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15th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2021

作者： Ahdan, Syaiful Nurhayati, Ade Nurkahfi, Galih Nugraha Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung 35132 Indonesia Institut Teknologi Telkom Jakarta Faculty of Engineering Jakarta11710 Indonesia

ISBN: (纸本)9781665428927

Named Data Networking (NDN) is a new network Architecture, the design principle of NDN comes from the success of today's internet, NDN changes the current network paradigm, sending a packet to be sent to the destination address based on the name, Strategy Forwarding is a decision making for the purpose of forwarding, when and where the interest will be forwarded. In contrast to the current concept of IP addresses, NDN uses Strategy Forwarding (SF) as the concept used in sending packets to their destination. And this is the purpose of writing this paper. This paper discusses some of the results of Strategy Forwarding from previous research. Each application will require different forwarding behavior, to know the characteristics of each proposed forwarding, this paper will present information about the measurement metrics and information about the topological model used in each implementation of the proposed forwarding strategy. The measurement metrics section also presents a summary in a comparison chart, and a classification of the proposed forwarding strategy. 50% on testing the Strategy Forwarding using interest metrics, 32% Delay, 29% Data, 21% Hop count and Delivery, and 57% Other Metrics. © 2021 IEEE.

关键词： Network architecture

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The comparison of EEG features in specific Mandarin words’ speech

The comparison of EEG features in specific Mandarin words’ ...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Wen-Shan Tsai Syue-Guan Huang Kuan-Chih Huang Jung-Chih Chen Chin-Teng Lin Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Brain Science and Technology Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Human-Centric AI Centre (HAI) Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

Due to advancements in brain signal application technology, there has been a growing focus on the human speech Brain-computer Interface (BCI) in recent years. An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing. To ensure the decoding capability of human speech BCI, researchers have to investigate the differences between overt speech and imagined speech, and they also need to find the best paradigm to build a reliable BCI. Thus, this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words "上" (up), "下" (down), "左" (left), and "右" (right). We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca's and Wernicke's areas (specifically F5, F7, FC3, FC5, and C5), to analyze the brain activity changes while speaking or imagining speech. Second, we used Daubechies 4 (db4) wavelet to do a 7-level decomposition to the EEG signal. The corresponding discrete wavelet transform (DWT) coefficients were further calculated with six statistical features for support vector machine (SVM) classification. We found that the "speak (overt speech)" task and the "imagine (imagined speech)" task resulted in significantly different alpha band power changes, and the beta band shows the same phenomenon. The result of average classification accuracy is 71.97% for "speak" and "imagine" distinction. In conclusion, we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features. This may pave the way to establish a Mandarin imagined speech BCI.

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Quantum-enhanced Federated Learning for Metaverse-empowered Vehicular Networks

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IEEE Transactions on Communications 2024年

作者： Hazarika, Bishmita Singh, Keshav Dobre, Octavia A. Li, Chih-Peng Duong, Trung Q. National Sun Yat-sen University Institute of Communications Engineering Kaohsiung804 Taiwan Memorial University Faculty of Engineering and Applied Science St. John's Canada Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-FEDCOM, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-FEDCOM is strengthened by key components like quantum sequential-training-program, with reinforcement learning-based dynamic mode switching to reduce communication costs and manage vehicle states adaptively, and the quantum vehicle-context-grouping utilizing hierarchical clustering and simulated annealing for effective vehicle grouping based on contextual data similarity, addressing the complexities of data heterogeneity. Additionally, the integration of quantum-inspired principal component analysis (Q-PCA) enhances memory efficiency, further optimizing the framework. These elements converge in the QV-FEDCOM algorithm, establishing a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Our study also introduces an innovative quantum trajectory loss (QTL) function, specifically designed for trajectory prediction tasks, which combines the Huber loss with an angular deviation penalty to robustly handle errors and penalize large deviations in the predicted trajectory angle. The effectiveness of the QV-FEDCOM framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem. © 1972-2012 IEEE.

关键词： Quantum communication

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Bacteria cells estimation in wastewater treatment plants using data-driven models ⁎

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IFAC-PapersOnLine 2024年第14期58卷 718-723页

作者： Fahad Aljehani Ibrahima N’Doye Pei-Ying Hong Mohammad K. Monjed Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Biology Faculty of Science Umm Al-Qura University Makkah 21961 Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Saclay France

Estimating or predicting the concentrations of bacteria cells is crucial for achieving better control of the operation of wastewater treatment plants. However, measuring the bacterial concentration along the influent wastewater stage to the treated effluent process is challenging as it involves lab access and trained personnel. Additionally, wastewater plants are generally nonlinear systems involving time-varying physical and biological characteristics, increasing the difficulty in estimating the bacterial concentration from a model-based approach. This paper proposes data-driven models based on four machine-learning models to estimate the bacterial cell density with a limited dataset in a wastewater treatment plant. The performance results demonstrate that the machine-learning models (i.e., K-Nearest Neighbour (kNN), Random Forest (RF), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGB)) have the potential to estimate accurately the bacterial concentration. RF displays better bacteria estimation in the influent by 10.7% compared to GBR and 7.4% compared to XGB and kNN. Whereas for the effluent, XGB improved the estimation by 12.8%, 2.4%, 14.6% compared to GBR, RF, and kNN, respectively. Also, results show that conductivity as a single feature is the most significant parameter affecting the bacterial cell estimation in the influent stage for the four machine learning algorithms. Similarly, the chemical oxygen demand (COD) and turbidity have pronounced effects in the effluent stage. These results reveal potential signs of designing a universal data-driven model-based approach applicable for bacteria estimation at influent and effluent based on the minimum feature combinations (conductivity, COD, and turbidity).

