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19.02Gbps/25m Underwater Wireless optical Communication Adopting Probabilistic Constellation Shaping QAM-DMT Transmission

19.02Gbps/25m Underwater Wireless Optical Communication Adop...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Zhang, Tianyi Tian, Jiahan Wang, Yuan Fei, Chao Zhang, Junwei Zhang, Fei Xie, Yitong Zhang, Guowu Wang, Gaoxuan Du, Ji Hong, Xiaojian He, Sailing National Engineering Research Center for Optical Instruments College of Optical Science and Engineering Ningbo Innovation Center Zhejiang University Hangzhou310058 China Photonics Research Institute The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong School of Electrical Engineering Royal Institute of Technology Department of Electromagnetic Engineering StockholmSE-10044 Sweden

ISBN: (纸本)9798350312614

We experimentally demonstrated probabilistic constellation shaping quadrature amplitude modulation discrete multitone (PCS QAM-DMT) for 25-m underwater wireless optical communication (UWOC) system with a net data rate of 19.02Gbps. 28.1% capacity improvement is achieved in comparison with conventional bit-power loading DMT scheme. To the best of our knowledge, this is the highest net data rate ever reported for a single LD in current UWOC. © 2023 IEEE.

关键词： laser communication probabilistic constellation shaping (PCS) underwater wireless optical communication (UWOC)

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Applying Reinforcement Learning to Combine Multiple Swarm-based Algorithms

Applying Reinforcement Learning to Combine Multiple Swarm-ba...

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2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

作者： Lira, Rodrigo Cesar Macedo, Mariana Siqueira, Hugo Valadares Bastos-Filho, Carmelo Computer Engineering Department University of Pernambuco Recife Brazil Center for Collective Learning ANITI University of Toulouse Occitania France Department of Electrical Engineering Federal University of Technology Paraná Brazil

ISBN: (纸本)9798350348071

Swarm intelligence is a very efficient field for the optimization of high-dimensional functions. Nevertheless, choosing the best swarm-based algorithm is still challenging because it requires expertise in the field. Here, we propose to use reinforcement learning to dynamically select the swarm-based techniques to solve a benchmark function based on the current simulation state. First, we created a swarm capable of modifying its metaphor over iteration. Next, we created a reinforcement learning environment to solve benchmark functions. Then, we trained Proximal Policy Optimization to select the well-suited metaheuristic (GWO, GPSO or LPSO) to solve Rastrigin and F3 based on the information retrieved from the simulation. Our proposal reached competitive results in all simulated scenarios. Moreover, we found that the use of GPSO is consistently more efficient at the middle of the convergence and that using GWO is more efficient than using the other selected algorithms at the beginning of the convergence. Future works will bring us more robustness in combining swarm-based techniques while decreasing the computational cost. Thus, we show that reinforcement learning has the potential to overcome the effort of choosing the well-suited metaheuristic for a specific problem. © 2023 IEEE.

关键词： Swarm intelligence

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Battery Degradation Modeling in Hybrid Power Plants: An Island System Unit Commitment Study 4

Battery Degradation Modeling in Hybrid Power Plants: An Isla...

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4th Annual IEEE Kansas Power and Energy Conference, KPEC 2023

作者： Huang, Jianqiao Fang, Xin Zhou, Xinyang Tan, Jin Dong, Shuan Hoke, Andy National Renewable Energy Laboratory Power System Engineering Center GoldenCO United States Illinois Institute of Technology Electrical and Computer Engineering Chicago United States Mississippi State University Electrical and Computer Engineering Department StarkvilleMS United States

ISBN: (纸本)9798350335408

As hybrid power plants (HPPs), such as photovoltaic (PV) and battery combinations, become increasingly important in power systems with high renewable energy penetration to address PV variability and ensure grid stability. This paper focuses on the urgent need to model the coordination between PV and battery systems in HPPs while accounting for battery degradation. We present a generation scheduling model that explicitly incorporates PV-battery hybridization in the unit commitment problem. Moreover, the cost function of the HPP scheduling problem endogenously considers battery degradation with adjustable weights to strike a balance between minimizing production costs and prolonging battery life, particularly when providing energy arbitrage and ancillary services. Using a realistic island system simulation, we demonstrate that accounting for battery degradation in the scheduling problem can significantly extend battery life with only minor additional production costs. © 2023 IEEE.

关键词： Cost functions

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SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity 22

SELF-Clustering Graph Transformer Approach to Model Resting ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Thapaliya, Bishal Akbas, Esra Sapkota, Ram Ray, Bhaskar Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331520526

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the sub clusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures. © 2025 IEEE.

关键词： Brain mapping

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Identification of Lung Disease via X-Ray Images Using Knowledge Distillation and Vision Transformer 4

Identification of Lung Disease via X-Ray Images Using Knowle...

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4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Galih, Brilliant Syifaa Nang Novamizanti, Ledya Akhyar, Fityanul School of Electrical Engineering Telkom University Bandung Indonesia School of Electrical Engineering Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia Universiti Tunku Abdul Rahman Department of Computer and Communication Technology Perak Kampar Malaysia

ISBN: (纸本)9798331506490

Lung disease is a widespread health concern affecting millions of people worldwide. Accurate diagnosis is essential for effective treatment, yet it often requires expert interpretation of chest X-rays to identify specific disease types. Unfortunately, many healthcare facilities, particularly in remote or resource-limited areas, lack access to skilled radiologists, leading to delayed diagnoses and potentially poor patient outcomes. To address this gap, this research introduces an automated lung disease classification system that leverages knowledge distillation for accurate and efficient diagnosis. The proposed system employs the Vision Transformer (ViT) architecture as the teacher model and MobileNet as the student model, aiming to classify chest X-ray images into five categories: normal, COVID-19, viral pneumonia, bacterial pneumonia, and tuberculosis. Knowledge distillation enables the student model to learn critical diagnostic features from the teacher model, resulting in a compact, high-performing model suitable for real-world deployment in settings with limited computational resources. Experimental evaluation on an original dataset of X-ray images demonstrated a classification accuracy of 93.07%, with high precision, recall, and F1-score, indicating robust performance across multiple disease types. This system holds promise as a valuable tool for assisting healthcare providers in making quick, accurate diagnoses, potentially improving treatment timelines and outcomes for patients in underserved regions. © 2024 IEEE.

