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A Double Layer Frequency Selective Surface using Interdigital Split Ring Resonators and Applications

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A Double Layer Frequency Selective Surface using Interdigita...

2020 International Conference on Power, Energy and Innovations, ICPEI 2020

ISBN: (纸本)9781728172408

This paper proposes a split ring resonator unit cell using interdigital structure. The size can be reduced from λ/2 to about λ/4 caused by the slow wave effect. An array of 14×14 unit cells can be formed to act as a frequency selective surface (FSS). The proposed FSS is designed at frequency of 1.8 GHz, which the length of unit cell is 11.48 mm and the length of FSS is 172.50 mm. Using double layer FSS, the effect of coupling between layers will result in a larger bandwidth from 1.8 GHz to 2.45 GHz. The simulation results when applying a dipole antenna over the double layer FSS show that the high antenna gains of 8.34 dB and 9.75 dB are obtained at 1.8 GHz to 2.45 GHz, respectively. The proposed double layer FSS with a dipole can be applied for modern communication systems including LTE and WLAN. © 2020 IEEE.

关键词： Frequency selective surfaces

Eliminating NB-IoT interference to LTE system: A sparse machine learning based approach

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arXiv 2020年

作者： Liu, Sicong Xiao, Liang Han, Zhu Tang, Yuliang Department of Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

—Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

A Review on Current and Old SCADA Networks Applied to Water Distribution Systems 1

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A Review on Current and Old SCADA Networks Applied to Water ...

1st International Conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Franco, Daniel Jose Muhammed, Abdullah Bin Subramaniam, Shamala K. Abdullah, Azizol Silva, Rui Miguel Akram, Omar Khasro University Putra Malaysia Department of Communication Technology and Network Serdang Malaysia Lab Ubinet Computer Security and Cybercrime Polytechnic Institute of Beja Department of Engineering Beja Portugal University Putra Malaysia Department of Architecture Serdang Malaysia

ISBN: (纸本)9781728144870

Water is probably the oldest factor receiving attention from the population, since the beginning of the human race. The evolution of water supply systems is directly related to the urban growth of the cities and their geographical location. This study focuses on SCADA (Supervisory Control And Data Acquisition) systems used to control and managing water distribution systems all around the world, highlighting their components and deeply approaching SCADA networks and communication protocols. Based on the need of protection and security on SCADA networks, this study aims to provide a review of the existing literature, through the case study of water distribution systems, identifying the different components of a SCADA system, as well as its communication architectures and protocols and some known attacks, to design a framework on current and old SCADA networks. Results show that SCADA systems are not just applied to water distribution and water waste systems, but also to many other industrial automated systems that control on crucial systems. The security of such systems is yet weak and faces many vulnerabilities and threats, where security mechanisms must be applied. For this purpose, the study of SCADA networks and communications is considered a crucial point for the correct development of security tools. © 2019 IEEE.

关键词： SCADA systems

Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

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arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach

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arXiv 2025年

作者： Pan, Wenxuan Yang, Yang Chen, Mingzhe Wei, Dong Guo, Caili Mao, Shiwen Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering the Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Wireless Engineering Research and Education Center Auburn University AuburnAL36849 United States

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

Confidence Estimation Using Machine Learning in Immersive Learning Environments

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Confidence Estimation Using Machine Learning in Immersive Le...

IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Yudong Tao Erik Coltey Tianyi Wang Miguel Alonso Mei-Ling Shyu Shu-Ching Chen Hadi Alhaffar Albert Elias Biayna Bogosian Shahin Vassigh Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA School of Computing and Information Sciences Florida International University Miami FL USA College of Communication Architecture and the Arts Florida International University Miami FL USA

ISBN: (数字)9781728142722

ISBN: (纸本)9781728142739

As the development of Virtual Reality and Augmented Reality (VR/AR) technology rapidly advances, learning in an artificial immersive environment becomes increasingly feasible. Such emerging technology not only facilitates and promotes an efficient learning process, but also reduces the cost of access to learning materials and environments. Current research mainly focuses on the development of immersive learning environments and the adaptive learning methods based on interactions between trainees and the environment. However, valuable human biometric data available in immersive environments, such as eye gaze and controller pose, have not been explored and utilized to help understand the affective state of the trainees. In this paper, we propose a machine-learning based research framework to estimate trainees' confidence about their decisions in immersive learning environments. Using this framework, we designed an experiment to collect biometric data from a multiple-choice question and answer session in an immersive learning environment. This includes collecting answers from 10 participants on 35 questions and their self-reported confidence in their answers. A Long Short-Term Memory neural network model was used to analyze the data and estimate the confidence with 85.6% accuracy.

关键词： Conferences Information processing Two dimensional displays

An Extension Network of Dendritic Neurons

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Computational intelligence and neuroscience 2023年第1期2023卷 7037124页

作者： Qianyi Peng Shangce Gao Yirui Wang Junyan Yi Gang Yang Yuki Todo Faculty of Engineering University of Toyama Toyama-shi 930-8555 Japan. Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang 315211 China. Department of Computer Science & Technology Beijing University of Civil Engineering and Architecture Beijing 100044 China. School of Information Renmin University of China Beijing China. Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Ishikawa 9201192 Japan.

Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch-Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability. In this study, a novel extended network based on the dendritic structure is innovatively proposed, thereby enabling it to solve multiple-class classification problems. Also, for the first time, an efficient error-back-propagation learning algorithm is derived. In the extensive experimental results, the effectiveness and superiority of the proposed method in comparison with other nine state-of-the-art classifiers on ten datasets are demonstrated, including a real-world quality of web service application. The experimental results suggest that the proposed learning algorithm is competent and reliable in terms of classification performance and stability and has a notable advantage in small-scale disequilibrium data. Additionally, aspects of network structure constrained by scale are examined.

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Matching Face Images for Biometric Authentication

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Matching Face Images for Biometric Authentication

IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT)

作者： Abdulmawla Najih Syed Alhaddad Abd Rahman Ramli S. J. Hashim Nabila Albannai Department of Computer & Communication Systems Engineering University Putra Malaysia Darul Ehsan Selangor Department of Computer & Communication Systems Engineering University Putra Malaysia Faculty of Engineering and Architecture Kastamonu University Kastamonu TURKEY

ISBN: (数字)9781728132761

ISBN: (纸本)9781728132778

The use of biometric features, to authenticate users of different applications, is growing rapidly in recent years, according to the high sensitivity of the protected information and the good security that biometric authentication provides. In this study, a method is proposed to measure the similarity between two face images, using convolutional neural networks, directly from the raw pixel values of the input image. As the neural network is used for matching instead of classifications, modifying the users that the proposed method can recognize is a matter of adding or removing model images of the user's faces, without the need to retrain the model. The similarity between the face image and every model image is measured in order to select the user with the highest similarity to the input image as the recognized user, where that similarity measure is compared to a threshold value in order to authenticate that user. The evaluation results of the proposed method, using ORL shows that the proposed method has 99.48% accuracy with 0.22% False Acceptance Rate (FAR) and 3.2% False Rejection Rate (FRR). Hence, the proposed method has been able to maintain high accuracy while eliminating the vulnerabilities of biometric authentication systems imposed by the use of separate stages for features extraction and similarity measurement.

关键词： Faces Face recognition Feature extraction Image recognition Authentication computer vision Data mining

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement