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Hardware Acceleration with Zero-Copy Memory Management for Heterogeneous computing

Hardware Acceleration with Zero-Copy Memory Management for H...

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

作者： Oren Bell Chris Gill Xuan Zhang Comp. Sci. and Eng. Dept. Washington University in St Louis St Louis MO USA Electrical and Systems Engineering Dept. Washington University in St Louis St Louis MO USA

the ROS2 software framework is increasingly prevalent in component-based applications for robots and other autonomous systems. Recently added ROS2 features to support zero-copy semantics may significantly reduce latency and latency variation when passing data from one component to another. Additionally, there is a growing trend of developing autonomous robotic systems on heterogeneous computing platforms to exploit hardware acceleration. However, support for portable and reusable zero-copy semantics on heterogeneous compute systems is limited. Such systems thus must either use low-level techniques to manage memory operations directly, which may be tedious and error-prone, or they may not adequately address substantial memory overheads that can arise from repeatedly copying messages and data into and out of device memory associated with GPUs and FPGAs. Towards addressing that limitation of the current state of the art, this paper introduces Hazcat, a new zero-copy framework that automatically performs device memory operations when needed, and avoids copying and other costly operations otherwise. Hazcat is integrated specifically with ROS2 but is also designed for portability to other component-based software frameworks.

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Indian Sign Language Recognition System

Indian Sign Language Recognition System

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international conference on Soft computing and Machine Intelligence (ISCMI)

作者： S Siddharth R Jayashree K N Shwetha CSE - AIML PES University Bangalore India

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

this paper presents an Indian Sign Language (ISL) recognition system, leveraging advanced deep learning techniques to address the communication barriers faced by the hearing-impaired community. the system is designed to recognize and interpret ISL gestures into corresponding text or speech, facilitating effective interaction between hearing-impaired individuals and the broader population. Our approach involves a Convolutional Neural Network (CNN) architecture, specifically ResNet-50, trained on the ISL dataset. Key features include image preprocessing, data augmentation, and real-time gesture recognition. Experimental results demonstrate the system’s high accuracy and robustness, showcasing its potential for practical deployment in various real-world applications. this work contributes significantly to the field of assistive technology, aiming to enhance the inclusivity and accessibility of communication for the hearing-impaired community in India.

关键词： Deep learning Sign language Accuracy Speech recognition Assistive technologies Data augmentation Robustness real-time systems Data models Convolutional neural networks

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Implementation of a Neural Network Model on the Aurora Operating System Using ARM Architecture 7

Implementation of a Neural Network Model on the Aurora Opera...

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7th international Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Markevich, Daniil S. Vidmanov, Dmitry A. Bauman Moscow State Technical University Department of Information Systems and Telecommunications Moscow Russian Federation

ISBN: (纸本)9798331531836

Deploying and training deep neural networks on mobile devices is a challenging task as machine learning applications increasingly require real-time data processing and on-device training capabilities. the ARM microprocessor architecture for mobile devices and embedded systems offers attractive platforms for edge machine learning. there are various machine learning libraries optimized for resource-constrained environments. this paper discusses the TensorFlow Lite library, the process of running and training machine learning models directly on ARM devices using TensorFlow Lite. It discusses the technical implementation, challenges, and optimizations required to achieve efficient training and running of neural network models using mobile devices with the Aurora operating system as an example. Finally, an example of implementing and training a neural network model on the Aurora OS in the C++ programming language without using additional libraries is given as an alternative to the TensorFlow Lite library. © 2025 IEEE.

关键词： ARM platform edge computing mobile deep learning model architecture

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Formalizing an Architectural Model of a Trustworthy Edge IoT Security Gatewayz 27

Formalizing an Architectural Model of a Trustworthy Edge IoT...

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27th IEEE international conference on embedded and real-time computing systems and applications (RTCSA)

作者： McCormack, Matt Vasudevan, Amit Liu, Guyue Sekar, Vyas Carnegie Mellon Univ CyLab Pittsburgh PA 15213 USA Carnegie Mellon Software Engn Inst Pittsburgh PA USA

ISBN: (纸本)9781665441889

Today's edge networks continue to see an increasing number of deployed IoT devices. these IoT devices aim to increase productivity and efficiency;however, they are plagued by a myriad of vulnerabilities. Industry and academia have proposed protecting these devices by deploying a "bolt-on" security gateway to these edge networks. the gateway applies security protections at the network level. While security gateways are an attractive solution, they raise a fundamental concern: Can the bolt-on security gateway be trusted? this paper identifies key challenges in realizing this goal and sketches a roadmap for providing trust in bolt-on edge IoT security gateways. Specifically, we show the promise of using a micro-hypervisor driven approach for delivering practical (deployable today) trust that is catered to both end-users and gateway vendors alike in terms of cost, generality, capabilities, and performance. We describe the challenges in establishing trust on today's edge security gateways, formalize the adversary and trust properties, describe our system architecture, encode and prove our architecture trust properties using the Alloy formal modeling language. We foresee our trustworthy security gateway architecture becoming a practical and extensible formal foundation towards realizing robust trust properties on today's edge security gateway implementations.