关键词： Lung cancer

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Analysis on D2D Heterogeneous Networks with State-Dependent Priority Traffic

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computers, Materials & Continua 2023年第2期74卷 2981-2998页

作者： Guangjun Liang Jianfang Xin Linging Xia Xueli Ni Yi Cao Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina National and Local Joint Engineering Laboratory of Radio Frequency Integration and Microassembly Technology Nanjing University of Posts and TelecommunicationsNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center NanjingChina Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

In this work,we consider the performance analysis of state dependent priority traffic and scheduling in device to device(D2D)heterogeneous *** are two priority transmission types of data in wireless communication,such as video or telephone,which always meet the requirements of high priority(HP)data transmission *** there is a large amount of low priority(LP)data,there will be a large amount of LP data that cannot be *** situation will cause excessive delay of LP data and packet dropping *** order to solve this problem,the data transmission process of high priority queue and low priority queue is *** the priority jump strategy to the priority queuing model,the queuing process with two priority data is modeled as a two-dimensionalMarkov chain.A state dependent priority jump queuing strategy is proposed,which can improve the discarding performance of low priority *** quasi birth and death process method(QBD)and fixed point iterationmethod are used to solve the causality,and the steady-state probability distribution is further ***,performance parameters such as average queue length,average throughput,average delay and packet dropping probability for both high and low priority data can be *** simulation results verify the correctness of the theoretical ***,the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.

关键词： Stochastic geometry queuing theory D2D heterogeneous networks quasi-birth and death process

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

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computers, Materials & Continua 2023年第5期75卷 2491-2508页

作者： Ashir Javeed Ana Luiza Dallora Johan Sanmartin Berglund Arif Ali Peter Anderberg Liaqat Ali Aging Research Center Karolinska InstitutetStockholm17165Sweden Department of Health Blekinge Institute of TechnologyKarlskrona37141Sweden School of Health Sciences University of SkövdeSkövdeSE 54128Sweden Department of Computer Science University of Science and Technology BannuBannu28100Pakistan Department of Electrical Engineering University of Science and Technology BannuBannu28100Pakistan

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity,mortality,and *** there is a consensus that dementia is a multifactorial disorder,which portrays changes in the brain of the affected individual as early as 15 years before its onset,prediction models that aim at its early detection and risk identification should consider these *** study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data,which comprised 75 *** are two automated diagnostic systems developed that use genetic algorithms for feature selection,while artificial neural network and deep neural network are used for dementia *** proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%,sensitivity of 93.15%,specificity of 91.59%,MCC of 0.4788,and performed superior to other 11 machine learning techniques which were presented in the past for dementia *** identified best predictors were:age,past smoking habit,history of infarct,depression,hip fracture,single leg standing test with right leg,score in the physical component summary and history of TIA/*** identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.

关键词： Dementia prediction feature selection genetic algorithm neural networks

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A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1304-1318页

作者： Linling Wang Daqi Zhu Wen Pang Chaomin Luo the Shanghai Engineering Research Center of Intelligent Maritime Search&Rescue and Underwater Vehicles Shanghai Maritime UniversityShanghai 201306China the Shanghai Maritime University Shanghai 201306China the School of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai 200093China the Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMS 39762 USA

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control *** stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

关键词： Autonomous underwater vehicle(AUV) event-triggered control fixed-time consensus formation obstacle avoidance improved artificial potential field and leader-follower strategy(IAPF-LF)

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Modernizing Home Protection: An IoT-Driven Approach with Smart Lock and Android Application 14

Modernizing Home Protection: An IoT-Driven Approach with Sma...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Rajput, Summaiya Talpur, Noureen Boudville, Rozan Abro, Ghulam Mustafa E Talpur, Beenish Zahid, Faizan Mehran University of Engineering and Technology Department of Telecommunications Engineering Jamshoro Pakistan Computer Information Science Department Seri Iskandar32610 Malaysia Electrical Engineering Studies College of Electrical Engineering Universiti Teknologi MARA Permatang Pauh Pulau Pinang13500 Malaysia University of Petroleum and Minerals Dhahran Interdisciplinary Research Center for Aviation and Space Exploration King Fahd Dammam31261 Saudi Arabia University of Catania Electronics and Computer Engineering Department of Electrical Italy

ISBN: (纸本)9798350364507

In the field of access control locks, conventional methods often require users to download applications with Bluetooth connectivity, and biometric measures such as fingerprints or face recognition that introduce challenges like access to only authorized parties, limited device accessibility and intricate registration processes. To address these challenges, the research paper presents an innovative IoT-based Smart Door Lock System, aiming to redefine access control. Diverging from traditional approaches, this system eliminates the need for application downloads and simplifies the complexities tied to biometrics. The core innovation lies in the simplicity of a user-friendly 4-digit PIN for primary access, prioritizing ease of use. To overcome real-time access constraints, the system introduces One-time Password (OTP) and One Touch Access (OTA) modes. These modes eliminate the intricacies linked with traditional methods, ensuring immediate and hassle-free access for both authorized and unauthorized users. © 2024 IEEE.

关键词： Face recognition

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