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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Optimization of Correlation Coefficient Calculation for Channel Matrix Characterization on FPGA 25

Optimization of Correlation Coefficient Calculation for Chan...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Sibio, Simona Ben Smida, Souheil Ding, Yuan Goussetis, George Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9798350330014

Current and future wireless communication networks need to support the demand for an ever-increasing data rate. Modern wireless communication air-interfaces use various modulation schemes with embedded pilot signals that are used to probe the channel state information (CSI). Since frequency resources are increasingly scarce, it becomes favorable to reduce the bandwidth occupied by the resources allocated to the pilot signals without compromising the system performance. In a MIMO scenario, the knowledge of the correlation coefficients between receiving antennas can help to optimize the transmission reducing the need of additional transmitted data. In this paper, a novel hardware implementation for calculating the correlation coefficient between two unknown signals is presented. The proposed approach aims to maintain a low mean-square error (MSE) with the minimum number of samples. Additionally, hardware optimizations are applied to the design resulting in a valuable reduction of the required digital resources and latency. The measurements, based on Xilinx XC7Z020 FPGA SoC, are compared with the results in a simulation environment to demonstrate its efficacy in the supported 5G NR frequency ranges. © 2023 ieee.

关键词： Field programmable gate arrays (FPGA)

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Recurrent Penalized Splines for real-time Forecasting of the Parameters of Technical systems

Recurrent Penalized Splines for Real-Time Forecasting of the...

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2023 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2023

作者： Kochegurova, Elena Kaida, Anastasiia Galkina, Maria Tomsk Polytechnic University Information Systems and Technologies Dep. Tomsk Russia Tomsk Polytechnic University School of Nuclear Science and Engineering Tomsk Russia

ISBN: (纸本)9781665475952

Forecasting time series (TS) from their historical data is used in many preventive monitoring and decision support systems in industry. For systems operating in real-time, an urgent problem is to reduce forecasting time without compromising other quality indexes (accuracy and trend continuation). The purpose of this paper is to describe a forecasting model based on a recurrent penalized P-spline. The spline model has a compact computational scheme where the efficiency is achieved by using a short input of historical data a simple mathematical description. The model was compared to 11 most well-known forecasting algorithms. The performance of the proposed model compared quite well to the competitive forecasting algorithms. The model was used to forecast a real-world TS of a technological parameter of a boiler plant. © 2023 ieee.

关键词： Decision support systems

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Evoking and Evaluation of Non-invasive Sensory Feedback for applications in Neural Rehabilitation: A Pilot Study

Evoking and Evaluation of Non-invasive Sensory Feedback for ...

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2023 ieee international conference on real-time computing and Robotics, RCAR 2023

作者： Dong, Yuanzhe Tang, Xi Jiang, Naifu Xie, Jun Liang, Wenyuan Shang, Peng Li, Guanglin Fang, Peng Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems The Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China National Research Center for Rehabilitation Technical Aids Beijing100176 China

ISBN: (纸本)9798350327182

Protheses are very useful for limb amputees to restore their lost motor functions. However, most commercially available prostheses lack an intuitive and natural sensory feedback function, which may strongly limit the performance of prosthetic control and lowers the acceptance of amputees. As a non-invasive stimulation method, Transcutaneous Electrical Nerve Stimulation (TENS) has been proven to effectively evoke intuitive and natural sensations, which is practical for upper-limb amputees in clinical applications. In this paper, we explored the relationship between TENS modalities and induced sensory information based on able-bodied subjects by using electroencephalogram (EEG). We computed the Event-related Potential (ERP) and Brain Electrical Activity Mapping (BEAM) to evaluate the performance of sensation evoking. The results demonstrated that the TENS applied on wrist areas with proper parameters could stably induce multiple types of intuitive sensations of different fingers, including touching, vibrating, and flapping, and the mapping relationship between stimulation modality and sensory feedback were objectively verified and evaluated with EEG. This pilot study might provide a support for future researches on restoration of sensory paths and feedback for upper-limb amputees. © 2023 ieee.

关键词： Electroencephalography

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Empowering computing: AI and CPU Synergy 4

Empowering Computing: AI and CPU Synergy

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kaushik, Priyanka Chamarthi, Subbarao Wasim, Javed Khatoon, Naghma Kumar, B Rajesh Tyagi, Nancy Chandigarh University Dept. of Cse Chandigarh India Lingayas Vidyapeeth Dept. of Mechanical Engineering Faridabad India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Computing & It Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

The objective of this research is to redefine the future of computing systems, focusing on enhancing performance, efficiency, and adaptability by identifying the cutting-edge innovations driven by AI in CPU design. This study conducts a comprehensive literature review and empirical evaluations to survey front-end optimizations, new architectural concepts, and emerging paradigms that could potentially revolutionize CPU technology. By leveraging advanced artificial intelligence techniques such as machine learning, deep learning, and reinforcement learning, novel CPU architectures and optimization strategies can be explored, unlocking unprecedented computational capabilities. The integration of AI methods into CPU architecture addresses key concerns including scalability, energy efficiency, security, and reliability. Moreover, real- world case studies across various domains such as autonomous systems, healthcare informatics, and edge computing demonstrate the transformative potential of AI- driven CPUs. This paper provides insights into the roadmap for the next wave of innovation in AI-infused computing, outlining potential avenues for further research and collaboration. © 2024 ieee.

