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Autonomous navigation of a small-scale ground vehicle using low-cost IμGPS integration for outdoor applications 13

Autonomous navigation of a small-scale ground vehicle using ...

引用

13th Annual ieee international systems conference, SysCon 2019

作者： Almeida, Humberto Pessoa Junior, Cairo Lucio Nascimento Santos, Douglas Soares Dos Leles, Michel Carlo Rodrigues Division of Electronic Engineering Instituto Tecnológico de Aeronáutica São José dos Campos SP12228-900 Brazil Department of Technology Federal University of São João Del-Rei Ouro Branco MG36420-000 Brazil

ISBN: (纸本)9781538683965

this paper describes the development of an autonomous navigation system for a small-scale ground vehicle which uses four independent DC motors for propulsion and an Arduino Mega2560 as its onboard computing plataform. the vehicle sensory system is composed of a low-cost Inertial Measurement Unit (which includes 3 magnetometers) and a standard GPS receiver. the onboard computer sends data from the onboard sensors to a Ground Control Station (GCS) and receives navigation commands from it. these commands are then used to drive the ground vehicle's DC motors using PWM (Pulse-Width Modulation) signals. the Ground Control Station software implements an Extended Kalman Filter for sensor data fusion. An ethernet shield and an ethernet wifi bridge are used for wireless communication between the vehicle onboard computer and the Ground Control Station. A two-wheeled vehicle model was used to develop the vehicle controller and to tune the Kalman Filter parameters. Simulations and real-world test results of the proposed autonomous navigation procedure are presented and discussed for a path-following experiment. © 2019 ieee.

关键词： Digital storage

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A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks

A New Clustering-Based Technique for the Acceleration of Dee...

引用

international conference on Machine Learning and applications (ICMLA)

作者： Erion Vasilis Pikoulis Christos Mavrokefalidis Aris S. Lalos Industrial Systems Institute Greece University of Patras Greece

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

Deep learning and especially the use of Deep Neural Networks (DNNs) provides impressive results in various regression and classification tasks. However, to achieve these results, there is a high demand for computing and storing resources. this becomes problematic when, for instance, real-time, mobile applications are considered, in which the involved (embedded) devices have limited resources. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Within the MCA framework, we propose a clustering-based approach that is able to increase the number of employed centroids/representatives, while at the same time, have an acceleration gain compared to conventional, k-means based approaches. this is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand. Moreover, the theoretical acceleration gains are presented and the key system hyper-parameters that affect that gain, are identified. Extensive evaluation studies carried out using various state-of-the-art DNN models trained in image classification, validate the superiority of the proposed method as compared for its use in MCA tasks.

关键词： Quantization (signal) Computational modeling Neural networks Transforms real-time systems Acceleration Task analysis

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Model Based Test Framework for Communications-Critical Internet of things systems 13

Model Based Test Framework for Communications-Critical Inter...

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13th international Baltic conference on Databases and Information systems (DB and IS)

作者： Pal, Deepak Vain, Juri Tallinn Univ Technol Dept Software Sci Akad Tee 15A EE-19086 Tallinn Estonia

ISBN: (纸本)9781614999416;9781614999409

In Industry 4.0 large-scale discrete event systems (DES) are becoming increasingly dependent on internet of things (IoT) based real-time distributed supervisory control systems. In order to meet the IoT growing demand, new technologies and skills are being developed which require an automated systematic testing to achieve success in adoption. Testing such systems requires an integration of computation, communication and control in the test architecture. this may pose number of issues that may not be suitably addressed by traditional centralized test architectures. In this paper, a distributed test framework for testing distributed real-time systems is presented, where online monitors (executable code as annotations) are integrated to systems to record relevant events. the proposed test architecture is more scalable than centralized architectures in the sense of timing constraints and geographical distribution. By assuming the existence of a coverage correct centralized remote tester, we give a partitioning algorithm of it to produce distributed local testers which enables to meet more flexible performance constraints while preserving the remote tester's functionality. the proposed approach not only preserves the correctness of the centralized tester but also allows to meet stronger timing constraints for solving test controllability and observability issues. the effectiveness of the proposed architecture is demonstrated by an illustrative example.

关键词： model-based testing discrete-event systems internet of things

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CFP: A Cross-layer Recommender System with Fine-grained Preloading for Short Video Streaming at Network Edge

CFP: A Cross-layer Recommender System with Fine-grained Prel...

