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Small-Scale Testbed for Evaluating C-V2X applications on 5G Cellular Networks 35

Small-Scale Testbed for Evaluating C-V2X Applications on 5G ...

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ieee Intelligent Vehicles symposium (IV)

作者： Arfvidsson, Kaj Munhoz Fragkedaki, Kleio Jiang, Frank J. Narri, Vandana Lindh, Hans-Cristian Johansson, Karl H. Martensson, Jonas KTH Royal Inst Technol Div Decis & Control Syst EECS Malvinas Vag 10 S-10044 Stockholm Sweden Scania CV AB Res & Dev S-15187 Sodertalje Sweden Ericsson AB Stand & Technol Torshamnsgatan 21 S-16440 Kista Stockholms Lan Sweden

ISBN: (纸本)9798350348811;9798350348828

In this work, we present a small-scale testbed for evaluating the real-life performance of cellular V2X (C-V2X) applications on 5G cellular networks. Despite the growing interest and rapid technology development for V2X applications, researchers still struggle to prototype V2X applications with real wireless networks, hardware, and software in the loop in a controlled environment. To help alleviate this challenge, we present a testbed designed to accelerate development and evaluation of C-V2X applications on 5G cellular networks. By including a small-scale vehicle platform into the testbed design, we significantly reduce the time and effort required to test new C-V2X applications on 5G cellular networks. With a focus around the integration of small-scale vehicle platforms, we detail the design decisions behind the full software and hardware setup of commonly needed intelligent transport system agents (e.g. sensors, servers, vehicles). Moreover, to showcase the testbed's capability to produce industrially-relevant, real world performance evaluations, we present an evaluation of a simple test case inspired from shared situational awareness. Finally, we discuss the upcoming use of the testbed for evaluating 5G cellular network-based shared situational awareness and other C-V2X applications.

关键词： Testbeds

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Implementation of Serial FPDP and 10G Ethernet in an FPGA Based real time embedded Hardware

Implementation of Serial FPDP and 10G Ethernet in an FPGA Ba...

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2024 ieee Wireless Antenna and Microwave symposium, WAMS 2024

作者： Sarkar, Abhinandan Chakrabarty, Jayabrata Althaf, M Sheik DRDO Bengaluru Electronics & Radar Development Establishment India

ISBN: (纸本)9798350358926

Today's radar technology demands the use of huge aperture antenna arrays for detection and tracking of targets from a range of hundreds of kilometers. This results in a complex RF manifold where a large number of RF channels in parallel are provided as input to the radar receiver. Subsequently, these inputs are digitized and forwarded to a real time embedded hardware for further processing. Since the number of RF channels are quite high and reception as well as analysis of the incoming data have to be completed for a particular dwell within a matter of few milliseconds, therefore we require high speed serial interfaces from receiver to receive the data and process it in shortest time. Moreover, a copy of the raw data is also required to be forwarded to the signal processor over 10G Ethernet interface. In this paper, we highlight such a design and implementation of a serial Front Panel Data Port (FPDP) interface working at 2.5 Gbaud/lane and a number of such lanes are processed successfully in parallel. Next, we present the design and implementation of the 10G Ethernet interface along with the validation of data integrity at the destination. © 2024 ieee.

关键词： Field programmable gate arrays (FPGA)

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An integrated approach of Genetic Algorithm and Machine Learning for generation of Worst-Case Data for real-time Systems 26

An integrated approach of Genetic Algorithm and Machine Lear...

引用

26th ieee/ACM International symposium on Distributed Simulation and real time applications (DS-RT)

作者： Kumar, Vikash Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (数字)9781665497992

ISBN: (纸本)9781665497992

Determining Worst-Case Execution time (WCET) is essential for temporal verification of real-time and embedded Systems. These systems are designed to meet the stringent timing constraints imposed by the regulations. If a system gets delayed due to non-compliance with the deadline, it will lead to disastrous events. Worst-Case Data which gives maximum execution time, plays a vital role in the estimation of WCET. An evolutionary algorithm such as the Genetic Algorithm has been employed to generate the Worst-Case Data. The complexity of an evolutionary algorithm requires the use of several computational resources. This paper presents a novel method to replace the hardware and simulator used in the evolution process with machine learning models. This method reduces the overall time required to generate Worst-Case Data. Different machine learning models are trained to integrate with genetic algorithms. Our machine learning models are created using the Pygad Framework. The feasibility of the proposed approach is validated using benchmarks from different domains. The results show the speedup in the generation of Worst-Case Data.

关键词： embedded System Genetic Algorithm Machine Learning real-time System

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Experimental Measurements on RTK-Corrected embedded real-time Navigation

Experimental Measurements on RTK-Corrected Embedded Real-Tim...

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2024 International Conference on DGON Inertial Sensors and applications, ISA 2024

作者： Bendel, P. Hintz, C. Newzella, A. MBDA Germany GmbH Schrobenhausen86529 Germany

ISBN: (纸本)9798350387513

real-time Kinematic (RTK) positioning is currently a state-of-the-art, high-precision solution within the domain of Global Navigation Satellite Systems (GNSS). Its potential and limitations in military systems such as missile guidance or aerial drone navigation in difficult environments have yet to be fully explored. This paper investigates the performance of RTK in a series of controlled experiments, aiming to assess the accuracy of RTK-based position solutions. A particular focus is placed on examining the impacts of interruptions in correction data communication and increasing distances from the base station. © 2024 ieee.

关键词： Global positioning system

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Machine learning in ASL fingerspelling recognition: A Literature Review 24

Machine learning in ASL fingerspelling recognition: A Litera...

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24th ieee International symposium on Computational Intelligence and Informatics, CINTI 2024

作者： Pinnington, Jamie Souag, Amina Azhar, M.A. Hannan Bin School of Engineering Technology and Design Canterbury Christ Church University Canterbury United Kingdom

ISBN: (纸本)9798350353433

The recognition of American Sign Language (ASL) fingerspelling through machine learning has seen significant advancements over the past few years. This literature review explores various machine learning models and their applications in ASL fingerspelling recognition, focusing on the transition from hardware-based approaches to sophisticated deep learning models. Key models discussed include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, highlighting their performance, challenges, and advancements. The review emphasises the importance of dataset quality and diversity, and the necessity for models to handle real-time, varied conditions for practical applications. The findings suggest that while significant progress has been made, challenges such as dataset variability, environmental conditions, and real-time processing remain. Future research should aim at developing more adaptive models and fostering collaboration between technology and the ASL community to enhance real-world communication for the deaf and hard of hearing. © 2024 ieee.

关键词： Adversarial machine learning

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Proposal for the implementation of a system based on augmented reality for the monitoring of an industrial process in real time

Proposal for the implementation of a system based on augment...

引用

ieee Colombian Conference on Communications and Computing (ieee COLCOM)

作者： Encarnacion-Diaz, Joan D. Jimenez-Quevedo, Cristian G. Chasi-Pesantez, Paul A. Guerrero-Vasquez, Luis F. Torres-Torres, Felix A. Ordonez-Ordonez, Jorge O. Univ Politecn Salesiana Mecatron Engn Cuenca Ecuador Univ Politecn Salesiana Appl Embedded Hardware Res Grp Cuenca Ecuador Univ Politecn Salesiana Telecommun & Telemat Res Grp Cuenca Ecuador

ISBN: (纸本)9798331504724;9798331504731

This study emphasizes the innovative implementation of an augmented reality (AR) system for real-time monitoring of industrial processes through a cost-effective data acquisition setup. By integrating AR, the system enables operators to visually interact with the process, enhancing understanding and operational efficiency. Utilizing AR technology, the system projects virtual 3D objects into the real environment, providing detailed insights into process dynamics and machinery interactions. The methodology involves the use of Raspberry Pi for data acquisition and Firebase for real-time data updates, ensuring accurate and timely information flow. Specific challenges addressed include the synchronization of real-time data and the integration of AR with existing industrial machinery. This approach not only improves monitoring capabilities but also facilitates a more immersive and interactive experience for operators, contributing to the broader field of Industry 4.0. The results highlight the potential of AR in enhancing operational efficiency and underscore its application in various industrial scenarios.

关键词： Augmented reality (AR) applications real-time Monitoring Industrial Automation Industrial Internet of Things (IIoT) Human-Machine Interaction

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Next-Gen AI-embedded Vision Systems: real-time Optimization for Surveillance, Autonomous Vehicles, and Industry 4.0

Next-Gen AI-Embedded Vision Systems: Real-Time Optimization ...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Prabhavathi, K. Anusha, M.N. Manjunatha, G. Sowjanya, N.B. Puttegowda, Kiran Naveen, K.B. Bgs Institute of Technology Adichunchanagiri University Dept. of Ece Mandya India Cauvery Institute of Technology Vtu Dept. of Ece Mandya India Vidyavardhaka College of Engineering Dept. of Ece Mysuru India

ISBN: (纸本)9798350375442

AI-powered embedded vision systems are transforming visual data processing by enabling real-time decision-making across critical applications such as autonomous vehicles, industrial automation, and surveillance. This paper introduces a system that merges advanced machine learning algorithms with energy-efficient hardware, designed to optimize performance and minimize power consumption. The system achieves a detection accuracy of 94.5%, processes images at 30 FPS, and maintains computational efficiency at 2.5 MB/Watt. These results underscore the system's effectiveness in real-time object detection with minimal latency. Despite these advances, challenges remain in maintaining accuracy under varying conditions and further optimizing the system. Future work will focus on improving robustness, exploring new applications, and integrating emerging technologies. This research highlights the significant impact and future potential of AI-driven vision technology, providing a framework for advancing capabilities in embedded vision systems. © 2024 ieee.

关键词： Machine vision

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Is the powersave governor really saving power? 24

Is the powersave governor really saving power?

