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Energy Efficient Scheduler of Aperiodic Jobs for real-time Embedded systems

International Journal of Automation and computing 2020年第5期17卷 733-743页

作者： Hussein El Ghor El-Hadi MAggoune University Institute of Technology Lebanese UniversityLebanon Sensor Networks and Cellular Systems(SNCS)Research Center UTSaudi Arabia Electrical Engineering Department University of Tabuk Sensor Networks and Cellular Systems(SNCS)Research Center 71491 TabukSaudi Arabia

Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of *** improvement is no longer the only metric by which optimality is *** fact,energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system's *** this work,we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy ***,we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling(DVFS)*** address a variant of earliest deadline first(EDF)scheduling algorithm called energy saving-dynamic voltage and frequency scaling(ES-DVFS)algorithm that is suited to unpredictable future energy production and irregular job *** prove that ES-DVFS cannot attain a total value greater than C/ˆSα,whereˆS is the minimum speed of any job and C is the available energy *** also investigate the implications of having in advance,information about the largest job size and the minimum speed used for the competitive factor of *** show that such advance knowledge makes possible the design of semi-on-line algorithm,ES-DVFS∗∗,that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive *** experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.

关键词： real-time systems energy efficiency aperiodic jobs scheduling dynamic voltage scaling low-power systems embedded systems

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real-time 3D Ultrasound Imaging System Based on a Hybrid Reconstruction Algorithm

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Chinese Journal of Electronics 2024年第1期33卷 245-255页

作者： Yifei LYU Yu SHEN Mingbo ZHANG Junchen WANG School of Mechanical Engineering and Automation Beihang University The First Medical Center General Hospital of Chinese PLA

As a safe and convenient imaging technology in clinical routine diagnosis, ultrasound imaging can provide real-time 2D images of internal tissues and organs. To realize real-time 3D image reconstruction, pixel nearest neighbor interpolation(PNN) reconstruction algorithm and Bezier interpolation algorithm are combined into a hybrid reconstruction algorithm. On this basis, a real-time interactive 3D ultrasound imaging system is *** temporal calibration and spatial calibration, the six degrees of freedom poses of 2D ultrasound images can be accurately collected. The 3D volume reconstructed by the proposed 3D reconstruction algorithm is visualized by volume rendering. A multi-thread software system allows parallel operation of data acquisition, 3D reconstruction,volume visualization and other functions. 3D imaging experiments on a 3D printing femur model, a neck phantom and the neck of human volunteers were performed for systematic evaluation. When the reconstruction voxel size was set to be(0.53 mm3, 1.03 mm3, 1.53 mm3), the reconstruction errors of the femur and trachea model were respectively(0.23 mm, 0.31 mm, 0.56 mm) and(0.62 mm, 0.88 mm, 1.41 mm). Clinical feasibility was demonstrated by application of the 3D ultrasound imaging on the neck of human volunteers.

关键词： Interpolation Three-dimensional displays Ultrasonic imaging Imaging Data visualization Reconstruction algorithms real-time systems

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Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 167-177页

作者： Xiuchun Xiao Chengze Jiang Qixiang Mei Yudong Zhang School of Electronics and Information Engineering Guangdong Ocean UniversityZhanjiangChina School of Cyber Science and Engineering Southeast UniversityNanjingChina School of Computing and Mathematical Sciences University of LeicesterLeicesterUK

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering *** the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence ***,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed ***,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed ***,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem *** with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

关键词： adaptive intelligent systems neural network real-time systems

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Double integral-enhanced Zeroing neural network with linear noise rejection for time-varying matrix inverse

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 197-210页

作者： Bolin Liao Luyang Han Xinwei Cao Shuai Li Jianfeng Li College of Computer Science and Engineering Jishou UniversityJishouChina School of Management Shanghai UniversityShanghaiChina School of Engineering Swansea UniversitySwanseaUK

In engineering fields,time-varying matrix inversion(TVMI)issue is often *** neural network(ZNN)has been extensively employed to resolve the TVMI ***,the original ZNN(OZNN)and the integral-enhanced ZNN(IEZNN)usually fail to deal with the TVMI problem under unbounded noises,such as linear ***,a neural network model that can handle the TVMI under linear noise interference is urgently *** paper develops a double integral-enhanced ZNN(DIEZNN)model based on a novel integral-type design formula with inherent linear-noise ***,its convergence and robustness are verified by deriva-tion *** comparison and verification,the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise *** experi-ments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear *** general,the DIEZNN model is an innovative work and is proposed for the first ***,the errors of DIEZNN are always less than 1�10−3 under linear noises,whereas the error norms of OZNN and IEZNN models are not convergent to *** addition,these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.

