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

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Risk and Mitigation of Nondeterminism in Distributed Cyber-Physical systems 23

Risk and Mitigation of Nondeterminism in Distributed Cyber-P...

引用

21st ACM/ieee international Symposium on Formal Methods and Models for System Design (MEMOCODE)

作者： Bateni, Soroush Lohstroh, Marten Wong, Hou Seng Kim, Hokeun Lin, Shaokai Menard, Christian Lee, Edward A. Univ Calif Berkeley Berkeley CA 94720 USA Arizona State Univ Tempe AZ USA Tech Univ Dresden Dresden Germany

ISBN: (纸本)9798400703188

Asynchronous frameworks for distributed embedded systems, like ROS and MQTT, are increasingly used in safety-critical applications such as autonomous driving, where the cost of unintended behavior is high. The loose coordination between the components in these frameworks gives rise to nondeterminism, where factors such as communication timing can lead to arbitrary ordering in the handling of messages. In this paper, we show that this problem compromises safety and complicates system design in Autoware. Auto 1.0, a popular open-source autonomous driving framework based on ROS 2. We extend the Lingua Franca coordination language to support distributed execution, port *** to Lingua Franca, and show that our solution avoids the identified problems. We assess the performance of our federated runtime implementation and show that it is competitive for this application. We also compare our achievable throughput to ROS 2 and MQTT using microbenchmarks and find that we can match or exceed the throughput of those frameworks while preserving determinism.

关键词： Distributed systems Deterministic models Languages real-time and embedded systems

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Radar-based breathing sensor for child presence detection in automotive applications 67

Radar-based breathing sensor for child presence detection in...

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67th ieee international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Gillner, Jonas Taylor, Roy Thelen, Klaus Ruhr West Univ Appl Sci Measurement & Sensor Technol Mulheim Germany Wayne State Univ Elect & Comp Engn Detroit MI USA

ISBN: (纸本)9798350387186;9798350387179

This study presents a novel sensor system aimed at preventing child heatstroke in unattended vehicles. The developed system integrates a 24 GHz Continuous Wave (CW) radar, engineered to detect the subtle breathing signals of infants. A custom-designed 4-by-1 patch array antenna, optimized for this specific application, is seamlessly embedded into a designed PCB. To enhance efficiency and accuracy, the system incorporates a low-cost 8-bit Microcontroller (MC) and a dedicated hardware filter. These components facilitate a tailored signal processing algorithm specifically designed for efficient and rapid computation of the child's Respiratory Rate (RR). The system's performance evaluation involved monitoring a sleeping infant's respiratory patterns in real-time. Collected data was then compared against established reference standards to assess the system's accuracy in detecting the child's RR, providing compelling evidence of its efficacy. Furthermore, the system has demonstrated compliance with automotive industry standards, assuring seamless integration into existing vehicle frameworks. This innovation holds significant potential to proactively enhance automotive safety standards.

关键词： radar sensor 24 GHz radar patch antenna child safety heatstroke prevention automotive safety vital signs monitoring breathing detection

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An Overview of Optimized Inventory Management Models in Technology Companies: Historical Developments, Practical applications, and AI-Driven Approaches 7

An Overview of Optimized Inventory Management Models in Tech...

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7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： Arnawtee, Mohammed Mahde Mahmood Zaiter, Murooj Fadhil Zaiter Institute of radio electronics and information technologies Ural Federal University Dept. Big Data Analysis and Methods of Analysis Ekaterinburg Russia

ISBN: (纸本)9798331529314

This article provides a comprehensive overview of improved inventory management models in technology companies and how AI methodologies such as machine learning, predictive analytics, and optimization algorithms are revolutionizing inventory management by enabling real-time decision making, accurate demand forecasting, and automated inventory replenishment by integrating historical context with state-of-the-art AI techniques. This article provides a thorough understanding of how technology companies can leverage advanced inventory management models to maintain a competitive edge and challenges associated with implementing AI- driven inventory systems. The findings offer valuable insights for both industry practitioners and academics, contributing to the development of more resilient and adaptive inventory management strategies within the rapidly evolving technology sector. © 2024 ieee.

关键词： Inventory control

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Channel Adaptive Decoding in Underwater Optical Camera Communication systems 5

Channel Adaptive Decoding in Underwater Optical Camera Commu...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Aberathna, Thimuthu Kumarage, Miyuni Atthanayake, Nuwan Sri Lanka Technology Campus Faculty of Engineering Padukka Sri Lanka

