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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Timoteo, Adriel Benyamin, Fabian Rafinanda Anargyarahman, Naufal Bhanu Iskandar Yuliana, Hajiar School of Electrical Engineering and Informatics Telecommunications Engineering Program Bandung Institute of Technology Bandung Indonesia Jenderal Achmad Yani University Faculty of Engineering Department of Electrical Engineering Cimahi Indonesia

ISBN: (纸本)9798331527686

Since the initial launch of 5G networks in 2018, 5G technology has rapidly expanded to cover about 40% of the world's population, offering higher bandwidth, faster connectivity, and lower latency than 4G. However, due to its higher frequency operation, 5G necessitates more frequent handovers, making efficient handover performance crucial. This research focuses on enhancing handover efficiency by predicting user movement using Long Short-Term Memory (LSTM) models for multi-step-ahead prediction. The model created will utilize 60 seconds of historical data to predict 10 seconds ahead, with a target to achieve an average prediction accuracy of less than 20 meters with a latency under 100 ms. The study employs TensorFlow for model training, leveraging the Grab-Posisi dataset, and utilizes Plotly and TensorBoard for result visualization. Six model variations were trained, with the 16-unit model trained for 3106 epochs performing best, achieving 16.99 meters accuracy and 31 ms latency. The findings suggest that LSTM is effective for predicting user movement in 5G networks, with potential accuracy improvements through extended training epochs. © 2024 IEEE.

关键词： Prediction models

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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPERATURE PREDICTION IN VANADIUM REDOX FLOW BATTERIES

COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPE...

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International Conference on Green Energy, Computing and Intelligent technology 2024, GEn-CITy 2024

作者： Zhang, Anxiu Juwono, Filbert H. Li, Hongfei Purwanto, Erick Wong, W.K. Liu, Linfeng Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781837242689

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction. © The Institution of engineering & technology 2024.

关键词： machine learning prediction Vanadium redox flow battery

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Multi-Step-Ahead Prediction of User Movement on 5G Networks Using the Long Short-Term Memory Method

Multi-Step-Ahead Prediction of User Movement on 5G Networks ...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Adriel Timoteo Fabian Rafinanda Benyamin Naufal Bhanu Anargyarahman Iskandar Hajiar Yuliana School of Electrical Engineering and Informatics Telecommunications Engineering Program Bandung Institute of Technology Bandung Indonesia Department of Electrical Engineering Faculty of Engineering Jenderal Achmad Yani University Cimahi Indonesia

ISBN: (数字)9798331527686

ISBN: (纸本)9798331527693

关键词： Training Meters Accuracy 5G mobile communication Quality of service Predictive models Handover Data models telecommunications Long short term memory

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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2023 Laser Science, LS 2023

作者： Gül, G. On, M. Berkay Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

关键词： Quantum electronics

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Evolutionary Trends of Intelligent IoT Networking for Commercial and Industrial Use Cases

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中国通信（英文版） 2023年第8期20卷前插1-前插4页

作者： Shuai Han Rong Bo Nadra Guizani Sai Xu Ruoyu Su Liang Xiao Cheng Li Department of Electronics and Communication Engineering Harbin Institute of Technology Communications Research Centre Canada Engineering program and the computing Research Association School of Cybersecurity Northwestern Polytechnical UniversityXi'anChina Nanjing University of Posts and Telecommunications Department of Informatics and Communication Engineering Xiamen Univer- sityFujianChina Department of Electrical and Computer Engineering of Memorial University St.John'sCanada

The intelligent Internet of Things(IIoT)is one of the key enablers for the digital transformation of traditional industries towards Industry *** services have diversified requirements due to the nature of their commercial and industrial *** various devices in IIoT environments have their unique properties in terms of resource limita-tions,network lifetimes,and application Quali-ty-of-Service(QoS)requirements which affect the performance under such applications ***,IoT devices typically have constrained power,storage,computing,high latency,massive multiple access and communications resources which limit the performance of IIoT for commercial and industrial Use *** the other hand,devices used in IIoT were not initially designed with security in mind and they had been considered *** response to the above challenges,domestic and foreign scholars have gradually carried out research on network architecture,collaborative scheduling strategy,energy supply technology,resource man-agement,and ***,the research on IIoT is still in its infancy,and has not yet formed comprehen-sive and systematic theoretical methods and technical standards.

