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An X-Band Frequency Beam-Scanning Circularly-Polarized Leaky-Wave Antenna with Low Axial Ratio in All Half-Power Beamwidth Ranges

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Progress In Electromagnetics Research C 2024年 139卷 129-137页

作者： Zhang, Dezhuang Wang, Zhongbao Xu, Zhixia Liu, Hongmei Gao, Mingming Fang, Shaojun School of Information Science and Technology Dalian Maritime University Liaoning Dalian116026 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao Liaoning 125105 China

A novel frequency beam-scanning circularly-polarized leaky-wave antenna with the axial ratio (AR) of less than 3 dB in all the half-power beamwidth ranges based on an H-shaped slow-wave transmission line is proposed for X-band applications. The proposed circularly-polarized leaky-wave antenna is composed of a novel slow-wave transmission line and two rows of elliptical patch structures, leading to wide AR bandwidth, backward-to-forward beam scanning, and stable gain. To verify the proposed antenna, one prototype with a center frequency of 10 GHz is designed and fabricated. Measured results indicate that the main beam scans from −25◦ to +28◦ within the operating frequency variation from 8.7 to 11.5 GHz. The 3-dB AR bandwidth and maximum gain are 27.7% and 10.28 dBic, respectively. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Slot antennas

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Random Security Generators with Enhanced Cryptography for Cybersecurity in Financial Supply Chains 1

Random Security Generators with Enhanced Cryptography for Cy...

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1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thammareddi, Latha Anumolu, Venkateswara Rao Kotte, Kulasekhara Reddy Chowdari Marella, Bhagath Chandra Arun Kumar, K. Bisht, Jatin India ERP Solutions-Information Technology India SLRI Solutions INC India Insights & Data Department of Financial Services India Big Data Architect India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science & Engineering Almora India

ISBN: (纸本)9798331518578

Blockchains, like Ethereum, depend on a distributed system of collaborative servers referred to as inspectors or mine workers to validate transactions and generate novel legal blocks. In today's electronic world, it is critical to secure financial supply chains (FSCs) from complex cyber-attacks. This study combines Quantized Random Security Generators (QRSGs) with Artificial Intelligence (AI) to improve cybersecurity in FSC. QRSGs use quantum physics to produce random outcomes, which is critical for establishing safe cryptography credentials and resolving the flaws of regular predictable RSGs. This study found that the QRSG-based approach outperformed traditional RSG methods. The QRSG demonstrated strong entropy and uncertainty, transferring both NIST SP800-22 and Diehard examinations at much greater levels. The platform's real-time safety features were improved by the AI element's outstanding performance measures in identifying security breaches. More rapid decoding and encryption times, reduced delay, and increased resilience to brute-force, quantum, and prediction assaults were all demonstrated by the coupled QRSG and AI methods. This study emphasizes how crucial it is to use AI and QRSGs to safeguard FSCs. The combined platform provides a complete option to reduce cyber-attacks by offering strong cryptography security and real-time attack identification. These results demonstrate how QRSG and AI innovations can completely transform cybersecurity in the banking industry, safeguarding vital financial systems and guaranteeing the privacy and reliability of financial payments. © 2025 IEEE.

关键词： Quantum optics

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Lay CO:Achieving Least Lossy Accuracy for Most Efficient RRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization

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计算机科学技术学报（英文版） 2023年第2期38卷 328-347页

作者： Shao-Feng Zhao Fang Wang Bo Liu Dan Feng Yang Liu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518057China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China

Resistive random access memory(RRAM)enables the functionality of operating massively parallel dot prod-ucts and ***-based accelerator is such an effective approach to bridging the gap between Internet of Things devices'constrained resources and deep neural networks'tremendous *** to the huge overhead of Analog to Digital(A/D)and digital accumulations,analog RRAM buffer is introduced to extend the processing in analog and in *** analog RRAM buffer offers potential solutions to A/D conversion issues,the energy consumption is still challenging in resource-constrained environments,especially with enormous intermediate data ***,criti-cal concerns over endurance must also be resolved before the RRAM buffer could be frequently used in reality for DNN in-ference *** we propose LayCO,a layer-centric co-optimizing scheme to address the energy and endurance con-cerns altogether while strictly providing an inference accuracy *** relies on two key ideas:1)co-optimizing with reduced supply voltage and reduced bit-width of accelerator architectures to increase the DNN's error tolerance and achieve the accelerator's energy efficiency,and 2)efficiently mapping and swapping individual DNN data to a correspond-ing RRAM partition in a way that meets the endurance *** evaluation with representative DNN models demonstrates that LayCO outperforms the baseline RRAM buffer based accelerator by 27x improvement in energy effi-ciency(over TIMELY-like configuration),308x in lifetime prolongation and 6x in area reduction(over RAQ)while main-taining the DNN accuracy loss less than 1％.