关键词： Deep reinforcement learning

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Sustainable Fertilizers in Coffee Plantation: Hybrid Recommendation for Agricultural Producers 5

Sustainable Fertilizers in Coffee Plantation: Hybrid Recomme...

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5th international conference on Inventive Research in computing applications, ICIRCA 2023

作者： Raveena, S. Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

To improve soil fertility and crop yields, early agricultural practices include the use of natural materials such as animal dung, compost, and crop wastes. They apply fertilizers at random not realizing the deficient type or amount. As a result, crop yield is immediately affected, as well as soil acidification and harm to the uppermost stratum. The hybrid recommendation system with a machine learning strategy was applied in the proposed framework. In contrast to numerous algorithms for estimating and evaluating historical yield concerning nutrient parameters. Farmers can use an interactive web source to post concerns to get a prompt reply with accurate fertilizer recommendations. Beyond that, extensive testing on real-world datasets obtained 92.5% accuracy, demonstrating the usefulness and efficiency of the proposed methodology. © 2023 ieee.

关键词： Recommender systems

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Mitigating Emergency Stop Collisions in AGV Fleets in Case of Control Failure 17

Mitigating Emergency Stop Collisions in AGV Fleets in Case o...

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7th ieee international conference on Robotic computing (IRC)

作者： Ogorelysheva, Natalia Vasileva, Anna Stadtler, Jannik Roidl, Moritz Howar, Falk Fraunhofer Inst Mat Flow & Logist IoT Dortmund Germany Fraunhofer Inst Mat Flow & Logist Embedded Syst Dept Dortmund Germany Fraunhofer Inst Mat Flow & Logist AI & Autonomous Syst Dortmund Germany Tech Univ Dortmund Chair Mat Handling & Warehousing Dortmund Germany Tech Univ Dortmund Chair Software Engn Dortmund Germany

ISBN: (纸本)9798350395747

Automated guided vehicles (AGVs) are an essential area of research for the industry to enable dynamic transport operations. Furthermore, AGV-based multi-robot systems (MRS) are being utilized in various applications, e.g. in production or in logistics. Most research today focuses on ensuring that the system is operational, which is not always achieved. In daily use, faults and failures in an AGV-based MRS are most likely inevitable. So industrial systems must support some safety methods, e.g. an emergency stop function. Although emergency stop functions are designed to prevent larger issues, their usage leads to a failure of the control system, since the affected systems are typically shut down immediately. Depending on the AGV type, an uncontrolled behaviour can occur. In case of control failure, this behaviour can lead to collisions and associated high costs. In this paper, we present and compare three approaches for avoiding collisions in an intralogistics scenario in the case of control failure. In the said scenario, the trajectory planing is being adapted to minimize or avoid collisions. The first approach calculates the next collision-free time slot when an emergency stop occurs and continues the planned trajectories until this time. The second approach calculates several alternative trajectories with the existing trajectory planning without considering emergency stop collisions. The third approach aims to completely avoid emergency stop collisions by extending trajectory planning to include the detection of possible emergency stop collisions. We evaluate and compare the approaches by employing multiple metrics to assess their performance, including runtime, number of collisions that occur, and the system's throughput. We thoroughly discuss and analyse the results, offering insights into the strengths and weaknesses of the approaches.

关键词： automated guided vehicles AGV multi-robot system collision avoidance emergency stop collision mitigation control failure fleet robotics fleet management system trajectory planning emergency stop collisions

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Enhancing the Security of Healthcare Data in Cloud computing Environments 2

Enhancing the Security of Healthcare Data in Cloud Computing...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sajwan, Himani Kumari, Anjali Chauhan, Garima Kumar, Raj School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9798331530389

Cloud computing has completely evolved the way health care data is managed, but it also introduces critical security vulnerabilities that can compromise the privacy of patient information and threaten regulatory compliance. Herein, we present one of the most severe challenges: securing EHRs in a cloud environment with access to data while maintaining HIPAA compliance. We introduce a novel hybrid framework combining homomorphic encryption with blockchain-based access control and dynamic audit logging. The framework was used with a three-phase experimental approach and was deployed over five major healthcare institutions while being analyzed for 2.3 million patient records over 18 months. Results showed a high success rate of 99.97% against denial-of-service attacks while showing reductions in latency time about data retrieval up to 3 4 % compared to traditional methods of encryption. Security-incident-based variation was reduced up to 78%, and 99.99% data integrity was achieved during concurrent access scenarios. Cost analysis shows that overheads in security management amount to a reduction of 4 5 % against conventional systems. Our results show that the proposed framework is far better for healthcare data security, along with improved operational efficiency and regulatory compliance. This work contributes to the literature by providing a scalable and cost-efficient solution addressing the most complex security requirements of modern health care systems in cloud environments. The fact that the framework is implemented successfully in real life seems to reflect its effectiveness for mass-scale implementation in health organizations. © 2025 ieee.

