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IEEE International Conference on Design & Test of Integrated Micro & Nano-systems (DTS)

作者： Jalel Ktari Hedi Choura Tarek Frikha Mouna Baklouti Dhia Mejdi Molka Kotti Ahmed Zribi Hassine Touil Ghaith Oueslati Computer Embedded Systems Lab National Engineering School of Sfax Sfax Tunisia Data Engineering and Semantics Unit National Engineering School of Sfax Sfax Tunisia Computer Sciences and Applied Mathematiques Departement National Engineering School of Sfax Sfax Tunisia

Despite being a promising area of exploration, the application of embedded systems in blockchain technology, particularly with FFGA or Raspberry technology, remains relatively limited. Despite the advantages offered by FPGAs, such as high-speed memory, parallel computing blocks, and a flexible architecture, their use in blockchain has primarily been focused on Proof of Work (PoW) consensus *** this paper, our goal is to address this gap by presenting the implementation of a blockchain application specifically tailored for the STM32 platform. By leveraging the capabilities of the STM32 microcontroller, we aim to demonstrate the practicality and effectiveness of embedded systems in blockchain technology, overcoming the limitations observed with previous platforms.

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Teleoperation as a Step Towards Fully Autonomous systems

Teleoperation as a Step Towards Fully Autonomous Systems

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Design, Automation and Test in Europe Conference and Exhibition

作者： Alex Bendrick Daniel Tappe Nora Sperling Rolf Ernst Andrea Nota Selma Saidi Frank Diermeyer Institute of Computer and Network Engineering Technische Universität Braunschweig Germany Professorship Embedded Systems Technische Universität Dortmund Germany Institute of Automotive Technology Technische Universität München Germany

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

In the foreseeable future, highly automated mobile systems, such as vehicles, robots, UAVs, or trains, will be confronted with difficult situations that require external support. The availability of such external support corresponds to level 4 driving automation and is an essential feature in current robotaxis and automated public transportation. While the first generation of level 4 prototypes relied on safety driver support, commercial systems are gradually moving towards support by teleoperation. Designing teleoperation support for level 4 systems is an end-to-end problem involving two main research and practical challenges, the teleoperation function defining the remote human interface with its scene representation and available control functions, and the real-time communication channel involving wired and wireless segments, which must provide reliable end-to-end data transport. Both challenges are tightly linked, and combined solutions are needed to reach the required safe teleoperation. Solutions can make use of the rich sensing and control system of a level 4 vehicle, which however can only be exploited if the communication channel provides adequate real-time access.

关键词： Wireless communication Wireless sensor networks Teleoperators Communication channels Ultra reliable low latency communication Robot sensing systems Real-time systems Safety Sensors Vehicles

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Admission Experts based on Android+VR, AR Technology and Voice Operation 6

Admission Experts based on Android+VR, AR Technology and Voi...

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6th International Conference on Digital Technology in Education, ICDTE 2022

作者： Xiao, Juan Yang, Weichen Li, Faying School of Computer and Artificial Intelligence Xiangnan University Hunan Chenzhou China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Hunan Chenzhou China Xiangnan University Hunan Chenzhou China

ISBN: (纸本)9781450398091

With the improvement of people’s living standards and the increasing abundance of materials, acceptance has become an indispensable part of people’s life. Nowadays, the application of smartphone is more and more widely used. A mobile APP based on Android system with the theme of acceptance is developed. Augmented Reality (AR) and Virtual Reality (VR) technologies as well as voice operation functions are applied to bring users a technological, convenient and novel experience of acceptance. © 2022 Association for Computing Machinery.

关键词： Augmented reality

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A New Compact Cantor Set Fractal MIMO Antenna for 5G Millimeter-Wave Applications

A New Compact Cantor Set Fractal MIMO Antenna for 5G Millime...

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Microwave and Antenna Symposium (IMAS), International

作者： Omaima Benkhadda Abdelati Reha Mohamed Saih Meryama Harrou Laboratory of Mathematics Computer Science Electrical Engineering and Physics (LAMIGEP) EMSI-Marrakech Marrakech Morocco Team of Microelectronics Embedded Systems and Telecommunication FST Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (数字)9798350356359

ISBN: (纸本)9798350356366

This article outlines the development of a compact MIMO antenna with two ports crafted for millimeter-wave band operations, specifically tailored to support fifth-generation (5G) wireless applications. The suggested antenna is constructed on a Rogers RT/duroid 5880 substrate, measuring ${12} \times {6}\times {0.508} \text{mm}{3}$ . The proposed antenna has a slotted ground on the bottom and two Fractal-shaped radiating elements inspired by the Cantor Set pattern, positioned atop the dielectric material. The suggested antenna covers the millimeter-wave spectrum utilized in 5G, showcasing a wide bandwidth extending from 26.6 GHz to 29.08 GHz and 38.5 to 39.2 GHz. The antenna attains a maximum gain of 7.64 dBi, robust isolation exceeding 26 dB and demonstrates exceptional diversity performance, with an envelope correlation coefficient (ECC) below 0.003 and a diversity gain (DG) surpassing 10 dB. The obtained results Confirm the validity of the designed MIMO antenna's robustness and make it a viable option for 5G wireless equipment.

