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Heterogeneous embedded Resource Management under Space-based Edge computing Environment with Kubernetes Device Plugin 3

Heterogeneous Embedded Resource Management under Space-based...

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3rd ieee international conference on Power, Electronics and Computer applications, ICPECA 2023

作者： Wang, Xin Yang, Xinyu Ye, Wenjie Yan, Lei Cao, Suzhi University of Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665472784

Edge devices are widely applied in space scenarios for their compact size and diminished power consumption. To avoid the collision of different applications, containerizing applications becomes a more welcomed option compared to virtual machines for its light-weightiness and short loading latency. Kubernetes, considered a de facto framework for orchestrating containers, allows automatic allocations of resources connected to nodes, but it only supports management on CPU, I/O, network, and storage by default. Supported devices can be extended with its device plugin mechanism however, official device plugins might not work on edge devices, as their functionalities are constrained due to its compactness, such as NVidia TX2 that is not supported by the official Nvidia GPU device plugin. Moreover, devices vary in type, and architecture and even some do not support operating systems, making it unintelligent to develop device plugins separately. Therefore, managing device plugins becomes an urgent problem for edge cloud providers. In this paper, we proposed and implemented a paradigm of orchestrating devices that allows Kubernetes manage edge devices, even if they do not run operating systems. As for the obstacles of being unable to report the device topology of a single node to the scheduler, we proposed an approach to hijack the scheduling process without establishing a new scheduler. We conducted experiments and the results showed that the platform we proposed can enable Ku-bernetes to manage various devices without device plugins, both on on-board embedded devices and non-OS-supported devices. We tested its performance and capacity with a simulated device, and the result shows that it only causes 6.34 % overhead, and its embedded scheduling method has an adequate performance compared to optimum method, which is only 7.7 % slower. © 2023 ieee.

关键词： Containers

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realtime Object Recognition Method Inspired by Multimodal Information Processing in the Brain for Distributed Digital Twin systems 18

Realtime Object Recognition Method Inspired by Multimodal In...

引用

18th ieee international Wireless Communications and Mobile computing conference (IWCMC)

作者： Seki, Ryoga Kominami, Daichi Shimonishi, Hideyuki Murata, Masayuki Fujiwaka, Masaya Osaka Univ Grad Sch Informat Sci & Technol Osaka Japan NEC Corp Ltd Tokyo Japan NEC Corp Ltd Syst Platform Res Labs Yokohama Kanagawa Japan

ISBN: (纸本)9781665467490

Recently, digital twins have been paid much attention as a major application towards Beyond 5G/6G network, and real-time object recognition methods are key technology to digitize the real world as a digital twin. However, it is challenging to make a fast and accurate decision on what the object is from real-time streaming information such as video because accurate object recognition algorithms require a huge computation. To satisfy delay requirement of digital twin applications, such computations have to be moved from cloud to edges or even small terminal devices, where computing capacity is very limited. Thus, recognition mechanisms have to be simplified for small devices but they would result in degraded accuracy. In this paper, we focus on the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information, to improve accuracy of simplified recognition mechanisms. We first propose a unimodal object recognition mechanism based on the Bayesian attractor model, which continuously recognizes objects from noisy streaming media data. Then, we extend the mechanism with Bayesian causal inference to fuse the results of unimodal media recognition. Through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.

关键词： Mobile AR digital twin multimodal recognition Bayesian attractor model Bayesian causal inference

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Method for Finding the Maximum Dynamic Flow in Periodic Fuzzy Graphs

Method for Finding the Maximum Dynamic Flow in Periodic Fuzz...

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2024 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2024

作者： Bozhenyuk, Alexander Gerasimenko, Evgeniya Nikashina, Polina Southern Federal University Department Of Information Security Systems Taganrog Russia

ISBN: (纸本)9798350395013

The article presents a method for determining the maximum flow value in dynamic networks using periodic graphs. These networks are generalized networks that have a wide range of practical applications, such as water distribution, currency exchange, and transportation of perishable or valuable goods. Due to the variation in bandwidth values in different areas of the network, dynamic networks are used. These parameters are presented in a fuzzified form, accounting for environmental factors and human activities. The selection of periodic graphs is based on the presence of cycles and frequency of time intervals. These types of networks can be applied in real-world transportation scenarios. © 2024 ieee.

