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Self-Decoupled Square Patch Antenna Arrays by Exciting and Using Mixed Electric/Magnetic Coupling Between Adjacent Radiators

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Chinese Journal of Electronics 2025年第2期34卷 483-494页

作者： Liu Qianwen Zhu Lei Lu Wenjun College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau 999078 China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing 210003 China

This article presents and develops a simple decoupling method for the planar square patch antenna arrays by virtue of mixed electric and magnetic coupling property. Since the resonant modes of TM10 and TM01 are a pair of degenerate modes in the square patch radiator which are intrinsically orthogonal, a superposed mode of them can be generated to possess consistent field distributions along all the four sides of the patch by adjusting the feeding position. By employing such superposed mode, the mutual coupling between two horizontally adjacent patch elements will become identical to that between two vertical ones, indicating an expected possibility that the complex 2-D decoupling problem in a large-scale antenna patch array can be effectively facilitated and simplified to a 1-D one. Subsequently, metallic pins and connecting strip are properly loaded in each square patch resonator, such that appropriate electric and magnetic coupling strengths can be readily achieved and thus the mutual coupling can get highly decreased. A 1 × 2 antenna array with an edge-to-edge separation of 1 mm, which corresponding to 0.0117λ0, is firstly discussed, simulated, and fabricated. The measured results show that the isolation can be highly improved from 4 dB to 17 dB across the entire passband. In final, 1 × 3, 2 × 2, and 4 × 4 antenna array prototypes are constructed and studied for verification of the expansibility and feasibility of the proposed decoupling method to both linear and 2-D antenna arrays.

关键词： Large-scale antenna array Mutual coupling Mixed electric and magnetic coupling effect Self-decoupling Square patch antenna

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A Logical Design of Internet of Things Assisted Digital Health Record Monitoring System using Deep Learning Principles

A Logical Design of Internet of Things Assisted Digital Heal...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gayathri, S. Vijayanandh Thirumoorthy, P. Valarmathy, A.S. Pandi, V. Samuthira Shobana, D. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350308167

Intelligent systems for monitoring health have been made possible by the IoT and the mental/physical health of an individual may be monitored using such a system. Determinants of numerous diseases, both physical and mental include tension, worry and high blood pressure. Problems associated with aging including anxiety, depression, stress and pressure require specialized care in this context. The prompt identification of health issues by monitoring of nervousness tension, stress, and heart rate is a key to avoiding irreversible harm. This will improve health outcomes;lessen the burden on caregivers, and save money. Find innovative technological answers for continuous monitoring of stress, anxiety, and blood pressure through the use of covert embedded sensors as well as deep learning methods. As a result of the suggested smart health monitoring system, health monitoring services may now be provided quickly, cheaply, and reliably from remote places, whereas this was not previously achievable with conventional health care systems. The implementation of a blockchain-based framework has increased the safety and privacy of patients' private information and reduced the likelihood of it being misused. Using Deep Learning and related technologies to evaluate health data for numerous goals has aided in the achievement of both preventative healthcare and fatality control. This has allowed for the early diagnosis of chronic illnesses, which was previously impossible. The combination of computing resources in the cloud and electronic storage has been introduced in order to render operations cheaper and instantaneously. This research provides a comprehensive overview of the state of health monitoring technology, including the most up-to-date developments and the most pressing problems. © 2023 IEEE.

关键词： Deep Learning Health Record Monitoring Healthcare Internet of Things IoT Sensors

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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Development of a Converter-based Supercapacitor System Emulator for PV Applications

Development of a Converter-based Supercapacitor System Emula...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Kritprajun, Paychuda Sutton, Elizabeth Tolbert, Leon M. Liu, Yunting Wang, Jingxin Praisuwanna, Nattapat Ferrari, Maximiliano The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States The Pennsylvania State University Department of Electrical Engineering University ParkPA United States Grid Components and Control Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350316445

As the utilization of supercapacitors in power system applications continues to increase, it is important to observe their behavior under transient and long-term operations in order to understand their impact on power grids. A real-time reconfigurable hardware testbed (HTB) is a power network emulator that provides flexibility to study various power system scenarios. This paper presents an emulation of a supercapacitor for a photovoltaic (PV) system on the HTB platform such that its dynamic behavior during power system scenarios can be observed. The developed emulator on the HTB is verified by comparing the emulation results with the model developed in MATLAB/Simulink. The experimental results of the emulator are consistent with the simulation results under the grid support scenarios. This supercapacitor emulator can be potentially used for various power system scenarios in addition to the PV applications presented in this paper. © 2023 IEEE.

关键词： grid ancillary services grid-connected photovoltaic system hardware testbed supercapacitor

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Convolution-Transformer for Image Feature Extraction

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computer Modeling in engineering & sciences 2024年第10期141卷 87-106页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

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Search for Extraterrestrial Intelligence as One-Shot Hypothesis Testing

Search for Extraterrestrial Intelligence as One-Shot Hypothe...