关键词： Wastewater treatment plant data-driven models bacterial estimation models machine learning algorithms

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Video Streaming QoS Analysis with TIPHON Standard NDNts

Video Streaming QoS Analysis with TIPHON Standard NDNts

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International Conference on Wireless and Telematics (ICWT)

作者： I Made Aditya Putra J.D Lisdatul Alifa Muhammad Hilmi Fawwaz Nissha Kinaya Mauren Sibayak Ratna Mayasari Hasan Nur Arifin Syaiful Ahdan Ridha Muldina Negara Nana Rachmana Syambas Dept. of Telecommunication Engineering Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia

Named data networks are a new concept in network architecture that can process data transmission by name. This concept can be utilized in the case of video streaming. TIPHON is a standard used to support streaming multimedia activities. According to TIPHON, three factors-delay, packet loss, and throughput-can influence how a stream behaves. This study's goal is to evaluate the quality of service (QoS) of video streaming services using an NDN network that adheres to the TIPHON NDN standard. Several things can affect throughput, such as bottlenecks on the computer or network device used.

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A Framework of FAS-RIS Systems: Performance Analysis and Throughput Optimization

arXiv

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arXiv 2024年

作者： Yao, Junteng Lai, Xiazhi Zhi, Kangda Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Yuen, Chau Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Computer Science Guangdong University of Education Guangdong Guangzhou China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory the School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems. © 2024, CC BY.

关键词： Antennas

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A Novel F-SVM based on PSO for Gait Phase Recognition in Lower Limb Exoskeleton 18

A Novel F-SVM based on PSO for Gait Phase Recognition in Low...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lv, Pingping Zhang, Chi Yi, Feng Yuan, Ting Li, Shupei Zhang, Meitong Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9798350312201

To meet the requirements of lower limb exoskeleton robots for lightweight equipment and accurate gait phase detection under different speeds, the kinematics and dynamics information are investigated. In this paper, we propose a novel radius-margin-based supporting vector machine (SVM) model with particle swarm optimization algorithm (PSO) in feature space called PSO-F-SVM for gait phase detection. The proposed method considers both the maximization of margin and the minimization of radius and uses PSO to optimize the parameter in F-SVM, which can enhance the performance of the SVM classifier. Plantar pressure insoles with flexible and elastic sensors are designed for gait feature measurement. We conduct comparative experiments between the proposed method and other variants of SVM under four speeds. The experimental result indicates that the proposed method improves the accuracy by 2.68% compared with the standard SVM. Moreover, more than 98% accuracy can be obtained at four speeds, which demonstrates that the proposed method outperforms the other variants of SVM. © 2023 IEEE.

关键词： Support vector machines

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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The effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks

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Journal of Optical Communications 2024年第s1期45卷 s253-s259页

作者： Eid, Mahmoud M. A. Zaki Rashed, Ahmed Nabih Ahammad, Mohd. Sultan Paul, Bikash Kumar Ahmed, Kawsar Department of Electrical Engineering College of Engineering Taif University Taif Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Dhanmondi Sukrabad Dhaka1207 Bangladesh

This study presents the effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks. The received signal power and max Q factor are measured in the presence of vertical-cavity surface-emitting lasers (VCSELs) bias current and modulation current at maximum propagation distance for the wireless network. The signal is enhanced with increasing of bias and modulation peak currents of the VCSEL device. The optimum received power and max. Q factor is also examined at Tx./Rx. the pointing error of 0.1 mrad and propagation reach of 5 km at available bit rates transmission of 10 Gb/s. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Surface emitting lasers

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Image Inpainting and Classification Agent Training Based on Reinforcement Learning and Generative Models with Attention Mechanism

Image Inpainting and Classification Agent Training Based on ...

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2021 International Conference on Microelectronics, ICM 2021

作者： Ukwuoma, Chiagoziem C. Belal Bin Heyat, Md Masadeh, Mahmoud Akhtar, Faijan Zhiguang, Qin Bondzie-Selby, Emmanuel Alshorman, Omar Alkahtani, Fahad University of Electronic Science and Technology of China School of Information and Software Engineering Sichuan Chengdu China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Yarmouk University Hijjawi Faculty for Engineering Computer Engineering Deprtment Irbid21163 Jordan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu China Najran University Faculty of Engineering AlShrouk Trading Company Najran Saudi Arabia Najran University Faculty of Engineering Electrical Engineering Department Najran Saudi Arabia

ISBN: (纸本)9781665408394

What distinguishes the field of artificial intelligence (AI) from others is to develop fully independent agents that learn optimal behavior, change, and evolve solely through the communication of trial and error with the surrounding environment. Reinforcement learning (RL) can be seen in multiple aspects of Machine Learning (ML), provided the environment, reward, actions, the state will be defined. Agent training in previous years is seen to only relate to robotics, games, and self-driving cars. While trying to divert the focus of researchers from the view of self-driving cars, games, robots, etc. Here, we investigated using reinforcement learning in the aspect of task completion. We deployed our architecture in an inpainting task where the agent generates the distorted or missing image content into an eminent fidelity completed the image by using reinforcement learning to influence the generative model utilized. The Generative Adversary Network (GAN) problem of not being steady and challenging to train was overwhelmed by utilizing latent space representation. The dimension is reduced compared to the distorted or corrupted image in training the GAN. Then reinforcement learning was deployed to pick the correct GAN input to get the image's latent space representation that is most suitable for the current input of the missing or distorted image region. In this paper, we also learned that the trained agent enhances the accuracy in a classification task of images with missing data. We successfully examined the classification enhancement on images missing 30%, 50%, and 70%. © 2021 IEEE.

关键词： Reinforcement learning

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