关键词： Productivity Industries Costs conferences systems architecture Metals Computer architecture

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Manipulator's Control System with Application of the Machine Learning 11

Manipulator's Control System with Application of the Machine...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and applications (IDAACS)

作者： Kondratenko, Yuriy Sichevskyi, Stanislav Kondratenko, Galyna Sidenko, Ievgen Petro Mohyla Black Sea Natl Univ Intelligent Informat Syst Dept 68th Desantnyk Str10 UA-54003 Kolaiv Ukraine

ISBN: (纸本)9781665426053

this paper provides an analytical review of the subject area, approaches and methods for real-time control systems with application of the machine learning. the review helps to choose approaches to control robotic devices, methods of artificial intelligence to perform pattern recognition and classification, technologies to design a control system and simulate a robotic device. the design and software implementation stage involved the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, software implementation of control system components. the control system was implemented using the Swift programming language and other technologies, and the configuration and creation of a neural network using the CreateML framework.

关键词： real-time system device control system manipulator arm robotics pattern recognition and classification artificial intelligence

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5th international conference on Recent Innovations in computing , ICRIC 2022

5th International Conference on Recent Innovations in Comput...

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5th international conference on Recent Innovations in computing , ICRIC 2022

ISBN: (纸本)9789819906000

the proceedings contain 66 papers. the special focus in this conference is on Recent Innovations in computing. the topics include: the Importance of Selected LMS Logs Pre-processing Tasks on the Performance Metrics of Classification Models;analysis of Deep Pre-trained Models for Computer Vision applications: Dog Breed Classification;an Efficient Recognition and Classification System for Paddy Leaf Disease Using Naïve Bayes with Optimization Algorithm;development of Decision-Making Prediction Model for Loan Eligibility Using Supervised Machine Learning;prediction of Osteoporosis Using Artificial Intelligence Techniques: A Review;analyzing Stock Market with Machine Learning Techniques;machine Learning Techniques for Image Manipulation Detection: A Review and Analysis;improving real-time Intelligent Transportation systems in Predicting Road Accident;Consumer Buying Behavior Analysis During COVID-19 Phase Using Statistical Data Mining and Multi-verse Stochastic Algorithm;clinical Named Entity Recognition Using U-Net Classification Model;addressing Role of Ambient Intelligence and Pervasive computing in Today’s World;Improving the AODV Routing Protocol Against Network Layer Attacks Using AntNet Algorithm in VANETs;a Comparative Study of IoT-Based Automated Hydroponic Smart Farms: An Urban Farming Perspective;Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review;Intelligent Communication for Internet of things (IoRT);energy-Efficient Priority-Based Routing Model for Smart Health Care;An Internet of things-Based Mining Worker Safety Helmet Using ESP32 and Blynk App;time Series-Based IDS for Detecting Botnet Attacks in IoT and embedded Devices;swarm Intelligence-Based Energy-Efficient Framework in IoT;Performance Analysis of MADM Techniques in Cognitive Radio for Proximity Services;neural Machine Translation from English to Marathi Using Various Techniques.

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MERGERS: Multi-Access Edge Resource Governance for real-time SaaS systems

MERGERS: Multi-Access Edge Resource Governance for Real-Time...

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international conference on VLSI Design

作者： Aakashjit Bhattacharya Arnab Sarkar Ansuman Banerjee ATDC IIT Kharagpur India ACMU ISI Kolkata India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

the Multi-access Edge computation (MEC) paradigm is being regarded as a viable alternative for handling data and computation requirements of complex real-time and safety-critical applications in upcoming IoT systems. this paper presents a resource arbitration technique called MERGERS that utilizes a central adaptive orchestrator called Resource Arbiter (RA), to dynamically allocate real-time service requests among heterogeneous MEC servers of a 5G MEC Software-as-a-Service (SaaS) provider. the objective of MERGERS is to maximize overall resource utilization of the given set of MEC servers through efficient load balancing. the approach partitions time into uniform intervals known as epochs. At the start of each epoch, RA takes into account the computational, storage, and deadline requirements of new client requests received in the previous epoch. It then uses a strict admission control policy to assign the new requests to suitable MEC edge servers, while ensuring that the hard real-time requirements of already scheduled tasks are not compromised. A newly arrived request is rejected if it's inclusion in the system can lead to deadline misses. Experimental results show MERGERS is able to achieve high task acceptance rates over a variety of simulation scenarios.

关键词： Multi-access edge computing embedded systems Corporate acquisitions Software as a service Admission control Very large scale integration Load management real-time systems Servers Resource management

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Assessing the Resistance of Internet of things applications Against Memory Corruption Attacks: A Case Study for Contiki and Tizen 5th

Assessing the Resistance of Internet of Things Applications ...