引用

24th ieee/ACM International symposium on Cluster, Cloud, and Internet Computing (CCGrid)

作者： Huang, Darong Costerot, Luis Atienza, David Ecole Polytech Fed Lausanne Embedded Syst Lab ESL Lausanne Switzerland UCM Dept Comp Architecture & Automat Madrid Spain

ISBN: (纸本)9798350395679;9798350395662

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application's performance and Dynamic Voltage and Frequency Scaling (DVFS) actions, alongside assessments of server power consumption and operating temperature, indicates that existing Linux scaling governors often fall into non-optimal DVFS strategies, especially for cloud applications with varying workloads and requests. This shortfall comes from the misleading CPU load metrics, which fail to accurately capture the applications' true performance requirements and demands. In this context, we introduce a novel scaling governor named GreenDVFS. First, it identifies the optimal frequencies for the application in a range of workload scenarios. Optimal frequencies are used to maintain application performance, reduce server power consumption, and maintain a balanced operating temperature in different workload scenarios. Furthermore, we design a long short-term memory (LSTM)-based time series methodology to detect the real-time workloads of cloud applications accurately and timely. Building on these foundations, the proposed method takes optimal DVFS actions, tailored for cloud applications under different workload conditions, to optimize performance, energy efficiency, and temperature. The experimental results highlight the effectiveness of the proposed GreenDVFS, with up to 18% savings in energy consumption and a 30% decrease in operational temperature by comparing against the default Linux governor, all while not compromising the application's performance. Such improvements help to optimize cloud computing operations for enhanced efficiency and sustainability.

关键词： Cloud Dynamic voltage and frequency scaling (DVFS) Energy efficiency Long short-term memory (LSTM) Scaling governor

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real-time Signal Processing in IoT-Based embedded Systems Using Hybrid AI-Enhanced Edge Computing

Real-Time Signal Processing in IoT-Based Embedded Systems Us...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Rajan, Chozha P. Ahmed, A. Sarfaraz Divya, Vyshnava Nagendar, Y. Mohan, Ashwala Atheeswaran, Athiraja Bannari Amman Institute of Technology Department of AIDS Tamilnadu Erode India Sri Eshwar College of Engineering Department of CSE Tamil Nadu Coimbatore India Malla Reddy College of Engineering Department of CSE Hyderabad India SR University Department of School of Computer Science and Artificial Intelligence Telangana Warangal India Guru Nanak Institutions Technical Campus Department of ECE Telangana Ranga Reddy India Hyderabad India

ISBN: (纸本)9798331517953

The rapid expansion of the Internet of Things (IoT) has necessitated real-time signal processing capabilities in embedded systems, but traditional cloud-based models often fall short in meeting the low-latency demands of these applications. To address this, we propose a hybrid AI-enhanced edge computing framework that integrates machine learning with conventional signal processing techniques, optimizing real-time processing at the edge. Our framework was tested in a smart factory environment, where edge devices equipped with lightweight AI models were employed for signal classification and processing tasks. The results demonstrate a 40% reduction in latency, with processing times as low as 50 milliseconds per signal, and an accuracy improvement of 15% over traditional methods, achieving a 92% accuracy rate in signal classification. These findings underscore the potential of hybrid AI at the edge to significantly enhance both the speed and accuracy of real-time signal processing in IoT-based embedded systems. This approach not only meets the critical latency requirements of modern IoT applications but also improves overall system reliability, making it a promising solution for applications in smart manufacturing, healthcare, and autonomous systems. Future work should explore further optimization of AI models for edge deployment and scalability across broader IoT ecosystems. © 2024 ieee.

关键词： Smart manufacturing

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Intelligent Water-Efficient Street Cleaning Vehicle for Road Pollution Identification applications 11

Intelligent Water-Efficient Street Cleaning Vehicle for Road...

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11th ieee International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) - Empower of Innovative Consumer technology

作者： Wang, Szu-Hong Liao, Yan-Xin Zhang, Liang-Xu Yunlin Univ Sci & Technol Dept Elect Engn Yunlin Taiwan

ISBN: (纸本)9798350386851;9798350386844

The phenomenon of road dust during vehicle operation poses threats to visibility and road safety, while also negatively impacting respiratory health and air quality, becoming an environmental concern. In order to address this issue, this study proposes an AI-based street cleaning vehicle system aimed at reducing dust emissions and providing a clean and safe driving environment, while also preventing driver distraction leading to traffic accidents. The system utilizes two cameras on the street cleaning vehicle to capture images of road conditions and surface pollution, which are then input into an embedded system for real-time image analysis. This technology requires classification of 8 categories, and leveraging deep learning technology like YolactEdge, real-time detection of road pollution is achieved. AI automatically adjusts water pressure for road cleaning and categorizes road pollution into four levels, adjusting water pressure accordingly. This method achieves good results in road pollution identification and adjusting the amount of water for road cleaning. Firstly, in terms of road pollution identification, the average accuracy is 99.85%, and the frame rate on the NVIDIA Jetson Xavier NX embedded system is 13 frames per second. Secondly, in adjusting the amount of water for road cleaning, after measurement, it is found to reduce water consumption by over 40% compared to traditional methods. This system achieves efficient water usage by accurately identifying road surface dirt and debris for application in low-speed street cleaning vehicles traveling at 20 km/hr.

关键词： Image recognition processing road pollution recognition YolactEdge street cleaning vehicle embedded system

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