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

共 36894 条记录，以下是351-360 订阅

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Software Reliability Modeling and Prediction of Residual Defects for embedded systems 26

Software Reliability Modeling and Prediction of Residual Def...

引用

26th international conference on Soft computing and Measurements, SCM 2023

作者： Dushutina, Elena V. Saint Petersburg Electrotechnical University LETI St. Petersburg Russia

ISBN: (纸本)9798350322484

one of the possible approaches to assessing the reliability of real-time embedded systems software is substantiated;the features of the system functioning, criteria and metrics are specified. There are a lot of frameworks based on software reliability growth models (SRGMs) and the Non-Homogeneous Poisson Process (NHPP) for measuring the reliability of software products and predicting residual defects. Numerous SRGM extensions take into account bug correction, debugging workflow, software metrics, programmer qualifications, etc. However, few works are devoted to approaches to evaluation taking into account the features of real-time software. The applicability, goodness of fit, accuracy of the models, and prediction of residual defects are analyzed and compared depending on the initial/input data and the type of real-time tasks to be solved. The novel framework is proposed;the most suitable reliability models included in the framework are briefly considered, in accordance with the different behavior of the risk function over time, and assumptions. The developed framework allows you to choose an appropriate model or set of models to predict the remaining defects in the software. The case study draws on a data set of the results of debugging and functioning of the system for monitoring the state of electrical equipment. The software of such a system implements tasks of mixed criticality with a certain set of periodic and sporadic tasks in real-time. © 2023 ieee.

关键词： embedded systems

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Hybrid AI-Electronic systems for real-time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 ieee.

关键词： Adversarial machine learning

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k-Robust Conflict-Based Search with Continuous time for Multi-robot Coordination

k-Robust Conflict-Based Search with Continuous time for Mult...

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2024 international conference on Intelligent Robots and systems

作者： Daudt, Guilherme Dymytry, Alleff Kolberg, Mariana Maffei, Renan Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9798350377712;9798350377705

Coordinating multiple robots is crucial for various real-life applications. Many Multi-Agent Path Finding (MAPF) algorithms have been proven to be successful in addressing this challenge. Nevertheless, some problems, such as unexpected delays during navigation, commonly arise when handling high-level abstractions, potentially leading to failures or collisions in live executions. This paper proposes k-Robust Continuous-time Conflict-Based Search (kR-CCBS), a novel algorithm that overcomes some of these limitations. Our approach offers path planning with continuous time, leading to more precise routes than discrete time approaches. Additionally, we increase safety by incorporating k-robustness, enabling the system to adapt to agent failures due to delays and minimize collision risks. Comparative evaluations demonstrate that kR-CCBS outperforms similar works in effectiveness while maintaining reasonable costs, making it a promising solution for real-world multi-agent coordination scenarios.

关键词： Continuous time systems

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The Generalized Graph real-time Task Model 20

The Generalized Graph Real-Time Task Model

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20th ieee international conference on embedded and Ubiquitous computing (EUC)

作者： Doose, David Santinelli, Luca Off Natl Etud & Rech Aerosp Toulouse France BMW Munich Germany

ISBN: (纸本)9798350396355

Safety-critical real-time embedded systems are becoming difficult to model and analyze as they accommodate more and more functionalities, and they rely on more and more complex HW and SW implementations. One of the main difficulty with such systems is to find the right coupling between the task model and the analysis method. Indeed, if the task model is too simple it may add pessimism to the result of the analysis, and it may not be possible to prove the correctness of the systems. If the model is too complex, the analysis may be not efficient enough to be usable;or worst the model may have an incomplete method only or no method at all, so the system cannot be analyzed. Finding, the right couple task model analysis method is a balance between expressiveness and analysis efficiency. In this article, we introduce a new graph real-time task model (noted GGTM) which has been used for several years for the deployment and the analysis of different embedded systems. We discuss its expressiveness compared to the literature and demonstrate its advantages. We also, present a decidable and efficient, exact schedulability analysis.