关键词： Microwave antennas Wireless communication Correlation coefficient Antenna measurements Slot antennas 5G mobile communication Millimeter wave measurements Bandwidth Fractals Substrates

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Hardware Implementation and Evaluation of an Information Processing Factory

Hardware Implementation and Evaluation of an Information Pro...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Walaa Amer Mariam Rakka Rachid Karami Minjun Seo Mazen A. R. Saghir Rouwaida Kanj Fadi J. Kurdahi Center for Embedded and Cyber-Physical Systems University of California at Irvine Irvine CA USA Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon

The Information Processing Factory (IPF) utilizes factory management principles to tackle the complexities of integrated embedded systems, ensuring continuous safe operation and optimization at runtime. This paper presents a hardware implementation of IPF that enables dynamic task migration across system resources, ensuring reliability in the face of internal or external failures. We demonstrate the effectiveness of IPF through the efficient migration of tasks in multiprocessor SoCs using a safety-critical pacemaker application as a case study. Despite the additional software and hardware requirements, implementing IPF in a pacemaker results in comparable reliability to dual modular redundancy (DMR) with faster service resumption and improved resource utilization.

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Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators

arXiv

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arXiv 2024年

作者： Pfeiffer, Kilian Balaskas, Konstantinos Siozios, Kostas Henkel, Jörg Embedded Systems Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Computer Engineering and Informatics University of Patras Patras26504 Greece Department of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece

In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tackled this problem on an algorithm level, e.g., by letting constrained devices train a subset of the server neural network (NN) model. However, it has been observed that these techniques are not effective w.r.t. accuracy. Importantly, they make simplistic assumptions about devices’ resources via indirect metrics such as multiply accumulate (MAC) operations or peak memory requirements. We observe that memory access costs (that are currently not considered in simplistic metrics) have a significant impact on the energy consumption. In this work, for the first time, we consider on-device accelerator design for FL with heterogeneous devices. We utilize compressed arithmetic formats and approximate computing, targeting to satisfy limited energy budgets. Using a hardware-aware energy model, we observe that, contrary to the state of the art’s moderate energy reduction, our technique allows for lowering the energy requirements (by 4×) while maintaining higher accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

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IEEE Networking Letters 2025年第2期7卷 83-87页

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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Vote and KNN outlier detection in Wireless Sensor Networks

Vote and KNN outlier detection in Wireless Sensor Networks

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2021 International Symposium on Networks, computers and Communications, ISNCC 2021

作者： Abid, Aymen Khediri, Salim El Moulahi, Tarek Cheour, Rym Kachouri, Abdennaceur Computer And Embedded Systems Lab National Engineering School Sfax Tunisia College Of Computer Department Of Infomration Technology Gabes Tunisia Advanced Fluid Dynamics Energetics And Evironment Lab National Engineering School Sfax Tunisia

ISBN: (纸本)9780738113166

The target of a Wireless Sensor Network (WSN) designer is to improve the robustness of the network while taking into account constraints such as verifying the detection of anomalies. Preventing anomalies can be assured by data analysis via outliers identification as it strongly affects decision-making in the case of smart-home, e-health and other high-risk applications. In this paper, we study some families of outliers and we propose to detect data anomalies using K Nearest Neighbours (KNN) and Voting techniques by a benchmark suitable to be a simulation for a smart-house. In order to evaluate the dependability, some performance metrics are analyzed such as data rate detection, precision, specificity and accuracy. Performance results are in the order of 100% detection rate with reduced false alarm and fast response time compared to some other existing ones. © 2021 IEEE.

关键词： Wireless sensor networks

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Cooperative Channel Capacity Learning

arXiv

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arXiv 2023年

作者： Letizia, Nunzio A. Tonello, Andrea M. Poor, H. Vincent Universität Klagenfurt Institute of Networked and Embedded Systems Klagenfurt9020 Austria The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In this paper, the problem of determining the capacity of a communication channel is formulated as a cooperative game, between a generator and a discriminator, that is solved via deep learning techniques. The task of the generator is to produce channel input samples for which the discriminator ideally distinguishes conditional from unconditional channel output samples. The learning approach, referred to as cooperative channel capacity learning (CORTICAL), provides both the optimal input signal distribution and the channel capacity estimate. Numerical results demonstrate that the proposed framework learns the capacity-achieving input distribution under challenging non-Shannon settings. © 2023, CC BY-NC-SA.

关键词： Game theory

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