关键词： deep learning accelerator resistive random access memory (RRAM) energy efficiency error tolerance

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TMPVT: Few-Shot classification of Tibetan medicinal plants using Vision Transformer 5

TMPVT: Few-Shot classification of Tibetan medicinal plants u...

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5th International Conference on Telecommunications, Optics, and Computer science, TOCS 2024

作者： Jiang, Jinghan Xiang, Hao School of Mathematics and Big Data Anhui University of Science and Technology Huainan China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510691667

In this paper, a novel Transformer Vision framework for Tibetan medicinal plants (TMPVT) is studied, aware of a few-shot Tibetan medicinal plants' (TMP) image classifications and recognition. Tibetan medicinal herbs are valuable natural medicinal plants that have important healthcare values and are useful in treating a wide range of diseases. In the system we studied, we utilized the self-attention mechanism in the Vision Transformer model to identify and classify Tibetan medicinal herbs. This approach allowed us to achieve accurate classification for image annotation of these herbs, including the localization of the herbs' targets and their respective categories. In addition, we constructed a dataset that contains annotated images of various types of Tibetan medicinal herbs extracted from a database of real plant images. We simulated and evaluated the proposed solution and demonstrated that the performance of TMPVT for solving the problem of classifying images of Tibetan medicinal herbs with few samples is significantly better than other available options. © 2025 SPIE.

关键词： Image annotation

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A Novel Bandpass-to-All-Stop Switchable Absorptive Filter with Ultra-Wideband Reflectionless Range

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Progress In Electromagnetics Research C 2024年 147卷 99-107页

作者： Yang, Bingjie Wang, Zhongbao Zhao, Shipeng Liu, Hongmei Gao, Mingming Fang, Shaojun School of Information Science and Technology Dalian Maritime University Liaoning Dalian116026 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Liaoning Huludao125105 China

A novel bandpass-to-all-stop switchable absorptive filter with an ultra-wideband reflectionless range is proposed in this paper. The bandpass section of the filter consists of a dual-mode resonator and two L-shaped feeding lines. The dual-port reflectionless characteristic is achieved by loading absorption networks at the end of the open stubs of the feeding lines, which are composed of two parallel coupled lines and absorption resistors. The switching of the reflectionless bandpass filter (RBPF) to the all-stop filter (ASF) is realized by controlling the on/off behavior of the PIN diode through the bias voltage. Measurements show that the filter prototype at the center frequency of 2.43 GHz with the 3-dB fractional bandwidth (FBW) is 8.23%. For the RBPF state, the filter has an ultra-wideband reflectionless FBW of 214% and upper stopband rejection better than 33 dB up to 6 GHz. Besides, the rejection is better than 30 dB from 0 to 5.32 GHz in the ASF state. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Ultra-wideband (UWB)

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Dynamic Task Offloading and Service Migration Optimization in Edge Networks

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International Journal of Crowd science 2023年第1期7卷 16-23页

作者： Han, Yibo Li, Xiaocui Zhou, Zhangbing Nanyang Institute of Big Data Research Nanyang Institute of Technology Nanyang473004 China Beijing100083 China Computer Science Department TELECOM SudParis Evry 91000 France

In recent years, edge computing has emerged as a promising paradigm for providing flexible and reliable services for Internet of things (IoT) applications. User requests can be offloaded and processed in real time at the edge of a network. However, considering the limited storage and computing resources of IoT devices, certain services requested by users may not be configured on current edge servers. In this setting, user requests should be offloaded to adjacent edge servers or requested edge servers should be configured by migrating certain services from the former, further reducing the service access delay of user requests and the energy consumption of IoT devices in such networks. To address this issue, in this study, we model this dynamic task offloading and service migration optimization problem as the multiple dimensional Markov decision process and propose a deep q-learning network (DQN) algorithm to achieve fast decision-making, an approximate optimal task offloading, and service migration solution. Experimental results show that our algorithm performs better than existing baseline approaches in terms of reducing the service access delay of user requests and the energy consumption of IoT devices in edge networks. © The author(s) 2023. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://***/licenses/by/4.0/).