关键词： neural network real-time systems

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Formal Modeling of Frame Selection in Asynchronous TSN Communications

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Chinese Journal of Electronics 2024年第2期33卷 549-563页

作者： Ershuai LI Xuan ZHOU Jinjing SUN Huagang XIONG Feng HE School of Electronic and Information Engineering Beihang University

The asynchronous time-sensitive networking(TSN) based on IEEE 802.1Qcr is expected to be a promising solution for the asynchronous transmissions of safety-critical flows without the support of clock *** the asynchronous traffic shaping(ATS) mechanism is adopted to meet the deadline requirements for transmissions of safety-critical flow, it is necessary to formally verify the real-time properties and corresponding network ***, it is still unclear how to build an efficient formal model to evaluate different frame selection methods during the ATS scheduling process, which originate from the dominations of priority or eligibility time. In this paper, we present a formal modeling framework to compare the impacts of different frame selection on transmission sequence under the ATS mechanism. According to the priority level(p ATS) or eligibility time(e ATS) for flows, two transmission selection methods in ATS are modeled and compared. Then, we verify the real-time properties of ATS. The result shows that the shaping-for-free property can be satisfied with the p ATS method but can not be fulfilled with the e ATS method. Besides, the timing analysis results illustrate that the e ATS method can provide more fairness than the p ATS method for the transmission of low-priority flows in TSN networks.

关键词： System recovery real-time systems Delays Synchronization

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Partitioned k-Exclusion real-time Locking Protocol Motivated by Multicore Multi-GPU systems

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Journal of Electronic Science and Technology 2016年第3期14卷 193-198页

作者： Mao-Lin Yang Hang Lei Yong Liao Ze-Wei Chen School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China

Graphic processing units （GPUs） have been widely recognized as cost-efficient co-processors with acceptable size, weight, and power consumption. However, adopting GPUs in real-time systems is still challenging, due to the lack in framework for real-time analysis. In order to guarantee real-time requirements while maintaining system utilization ~in modern heterogeneous systems, such as multicore multi-GPU systems, a novel suspension-based k-exclusion real-time locking protocol and the associated suspension-aware schedulability analysis are proposed. The proposed protocol provides a synchronization framework that enables multiple GPUs to be efficiently integrated in multicore real-time systems. Comparative evaluations show that the proposed methods improve upon the existing work in terms of schedulability.

关键词： Embedded systems graphic processing units(GPUs) heterogeneous systems locking protocols multicore real-time systems

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VNet: a versatile network to train real-time semantic segmentation models on a single GPU

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Science China(Information Sciences) 2022年第3期65卷 272-273页

作者： Wenxing LI Ning LIN Mingzhe ZHANG Hang LU Xiaoming CHEN Xiaowei LI College of Computer Science and Technology Guizhou University State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

Dear editor,Modern semantic segmentation, which has important applications such as medical image analysis, image editing, and video surveillance, has made remarkable progress using deep convolution neural network models. Recently, an efficient real-time semantic segmentation method has received considerable attention, as intelligent edge devices not only have faster inference speed requirements for semantic segmentation models but also cannot rely on the cloud services of data centers. There are two feasible approaches to develop an efficient semantic segmentation model.

关键词： Efficiency Machine learning real-time systems Segmentation Light-weight

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The Novel Instance Segmentation Method Based on Multi-Level Features and Joint Attention

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Chinese Journal of Electronics 2023年第5期32卷 1160-1168页

作者： XU Bowen LU Yinan WU Tieru GUO Xiaoxin College of Computer Science and Technology Jilin University College of Mathematics Jilin University

Instance segmentation is an important task in computer vision. In order to enhance the multilevel features expression ability of the segmentation networks, a novel module is proposed in this paper. Firstly,we design a weighted bi-directional feature fusion way by computing the weight distribution function of bi-directional feature pyramid network. Secondly, we propose a joint attention mechanism to effectively filter different levels of feature information by adopting serial and parallel ways to combine the channel attention and spatial attention modules. At the same time, the module uses dynamic convolution to stabilize the calculation speed while improve the 6.7% mean average precision of *** experiments on the COCO dataset demonstrate that the module can effectively improve the performance of the existing instance segmentation networks.

关键词： Computer vision Convolution Neural networks Bidirectional control Information filters real-time systems Task analysis

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HRPose: real-time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 189-198页

作者： GUAN Qi SHENG Zihao XUE Shibei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

关键词： Knowledge engineering Computational modeling Pose estimation Grasping Benchmark testing Feature extraction real-time systems

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An Adaptive Interactive Multiple-Model Algorithm Based on End-to-End Learning

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Chinese Journal of Electronics 2023年第5期32卷 1120-1132页

作者： ZHU Hongfeng XIONG Wei CUI Yaqi Institute of Information Fusion Naval Aviation University

The interactive multiple-model(IMM) is a popular choice for target tracking. However, to design transition probability matrices(TPMs) for IMMs is a considerable challenge with less prior knowledge, and the TPM is one of the fundamental factors influencing IMM performance. IMMs with inaccurate TPMs can make it difficult to monitor target maneuvers and bring poor tracking results. To address this challenge, we propose an adaptive IMM algorithm based on end-to-end learning. In our method, the neural network is utilized to estimate TPMs in real-time based on partial parameters of IMM in each time step, resulting in a generalized recurrent neural network. Through end-to-end learning in the tracking task, the dataset cost of the proposed algorithm is smaller and the generalizability is stronger. Simulation and automatic dependent surveillance-broadcast tracking experiment results show that the proposed algorithm has better tracking accuracy and robustness with less prior knowledge.

关键词： Adaptation models Target tracking Recurrent neural networks Costs Simulation Robustness real-time systems

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