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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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Exploring Segnet Architectures for iGPU embedded Devices 15

Exploring Segnet Architectures for iGPU Embedded Devices

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15th international Joint conference on Computational Intelligence, IJCCI 2023

作者： Chaudron, Jean-Baptiste Mascarenas-Gonzalez, Alfonso ISAE-SUPAERO Université de Toulouse France

ISBN: (纸本)9789897586743

Image segmentation is an important topic in computer vision which encompasses a variety of techniques to divide image into multiple areas or sub-regions in order to extract meaningful information. Artificial Neural Networks (ANNs), biologically inspired algorithms, are nowadays widely used to perform such tasks and popular models are usually based on encoder-decoder architectures. Segnet was one of the first proposed model of this kind in the literature and, despite its efficiency, it has several drawbacks for embedded systems especially due to the huge amount of arithmetic operations and memory used in the original version. However, its simple sequential based architecture offers interesting properties for optimization and real-time analysis. In this paper, we deeply investigate how to tune and adapt original Segnet architecture to allow efficient run-time execution on embedded targets equipped with an iGPU. We propose our own implementation design which is experimented and validated on iGPU embedded devices for two state of the art datasets from Unmanned Aerial Vehicle (UAV) applications. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： embedded systems

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Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection and Early Warning Control System 3

Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection ...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Li, Wenshan Hunan Institute of Traffic Engineering Hunan Hengyang China

ISBN: (纸本)9798331508456

Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signal processing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advanced algorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential. © 2025 ieee.

关键词： Electronic guidance systems

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The Digital Twin Model of Chemical Production systems in Smart Factories: A Case Study 23

The Digital Twin Model of Chemical Production Systems in Sma...

引用

23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Heng Liu, Zheng Yuan, Wei Chen, Guiheng Chen, Xiaolong Yang, Yingze Peng, Jun School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665494571

Nowadays, more and more information technology such as internet-of-things (IoT) and artificial intelligence (AI) are applied to all walks of life. Facing the pressure of technological innovation, traditional manufacturers have paid attention to digital twin to improve production lines. However, most of the research on digital twin have not implement the concept of digital twin into actual production lines. To fill this gap, this paper demonstrates a digital twin application case study with whole process analysis based on machine learning. More specifically, data preprocessing, clustering, classification and regression are utilized to build a digital twin model. Moreover, we deploy digital twin model to cloud server and develop web application with real-time diagnosis function which has user-friendly interactive interface. Furthermore, the digital twin model of chemical production system has already been implemented in a physical chemical process plant. So the results of the experiment can prove the effectiveness of our proposed model building approaches. © 2021 ieee.

关键词： Production systems Technological innovation Smart cities Buildings real-time systems Digital twins Servers

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Cyber Physical systems: Security Technologies, Application and Defense 9

Cyber Physical Systems: Security Technologies, Application a...

引用

9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Malarkodi, K.P. Sugumar, R. Baswaraj, D. Hasan, Arif Kousalya, A. Nirmala, S. Sri Krishna Arts and Science College Department of Computer Applications Coimbatore India Vasavi College of Engineering Hyderabad India Amity University ABS Madhya Pradesh India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350397376

New security threats have surfaced in response to the meteoric rise in the prevalence of Cyber-Physical systems (CPS). The latest iteration of CPS has a wide range of flaws, dangers, attacks, and safeguards. However, there hasn't been any comprehensive research done on CPS security problems. In particular, it has been challenging to examine the issue using a single generalized model due to the variability of CPS elements and the diversity of CPS systems. In this study, we use a holistic security model to examine CPS safety. We examine the technologies at the perceptual, network, and application levels. Smart grids, transportation, healthcare, and residential applications, along with the most common problems and solutions. Since many studies did not focus on a particular use case, we categorize them into broad categories, such as ICSs, cloud servers, and embedded devices. © 2023 ieee.

关键词： Cyber Physical System

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Programming Autonomous Machines Special Session Paper 22

Programming Autonomous Machines Special Session Paper

引用

22nd ACM SIGBED international conference on embedded Software (EMSOFT) as part of the embedded systems Week (ESWEEK)

作者： Liu, Shaoshan Li, Xiaoming Geng, Tongsheng Zuckerman, Stephane Gaudiot, Jean-Luc PerceptIn Santa Clara CA 95054 USA Univ Delaware Newark DE USA Univ Calif Irvine Irvine CA USA CY Cergy Paris Univ ENSEA Lab ETIS UMR 8051CNRS F-95000 Cergy France

ISBN: (数字)9781665472982

ISBN: (纸本)9781665472982

One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomous machines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomous machines.

关键词： Programming Languages Compiler Autonomous Machine computing Runtime systems

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Scene-level Tracking and Reconstruction without Object Priors

Scene-level Tracking and Reconstruction without Object Prior...

引用

ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Chang, Haonan Boularias, Abdeslam Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

We present the first real-time system capable of tracking and reconstructing, individually, every visible object in a given scene, without any form of prior on the rigidness of the objects, texture existence, or object category. In contrast with previous methods such as Co-Fusion and MaskFusion that first segment the scene into individual objects and then process each object independently, the proposed method dynamically segments the non-rigid scene as part of the tracking and reconstruction process. When new measurements indicate topology change, reconstructed models are updated in real-time to reflect that change. Our proposed system can provide the live geometry and deformation of all visible objects in a novel scene in real-time, which makes it possible to be integrated seamlessly into numerous existing robotics applications that rely on object models for grasping and manipulation. The capabilities of the proposed system are demonstrated in challenging scenes that contain multiple rigid and non-rigid objects. Supplementary material, including video, can be found at https://***/changhaonan/STAR-no-prior.

