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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Fanzi Zeng Chen Liu Jisheng Xu Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China

ISBN: (纸本)9781509040940

In this paper, we presented a fairness-aware scheduling scheme in OFDM-based cognitive radio networks. Our objective is to maximize the systems energy efficiency while maintain the fairness among secondary users. In addition, many factors will be considered such as the channel quality, the interferences taken to the primary system and the total transmission power of the cognitive radio system. The resource allocation process consists of two steps. First, we use an improved heuristic algorithm to assign subcarriers to secondary users and then use an iterative algorithm to distribute power to each subcarrier. Simulation results demonstrate that our scheme is able to gain a higher level for energy efficiency and guarantee the minimum transmission requirements of secondary users.

关键词： Resource management Heuristic algorithms Interference OFDM Quality of service Cognitive radio Iterative methods

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A Power Model Combined of Architectural Level and Gate Level for Multicore Processors

A Power Model Combined of Architectural Level and Gate Level...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Manman Peng Yan Hu Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China

ISBN: (纸本)9781479914449

Low power consumption is becoming a critical factor for multicore processors. As the multicore processor design complexity increases, power estimation for multicore processors has gained more importance. This paper presents a new power model combined of architectural level and gate level for multicore processors. The model maps the multicore processors to a combination of building blocks, and estimates the gate-level power of these blocks using parameterized RTL. Then, the power numbers are made in the form of look-up tables, and integrated in architecture simulators. The experiments show that for peak power estimation, an excellent accuracy has been reached and simulation performance is greatly improved compared to the gate level.

关键词： Program processors Computational modeling Multicore processing Logic gates Random access memory Hardware Analytical models

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The EDF with Dual Virtual Deadline Algorithm for Mixed Criticality Task System

The EDF with Dual Virtual Deadline Algorithm for Mixed Criti...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Lining Zeng Cheng Xu Renfa Li Key Laboratory for Embedded and Network Computing Hunan University Changsha Hunan Province China

ISBN: (纸本)9781538665664;9781538665657

In the area of real-time embedded systems, one of the hot topics is scheduling the mixed-criticality tasks to execute the LO tasks better when guaranteeing the correctness of HI tasks. In this paper, we research the schedule mechanism for dual-criticality tasks. Firstly, we modify the correctness condition of MC scheduling. Then, we present an optimized MC scheduling algorithm EDF -DVD, which assigns scale factors for both HI jobs and LO jobs. We analyze the different situation based on system utilizetion and present the strategy of calculating the value of scale factor corresponding to the utilization. The experiments show that our mechanism has better performance in scheduling the dual-criticality tasks.

关键词： Task analysis Schedules Scheduling embedded systems Switches Scheduling algorithms Real-time systems

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AutoToolVCC: Variable consistency check tool for automotive software projects

AutoToolVCC: Variable consistency check tool for automotive ...

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作者： Xie, Pingfu Wang, Yunlong He, Bo Xie, Guoqi College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China United Automotive Electronic Systems Co. Ltd. Shanghai China

Automotive software systems have become increasingly complex and huge, leading to long development cycle and increased development difficulty. When a complete automotive software project has reached a scale of several gigabytes, the complicated development process is prone to produce the following two irregular problems: (1) variable inconsistency problem and (2) code coexistence problem. These irregular problems may eventually lead to serious consequences, such as car crashes. In response to the above irregular problems, we develop a variable consistency check tool called AutoToolVCC for automotive software projects. AutoToolVCC divides the variable consistency check process into two stages: (1) extracting all variable information from the automotive software project and (2) checking the consistency of the variables through the variable preprocessing technique and the variable attribute check technique. After an actual check of multiple automotive software projects (approximately 500–1000 MB in size) provided by an automotive manufacturer, the accuracy of variable consistency check is as high as 100%, and the check time only takes about 10–30 s, whereas the pervious plain method needs 100–600 s. © 2020 John Wiley & Sons, Ltd.

关键词： Accidents

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Sensor data fusion based on an improved dempaster-shafer evidence theory in vehicular cyber-physical systems

Sensor data fusion based on an improved dempaster-shafer evi...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： Mengmeng Ma Jiyao An Zhong Huang Zhangbao Cao Key Laboratory of Embedded and Network Computing of Hunan Province Hunan University Changsha PR China

ISBN: (纸本)9781479977895

In Vehicular Cyber-Physical Systems (VCPS), the collected sensor data are always uncertain and conflicting. Dempster-Shafer (DS) evidence theory can effectively deal with uncertain information, but the Dempster's rule may produce counter-intuitive results when the information is conflicting. This paper proposed an improved approach for combining conflicting evidence with different weighting factors based on a novel dissimilarity measure. Firstly, a new dissimilarity measure mixing fuzzy nearness with the introduced correlation coefficient is proposed to characterize the divergence degree between two basic probability assignments (BPAs). Then, the weighting factors are developed by using the proposed dissimilarity measure. Finally, the Dempster's rule is chose to combine the revised sources. Simulation experiment shows that the improved method can effectively solve the problem of sensor data fusion in VCPS with better convergence performance.