关键词： real-time systems Graph Task Model Schedulability Analysis

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AMPER-X: Adaptive Mixed-Precision RISC-V Core for embedded applications 10

AMPER-X: Adaptive Mixed-Precision RISC-V Core for Embedded A...

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10th Nordic Circuits and systems conference

作者： Othman, Ahmad Kamaleldin, Ahmed Goehringer, Diana Tech Univ Dresden Chair Adapt Dynam Syst Dresden Germany

ISBN: (纸本)9798331517663;9798331517670

The rapid expansion of the Internet of Things, wireless sensor networks, and robotics applications have established embedded systems as a fundamental component of modern computing. Despite this growth, optimizing power consumption and computing resources remains critical. Traditional embedded platforms often struggle with fixed hardware configurations that limit their adaptability and efficiency. To address these issues, this paper presents a novel adaptive processor architecture based on RISC-V ISA which introduces reconfigurable execution regions capable of supporting mixed-precision operations (16/8/4-bit). These reconfigurable execution regions execute and continuously adapt to varying precision and operational demands triggered by custom RISC-V instructions that activate the desired region at specific precision levels. The reconfiguration process is managed through dynamic function exchange (DFX) targeting AMD/Xilinx XCZU7EV FPGA device. The evaluation demonstrates that the modified core efficiently utilizes FPGA resources, with 6,396 LUTs and 2,679 FFs allocated, and operates with a dynamic power consumption of 24 mW at 50 MHz. The reconfiguration time is 76 ms for each reconfiguration execution region, incorporating the overhead required for reconfiguration management and control. These results highlight the core's potential to allow the realization of RISC-V-based reconfigurable cores without degrading performance and energy efficiency for embedded applications.

关键词： embedded systems RISC-V Processor Architecture Dynamic Function Exchange FPGAs

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Enhancing Automotive embedded applications: A Comprehensive Evaluation of Control Flow Checking Methods

Enhancing Automotive Embedded Applications: A Comprehensive ...

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2024 ieee international conference on Design, Test and Technology of Integrated systems, DTTIS 2024

作者： Solouki, Mohammadreza Amel Sini, Jacopo Violante, Massimo Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350363128

Mitigating the risks posed by Random Hardware Failures (RHFs) is crucial to prevent data corruption and Control Flow Errors (CFEs) in embedded systems. This paper addresses these concerns through the application of Software-Implemented Hardware Fault Tolerance (SIHFT) methods, emphasizing compatibility with high-level programming languages such as C. Current SIHFT methods, often implemented in low-level Assembly, present challenges in terms of overhead to code size and real-time execution. Our proposed approach focuses on pre-compilation application of SIHFT methods, specifically Control Flow Checking (CFC), to identify CFEs within C-language-based code. We conducted a comparative analysis of two established software-based CFE detection methods in C, seamlessly integrating CFC methods into the application behavioral model. Our methodology ensures ISO26262 compliance, crucial for the automotive sector, offering a software-only strategy that aligns with safety and cost considerations. © 2024 ieee.

关键词： C (programming language)

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Preliminary Approach to Parallelizing Autonomous Driving applications Using High-Performance Many-core Processor

Preliminary Approach to Parallelizing Autonomous Driving App...

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

作者： Xuankeng He Takuya Azumi Graduate School of Science and Engineering Saitama University

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

Advancements in autonomous driving technologies leverage real-time computing and embedded systems to enable vehicles to make quick decisions based on dynamic road conditions. These increasingly complex systems face rising computational demands. This study introduces a preliminary approach to parallelizing autonomous driving applications using a high-performance many-core processor. By distributing tasks across multiple cores, the approach enhances concurrent execution, reduces conflicts, and minimizes resource contention, improving the efficiency and performance of autonomous driving systems.

关键词： embedded systems Roads Parallel processing real-time systems Vehicle dynamics Complex systems Autonomous vehicles Faces

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Reinforcement Learning for Dynamic Power Management in embedded systems 5

Reinforcement Learning for Dynamic Power Management in Embed...

