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EEG and fMRI Artifact Detection Techniques: A Survey of Recent Developments

SN Computer Science 2023年第5期4卷 528页

作者： Mili, Rahma Bouaziz, Bassem Maalel, Ahmed Gargouri, Faiez Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Faculty of Economics and Management of Sfax University of Sfax Sfax 3021 Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Higher Institute of Computer and Multimedia of Sfax Sfax 3021 Tunisia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba 2010 Tunisia Higher Institute of Applied Sciences and Technology University of Sousse Sousse 4003 Tunisia

The evolution of different techniques for exploring cerebral activity and the development of signal processing and analysis methods have enabled a better understanding of the dynamic cerebral mechanisms in favor of fine diagnosis and characterization of certain diseases such as dementia and epilepsy. Integration of functional magnetic resonance imaging (fMRI) with electroencephalography (EEG) has offered the possibility of understanding new insights into neuroscientific studies because of the higher temporal and spatial measurements of brain activity when compared with the use of each technique separately. However, the experimental limitations of fMRI/EEG are mainly the numerous artifacts, mainly due to switching of magnetic field gradients and cardiac activity. To provide a comprehensive overview of this research area, a systematic mapping study has been conducted. This mapping study aims to identify the topics studied within the artifacts’ detection in EEG and fMRI data, as well as examine the adopted research methodologies, identify the gaps in the current research and point to directions for future research. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artifacts Deep learning EEG fMRI Systematic mapping study

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Circuit level implementation of the Reduced Quantum Genetic Algorithm using Qiskit 16

Circuit level implementation of the Reduced Quantum Genetic ...

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16th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2022

作者： Ardelean, Sebastian Mihai Udrescu, Mihai Politehnica University of Timisoara Advanced Computing Systems and Architectures Laboratory Department of Computer and Information Technology Timisoara300223 Romania

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

Genetic Algorithm (GA) are common probabilistic optimization methods inspired by the process of natural selection. Quantum computers promise substantial speedups over conventional machines, and libraries allow the emulation of circuits on a quantum computer in different highly configurable noise models and even run on quantum computers. Therefore, we need to analyze this class of heuristic methods in the quantum context. We propose a circuit level implementation of the Reduced Quantum Genetic Algorithm (RQGA) using Python and Qiskit. Our main goal is to analyze the circuit complexity from the perspectives of the number of qubits required and the number of quantum gates used. To achieve our goal, we instantiate the framework for solving the knapsack problem, examine the results in a simulated environment, and analyze the circuit's complexity. © 2022 IEEE.

关键词： Heuristic methods

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HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection

HybridCRS-TMS: Integrating Collaborative Recommender System ...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Rekik, Mouna Grati, Rima Benmohamed, Ichrak Boukadi, Khouloud Higher Institute of Transport and Logistics of Sousse Tunisia University of Sfax Multimedia Information systems and Advanced Computing Laboratory Tunisia Zayed University College of Technological Innovation Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897587061

The pervasive influence of smartphones and mobile internet has revolutionized journey planning, particularly transportation. With navigation applications delivering real-time information, recommender systems have emerged as crucial tools for enhancing the travel experience. This paper introduces HybridCRS-TMS, a unique Hybrid Collaborative Recommender System for Transport Mode Selection, leveraging a dataset of 260 passengers. Through advanced data mining techniques, specifically k-Nearest Neighbors (k-NN) for collaborative recommendations and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis for objective evaluation, the system provides personalized transportation mode recommendations. The model not only demonstrates exceptional performance but also showcases the synergy between collaborative and objective decision-making approaches, contributing to efficient, personalized, and well-informed travel solutions. This study underscores the system’s versatility, illustrating its ability to optimize travel choices through a hybrid recommendation framework that integrates both collaborative and objective criteria. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Nearest neighbor search

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of Computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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Computational Complexity Analysis of Ant Colony Clustering Algorithms: Application to Student's Grouping Problem

Computational Complexity Analysis of Ant Colony Clustering A...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Chniter, Malak Abid, Abir Kallel, Ilhem Kanoun, Slim Mir@cl Lab: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax ReGIM-Lab: Research Groups in Intelligent Machines Tunisia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time. © 2022 IEEE.

关键词： Computational complexity

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Parameter-efficient fine-tuning of Whisper for low-resource speech recognition

Parameter-efficient fine-tuning of Whisper for low-resource ...

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Artificial Intelligence, Networking and information Technology (AINIT), International Seminar on

作者： Yunpeng Liu Dan Qu Department of Artificial Intelligence Laboratory for Advanced Computing and Intelligence Engineering School of Information Systems Engineering University of Information Engineering Henan China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper's parameter-efficient fine-tuning capabilities, the advantages and disadvantages are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper's low-resource speech recognition performance using two parameter-efficient fine-tuning strategies with limited supervised data from multiple low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper's performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available.

关键词： Seminars Costs Target recognition Error analysis Speech recognition Data models Task analysis

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Two-mode squeezing over deployed fiber coexisting with conventional communications

Two-mode squeezing over deployed fiber coexisting with conve...

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Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Chapman, Joseph C. Miloshevsky, Alexander Lu, Hsuan-Hao Rao, Nageswara Alshowkan, Muneer Peters, Nicholas A. Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods for Engineered Systems Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We demonstrate distribution of two-mode squeezing to two separate locations and implement joint homodyne detection. We also show multiplexing with multiple classical signals. We distribute squeezing over several 5-km ... 详细信息

ISBN: (纸本)9781957171272

关键词： Analog to digital converters Homodyne detection Multiplexing Optical networks Phase noise Squeezed states

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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