关键词： Special issues and sections Internet of Things Intelligent systems Commercialization Requirements engineering Industrial engineering

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Broadband Beamforming via Frequency Invariance Transformation and PARAFAC Decomposition for Jamming Mitigation in V2X Scenarios

Broadband Beamforming via Frequency Invariance Transformatio...

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IEEE Conference on Vehicular technology (VTC)

作者： Daniel V. de Lima João Paulo J. da Costa Antonio Arlis S. da Silva Giovanni A. Santos José Alfredo R. Vargas Auzuir R. de Alexandria Postgraduate Program in Telecommunications Engineering (PPGET) IFCE Fortaleza Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Lippstadt Germany Graduate School for Applied Research in North Rhine-Westphalia (PKNRW) Bochum Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Gennany Graduate Program in Computing (PPGC) UFRGS Porto Alegre Brazil Department of Electrical Engineering UnB Brasilia Brazil

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Vehicle-to-Everything (V2X) communication can considerably improve the efficiency and safety of autonomous driving and advanced driver-assistance systems (ADASs). However, V2X communication can be considerably degraded in the presence of cyberattacks, such as radio jamming. Traditionally, beamforming techniques can be applied to increase the signal-to-interference plus noise ratio (SINR). This paper evaluates broadband beamforming in the mmWave spectrum against radio jamming in V2X communication. The exploitation of the mm Wave spectrum in 5G-V2X communication has a natural advantage against radio jamming. First, attenuation is stronger in the mmWave spectrum in the range of 40 GHz or higher than in the traditional 5.9 GHz. Second, to generate broadband radio jamming, the radio jammer requires much more complex hardware and energy consumption. Third, by using broadband beamforming, broadband radio jamming can be considerably attenuated, limiting the degradation of the spectrum by the radio jamming. According to our numerical results, gains of close to 30 dB SINR can be achieved. We propose a broad-band beamforming technique based on the canonical polyadic decomposition via generalized eigenvalue decomposition (CPD-GEVD). The CPD-GEVD broadband beamforming outperforms state-of-the-art beamforming algorithms in most V2X scenarios presented in this paper.

关键词： Vehicular and wireless technologies Array signal processing Interference Attenuation Broadband communication Jamming Millimeter wave communication

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Comparative performance of machine learning models for temperature prediction in vanadium redox flow batteries

Comparative performance of machine learning models for tempe...

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International Conference on Green Energy, Computing and Intelligent technology 2024 (GEn-CITy 2024)

作者： Anxiu Zhang Filbert H. Juwono Hongfei Li Erick Purwanto W. K. Wong Linfeng Liu Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China

ISBN: (数字)9781837242863

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VR-Based Implicit Authentication Using Electromyogram and Inertial Measurement Unit

VR-Based Implicit Authentication Using Electromyogram and In...

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International Conference on Cyberworlds

作者： Da-Young Yu Hyung-Tak Lee Youngsam Kim Jong-Hyuk Roh Soohyung Kim Jeonghun Ku Han-Jeong Hwang Dept. Electronics and Information Engineering Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea Department of Biomedical Engineering College of Medicine Keimyung University Daegu Republic of Korea

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Biometrics based on electromyogram (EMG) and inertial measurement unit (IMU) signals has received significant attention due to its resistance to forgery and tampering, as well as its relatively higher convenience and security compared to other biometric modalities. However, most EMG and IMU signal-based authentication methods require explicit authentication using unnatural gestures, which is not userfriendly. To address this, we collected EMG and IMU data during virtual reality (VR) rhythm game play, which does not require explicit gestures, and performed implicit authentication. Using a linear discriminant analysis (LDA) classifier, we achieved classification accuracies of 91.71% for human identification and 87.91% for human authentication. The result demonstrated the feasibility of a seamless and implicit authentication in VR environments.

关键词： Biometrics Solid modeling Measurement units Accuracy Authentication Inertial navigation Games Virtual reality Rhythm Electromyography

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