关键词： Graphic methods

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Auto-tuning of Large-Scale Parallel computing applications Based on Bayesian Optimization 4

Auto-tuning of Large-Scale Parallel Computing Applications B...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhang, Jinhu Sun, Bin Sun, Jianpeng Yu, Hao Xie, Xiguo Zhengzhou University Zhengzhou China Co. Ltd. Beijing China

ISBN: (纸本)9798331531225

The performance of large-scale parallel computing applications is highly dependent on the parameter settings within complex systems. Due to the high-dimensional and nonlinear nature of kernel environment parameter spaces, traditional manual tuning methods are both labor-intensive and inefficient when exploring optimal parameter combinations, often leading to suboptimal performance. To address this issue, this paper proposes an automated parameter tuning framework based on Bayesian optimization, aimed at efficiently exploring complex high-dimensional parameter spaces and predicting better-performing parameter combinations. This framework builds a surrogate model, iteratively selects potentially efficient parameter configurations, and integrates SHAP to analyze the influence of each parameter on performance optimization. We validated the effectiveness of this framework in several typical large-scale parallel computing application scenarios, including NAS Parallel Benchmarks (NPB), quantum chemistry simulation software Quantum ESPRESSO and VASP, and the Weather Research and Forecasting model (WRF). Experimental results show that Bayesian optimization significantly shortens the overall tuning time and performs traditional methods such as genetic algorithms in terms of performance optimization. Experimental results show that Bayesian optimization significantly shortens the overall tuning time by 50% and improves application performance by up to 11%, outperforming traditional methods such as genetic algorithms in terms of performance optimization. Furthermore, SHAP analysis reveals the importance of key kernel parameters in enhancing application performance. © 2024 ieee.

关键词： Bayesian networks

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Analysis, Trends, and Utilization of Security Information and Event Management (SIEM) in Critical Infrastructures 10

Analysis, Trends, and Utilization of Security Information an...

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Sashwin, K. Nithika, K.S. Priyadharshini, S. Maduvant, S.P. Saranya Sri Eshwar College of Engineering Department of Computer Science and Business Systems Computer Science and Business Systems Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Security Information and Event Management (SIEM) systems have become essential assets in the realm of cybersecurity. They fulfill a central role in the prevention, detection, and response to cyber threats. Over time, SIEM solutions have matured into comprehensive platforms that offer extensive visibility, enabling organizations to pinpoint high-risk areas and take proactive measures to mitigate potential threats. This proactive approach not only bolsters security but also streamlines incident response, reducing both costs and response times. Recent developments in the field have witnessed the convergence of SIEM systems with big data analytics tools, opening doors to more sophisticated threat detection and response capabilities. This survey delves into the core features of widely adopted SIEM platforms and evaluates the external factors influencing the trajectory of the SIEM landscape over the medium and long term. Furthermore, the study presents a catalog of potential enhancements for the next generation of SIEMs, offering a thorough analysis of their advantages and practical applications within critical infrastructure settings. © 2024 ieee.

关键词： Critical infrastructures

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Optimization and Design of Coding Algorithm for Monitoring System Based on embedded Structure 7

Optimization and Design of Coding Algorithm for Monitoring S...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Huang, Rubing Zhao, Lianyi Anhui Vocational and Technical College School of Information Engineering Hefei China Anhui Water Resources and Hydropower Vocational and Technical College School of Mechanical & Electrical Engineering Hefei China

ISBN: (纸本)9781665478571

In view of the problems of large transmission delay, low video image compression rate, and high network transmission overhead in traditional monitoring systems. According to the characteristics of ARM platform and H.264 encoding algorithm, an algorithm optimization method based on x264 encoder embedded platform is proposed. The New Three-step Search method is used to realize motion compensation prediction encoding. Based on streaming media technology, The RTP and RTSP protocol realizes the packaging and real-time transmission of video stream data. The optimized encoder is applied to the embedded video monitoring system, which effectively improves the video encoding efficiency of the system. © 2022 ieee.

关键词： Signal encoding

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2nd international conference on Intelligent systems Design and Engineering applications, ISDEA 2023

2nd International Conference on Intelligent Systems Design a...

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2nd international conference on Intelligent systems Design and Engineering applications, ISDEA 2023

ISBN: (纸本)9789819963027

The proceedings contain 14 papers. The special focus in this conference is on Intelligent systems Design and Engineering applications. The topics include: Cloud Distribution Forecasting Model Using Ground Altitude Information and CNN;UX and Industry 5.0: A Study in Repairing Equipment Using Augmented reality;augmented reality Towards Industry 5.0: Improving Voice and Tap Interaction Based on User Experience Feedback;food Allergen Database for Japanese Restaurants and Its Application to Menu Recommendation System to Foreign Travelers;analysis of Grasping Mechanism for Random Regular Object of Improved Prosthetic Robotic Arm;a Human-Like Intelligent Swing System Using the Machine Vision Approach;multi-robot Positioning and Anti-interference Based on Ultra Wide Band;designing Smart Disinfection Hangers in the Covid-19 Epidemic;an Automatic Kiss Camera System Using Deep Neural Network Technique;based on embedded Technology to real-time Control for Analogous Active Suspension System;Intelligent Manufacturing Transformation Development Strategy of Jilin City Automobile Industry: A Research Based on SWOT-AHP Model;Persistent UAV Formation Flight by Dynamic Agent Replacement and Leader Selection.