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Annual Conference on Information sciences and Systems (CISS)

作者： Ian George Xinan Chen Lav R. Varshney Department of Electrical and Computer Engineering Coordinated Science Laboratory Urbana IL USA

Both the search for extraterrestrial intelligence (SETI) and messaging extraterrestrial intelligence (METI) struggle with a strong indeterminacy in what data to look for and when to do so. This has led to attempts at finding both fundamental mathematical limits for SETI as well as benchmarks regarding specific signals. Due to the natural correspondence, previous information-theoretic work has been formulated in terms of communication between extraterrestrial and human civilizations. In this work, we instead formalize SETI as a detection problem, specifically (quantum) one-shot asymmetric hypothesis testing. This framework holds for all detection scenarios-in particular, it is relevant for detection of any technosignature, including quantum mechanical signals. To the best of our knowledge, this is the first work to consider the applicability of SETI for quantum signals. Using this formalism, we are able to unify the analysis of fundamental limits and benchmarking specific signals. To show a distinction between METI and SETI, we show that significantly weaker signals may be useful in detection in comparison to communication. Furthermore, the framework is computationally efficient, so it can be implemented by practicing astrobiologists.

关键词： Quantum computing Quantum mechanics Focusing Benchmark testing Computational efficiency Standards Information theory

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models 38

FIRE: A Dataset for Feedback Integration and Refinement Eval...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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Distributed Offloading in Multi-Access Edge Computing Systems: A Mean-Field Perspective

arXiv

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arXiv 2025年

作者： Aggarwal, Shubham Zaman, Muhammad Aneeq uz Bastopcu, Melih Ulukus, Sennur Başar, Tamer Coordinated Science Laboratory The Department of Mechanical Science and Engineering The University of Illinois at Urbana-Champaign [UIUC United States Analog Devices Inc United States Department of Electrical and Electronics Engineering Bilkent University Turkey Department of Electrical and Computer Engineering University of Maryland College Park United States Coordinated Science Laboratory The Department of Electrical and Computer Engineering UIUC United States

With the widespread adoption of internet-of-things (IoT) devices capable of supporting numerous intelligent applications, the demand for computational power has surged dramatically. Multi-access edge computing (MEC) technology is a promising solution to assist the often power-constrained IoT devices by providing additional computing resources for time-sensitive tasks. In this paper, we consider the problem of optimal task offloading in MEC systems with due consideration of the timeliness and scalability issues under two scenarios of equitable and priority access to the edge server (ES). In the first scenario, we consider a MEC system consisting of N devices assisted by one ES, where the devices can split task execution between a local processor and the ES, with equitable access to the ES. In the second scenario, we consider a MEC system consisting of one primary user, N secondary users and one ES. The primary user has priority access to the ES while the secondary users have equitable access to the ES amongst themselves. In both scenarios, due to the power consumption associated with utilizing the local resource and task offloading, the devices must optimize their actions. Additionally, since the ES is a shared resource, other users’ offloading activity serves to increase latency incurred by each user. We thus model both scenarios using a large user non-cooperative game framework. However, the presence of a large number of users makes it nearly impossible to compute the equilibrium offloading policies for each user, which would require a significant communication overhead to exchange information with each other. Thus, to alleviate such scalability issues, we invoke the paradigm of mean-field games to design completely distributed low complexity algorithms for the computation of approximate Nash equilibrium policies for each user based on only their local information. Further, by leveraging the novel age of information (AoI) metric, we study the trade-offs between inc

关键词： Computation offloading

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Dynamic counterpropagating all-normal dispersion(DCANDi)fiber laser

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Photonics Research 2024年第9期12卷 2033-2046页

作者： NEERAJ PRAKASH JONATHAN MUSGRAVE BOWEN LI SHU-WEI HUANG Department of Electrical Computerand Energy EngineeringUniversity of Colorado BoulderBoulderColorado 80309USA Key Laboratory of Optical Fiber Sensing and Communications(Ministry of Education) University of Electronic Science and Technology of ChinaChengdu 611731China

The fiber single-cavity dual-comb laser(SCDCL)is an emerging light-source architecture that opens up the possibility for low-complexity dual-comb pump-probe ***,the fundamental trade-off between measurement speed and time resolution remains a hurdle for the widespread use of fiber SCDCLs in dual-comb pump-probe *** this paper,we break this fundamental trade-off by devising an all-optical dynamic repetition rate difference(Δf_(rep))modulation *** demonstrate the dynamicΔf_(rep) modulation in a modified version of the recently developed counterpropagating all-normal dispersion(CANDi)fiber *** verify that our all-optical dynamicΔf_(rep) modulation technique does not introduce excessive relative timing *** addition,the dynamic modulation mechanism is studied and validated both theoretically and *** a proof-of-principle experiment,we apply this so-called dynamic CANDi(DCANDi)fiber laser to measure the relaxation time of a semiconductor saturable absorber mirror,achieving a measurement speed and duty cycle enhancement factor of *** fiber laser is a promising light source for low-complexity,highspeed,high-sensitivity ultrafast dual-comb pump-probe measurements.

关键词： fiber pump laser

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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

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computers, Materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia Department of CS University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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