关键词： Reinforcement learning

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Zero-shot Stance Detection with Logically Consistent data Augmentation

Zero-shot Stance Detection with Logically Consistent Data Au...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Bowen Li, Xu Ma, Jun Zhang, Xi Dai, Genan Ye, Jianhua College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Applied Science Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Zero-shot stance detection (ZSSD) is a challenging task that requires classifying stances towards unseen targets without large, well-curated training datasets. Existing data augmentation methods for ZSSD often suffer from semantic inconsistencies, hindering their effectiveness. To address these limitations, we propose a Logically Consistent data Augmentation (LCDA) framework that enhances data quality by maintaining logical coherence. LCDA comprises two key components: data augmentation and logical graph convolutional network. Our data augmentation approach leverages chain-of-thought (CoT) prompting to enable large language models (LLMs) to distill training text into first-order logic (FOL) expressions, which provide a clear and concise representation of the underlying logic for stance prediction. These FOL expressions are then used to generate high-quality augmented samples. Furthermore, we introduce a novel logical graph convolutional network that effectively exploits FOL knowledge and combines it with augmented data samples to train a more accurate stance detection model. Our experiments on benchmark datasets demonstrate that the LCDA framework significantly outperforms existing ZSSD techniques, highlighting the effectiveness of integrating FOL into data augmentation for improving stance detection accuracy. © 2025 IEEE.

关键词： Chain-of-thought data augmentation First-order logic Zero-shot stance detection

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A Write-Optimized PM-oriented B+-tree with Aligned Flush and Selective Migration 21

A Write-Optimized PM-oriented B+-tree with Aligned Flush and...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： He, Xianyu Zhang, Runyu Tian, Pengpeng Zhou, Lening Lian, Min Yang, Chaoshu Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350329223

Emerging Persistent Memories (PMs) usually have the severe drawback of expensive writes. Therefore, existing PM-oriented B+-trees mainly concentrate on alleviating the write overhead (i.e., reducing the writes to PM and the flush instructions). Unfortunately, these schemes still employ improper data organization in leaf nodes, which can lead to massive data migration during insertions or node splits. This paper proposes a write-optimized PM-oriented B+ -tree (WPB+ -tree) to solve the above problems. WPB+ -tree first adopts a buffer-assisted mechanism that temporarily stores the newly inserted data to reduce the overhead of data migration and flush. Second, WPB+ tree employs a selective migration of node entries to achieve less data migration when splitting a node. We implement the proposed WPB+ -tree with actual persistent memory. Compared with state-of-the-art solutions (i.e., FAST&FAIR and SSB-Tree), WPB+ -tree achieves up to 17%, 40%, and 20% higher throughput in the insert, search, and scan operations, respectively. © 2023 IEEE.

关键词： Cost reduction

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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AceGPT, Localizing Large Language Models in Arabic

AceGPT, Localizing Large Language Models in Arabic

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Huang, Huang Yu, Fei Zhu, Jianqing Sun, Xuening Cheng, Hao Song, Dingjie Chen, Zhihong Alharthi, Abdulmohsen An, Bang He, Juncai Liu, Ziche Zhang, Zhiyi Chen, Junying Li, Jianquan Wang, Benyou Zhang, Lian Sun, Ruoyu Wan, Xiang Li, Haizhou Xu, Jinchao Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China The Chinese University of Hong Kong Shenzhen China King Abdullah University of Science and Technology Thuwal Saudi Arabia Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798891761148

This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. Significant concerns emerge when addressing cultural sensitivity and local values. To address this, the paper proposes a comprehensive solution that includes further pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic, alongside Reinforcement Learning with AI Feedback (RLAIF) employing a reward model attuned to local culture and values. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities. Comprehensive evaluations reveal that the resulting model, dubbed ‘AceGPT’, sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. Codes, data, and models are in https://***/FreedomIntelligence/AceGPT. © 2024 Association for Computational Linguistics.

关键词： Reinforcement learning

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