关键词： Cloud security

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real-time Prediction Method of Remaining Useful Life Based on TinyML

Real-time Prediction Method of Remaining Useful Life Based o...

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2022 ieee international conference on real-time computing and Robotics, RCAR 2022

作者： Liu, Hongbo Song, Ping Qie, Youtian Li, Yifan Beijing Institute of Technology Mechanical Engineering Beijing China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Tiny Machine Learning (TinyML) is a new research area aimed at designing and developing machine learning (ML) techniques for embedded systems and IoT units. Due to the limited resources of embedded system, neural network pruning is widely used to reduce resource occupation. To solve the problem that the Remaining Useful Life (RUL) of the equipment is difficult to calculate accurately and in real time, a pruning method based on L1 norm weight was designed to reduce the memory footprint and computational load of the neural network, and a lightweight two-dimensional convolutional neural network was constructed. Experimental results show that compared with random pruning, this method greatly reduces the influence of neural network parameter reduction on the accuracy of inference results. Meanwhile, a retraining method based on Adam optimization was used to make the RUL curve predicted by the retrained model more close to the real RUL curve. When the weight parameters are reduced by 30%, the model still maintains good prediction accuracy, and can realize the real-time prediction of RUL in the embedded system with limited resources. © 2022 ieee.

关键词： embedded systems

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Unsupervised Deeply embedded Online Multi-Product Recommendation of Commercial Banks Based on Behavior Data Trajectory 5

Unsupervised Deeply Embedded Online Multi-Product Recommenda...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Liu, Xiaopeng Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798350379945

This study presents an innovative approach to online multi-product recommendation for commercial banks based on unsupervised deep learning and user behavior data trajectories. The model first captures and analyzes user behavior data trajectories, using filtering techniques and Markov modeling to understand and predict user behavior patterns. Then, the model proposes an unsupervised deep learning algorithm, DSRET, which integrates expert trust and similarity computations to provide real-time product recommendations. The proposed algorithm is evaluated using real data from ZA Bank, which demonstrates its superior performance in terms of recommendation accuracy and computational efficiency compared to existing methods. The results show that the designed approach achieves an average RMSE value of 0.768, highlighting its effectiveness in providing accurate recommendations while ensuring fast computation. This study contributes to the advancement of online recommendation systems for commercial banks and lays the foundation for future research aimed at further optimizing and extending the proposed algorithm. © 2024 ieee.

关键词： Data accuracy

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PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs 40

PlatoD2GL: An Efficient Dynamic Deep Graph Learning System f...

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40th ieee international conference on Data Engineering, ICDE 2024

作者： Huang, Xing Lin, Dandan Huang, Weiyi Sun, Shijie Wen, Jie Chen, Chuan WeChat Tencent Inc. Shenzhen Institute of Computing Sciences China

ISBN: (纸本)9798350317152

Recently, huge interests in both academic and in-dustry have been posed to Graph Neural Network due to its power on revealing the topological information inside the data. To support the real-world applications, most of (if not all) which contain large-scale graphs with billions of edges, a number of graph-based deep learning systems have been proposed and implemented. However, all of them fail to efficiently process the dynamic graphs in terms of both memory and time cost. The state-of-the-art suffers from two issues: (1) expensive memory consumption due to the huge indexing overhead of numerous key-value pairs in traditional key-value topology storage and (2) inefficient dynamic updating due to the heavy updates on indexing structures for weighted neighbor sampling. In this paper, we proposed a Dynamic Graph-based Learning System PiatoD2GL to address above two issues. Specifically, we design a novel and effective non-key-value data structure, termed samtree, for dynamic topology storage, which largely reduces the memory cost. In addition, we propose an efficient sampling indexing structure FSTable by utilizing Fenwick tree, guaranteeing the efficiency of both dynamic updates and weighted sampling. Comprehensive experiments have demonstrated that, for dynamic updating the graph topology, the efficiency of our system by up to 79.8% and by up to 6.3 times in terms of memory and time cost, respectively. Now, our system serves the major traffic in WeChat Platform for training various GNN models. © 2024 ieee.

关键词： Learning systems

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