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Low-profile,low sidelobe array antenna with ultrawide beam coverage for UAV communication

Chinese Journal of Aeronautics 2025年第1期38卷 444-454页

作者： Mei LI Zhiliang SHANG Lin PU Ming-Chun TANG Lei ZHU School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China Department of Electrical and Computer Engineering University of MacaoMacao SAR 999078China

This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage,low profile,and low Sidelobe Level(SLL)for the application of Unmanned Aerial Vehicle(UAV)air-to-ground *** array consists of ten broadside-radiating,ultrawide-beamwidth elements that are cascaded by a central-symmetry series-fed network with tapered currents following Dolph-Chebyshev distribution to provide low ***,an innovative design of end-fire Huygens source antenna that is compatible with metal ground is presented.A low-profile,half-mode Microstrip Patch Antenna(MPA)is utilized to serve as the magnetic dipole and a monopole is utilized to serves as the electric dipole,constructing the compact,end-fire,grounded Huygens source ***,two opposite-oriented end-fire Huygens source antennas are seamlessly integrated into a single antenna element in the form of monopole-loaded MPA to accomplish the ultrawide,broadside-radiating *** consideration has been applied into the design of series-fed network as well as antenna element to compensate the adverse coupling effects between elements on the radiation *** indicates an ultrawide Half-Power Beamwidth(HPBW)of 161°and a low SLL of-25 dB with a high gain of 12 d Bi under a single-layer *** concurrent ultrawide beamwidth and low SLL make it particularly attractive for applications of UAV air-to-ground communication.

关键词： Array antenna Broad beam Fan beam Low sidelobe level Chebyshev synthesis UAV communication

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The Guesswork of Ordered Statistics Decoding: Guesswork Complexity and Decoder Design

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IEEE Transactions on Information Theory 2025年第6期71卷 4167-4192页

作者： Yue, Chentao She, Changyang Vucetic, Branka Li, Yonghui University of Sydney School of Electrical and Computer Engineering NSW Sydney2006 Australia School of Electronic and Information Engineering Shenzhen China

This paper investigates guesswork over ordered statistics and formulates the achievable guesswork complexity of ordered statistics decoding (OSD) in binary additive white Gaussian noise (AWGN) channels. The achievable guesswork complexity is defined as the number of test error patterns (TEPs) processed by OSD immediately upon finding the correct codeword estimate. The paper first develops a new upper bound for guesswork over independent sequences by partitioning them into Hamming shells and applying Hölder’s inequality. This upper bound is then extended to ordered statistics, by constructing the conditionally independent sequences within the ordered statistics sequences. Next, we apply these bounds to characterize the statistical moments of the OSD guesswork complexity. We show that the achievable guesswork complexity of OSD at maximum decoding order can be accurately approximated by the modified Bessel function, which increases exponentially with code dimension. We also identify a guesswork complexity saturation threshold, where increasing the OSD decoding order beyond this threshold improves error performance without further raising the achievable guesswork complexity. Finally, the paper presents insights on applying these findings to enhance the design of OSD decoders. © 1963-2012 IEEE.

关键词： Bessel functions

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Max-Min Secrecy Rate and Secrecy Energy Efficiency Optimization for RIS-Aided VLC Systems: RSMA Versus NOMA

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年 6卷 1303-1316页

作者： Maraqa, Omar Aboagye, Sylvester Khoshafa, Majid H. Ngatched, Telex M. N. McMaster University Department of Electrical and Computer Engineering Hamilton Canada University of Guelph School of Engineering Guelph Canada

Integrating visible light communication (VLC) with the reconfigurable intelligent surface (RIS) significantly enhances physical layer security by enabling precise directional signal control and dynamic adaptation to the communication environment. These capabilities strengthen the confidentiality and security of VLC systems. This paper presents a comprehensive study on the joint optimization of VLC access point (AP) power allocation, RIS association, and RIS elements orientation angles for secure VLC systems, while considering rate-splitting multiple access (RSMA) and power-domain non-orthogonal multiple access (NOMA) schemes. Specifically, two frameworks are proposed to maximize both the minimum secrecy rate (SR) and the minimum secrecy energy efficiency (SEE) by jointly optimizing power allocation, RIS association, and RIS elements orientation angles for both power-domain NOMA and RSMA-based VLC systems. The proposed frameworks consider random device orientation and guarantee the minimum user-rate requirement. The proposed optimization frameworks belong to the class of mixed integer nonlinear programming, which has no known feasible solution methodology to guarantee the optimal solution. Moreover, the increased degree of freedom and flexibility from the joint consideration of power control, RIS association and element orientation results in a large set of decision variables and constraints, which further complicates the optimization problem. To that end, we utilize a genetic algorithm-based solution method, which through its exploration and exploitation capabilities can obtain a good quality solution. Additionally, comprehensive simulations show that the RSMA scheme outperforms the power-domain NOMA scheme across both the SR and SEE metrics over various network parameters. Furthermore, useful insights on the impact of minimum user rate requirement, number of RIS elements, and maximum VLC AP transmit power on the minimum SR and SEE performances are provided. © 2020

关键词： Integer programming

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Bidding Strategy for Hybrid PV-BESS Plants via Knowledge-data-complementary Learning

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 365-378页

作者： Xiaoge Huang Zhenhuan Ding Zhao Liu Tianqiao Zhao Pei Zhang Xiaojun Wang Department of Electrical and Computer Engineering State University of New York at BinghamtonBinghamtonUSA School of Artificial Intelligence Anhui UniversityHefei 230601China School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China Brookhaven National Lab UptonUSA School of Electrical Automation and Information Engineering Tianjin UniversityTianjin 300072China

The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power ***,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead *** paper proposes two coherent models to address these ***,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation ***,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast *** role of the DEQ model is to guide the knowledgedriven bidding ***,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational *** two models have independent optimizations based on misaligned *** address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding *** proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).

