家特征提取的红外弱小目标检测方法

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-15 DOI: 10.1049/ell2.70359

Zhengkui Weng, Xinjie Fu, Xu Zhang, Siyuan Sun

{"title":"MOE-IR: Infrared Dim Small Target Detection Method With Mixture of Experts Feature Extraction","authors":"Zhengkui Weng, Xinjie Fu, Xu Zhang, Siyuan Sun","doi":"10.1049/ell2.70359","DOIUrl":"10.1049/ell2.70359","url":null,"abstract":"Infrared small target detection holds significant importance across various domains, including military and security applications. Nevertheless, detecting small targets is highly challenging due to their minimal pixel presence in images, indistinct features, and complex backgrounds. Existing detection methods are often interfered by complex backgrounds, resulting in unsatisfactory detection results. To overcome this challenge, this study introduces the mixture of experts infrared small target detection (MOE-IR) method. The core idea of this method is to construct a mixture of experts feature extraction network to perform rich feature extraction and complex background suppression on small targets, respectively, so as to achieve robust infrared small target detection in complex backgrounds. Specifically, the MOE-IR comprises a target feature extraction expert and a background suppression expert. The target feature extraction expert focuses on enhancing the features of infrared small targets, while the background suppression expert aims to mitigate background clutter. Additionally, an adaptive gate controlled network is incorporated to dynamically assign weights to the two experts based on the input infrared image, ensuring effective detection of infrared small targets across diverse and complex scenarios. Extensive experiments demonstrate that the proposed algorithm surpasses existing infrared small target detection methods in terms of detection accuracy and false alarm rate. It can reliably and stably identify small targets within infrared images, thus offering an effective solution for practical infrared small target detection applications.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Least Squares-Based Near-Field Source Localisation With Enhanced Range Estimation 基于最小二乘的增强距离估计近场源定位

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-12 DOI: 10.1049/ell2.70361

TaeJin Jung, Geunhwan Kim

引用次数: 0

Quantum Implementation of S-Boxes Based on Polynomial Evaluation 基于多项式求值的s - box量子实现

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-11 DOI: 10.1049/ell2.70337

Doyoung Chung, Seungkwang Lee

引用次数: 0

Global Context Attention in YOLO for Broadband Signal Identification 宽带信号YOLO识别中的全局关注

IF 0.8 4区工程技术

Electronics Letters Pub Date : 2025-07-11 DOI: 10.1049/ell2.70358

Xiaoya Wang, Songlin Sun, Haiying Zhang

{"title":"Global Context Attention in YOLO for Broadband Signal Identification","authors":"Xiaoya Wang, Songlin Sun, Haiying Zhang","doi":"10.1049/ell2.70358","DOIUrl":"10.1049/ell2.70358","url":null,"abstract":"Detecting broadband communication signals is crucial for signal processing, as its accuracy directly impacts subsequent processing such as information restoration. However, due to the ever-growing complexity of the electromagnetic environment, traditional broadband detection algorithms are no longer suitable for current application needs. This paper proposes a broadband signal detection model, NLYOLO. We improve the precision of the image by analysing the characteristics of the target signal by selecting suitable parameters. We also select anchors adapted to the special shape of the target signals and introduce attention modules Non-local and SimAM in the feature extraction of NLYOLO and improve the loss function. This enhances the ability of high-level semantic information feature extraction and improves the precision rate and recall of small target signals. The experimental results show that compared with the traditional energy detection algorithm and the baseline model, the detection precision and recall rate are significantly improved, which effectively achieves the target detection task in the field of signal processing in complex scenes.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0