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On Multivariate Linear Regression applications 11

On Multivariate Linear Regression Applications

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11th international conference on Electrical, Electronic and computing Engineering, IcETRAN 2024

作者： Jovanović, Aleksa Krstić, Aleksandra Vujnović, Sanja Durović, Željko University of Belgrade School of Electrical Engineering The Department of Signals and Systems Belgrade Serbia

ISBN: (纸本)9798350386998

Linear regression is a very simple machine learning model that is supposed to find linear relations between input and output data. Its use is limited since real-world random variables are almost never linearly correlated, but when there is limited amount of data for training, using other larger and more sophisticated models might be impossible. Even though simple linear relations are hard to come by in nature, more complex mathematical dependencies can often be linearized i.e. approximated using linear models. Additionally, by preprocessing the input data using nonlinear mathematical operations linear regression can artificially account for nonlinear relations. This methodology was tested on real data representing measurements in thermal power plant while the goal was predicting the specific energy of the coal that was being burnt in the plant. Estimating coals caloric value is of tremendous value since using this information power plant can be configured to run more efficiently. © 2024 ieee.

关键词： Multiple linear regression

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EarlyBird: Energy belongs to those who wake up early

EarlyBird: Energy belongs to those who wake up early

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ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Hugo Reymond Jean-Luc Béchennec Mikaël Briday Sébastien Faucou Isabelle Puaut Erven Rohou Univ Rennes Inria CNRS IRISA France Nantes Université École Centrale Nantes CNRS LS2N Nantes France

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

By relying on ambient energy, battery-less devices significantly increase the autonomy of IoT devices, enabling maintenance-free operation in remote locations. However, due to the scarcity of ambient energy, these devices rely on capacitors to buffer energy, and alternate between power-off phases where the device is harvesting energy and computation bursts. In most existing techniques, the device resumes execution only when the capacitor is full. However, we argue that doing so is sub-optimal. Instead, we advocate that waking-up the device sooner may yield better performance since the microcontroller consumes less power when operating at lower voltage. To this extent, we introduce EarlyBird, a technique that automatically computes a fine-tuned wake-up voltage for each resume point. EarlyBird leverages static analysis to determine how much energy is needed before resuming from a given program location, and provides a runtime library to enforce the early wake-up strategy. We evaluated how EarlyBird improves existing checkpointing techniques and results show an increase in the number of benchmarks executed per minute of up to 5.65×.

关键词： Performance evaluation Checkpointing Runtime library Microcontrollers Capacitors Voltage Static analysis Benchmark testing real-time systems Internet of Things

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Memory-Efficient Gaussian Fitting for Depth Images in real time 39

Memory-Efficient Gaussian Fitting for Depth Images in Real T...

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ieee international conference on Robotics and Automation (ICRA)

作者： Li, Peter Zhi Xuan Karaman, Sertac Sze, Vivienne MIT Cambridge MA 02139 USA

ISBN: (纸本)9781728196817

computing consumes a significant portion of energy in many robotics applications, especially the ones involving energy-constrained robots. In addition, memory access accounts for a significant portion of the computing energy. For mapping a 3D environment, prior approaches reduce the map size while incurring a large memory overhead used for storing sensor measurements and temporary variables during computation. In this work, we present a memory-efficient algorithm, named Single-Pass Gaussian Fitting (SPGF), that accurately constructs a compact Gaussian Mixture Model (GMM) which approximates measurements from a depthmap generated from a depth camera. By incrementally constructing the GMM one pixel at a time in a single pass through the depthmap, SPGF achieves higher throughput and orders-of-magnitude lower memory overhead than prior multi-pass approaches. By processing the depthmap row-by-row, SPGF exploits intrinsic properties of the camera to efficiently and accurately infer surface geometries, which leads to higher precision than prior approaches while maintaining the same compactness of the GMM. Using a low-power ARM Cortex-A57 CPU on the NVIDIA Jetson TX2 platform, SPGF operates at 32fps, requires 43KB of memory overhead, and consumes only 0.11J per frame (depthmap). Thus, SPGF enables real-time mapping of large 3D environments on energy-constrained robots.

关键词： Three-dimensional displays Program processors Fitting Memory management Robot sensing systems Cameras Throughput

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