ISBN: (纸本)9798331530983

Underwater optical camera communication (UOCC) systems that use light-emitting diodes as transmitters and high-frame rate cameras as receivers offer robust and low-cost wireless communication solutions for underwater applications compared to traditional methods. However, its performance is greatly affected by the varying underwater environments such as turbulence, salinity, and oceanic currents. This is mainly due to fixed image processing and data decoding algorithms used in UOCC systems don't provide adaptive methods that change with the underwater environments. To this end, we introduce an adaptive threshold-based algorithm to enhance the system's performance by dynamically adjusting the threshold values used in the decoding algorithm on real-time channel *** this context, adaptive thresholding refers to selecting optimal threshold values for various transmission scenarios, ensuring robust data decoding tailored to specific environmental challenges. This paper presents a comprehensive analysis of the presented algorithm under four distinct channel conditions, such as clear water, muddy water, air bubbles, and chlorophyll water. Our simulations and experiments demonstrate the effectiveness of the adaptive threshold-based algorithm in improving bit error rate (BER), making UOCC more reliable and robust in diverse underwater conditions for short-range applications. The BER performance of the presented algorithm in clear water is nearly zero, while under the other three conditions, it is approximately 1 × 10-3 within a 1-meter communication distance. © 2025 ieee.

关键词： Signal systems

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Object Recognition based on Deep Learning Algorithms using embedded IoT with Interactive Interface 7

Object Recognition based on Deep Learning Algorithms using E...

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7th international conference on Intelligent computing and Control systems, ICICCS 2023

作者： Borde, Swapna Patil, Chandan Sonawane, Chinmay Singh, Mankrit Vidyavardhini's College of Engineering and Technology Vasai India B.E. Computer Engineering Vidyavardhini's College of Engineering and Technology Vasai India

ISBN: (纸本)9798350397253

Object detection is one of the most popular applications of machine learning in the modern era. With the growth of IOT in recent times, dedicated devices offering real-time object detection have seen overwhelming demand and applications in many sectors e.g. security, healthcare, workplace etc. Different algorithms and approaches have been implemented and studied in terms of object detection. To refer to a few the YOLO family, RCNN family, SSD etc. This research study compares the performance of YOLO and Faster RCNN based on a custom dataset containing different objects and items. The IOU of each data point (image) is calculated and compared. YOLO performs better for a small margin. In this research study, a language learning interactive model is demonstrated based on object detection(YOLO), NLP, python, flask and IoT (Raspberry Pi.) The web application is running on is divided into two parts, learning and practice. The learning part has a raspberry pi device which has a camera module that captures real-time footage, recognizes the object and reads out its name in the language the user is learning. The practice has the same setup, with the application asking the user to show a particular object. Points are awarded for correct guesses, making the learning process more interactive and involving. © 2023 ieee.

关键词： Object detection

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Visual Monitoring of Many Objects in real time Using embedded GPU

Visual Monitoring of Many Objects in Real Time Using Embedde...

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2023 international conference on Advanced and Global Engineering Challenges, AGEC 2023

作者： Singh, Mahesh K. Kaushik, K. J. Sai Venkat Sahithya, Masabathula Sankar, G. Ajay Aditya Engineering College Department of Electronics and Communication Engineering Surampalem India

ISBN: (纸本)9798350340969

In modern times, object tracking and detection play a crucial role in many real-life applications like the traffic sector, UAVs, and many other real-life applications. Generally, object detection and tracking have different approaches but we discuss a new approach in this article. Usually, object tracking and detection is a hard task it generally performs using AI and deep learning-based platforms so for that we require a heavy performance high-end hardware but previously we perform this task on low-power edge IoT devices but there are some drawbacks like limited computing and also compromising inaccuracy. So to improve the efficiency, accuracy, and performance we go for an embedded GPU platform which consists of a deep learning architecture and also the joint architecture of tracking and detection. By this combination system, the accuracy is increased on the MOT16 data set. And also the efficiency is increased to track the 45 simultaneous objects at a time with 25 fps. © 2023 ieee.

关键词： Graphics processing unit

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Dynamic Tasks Scheduling with Multiple Priorities on Heterogeneous computing systems

Dynamic Tasks Scheduling with Multiple Priorities on Heterog...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Tayeb, Hayfa Bramas, Berenger Faverge, Mathieu Guermouche, Abdou Bordeaux Univ INRIA ICube Lab Bordeaux France Strasbourg Univ INRIA ICube Lab Strasbourg France Bordeaux Univ INRIA Bordeaux INP CNRS Bordeaux France Bordeaux Univ INRIA LaBRI Lab Bordeaux France

ISBN: (纸本)9798350364613;9798350364606

The efficient utilization of heterogeneous computing systems is crucial for scientists and industrial organizations to execute computationally intensive applications. Task-based programming has emerged as an effective approach for harnessing the processing power of these systems. However, effective scheduling of task-based applications is critical for achieving high performance. Typically, these applications are represented as directed acyclic graphs (DAGs), which can be optimized through careful scheduling to minimize execution time and maximize resource utilization. In this paper, we introduce MultiPrio, a dynamic task scheduler that aims to minimize the overall completion time of parallelized task-based applications. The goal is to find a trade-off between resource affinity, task criticality, and workload balancing on the resources. To this end, we compute scores for each task and manage the available tasks in the system with a data structure based on a set of priority queues. Tasks are assigned to available resources according to these scores, which are dynamically computed by heuristics based on task affinity and criticality. We also consider workload balancing across resources and data locality awareness. To evaluate the scheduler, we study the performance of dense and sparse linear algebra task-based applications and task-based FMM application using the StarPU runtime system on heterogeneous nodes. Our scheduler shows interesting results compared to other state-of-the-art schedulers in StarPU for regular applications, and excels at optimizing irregular workloads, improving performance by up to 31%.

