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HWDSQP: A Historical Weighted and Dynamic Scheduling Quantum Protocol to Enhance Communication Reliability

IEEE Journal on Selected Areas in Communications 2025年

作者： Lin, Liwei Ma, Rongbo Wang, Zejian Cai, Zinuo Xu, Haochen Zhang, Baoheng Ma, Ruhui Buyya, Rajkumar Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Aerospace System Engineering Shanghai Shanghai201109 China University of Melbourne School of Computing and Information Systems MelbourneVIC Australia

Quantum computing holds the promise of solving problems difficult for classical computers. However, we are still in the era of Noisy Intermediate-Scale Quantum (NISQ) computers, necessary to establish effective distributed quantum communication protocols to distribute complex quantum computing tasks across different quantum computers for execution. Significant progress has been made in quantum communication technology, particularly in quantum path creation and resource scheduling. The establishment of quantum paths relies on quantum entanglement and quantum relay technologies, achieving long-distance, high-fidelity quantum state transmission through entanglement swapping between multiple relays. However, resources in quantum communication networks are limited and expensive, making efficient resource scheduling strategies crucial for improving overall network efficiency. To address these issues, we design a network protocol that includes the Historical Weighted Fidelity Routing (HWFR) algorithm and the Dynamic Multi-Priority Quantum Scheduling (DMPQS) algorithm to enhance communication reliability across quantum computers. Both algorithms aim to enhance the reliability of quantum links, optimize resource utilization, and adapt to dynamic changes in the links. The former algorithm dynamically selects the optimal path by considering factors such as link length, noise level, entanglement success rate, and quantum relay resource constraints, ensuring high-fidelity and reliable quantum communication. The latter dynamically adjusts request priorities based on the urgency of quantum service requests and fidelity requirements, optimizing resource utilization. Experimental results show that the proposed protocol performs excellently in terms of an average response time of requests and link utilization, effectively improving the utilization efficiency of network resources and the overall performance of the system. © 1983-2012 IEEE.

关键词： Qubits

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots via Dynamic Feedback Linearization in Forward and Backward Motion 10

Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Olyai, Mohammad Alipour, Khalil Tarvirdizadeh, Bahram Sorouri, Majid Ghamari, Mohammad Laboratory Department of Mechatronics Eng School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Tehran Iran Maynooth University Maynooth Department of Electronic Engineering Co. Kildare Ireland Islamic Azad University Science and Research Branch Department of Computer and Mechatronics Engineering Tehran Iran California Polytechnic State University Electrical Engineering Department San Luis ObispoCA93407 United States

ISBN: (纸本)9798331509972

One type of wheeled mobile robot widely used in public transportation and for carrying high payloads is the tractor-Trailer wheeled robots (TTWRs). This study considers a differentially-driven tractor under pure rolling conditions, which is subject to nonholonomic constraints. Controlling a TTWR in both backward and forward motion is challenging due to inherent instability. To address this issue and achieve trajectory tracking control for these systems, this paper employs dynamic feedback linearization (DFL) to overcome the limitations of static feedback linearization (SFL). The system's response will be examined under various trajectories and initial conditions for both forward and backward motion. © 2024 IEEE.

关键词： Tractors (truck)

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ChainPIM: A ReRAM-Based Processing-in-Memory Accelerator for HGNNs via Chain Structure

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Xiao, Wenjing Wang, Jianyu Chen, Dan Shi, Chenglong Ling, Xin Chen, Min Wu, Thomas School of Computer Electronics and Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China School of Computer Electronics and Information Guangxi University Nanning530004 China School of Computing National University of Singapore 119077 Singapore School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Pazhou Laboratory Guangzhou510330 China School of Electrical Engineering Guangxi University Nanning530004 China

Heterogeneous graph neural networks (HGNNs) have recently demonstrated significant advantages of capturing powerful structural and semantic information in heterogeneous graphs. Different from homogeneous graph neural networks directly aggregating information based on neighbors, HGNNs aggregate information based on complex metapaths. ReRAM-based processing-in-memory (PIM) architecture can reduce data movement and compute matrix-vector multiplication (MVM) in analog. It can be well used to accelerate HGNNs. However, the complex metapath-based aggregation of HGNNs makes it challenging to efficiently utilize the parallelism of ReRAM and vertices data reuse. To this end, we propose ChainPIM, the first ReRAM-based processing-in-memory accelerator for HGNNs featuring high computing parallelism and vertices data reuse. Specifically, we introduce R-Chain, which is based on a chain structure to build related metapath instances together. We can efficiently reuse vertices through R-Chain and process different R-Chains in parallel. Then, we further design an efficient storage format for storing R-Chains, which reduces a lot of repeated vertices storage. Finally, a specialized ReRAM-based architecture is developed to pipeline different types of aggregations in HGNNs, fully exploiting the huge potential of multi-level parallelism in HGNNs. Our experiments show that ChainPIM achieves an average memory space reduction of 47.86% and performance improvement by 128.29× compared to NVIDIA Tesla V100 GPU. © 1982-2012 IEEE.

关键词： Random access storage

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Joint Image and Feature Enhancement for Object Detection under Adverse Weather Conditions

Joint Image and Feature Enhancement for Object Detection und...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mengyu Yin Mingyang Ling Kan Chang Zijian Yuan Qingpao Qin Boning Chen School of Computer and Electronic Information Guangxi University Nanning China School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection under adverse weather conditions remains a challenging problem to date. To address this problem, a joint image and feature enhancement method called JE-YOLO is proposed. Firstly, a lightweight image enhancement network is used to enhance the low-quality image captured under adverse weather conditions. Secondly, to provide rich information for detection, two detection backbones are applied in parallel to extract features from both the low-quality image and its enhanced result. Afterwards, the extracted features are further enhanced by a foreground-guided feature refinement module (FFRM), which introduces a task-driven attention mechanism and explores inter-layer correlation. Finally, the enhanced features from different branches are fused by the adaptive multi-branch weighting (AMW) strategy, and then fed to the neck and head of detector. Experiments are carried out on both the low-light and foggy conditions, and the results demonstrate that compared with state-of-the-art (SOTA) methods, the proposed JE-YOLO is able to achieve the highest accuracy of detection in all cases. Code will be available at https://***/Murray-Yin/JE-YOLO.

