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Hardware Software Co-design Approch for ECG Signal Analysis 1

1st international conference on Machine Intelligence and Computer science Applications, ICMICSA 2022

作者： Bendahane, Bouchra Jenkal, Wissam Laaboubi, Mostafa Latif, Rachid Ibn Zohr University National School of Applied Science System Engineering and Information Technology Laboratory Agadir Morocco

ISBN: (数字)9783031293139

ISBN: (纸本)9783031288456

An essential tool for diagnosing heart diseases is the electrocardiogram (ECG) signal. The accuracy of the diagnosis is impacted by the noise that occurs while this signal is being acquired. Denoising turns into a foundational step in this context. DWT-ADTF is an effective ECG signal-denoising method. This work tries to provide an HW/SW co-design solution to this method. The signal is well denoised, according to the simulation results of the developed designs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of Computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Efficient Remote Memory Paging for Disaggregated Memory systems 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Wang, Tao Liu, Haikun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Memory disaggregation has attracted increasing attention in recent years because it is a cost-efficient approach to scale memory capacity for applications in a data center. However, the latency of remote memory access is a major concern in disaggregated memory systems. This paper presents VANDI, a virtual memory paging mechanism that allows applications to use remote memory pools transparently. VANDI enables effective data caching and prefetching mechanisms to address the problem of high access latency in disaggregated memory systems. VANDI exploits a low-complexity cache replacement strategy to optimize the asynchronous staging queue so that the remote write latency can be significantly reduced. VANDI can also prefetch data in multi-granularity from a remote memory pool in an adaptive manner, and thus further improves the hit rate of the local cache to reduce the read latency of remote memory. Our extensive experiments using micro-benchmarks show that VANDI can improve the performance of typical remote paging system–Infiniswap by up to 15 × –102 ×. VANDI can also improve the performance of state-of-the-art disaggregated memory system–Valet by 1.2 × –2.7 ×. For typical machine learning workloads, VANDI can achieve 20% to 80% performance improvement compared with the state-of-the-art Valet. © 2023, Springer Nature Switzerland AG.

关键词： Paging systems

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Unknown Task Selection and Worker Recruitment Using Two-Stage Multi-Armed Bandit in Crowdsensing

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IEEE Internet of Things Journal 2025年第13期12卷 24441-24456页

作者： Song, Haoyuan Li, Peng Du, Rui Yu, Hai Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hubei Polytechnic University School of Computer Huangshi435003 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Mobile Crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This paper tackles these challenges by modeling the long-term multi-task, multi-worker selection problem as a Two-Stage Multi-Armed Bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a Combinatorial MAB (CMAB) problem. We propose an extended Upper Confidence Bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods. © 2014 IEEE.

关键词： Personnel selection

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Real-time 3D Object Detection based on Dynamic Sparse Voxel Transformer in Mining Area 22

Real-time 3D Object Detection based on Dynamic Sparse Voxel ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Sun, Shengdi Wang, Zhangyu Liu, Runsen Yang, Songyue Wang, Liyun Liu, Wentao School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements. © 2024 IEEE.

关键词： Trucks

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Identification of Key Signaling Pathways and Novel Computational Drug Target for Depression and Coronary Artery Disease

Identification of Key Signaling Pathways and Novel Computati...

引用

2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Morshed, Monir Uddin, Muhammad Shahin Daffodil International University Department of Computing and Information System Dhaka1216 Bangladesh Department of Computer Science and Engineering Dhaka1230 Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798331529765

Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions. © 2024 IEEE.

关键词： Blood vessels

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AKDC: Ambiguous Kernel Distance Clustering Algorithm for COVID-19 CT Scans Analysis

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IEEE Transactions on systems, Man, and Cybernetics: systems 2024年第10期54卷 6218-6229页

作者： Singh, Pritpal Huang, Yo-Ping The Quantum Optimization Research Lab Department of Data Science and Analytics Central University of Rajasthan Ajmer305817 India National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City23741 Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung41349 Taiwan

Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this study, a novel soft clustering algorithm, the ambiguous kernel distance clustering (AKDC) algorithm, is presented. This algorithm is developed by applying ambiguous set theory and the Gaussian kernel function. The ambiguous set theory defines the ambiguities inherent in each feature with four membership values: 1) true;2) false;3) true-ambiguous;and 4) false-ambiguous. The degree of membership values here forms a low-dimensional feature space that is not linearly distributed. Therefore, these nonlinearly distributed membership values are mapped into a high-dimensional feature space using the Gaussian kernel function. This study focuses on performing cluster analysis of computerized tomography scans of COVID-19 (CTSC-19) cases using AKDC. COVID-19, recognized as one of the most life-threatening diseases of this century, is highly contagious, and early diagnosis may prevent one-to-one transmission. Extensive empirical studies have been conducted with different types of CTSC-19 to demonstrate its effectiveness against existing kernel-based clustering and nonkernel-based clustering algorithms, namely mercer kernel fuzzy c-mean (MKFCM), kernel generalized FCM (KGFCM), kernel intuitionistic fuzzy entropy c-means (KIFECMs), morphological reconstruction and membership filtering clustering (FRFCM), and intuitionistic FCM based on membership information transferring and similarity measurements (IFCM-MS). The effectiveness of the proposed algorithm compared to the existing algorithms is evaluated using standard statistical metrics, such as dice index (DI), Jaccard index (JI), structural similarity index (SI), and correlation coefficient (CC). The empirical results show that AKDC is more effective than existing algorithms based on DI, JI, SI, and CC. © 2013 IEEE.

关键词： Clustering algorithms

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Cable segmentation based on Mask2Former in open-pit mining area 22

Cable segmentation based on Mask2Former in open-pit mining a...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Liyun Zhou, Bin Yang, Songyue Liu, Wentao Li, Huazhi Sun, Shengdi School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications. © 2024 IEEE.

关键词： Mine trucks

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Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain systems 28th

Anole: A Lightweight and Verifiable Learned-Based Index for...

引用

28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Chang, Jian Li, Binhong Xiao, Jiang Lin, Licheng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031306365

Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index’s error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10 × and reduces the storage overhead by 99.4 % compared with the state-of-the-art vChain+. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Event-Triggered Mechanism-Based MPC for Path-Tracking Control of Four-Wheel Steering Vehicles 22

Event-Triggered Mechanism-Based MPC for Path-Tracking Contro...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Xiangyu Xu, Guoyan Li, Han Chen, Peng Xia, Qi Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

In this study, we tackle the path-tracking problem of a nonlinear four-wheel steering vehicle dynamics model subject to model mismatches and propose a model predictive control (MPC) algorithm based on an event-triggered mechanism (ET-MPC). The goal is to maintain closed-loop control performance while reducing the computational and communication burdens of traditional MPC. We introduce an ET-MPC framework utilizing a model-free reinforcement learning agent with proximal policy optimization (PPO). This agent interacts with the MPC system, progressively learning to determine the optimal event-triggered mechanism. To enhance exploration and training efficiency, we incorporate the Long Short-Term Memory (LSTM) technique into PPO. Experimental results show that the proposed ET-MPC framework, combined with reinforcement learning for reward optimization, demonstrates superior overall performance in path-tracking control of four-wheel steering vehicles. © 2024 IEEE.

关键词： Four wheel steering

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