关键词： Priority-based scheduling Runtime system heterogeneous computing systems

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real-time Pedestrian Detection in Hazy Weather for Surveillance systems 8

Real-Time Pedestrian Detection in Hazy Weather for Surveilla...

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8th ieee international conference on Image and Signal Processing and their applications, ISPA 2024

作者： Yassine, Bouafia Larbi, Guezouli Lakhlef, Hicham University of Batna2 Algeria LaSTIC Laboratory Computer Science Department Batna Algeria Environment and Sustainable Development LEREESI Laboratory Higher National School of Renewable Energy Batna Algeria Sorbonne University University of Technology of Compiègne Compiègne France

ISBN: (纸本)9798350309249

real-time pedestrian detection is an expanding research topic that is crucial in various vision-based applications. Effective detection of pedestrians in various environments and situations will significantly improve human safety. However, the performance of current pedestrian detection algorithms faces challenges in scenarios with reduced visibility and blurred appearance, such as hazy weather conditions. This study addresses this issue by seeking an optimal balance between accuracy and speed for deploying deep learning-based pedestrian detection in real-time intelligent surveillance systems. For this aim, we used the transfer learning technique to fine-tune small versions of state-of-the-art YOLO models (YOLONAS-small, YOLOv8-small, YOLOv6-small, and YOLOv5-small) to perform pedestrian detection in hazy weather effectively. Experiment results demonstrate the effectiveness of our methods in accurately detecting pedestrians under hazy conditions by achieving high average precision and F-Score. Moreover, they all operate in real time, with impressive processing speeds ranging from 52.41 to 186.22 FPS. © 2024 ieee.

关键词： Deep learning

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Safe Integration of Learning in SystemC using timed Contracts and Model Checking 23

Safe Integration of Learning in SystemC using Timed Contract...

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21st ACM/ieee international Symposium on Formal Methods and Models for System Design (MEMOCODE)

作者： Blohm, Pauline Adelt, Julius Herber, Paula Univ Munster Munster Germany

ISBN: (纸本)9798400703188

Intelligent components such as reinforcement learning are increasingly used in safety-critical embedded systems, e.g., in cars, trains or traffic control systems. While it is crucial to verify the correctness of such systems under all circumstances, their formal verification is difficult due to multiple challenges: the real-time and concurrent behavior, deeply intertwined hardware and software components, the lack of formal semantics in industrially used design languages such as SystemC, and the trial-and-error behavior of learning components. In this paper, we present an approach to safely integrate learning into systems that are designed in SystemC using timed contracts and model checking. Our main contribution is twofold: 1) We show how the safe behavior of a learning component within a concurrent real-time system can be defined by a timed contract. 2) We provide an embedding of such contracts into an existing transformation from SystemC to Uppaal timed automata. This enables us to use the Uppaal model checker to formally verify that the SystemC design is safe under the assumption that the learning component adheres to its timed contract. To ensure safe behavior at runtime, we integrate the contract as a runtime monitor in the SystemC design. We demonstrate the applicability of our approach with a case study of an intelligent traffic light controller that uses reinforcement learning.

关键词： embedded systems SystemC Safe Learning Formal Verification Model Checking

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Analysis of New embedded Simulation Technology based on Smart Internet of Things 1

Analysis of New Embedded Simulation Technology based on Smar...

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1st ieee international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Li, Min LANZHOU Resources and Environment VOC-Tech College Gansu Lanzhou730030 China

ISBN: (纸本)9798350323795

The integration of Intelligent Internet of Things (IoT) and embedded systems has made the design and development of embedded systems more intelligent and efficient. In embedded systems, simulation technology is an important tool that can be used to verify and test the correctness and reliability of the system. This article introduces a new embedded simulation technology based on the intelligent Internet of Things, which can be used to complete simulation testing more quickly, improve the accuracy and precision of simulation, and reduce the cost of system development and testing. The core of this technology is to utilize the data collection, transmission, and processing functions of the intelligent Internet of Things to input data from physical reality into the simulation system, achieving rapid construction of the simulation system and more accurate simulation results. At the same time, this technology also supports remote monitoring and control, enabling embedded systems to dynamically adjust and optimize during operation, improving system performance and reliability. © 2023 ieee.

关键词： Internet of things

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