关键词： Cyber-physical systems Yttrium Real-time systems Weight measurement Data integration Correlation coefficient Reliability

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TrafficGPT: Towards Multi-Scale Traffic Analysis and Generation with Spatial-Temporal Agent Framework

arXiv

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arXiv 2024年

作者： Ouyang, Jinhui Zhu, Yijie Yuan, Xiang Wu, Di Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha Hunan410082 China

The precise prediction of multi-scale traffic is a ubiquitous challenge in the urbanization process for car owners, road administrators, and governments. In the case of complex road networks, current and past traffic information from both upstream and downstream roads are crucial since various road networks have different semantic information about traffic. Rationalizing the utilization of semantic information can realize short-term, long-term, and unseen road traffic prediction. As the demands of multi-scale traffic analysis increase, on-demand interactions and visualizations are expected to be available for transportation participants. We have designed a multi-scale traffic generation system, namely TrafficGPT, using three AI agents to process multi-scale traffic data, conduct multi-scale traffic analysis, and present multi-scale visualization results. TrafficGPT consists of three essential AI agents: 1) a text-todemand agent that is employed with Question & Answer AI to interact with users and extract prediction tasks through texts;2) a traffic prediction agent that leverages multi-scale traffic data to generate temporal features and similarity, and fuse them with limited spatial features and similarity, to achieve accurate prediction of three tasks;and 3) a suggestion and visualization agent that uses the prediction results to generate suggestions and visualizations, providing users with a comprehensive understanding of traffic conditions. Our TrafficGPT system focuses on addressing concerns about traffic prediction from transportation participants, and conducted extensive experiments on five realworld road datasets to demonstrate its superior predictive and interactive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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Schedule length minimization of parallel applications with energy consumption constraints using heuristics on heterogeneous distributed systems

Schedule length minimization of parallel applications with e...

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作者： Xie, Guoqi Xiao, Xiongren Li, Renfa Li, Keqin College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China Department of Computer Science State University of New York AlbanyNY United States

Energy consumption is one of the primary design constraints in heterogeneous parallel and distributed systems ranging from small embedded devices to large-scale data centers. The problem of minimizing the schedule length of an energy consumption-constrained parallel application has been studied recently in homogeneous systems with a shared memory. To adopt the heterogeneity and distribution of high-performance computing systems, this study solves the problem of minimizing the schedule length of an energy consumption-constrained parallel application in heterogeneous distributed systems based on a dynamic voltage and frequency scaling energy-efficient design technique. The aforementioned problem is divided into 2 subproblems in this study, namely, satisfying energy consumption constraint and minimizing schedule length. The first subproblem is solved by transferring the energy consumption constraint of the application to that of each task, whereas the second subproblem is solved by heuristically scheduling each task with low time complexity. Experiments using both fast Fourier transform and Gaussian elimination parallel applications show that the actual energy consumption values do not always exceed but are close to the given energy consumption constraints. In addition, the minimum schedule lengths are generated using the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Energy utilization

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Online object tracking based on local sparse representation in a Bayesian inference framewo

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Journal of Computational Information Systems 2015年第11期11卷 3919-3928页

作者： Liu, Zekun Xu, Cheng Key Laboratory for Embedded Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha China

Significant research has been devoted to object tracking. Most existing algorithms can track the object well in the controlled environment. However, they may fail in the presence of drastic appearance changes or complex environment variation. In the paper, for robustness object tracking, we proposed an algorithm based on the local sparse representation. Different from most of the traditional object representation which only consider the holistic representation, in our proposed algorithm, the image patch of the object is represented sparsely in the form of a linear combination of dictionary elements constructed online to deal with the target appearance changes. And the aim of the object tracking is posed as a binary classification problem. A linear classifier is learned for discriminate the object target from the background. Furthermore, we combine them in a Bayesian inference framework. To alleviate the tracking drift, we proposed a two-stage particle filtering method. Experiments on several benchmark video sequences has demonstrate that our proposed algorithm performs favorably contrasted with other several state-of-art algorithms. ©, 2015, Binary Information Press. All right reserved.

关键词： Bayesian networks

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Neighbor Diversification-Based Collaborative Filtering for Improving Recommendation Lists

Neighbor Diversification-Based Collaborative Filtering for I...

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International Workshop on Intelligent Communication and Social networks

作者： Chao Yang Cong Cong Ai Renfa Li Key Laboratory for Embedded and Network Computing of Hunan University

ISBN: (纸本)9781479909735

Recommendation systems are popular information filtering tools that help people find what they want. Accuracy is the most widely used metric for evaluating recommendation systems. Recently, many research works have focused on new measurements beyond the accuracy of recommendation systems. In this paper, we propose a neighbor diversification collaborative filtering algorithm to improve the recommendation lists. By using Movielens dataset for empirical analysis, we investigated the influence of neighbor diversity to the recommendation accuracy, diversity, novelty and coverage. Intensive experimental results proved the efficiency of our proposed algorithm for improving recommendation lists.

关键词： Recommendation System Neighbor Diversification Diversity Novelty Coverage

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IoT data analytics using deep learning

arXiv

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arXiv 2017年

作者： Xie, Xiaofeng Wu, Di Liu, Siping Li, Renfa Key Laboratory for Embedded Networking Computing of Hunan Province Hunan University

Deep learning is a popular machine learning approach which has achieved a lot of progress in all traditional machine learning areas. Internet of thing (IoT) and Smart City deployments are generating large amounts of time-series sensor data in need of analysis. Applying deep learning to these domains has been an important topic of research. The Long-Short Term Memory (LSTM) network has been proven to be well suited for dealing with and predicting important events with long intervals and delays in the time series. LTSM networks have the ability to maintain long-term memory. In an LTSM network, a stacked LSTM hidden layer also makes it possible to learn a high level temporal feature without the need of any fine tuning and preprocessing which would be required by other techniques. In this paper, we construct a long-short term memory (LSTM) recurrent neural network structure, use the normal time series training set to build the prediction model. And then we use the predicted error from the prediction model to construct a Gaussian naive Bayes model to detect whether the original sample is abnormal. This method is called LSTM-Gauss-NBayes for short. We use three real-world data sets, each of which involve long-term time-dependence or short-term time-dependence, even very weak time dependence. The experimental results show that LSTM-Gauss-NBayes is an effective and robust model. Copyright © 2017, The Authors. All rights reserved.

关键词： Long short-term memory

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