引用

5th EAI international conference on Safety and Security in Internet of things, EAI SaSeIoT 2021

作者： Basiri, Mohammad Mouzarani, Maryam Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

ISBN: (纸本)9783030942847

the Internet of things helps objects to interact with each other and function more efficiently. However, one of the major challenges in this technology is security. Research on the security of the Internet of things mostly revolves around network security, but IoT software security has not garnered adequate attention. Due to the need for high-speed applications and the real-time structure of operating systems in the Internet of things, their applications are often coded in languages such as C and C++ and may be vulnerable to memory corruption attacks. In this article, we study the resistance of the applications running in two well-known operating systems in IoT, namely Contiki and Tizen, against memory corruption attacks and show how memory corruption vulnerabilities arise in these applications. We demonstrate the possibility of exploiting these vulnerabilities in Contiki and Tizen and the weakness or loss of memory protection mechanisms in these operating systems. By this analysis, we hope to notify the developers of Tizen and Contiki applications of the feasibility of exploiting memory corruption vulnerabilities in these operating systems and the importance of avoiding these vulnerabilities in their codes by paying enough attention to the secure coding principles. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Microservices Architecture to Improve the Performance of Machine Learning applications in eHealth 10th

Microservices Architecture to Improve the Performance of Mac...

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10th international conference on Technologies and Innovation

作者： Calderon-Gomez, Huriviades Mendoza-Pitti, Luis Gomez-Pulido, Jose Manuel Vargas-Lombardo, Miguel Univ Tecnolog Panama E Hlth & Supercomputing Res Grp GISES Panama City 081907289 Panama Univ Alcala Dept Comp Sci Hlth Comp & Intelligent Syst Res Grp HCIS Alcala De Henares 28805 Spain Ramon & Cajal Inst Hlth Res IRYCIS Renal & Vasc Physiol & Pathophysiol Res Grp Colmenar Viejo Spain

ISBN: (纸本)9783031757013;9783031757020

the objective of this paper is to propose an intergenerational software architecture, designated "mu e-Health 4.0," for the integration and interconnection of traditional eHealth applications utilized within medical institutions. these applications are commonly developed in accordance with the service-oriented architecture architectural style. Furthermore, in conjunction with modern applications, the microservice architecture style has been adapted to accommodate multiple machine learning-based inference systems, which are fed by a large database that integrates diverse and heterogeneous dynamic data sources. Proper standardization of these data sources is achieved using metadata. In contrast to other proposals, our approach prioritizes the replicability, scalability, and interoperability of the specialized services that comprise this architecture, leveraging our versatile, high-performance software features. To demonstrate the feasibility of mu eHealth 4.0, it was necessary to adapt this proposal to a real eHealth use case, specifically to the prediction of the occurrence of intradialytic hypotension during a hemodialysis session. these results were validated by quantitative values obtained from rigorous tests, such as response time, efficient use of the infrastructure, and network consumption. In conclusion, it can be posited that mu eHealth 4.0 has the potential to create an eHealth ecosystem in conjunction with the integration of multiple machine learning-based inference systems, such as logistic regression, random forest, or XGBoost, to perform specialized decision support tasks in the prevention, monitoring, and treatment of hypotension prediction in hemodialysis. It should be noted that mu eHealth 4.0 was developed with the intention of being adapted to other eHealth domains, including diabetes and colon cancer.

关键词： Cloud-native computing eHealth Intergenerational architecture software Interoperability Machine learning-based inference system Microservices architecture Versatile high-performance

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Road Lane Tracking Using Constrained Scale Space Radon Transform

Road Lane Tracking Using Constrained Scale Space Radon Trans...

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international conference on Soft computing and Machine Intelligence (ISCMI)

作者： Djemel Ziou Nafaa Nacereddine Aïcha Baya Goumeidane Département d’informatique Université de Sherbrooke Sherbrooke Qc Canada Research Center in Industrial Technologies P.O.Box 64 Chéraga Algiers Algeria

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

On a road, there are often parallel linear structures painted in white or color to demarcate the lanes. For automatic driving, these structures must be tracked in image sequences acquired in real time by an embedded camera so that a vehicle can stay in its lane or change lanes. these road markings appear in grayscale images as continuous or discontinuous linear structures of variable width, converging at vanishing points. In this article, we propose tracking these road markings using both the Scale Space Radon Transform (SSRT) and projective geometry. these markings are considered as a mixture of linear structures, with the model being updated under parallelism constraints. the proposed multiline tracker is less sensitive to noise and acquisition conditions due to simultaneous adjustment expressed in feature space. Experimental results demonstrate the superiority of SSRT and the simultaneous tracking of multiple lines.

关键词： Roads Training data Transforms Manuals Parallel processing Robustness real-time systems Noise measurement Projective geometry Optimization

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