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Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

Chinese Journal of Electronics 2024年第6期33卷 1478-1486页

作者： Feiyan JI Heng ZHANG Xiuqiong XING Wenjun LU Lei ZHU Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1and TE9/5,1within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%（i.e., 2.06–5.44 GHz）, in which the stable gain bandwidth in the backfire,-x-direction（θ = 90°, φ = 180°） with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3%（i.e., 3.74–5.44 GHz）. It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

关键词： Fluctuations Azimuth Prototypes Metals Resonance Frequency measurement Broadband antennas Impedance Gain Wideband

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Sparse Spatial Lane Change Increases SDM network Efficiency 49

Sparse Spatial Lane Change Increases SDM Network Efficiency

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49th European Conference on Optical Communications, ECOC 2023

作者： Brasileiro, Ítalo Drummond, André Jukan, Admela Department of Computer Science University of Brasília Brazil Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

We show that the sparse distribution of ports for core switching in optical SDM networks can significantly improve network performance. The results show that more than 70% of equipment capacity can be saved while obta... 详细信息

ISBN: (纸本)9781839539954

关键词： Switching networks

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RIS-Assisted UAV-Enabled JSAC System: Joint Radio Resource and Phase Shift Optimization Framework

RIS-Assisted UAV-Enabled JSAC System: Joint Radio Resource a...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Tun, Yan Kyaw Dán, György School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Division of Network and Systems Engineering Stockholm Sweden

ISBN: (纸本)9798350370218

We propose a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled joint communication and sensing (JSAC) system in which multiple JSAC nodes equipped with both sensing and communication functions are deployed to perform sensing and communication simultaneously, and a communication-enabled RIS is installed to provide strong wireless links for JSAC nodes to communicate with the UAV (i.e., aerial base station). We then formulate the problem of maximizing the sum communication rate of JSAC nodes while ensuring their minimum sensing performance requirements, i.e., minimum estimation rate requirements, by jointly optimizing sub-band assignment, adaptive bandwidth allocation, and the RIS phase shift. The resulting problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, thus, to solve the problem efficiently, we decompose the problem into two coupled sub-problems, a sub-band assignment and adaptive bandwidth allocation problem, and a RIS phase shift problem. We propose a low-complexity iterative algorithm for solving the sub-problems in an alternating manner. Extensive simulations show that the proposed algorithm can increase the communication rate by up to 27.51 %,48.747%, and 62.0942% compared to RSA, RPS, and RSRPS schemes. © 2023 IEEE.

关键词： dual decomposition Joint sensing and communication one-to-one matching game reconfigurable intelligent surface unmanned aerial vehicle whale optimization

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Adversarial Robustness of Multi-agent Reinforcement Learning Secondary Control of Islanded Inverter-based AC Microgrids 14

Adversarial Robustness of Multi-agent Reinforcement Learning...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Shereen, Ezzeldin Kazari, Kiarash Dan, Gyorgy Kth Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9781665455541

Secondary control of voltage magnitude and frequency is essential to the stable and secure operation of microgrids (MGs). Recent years have witnessed an increasing interest in developing secondary controllers based on multi-agent reinforcement learning (MARL), in order to replace existing model-based controllers. Nonetheless, unlike the vulnerabilities of model-based controllers, the vulnerability of MARLbased MG secondary controllers has so far not been addressed. In this paper, we investigate the vulnerability of MARL controllers to false data injection attacks (FDIAs). Based on a formulation of MG secondary control as a partially observable stochastic game (POSG), we propose to formulate the problem of computing FDIAs as a partially observable Markov decision process (POMDP), and we use state-of-the-art RL algorithms for solving the resulting problem. Based on extensive simulations of a MG with 4 distributed generators (DGs), our results show that MARL-based secondary controllers are more resilient to FDIAs compared to state of the art model-based controllers, both in terms of attack impact and in terms of the effort needed for computing impactful attacks. Our results can serve as additional arguments for employing MARL in future MG control. © 2023 IEEE.

关键词： Controllers

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Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment

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ACM Computing Surveys 2025年第8期57卷 1-36页

作者： Santos, Paulo Roberto Da Paz Ferraz Resende, Paulo Angelo Alves Gondim, João José Costa Drummond, André Costa Department of Computer Science University of Brasilia Brasilia Brazil C4I and Cyber Center George Mason University Fairfax United States GigaCandanga Association Brasilia Brazil Department of Electrical Engineering University of Brasilia Brasilia Brazil Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig Germany

Cyber attacks have become a growing threat in today's interconnected society, and taxonomies play a crucial role in understanding and preventing these attacks. However, the lack of comprehensive assessment methods for evaluating attack taxonomies represents a significant gap in the literature, hindering their development and applicability. This article aims to address this gap by conducting a survey of 20 attack taxonomies published between 2011 and 2022 and evaluating them with a novel set of qualitative and quantitative assessment criteria, grounded in fundamental taxonomy requirements and key structural attributes. In pursuit of clear and objective assessment criteria, the authors investigated the main taxonomy properties in the literature, identifying dependencies and relationships. This investigation extracted the fundamental requirements for a relevant and widely accepted attack taxonomy in the cybersecurity community. Noteworthy structural aspects, such as organization, scheme, labeling, and approach, are also addressed, considering their impact on taxonomy effectiveness and applicability constraints. Finally, the article poses some open questions and challenges, along with suggestions for future research directions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cyber attacks