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5th ieee international conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Pattanaik, Balachandra Pattanaik, Ojasvi Tilak Babu, S.B.G. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Adjunct Faculty Saveetha School of Engineering Tamilnadu Chennai India JNTU Vignan Institute of Management and Technology for Women Department of Computer Science and Engineering Hyderabad Ghatkesar India Aditya Engineering College Department of ECE Surampalem India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

Itaddresses the challenges that are associated with dynamic power management in embedded systems by utilizing techniques that are derived from the discipline of reinforcement learning (RL). Given the increasing complexity of embedded systems and the need for solutions that are efficient for energy consumption, RL is a potential technology that can dynamically optimize power utilization. This is particularly pertinent in light of the requirement for solutions that are efficient for energy consumption. The dynamic workload fluctuations that are inherent in embedded systems are taken into consideration in this research, which studies the integration of RL algorithms to control power levels in real time in an adaptive manner. This research also takes into account the variable workloads that are endemic to embedded systems. To evaluate the efficacy of RL-based dynamic power management strategies in comparison to traditional methods, with a specific emphasis on the potential for enhanced energy efficiency and system responsiveness, the goal of this is to evaluate the effectiveness of these strategies. The findings not only contribute to the improvement of understanding of the use of reinforcement learning in the setting of embedded systems, but they also provide insights into the utility of RL in meeting the evolving power management requirements in contemporary computing environments. © 2024 ieee.

关键词： Reinforcement learning

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Software Architecture for IoT-based Indoor Positioning systems for Ambient Assisted Living 20

Software Architecture for IoT-based Indoor Positioning Syste...

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ieee 20th international conference on Software Architecture (ICSA)

作者： Mendes, Lucas F. Aguilar, Paulo A. C. Bezerra, Carla I. M. Univ Fed Ceara Dept Comp Sci Quixada Brazil

ISBN: (纸本)9798350397499

Indoor positioning is an important element in context-aware applications, especially based on the internet of things (IoT). Elderly people or people with specific needs can benefit from such applications by providing them with an ambient assisted living (AAL). Indoor positioning system (IPS) applications can be deployed under an IoT infrastructure to provide location awareness to other applications, such as AAL solutions. However, software patterns and architectures in the context of IoT applications are still open research topics. This paper proposes a software architecture for IoT-based IPS applications to support AAL systems. We performed a systematic literature review (SLR) to identify solutions and non-functional requirements (NERs) to incorporate into the architecture of these applications. From the SLR results, we developed an architecture for IoT-based IPS applications, using Fog computing elements, to support AAL systems. We evaluate the architecture using the iFogSim simulation environment regarding latency, network usage, and energy consumption. The proposed approach was compared to a cloud-based deployment. Experimental results show that the proposed fog-based architecture significantly reduces latency, network usage, and operational costs and is suitable for real-time response scenarios. In addition, the energy consumption of cloud data centers is reduced by employing fog computing.

关键词： ambient assisted living fog computing indoor positioning system internet of things software architecture

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AI Driven Predictive Functional Safety Leveraging Key Performance Indicators in Autonomous Vehicles

AI Driven Predictive Functional Safety Leveraging Key Perfor...

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2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Sawarkar, Nikhilesh Harman International Automotive Intelligent Cockpit Platform Bangalore India

ISBN: (纸本)9798331517953

As the automotive industry transitions towards Software-Defined Vehicles (SDVs) with centralized computing architectures, managing functional safety becomes increasingly complex. This paper examines how AI-driven decision systems can enhance predictive functional safety in autonomous vehicles especially with the architectural shift from traditional to zonal and centralized computing. A combined approach utilizing Predictive Artificial Intelligence (AI) and Key Performance Indicators (KPIs) is proposed to ensure functional safety and optimize system performance. KPIs track real-time performance of critical functions, serving as diagnostic tools during development and enabling AI-based predictive monitoring post-deployment. This approach facilitates proactive risk mitigation and continuous system optimization. © 2024 ieee.

关键词： Automotive industry

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