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ieee international conference on Cloud computing, CLOUD

作者： Dezhi Ran Yuanxing Zhang Ye Yuan Kaigui Bian School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Nowadays, short video feed has attracted billions of mobile users all around the world to interact with content effortlessly, yielding an explosive growth of short video commerce. Typically, users watch full-screen short videos of a few seconds one-by-one in a watch-list generated by recommender systems, skipping those they are not interested in. However, the recommender system at the cloud makes a user-interest-specific decision mostly based on the users' behavior data collected within the application itself (e.g., users' view history), without examining the lower-layer network and communication statistics. When the playback choked due to the limited network bandwidth, the user will probably skip the video, leading to a waste of bandwidth and degradation of the user's quality of experience (QoE). Meanwhile, the excessive number of user requests to video contents raises a heavy computational load and communication cost for the recommender system at the cloud to determine which videos to be recommended and delivered to each user in a real-time manner. the advance of edge computing provides a promising avenue of deploying edge nodes with caches (e.g., household devices) beyond cloud and edge servers, such that the recommender system in the cloud can place popular video contents closer to client users, and meanwhile the contents are delivered to client users with good network condition. In this paper, we propose CFP, a cross-layer recommender system for short video streaming with fine-grained preloading technique at the network edge. CFP jointly optimizes the recommendation effect of the video application and the content preloading efficiency under various network conditions at the network edge. CFP takes a two-stage approach: the cloud server first seeks to perform edge-wise instead of user-interest-specific recommendation with neural collaborative filtering recommender, preloading a list of candidate videos to edge nodes, and each edge node, deploying the GRU with at

关键词： Cloud computing Emulation Bandwidth Streaming media Quality of experience Servers Recommender systems

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On the Use of Containers in High Performance computing Environments

On the Use of Containers in High Performance Computing Envir...

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ieee international conference on Cloud computing, CLOUD

作者： Subil Abraham Arnab K. Paul Redwan Ibne Seraj Khan Ali R. Butt Virginia Tech

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

the lightweight nature, application portability, and deployment flexibility of containers is driving their widespread adoption in cloud solutions. Data analysis and deep learning (DL)/machine learning (ML) applications have especially benefited from containerization. As such data analysis is adopted in high performance computing (HPC), the need for container support in HPC has become paramount. However, containers face crucial performance and I/O challenges in HPC. One obstacle is that while there have been HPC containers, such solutions have not been thoroughly investigated, especially from the aspect of their impact on the crucial HPC I/O throughput. To this end, this paper provides a first-of-its-kind empirical analysis of state-of-the-art representative container solutions (Docker, Podman, Singularity, and Charliecloud) in HPC environments. We also explore how containers interact with an HPC parallel file system like Lustre. We present the design of an analysis framework that is deployed on all nodes in an HPC environment, and captures CPU, memory, network, and file I/O statistics from the nodes and the storage system. We are able to garner key insights from our analysis, e.g., Charliecloud outperforms other container solutions in terms of container start-up time, while Singularity and Charliecloud are equivalent in I/O throughput. But this comes at a cost, as Charliecloud invokes the most metadata and I/O operations on the underlying Lustre file system. By identifying such trade-offs and optimization opportunities, we can enhance HPC containers performance and the ML/DL applications that increasingly rely on them.

关键词： Cloud computing Data analysis File systems High performance computing Containers throughput Servers

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Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study

Comparing Offline and Online Testing of Deep Neural Networks...

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international conference on Software Testing, Verification, and Validation, ICST

作者： Fitash Ul Haq Donghwan Shin Shiva Nejati Lionel C. Briand University of Luxembourg Luxembourg University of Ottawa Ottawa Canada

ISBN: (数字)9781728157788

ISBN: (纸本)9781728157795

there is a growing body of research on developing testing techniques for Deep Neural Networks (DNNs). We distinguish two general modes of testing for DNNs: Offline testing where DNNs are tested as individual units based on test datasets obtained independently from the DNNs under test, and online testing where DNNs are embedded into a specific application and tested in a close-loop mode in interaction with the application environment. In addition, we identify two sources for generating test datasets for DNNs: Datasets obtained from real-life and datasets generated by simulators. While offline testing can be used with datasets obtained from either sources, online testing is largely confined to using simulators since online testing within real-life applications can be time consuming, expensive and dangerous. In this paper, we study the following two important questions aiming to compare test datasets and testing modes for DNNs: First, can we use simulator-generated data as a reliable substitute to real-world data for the purpose of DNN testing? Second, how do online and offline testing results differ and complement each other? though these questions are generally relevant to all autonomous systems, we study them in the context of automated driving systems where, as study subjects, we use DNNs automating end-to-end control of cars' steering actuators. Our results show that simulator-generated datasets are able to yield DNN prediction errors that are similar to those obtained by testing DNNs with real-life datasets. Further, offline testing is more optimistic than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing.