关键词： Geometry Deformable models Visualization Stars Grasping Streaming media real-time systems

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Traffic Sign Recognition System with Hybrid CNN and Random Forest Models for Autonomous Driving applications 1

Traffic Sign Recognition System with Hybrid CNN and Random F...

引用

1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Vaidya, Akhil Tulsiyan, Darsh Surve, Omkar Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350352931

Accurate traffic sign recognition is crucial for driver assistance systems, yet challenges persist due to environmental factors and camera distortions. This study introduces a novel framework for real-time traffic sign detection and classification using vehicle dash-cams. The system employs a fine-tuned YOLOv8 model for detection, achieving mAP50 of 99.348%. Classification utilizes a two-stage approach: VGG16-based image embedding generation followed by Random Forest classification, resulting in an F 1 score of 98.61%. The model is trained on a diverse dataset combining GTSRB and web-scraped images, with extensive augmentation to enhance robustness. Additionally, Microsoft's TrOCR extracts numerical values from speed limit signs. This hybrid approach merges the strengths of convolutional neural networks and Random Forests, producing a robust solution even with a very limited amount of training data. The system's high accuracy and real-time performance significantly enhance driver safety by ensuring reliable traffic sign recognition across various conditions. © 2024 ieee.

关键词： Convolutional neural networks

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Construction and Operation of a Generative Artificial Intelligence-Assisted Language Learning System 3

Construction and Operation of a Generative Artificial Intell...

引用

3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Cao, Yilu Lu, Haoyu Zhou, Yingping Gao, Jiahui Wang, Han Jiao, Yabei College Of Foreign Languages Chongqing University Of Technology Chongqing China College Of Computer Science And Engineering Chongqing University Of Technology Chongqing China School Of Accountancy Chongqing University Of Technology Chongqing China Liangjiang International College Chongqing University Of Technology Chongqing China

ISBN: (纸本)9798350318609

The generative artificial intelligence-assisted language learning system is a system that utilizes artificial intelligence technology. It provides users with more personalized and efficient language learning services through core modules such as generative pre-trained models and personalized learning engines, It is currently one of the significant applications of artificial intelligence technology in the field of language learning. This paper aims to introduce the design, construction, and operation of a generative artificial intelligence-assisted language learning system. In this system, the generative pre-trained model GPT -3.5 is employed, incorporating core modules such as natural language processing, generative adversarial networks, and Transformer models. This paper also conducts detailed research and analysis on aspects such as text generation quality in the system, response time testing of the model within the system, and feedback on system application scenarios. Through the research presented in this paper, valuable reference and guidance can be provided for the development and application of generative artificial intelligence-assisted language learning systems. © 2024 ieee.

关键词： Engines

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Edge Based Skin Cancer Decision Support System Using Machine Learning Algorithms

Edge Based Skin Cancer Decision Support System Using Machine...

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2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Nawaz, Anum Irfan, Muhammad Sadiqa, Hafza Ayesha Westerlund, Tomi School of Information Science and Technology Fudan University Shanghai China University of Turku Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology Turku Finland

ISBN: (纸本)9798350304602

Skin lesions has grown rapidly in recent years. Detecting and treating skin lesions in its early stages can be extremely beneficial to its recovery. Using Digital image processing (DIP) to extract skin diseases from lesions emerged rapidly due to in accessibility of dermatologist and laboratory facilities in remote areas specifically in under developed countries. Out of many deep learning based feature extraction algorithms, convolutional neural networks (CNN) outperform along with different classifiers. In this study, we focused on edge based classifiers for robust skin lesions classification. To choose the best combination of classifier with existing CNN based feature extraction methods we used HAM-10000 dataset, supervised by ISIC. Different mobile based classifiers such as QDA, KNN and SVM, along with their different hybrid versions were tested in same conditions. Experimental results shows that the cubic kernel function used in the cubic SVM approach allows for more complex decision boundaries to be created for melanoma detection, as the characteristics of melanoma lesions can vary greatly. The cubic kernel function enables the SVM model to capture these complex patterns and make accurate predictions using less computational resources as compared to other mobile based classifiers. Cubic SVM shows the accuracy of 98% while using only 20 seconds of time. © 2023 ieee.

关键词： Support vector machines

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2024 ieee international conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

2024 IEEE International Conference on Intelligent Signal Pro...

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2024 ieee international conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

The proceedings contain 157 papers. The topics discussed include: a reversible data hiding scheme for ECG signals using CNN-PEE;design and development of in-memory-compute SRAM cell using 45nm technology;stand alone or non-standalone 5G tactical edge network architecture for military and use case scenarios;secrecy capacity optimization in RF/FSO systems: impact of mixed Rayleigh and log-normal fading with atmospheric turbulence;real-time eye-tracking mouse control system using OpenCV and facial landmark detection;a time-efficient path navigating landmine detection robot;optimizing GPS positioning: a deep learning approach to improve accuracy;a novel structure of on-chip multilayered half-turn inductor for RF applications;and low profile wideband 3 element parasitic hexagonal patch antenna.

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