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Dysarthric speech recognition: an investigation on using depthwise separable convolutions and residual connections

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Neural Computing and Applications 2025年第12期37卷 7991-8005页

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna DeepNet Discovery Network Faculty of Engineering and Design University of Auckland Private Bag 92019 Building 405 Level 6 Room 669 3 Garfton Road Auckland1142 New Zealand Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

Dysarthria is a neurological condition resulting from impairments affecting muscle control involved in speech articulation, leading to reduced intelligibility or unintelligible speech, which affects communication abilities. Although Automatic Speech Recognition (ASR) technologies hold the potential to improve the lives of people with dysarthria significantly, ASR systems designed for normal speech have shown limited effectiveness when presented with impaired speech. Consequently, researchers have focused on developing ASR systems specifically tailored for dysarthria. However, progress in this area has been gradual due to the scarcity of dysarthric speech for training and the increased variability of speech among dysarthric individuals, necessitating a larger dataset of dysarthric utterances. One potential solution to enhance the robustness of dysarthric ASR is to deepen the architecture of the acoustic model, which maps the speech signal to words or phonetic units. However, deeper architectures require more training data and pose challenges in dealing with issues such as the vanishing gradient problem and representational bottlenecks in deep learning models. In this study, we expanded on our previous findings and investigated the applications of Depthwise Separable Convolution neurons and the inclusion of Residual Connections to propose a deep dysarthric acoustic model, tackling both vanishing gradients and representational bottleneck issues in dysarthric ASR. Multiple speaker-adaptive dysarthric ASRs were trained and evaluated for 15 UA-Speech dysarthric subjects, then benchmarked against the state-of-the-art and our previous dysarthric ASRs. Our proposed architectures have delivered up to 22.58% word recognition rate (WRR) improvements over the reference models. We observed an average of 10.81% better WRRs over the base traditional dysarthric ASR for all speakers. Likewise, the proposed acoustic model outperformed the state-of-the-art Transformer-based dysarthric

关键词： Convolution

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A KUSHO WORDS RECOGNITION METHOD FOR CONTACTLESS INFORMATION MANAGEMENT IN RESTAURANT KITCHENS 51

A KUSHO WORDS RECOGNITION METHOD FOR CONTACTLESS INFORMATION...

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51st International Conference on computers and Industrial engineering, CIE 2024

作者： Usuki, Risa Usuki, Jun Department of Computer Science and Engineering Waseda University Tokyo Japan Department of Information Network and Communication Kanagawa Institute of Technology Kanagawa Japan

Advances in IT technology have led to research into KUSHO recognition, which enables the non-contact operation of devices without the use of devices such as keyboards or controllers. This is a technology that transmits information about the characters written in space towards the camera (hereafter this action is referred to as KUSHO) to the devices. KUSHO has advantages, such as the ability to transfer information without training. In restaurant kitchens, workers transmit various types of information using devices with input functions, however, hygiene considerations are essential when operating equipment in kitchens. In addition, in noisy kitchens, it is also difficult to use voice input, and there are limits to paper-based information transmission. Under these circumstances, in this research, the introduction of KUSHO input technology for non-contact information transfer in restaurant kitchens is being investigated. Therefore, the content handled by kitchen workers for information management is first analyzed and shown to be representable in three input formats. Subsequently, an additional hand signal to be used while doing KUSHO and an improved algorithm for determining them are proposed. Finally, an algorithm for KUSHO recognition of character strings using standard input time is proposed. Experiments on the implementation of these proposals have shown the effectiveness of the new decision algorithm for the addition of a hand signal, with a 100% correct rate. For strings of two or more uppercase alphabetic characters, the recognition rate was 92.5% for all eight types of "KUSHO operation" and the recognition rate was 70.0% for ten randomly selected from 100 different "product names." For "quantity," it was difficult to recognize multiple digits, however 100% recognition rate was achieved by dividing the digits into each single digit 0~9 using an improved hand signal algorithm. Thus, algorithm some problems remain, the effectiveness of all the proposed methods ha

关键词： Information management

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo 182-8585Japan Information Technology Center Nagoya UniversityNagoya 464-8601Japan National Institute of Informatics(NII) Tokyo 101-8430Japan Hohhot Minzu College Hohhot 010051China Toyota Motor Corporation Tokyo 112-8701Japan Department of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240China

Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central *** requirement outpaces the capacity of traditional communication *** tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise *** solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge *** system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient ***,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain *** results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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DTGCN: a method combining dependency tree and graph convolutional networks for Chinese long-interval named entity relationship extraction

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13361-13373页

作者： Li, Taoyuan Ma, Liangli Qin, Jiwei Ren, Wei College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

The extraction of relationships between military entities and descriptive entities is a key task for automated construction of military knowledge graphs. This category of text unstructured imposed problems such as unfixed entity length, possibly overlong intervals between entities, and excessive pronouns. Many studies rely on traditional pattern matching and statistical learning methods. Thanks to the expansion of data volume, deep learning methods are generally used to address similar problems. However, the traditional single neural network model lacks the ability to extract the relationship information of long-interval entities and cannot tackle the above problems well. To solve these problems, a new named entity relationship extraction model called DTGCN is proposed combining dependency tree and GCN (Graph Convolutional networks). By comparing with traditional models, the experimental results demonstrate that the method proposed in this paper can enhance F1-value by 2.28–11.11% and can provide support for the extraction of Chinese long-interval relationships. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Knowledge graph

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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