关键词： Testing Safety Cameras Autonomous systems Roads Electronic mail Reliability

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Predictability in Mixed-Criticality systems 24

Predictability in Mixed-Criticality Systems

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24th ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Kahil, Rany Poplavko, Peter Socci, Dario Bensalem, Saddek UGA VERIMAG F-38000 Grenoble France Mentor Siemens Business F-38334 Inovallee Montbonnot France

ISBN: (纸本)9781538677599

In this paper, we revisit and refine our previous research on schedulability testing for fixed set of dual-critical jobs. Such systems can be tested for tight job termination time bounds using simulation, under the prerequisite that the scheduling policy be predictable. We give a more precise, less restrictive generalization of the notion of predictability to mixed criticality systems. We prove that this property is inherent for an important class of scheduling policies and that it justifies testing the system using basic scenarios.

关键词： Testing Schedules Task analysis Sustainable development Switches Scheduling algorithms

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Evaluating Software Diversity in Branch Prediction Analyses for static WCET Estimation

Evaluating Software Diversity in Branch Prediction Analyses ...

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

作者： Joachim Fellmuth Jonas Zell Sabine Glesner Software and Embedded Systems Engineering Technical University Berlin Technical University Berlin Software and Embedded Systems Technical University Berlin

Static worst-case execution time analysis enables to obtain guaranteed timing bounds for programs, which is required for safety-critical hard real-time systems. this comprises micro-architectural analyses that rely on full knowledge of the executed program. An example are current approaches to statically bound the time penalty of mispredicted branches in systems using static or dynamic branch predictors. On the other hand, in artificial software diversity, uncertainty in program aspects is used to render code-reuse attacks useless, making the system considerably more secure. We solve this conflict by proposing adapted static analyses for static and dynamic branch prediction that are able to cope with diversity, and by quantifying the impact of diversity onto the analysis results through extensive evaluation.

关键词： History Software real-time systems Indexing Timing Static analysis Layout

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EDF-Based Mixed-Criticality Scheduling with Graceful Degradation by Bounded Lateness

EDF-Based Mixed-Criticality Scheduling with Graceful Degrada...

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

作者： Kecheng Yang Zhishan Guo Texas State University University of Central Florida

Mixed-criticality (MC) scheduling has been proposed for embedded real-time systems to alleviate the dilemma between runtime resource utilization and worst-case temporal guarantees for critical functions. the approach of dropping all low-criticality tasks upon a mode switch has been criticized for potentially over-degraded performance. In this paper, we focus on the graceful degradation for MC scheduling by providing bounded lateness for certain low-critical tasks. We define MCQOS-schedulability that massages the required bounded lateness into the definition of conventional MC-schedulability. A virtual deadline based scheduler (EDF-VDS) is proposed with utilization-based MCQOS-schedulability test and and closed-form lateness bounds.

关键词： Task analysis Quality of service Switches Degradation real-time systems Runtime Resource management

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embedded intelligent vacuum monitoring method of high voltage circuit breaker based on Internet of things 13

Embedded intelligent vacuum monitoring method of high voltag...

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13th EAI international conference on Mobile Multimedia Communications, Mobimedia 2020

作者： Liu, Wei-Xue Collage of Mathematics and Compute Science Chifeng University Chifeng024000 China

ISBN: (纸本)9781631902710

A certain degree of vacuum is the premise to ensure that the intelligent high-voltage circuit breaker will extinguish the arc during operation, and the degree of vacuum will drop in operation due to some reasons, so it needs to be monitored frequently. In view of the low accuracy of the traditional embedded intelligent high-voltage circuit breaker vacuum monitoring method, the embedded intelligent high-voltage circuit breaker vacuum monitoring method based on the Internet of things is proposed Law. Based on the analysis of the principle of embedded intelligent high-voltage circuit breaker vacuum degree monitoring, using the Internet of things sensor technology, the embedded intelligent high-voltage circuit breaker vacuum degree monitoring. the experimental results show that the proposed method has high accuracy and good monitoring effect, which is of great significance to the construction of smart grid. Copyright © 2020 EAI.

关键词： Internet of things

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