关键词： Head Neural networks Object detection Detectors Feature extraction Neck Reliability

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An overview on the designs of distributed observers in LTI multi-agent systems

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Science China(Technological Sciences) 2021年第11期64卷 2337-2346页

作者： PEI YingQing GU HaiBo LIU KeXin LU JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

With the rapid development of information technologies and cloud computing, sensor networks play an increasingly important role in our society. Over the past few decades, distributed observer theory has attracted unprecedented attention due to its wide potential applications in different areas. Meanwhile, various approaches and algorithms have been proposed and investigated. The design of distributed observers is one of the frontier topics of system and control research, which has the significant theoretical values and broad application prospects. This paper attempts to review the representative models and the corresponding approaches for distributed observer design in linear time-invariant(LTI) systems. Firstly, the research backgrounds and main advances of distributed observer designs are briefly reviewed. Then, recent results of distributed observer designs for discrete-time and continuous-time LTI multi-agent systems(MASs) are introduced in detail, respectively. Finally, the prospects and the future work directions of the design of distributed observers are put forward. The main purpose of this paper is to promote the emerging topic on the designs of distributed observers, with focuses on the interdisciplinary interest from technological sciences.

关键词： multi-agent system linear time-invarient system distributed observer consensus

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From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation

arXiv

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arXiv 2024年

作者： Liu, Xin Hao, Chao Yu, Zitong Yue, Huanjing Yang, Jingyu School of Electrical and Information Engineering Tianjin University China Computer Vision and Pattern Recognition Laboratory School of Engineering Sciences Lappeenranta-Lahti University of Technology LUT Finland School of Computing and Information Technology Great Bay University China

The action anticipation task refers to predicting what action will happen based on observed videos, which requires the model to have a strong ability to summarize the present and then reason about the future. Experience and common sense suggest that there is a significant correlation between different actions, which provides valuable prior knowledge for the action anticipation task. However, previous methods have not effectively modeled this underlying statistical relationship. To address this issue, we propose a novel end-to-end video modeling architecture that utilizes attention mechanisms, named Anticipation via Recognition and Reasoning (ARR). ARR decomposes the action anticipation task into action recognition and sequence reasoning tasks, and effectively learns the statistical relationship between actions by next action prediction (NAP). In comparison to existing temporal aggregation strategies, ARR is able to extract more effective features from observable videos to make more reasonable predictions. In addition, to address the challenge of relationship modeling that requires extensive training data, we propose an innovative approach for the unsupervised pre-training of the decoder, which leverages the inherent temporal dynamics of video to enhance the reasoning capabilities of the network. Extensive experiments on the Epic-kitchen-100, EGTEA Gaze+, and 50salads datasets demonstrate the efficacy of the proposed methods. The code is available at https://***/linuxsino/ARR. Copyright © 2024, The Authors. All rights reserved.

关键词： Video analysis

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Accurate Parameter Estimation for PMSMs using electrical and Mechanical Models Considering Temperature Effect and Inverter Distortion

Accurate Parameter Estimation for PMSMs using Electrical and...

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2022 Chinese Automation Congress, CAC 2022

作者： Tong, Zhe Meng, Bo Ding, Beichen Feng, Guodong Lai, Chunyan Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Sun Yat-sen University School of Intelligent Systems Engineering Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China Concordia University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9781665465335

This paper presents a decoupled parameter estimation method for PMSM considering temperature rise and inverter distortion. Both electrical and mechanical models are explored to derive the estimation model to achieve better parameter estimation accuracy. Specifically, the proposed approach firstly performs the initial estimation of the PM flux, winding resistance and friction coefficient by considering the temperature effect, and then derives the decoupled model for inductance estimation using both the electrical and mechanical measurements. Compared with other approaches, our decoupled approach employs the mechanical model and considers the temperature effect, which can make the parameter identification more accurate. The experiment is performed on a test PMSM to verify the estimation performance of the proposed approach. © 2022 IEEE.

关键词： Parameter estimation

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Comparison of Machine Learning Classifiers for Exchange Rate Trend Forecasting 13

Comparison of Machine Learning Classifiers for Exchange Rate...

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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Sarmas, Elissaios Koutsellis, Themistoklis Ververidis, Christopher Papapolyzos, Thomas Choumas, Stylianos Bitsikas, Anastasios Doukas, Haris National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece Hypertech Sa Athens Greece CAPITAL.GR Sa Athens Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

The market of foreign exchange is one of the largest markets worldwide. However, predicting the price of exchange currency pairs is a very difficult problem due to the fact that exchange rate time series demonstrate a highly non-linear and non-stationary behavior, being affected by a series of parameters which are difficult to model efficiently. This study attempts to compare five machine learning and neural network classifiers: Logistic Regression model, Support Vector Classifier, Gaussian Naive Bayes, Random Forest and Multi-layer Perceptron. The most highly correlated features are evaluated and compared for predicting the day ahead trend of the Euro-United States Dollar (EUR-USD) currency pair. Results indicate that model selection is not as significant as the combination of the most important features for the accuracy of the prediction. © 2022 IEEE.

关键词： Forecasting

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