关键词： Hybrid power plant(HPP) photovoltaic(PV) battery energy storage system(BESS) optimization uncertainty bidding strategy error quantification knowledge-data-complementary learning

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi'an University of Technology School of Electrical Engineering Xi'an Jiaotong University Smart Grid Research Institute Nanjing Institute of Technology Department of Energy Aalborg University

To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles' degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM) network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Autoencoder Battery degradation trajectory Early prediction Synthetic data generation

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Improving Machine Translation Formality with Large Language Models

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computers, Materials & Continua 2025年第2期82卷 2061-2075页

作者： Murun Yang Fuxue Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Electrical Engineering Yingkou Institute of TechnologyYingkou115014China

Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.

关键词： Neural machine translation formality large language model text style transfer style evaluation reinforcement learning

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Analytic Models for the Capacity Distribution in MDG-impaired Optical SDM Transmission

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Journal of Lightwave Technology 2025年第12期43卷 5558-5568页

作者： Zischler, Lucas Mello, Darli A. A. State University of Campinas School of Electrical and Computer Engineering Campinas13083-970 Brazil

In coupled space-division multiplexing (SDM) transmission systems, imperfections in optical amplifiers and passive devices introduce mode-dependent loss (MDL) and gain (MDG). These effects render the channel capacity stochastic and result in a decrease in average capacity. Several previous studies employ multi-section simulations to model the capacity of these systems. Additionally, relevant works derive analytically the capacity distribution for a single-mode system with polarization-dependent gain and loss (mode count $D=2$). However, to the best of our knowledge, analytic expressions of the capacity distribution for systems with $D>2$ have not been presented. In this paper, we provide analytic expressions for the capacity of SDM optical systems affected by MDG with arbitrary mode counts. The expressions rely on Gaussian approximations for the per-mode capacity distributions and for the overall capacity distribution, as well as on fitting parameters for the capacity cross-correlation among different modes. Compared to simulations, the derived analytical expressions exhibit a suitable level of accuracy across a wide range of practical scenarios. © 1983-2012 IEEE.

关键词： Stochastic systems

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Robust federated contrastive recommender system against targeted model poisoning attack

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Science China(Information Sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Combined Architecture of Destination Sequence Distance Vector (DSDV) Routing with Software Defined Networking (SDN) and Blockchain in Cyber-Physical Systems

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computers, Materials & Continua 2025年第2期82卷 2311-2330页

作者： Jawad Ahmad Ansari Mohamad Khairi Ishak Khalid Ammar School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong TebalPulau Pinang11800Malaysia Department of Electrical and Computer Engineering College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates

Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and monitoring system using switches and controllers that will route the packets through the network, identify any false nodes, take preventive measures against them and preventing them for any future problems. Next, the system is powered by Blockchain cryptographic features by utilizing the TrustChain features to create a private, secure, and temper-free ledger of the transactions performed inside the network. Results are achieved in the legitimate devices connecting to the network, transferring their packets to their destination under supervision, reporting whenever a false node is causing hurdles, and recording the transactions for temper-proof records. Evaluation results based on 1000+ transactions illustrate that the proposed mechanism not only outshines most aspects of Cyber-Physical systems but also consumes less computation power with a low latency of 0.1 seconds only.

关键词： DSDV intelligent authentication SDN control&monitoring blockchain recording of transactions

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Multi-Modal Self-Supervised Learning AlgorithmBased Product Recommendation

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15371-15380页

作者： Gao, Li Fan, Haiping Chen, Qingkui Yang, Heyu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

With the rise of e-commerce, personalized recommendation algorithms have received much attention in recent years. Meanwhile, multimodal recommendation algorithms have become the next competitive track of personalized recommendation. The current mainstream recommendation algorithms (e.g., NeuMF, NGCF) rely too much on empirical data and do not effectively utilize multimodal information to model user preferences, while there is the problem of sparsity of user preference data. Secondly, they are still limited to using the same weights to fuse the item features of different modalities, and have the problem of inaccurate recommendation. For this reason, this paper proposes Multi-Modal Self-Supervised Learning Algorithm Based Product Recommendation (MMSLRec). (1) Data augmentation by self-supervised adversarial perturbation generation learning is performed to supplement the missing interaction information of tailed products to solve the common data sparsity problem in product recommendation. (2) A multimodal representation learning module is introduced to capture deep features in image and text modalities using AlexNet and Bert, respectively, in order to avoid the recommendation accuracy degradation problem caused by insufficient data feature learning. (3) Learning the user's perceived strength in different modalities, using the user co-occurrence matrix to fuse the features and make recommendations. Experiments on Amazon-Electronics, Amazon-Clothing, and Amazon-Baby, with different degrees of data sparsity verify the effectiveness of our MMSLRec in solving the data sparsity problem and the multimodal user preference modeling problem, and compared to the state-of-the-art baseline recommendation model on the three datasets, the Recall@20 improved by 9.26%, 3.11% and 2.92%, respectively. © 2004-2012 IEEE.

关键词： Self-supervised learning

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