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检索条件"机构=Computer Programming"

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Concept of an Optical Distance and Speed Sensor Using Novel Offsetless Spatial Frequency Filters in the Area of Functional Safety 32

Concept of an Optical Distance and Speed Sensor Using Novel ...

引用

32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Wasinski, Frank Bonath, Werner Ricklefs, Ubbo Borcsok, Josef Schwarz, Michael Hahn, Eike University of Applied Sciences THM Dept. of Electrical Engineering and Information Technology Giessen Germany Institute for Computer Architecture and System Programming University of Kassel ICAS Kassel Germany

ISBN: (纸本)9798350399714

Optical speed sensors based on the spatial frequency filter method are a proven technology that offer high measurement accuracy over a wide speed range. Newly developed variants also enable measurements at very low speeds and increase the robustness of the method [1]. This makes the sensor very suitable for use in the area of functional safety. A measurement chain up to safety integrity level 3 (SIL3) should be feasible via a combination with a safety system on a chip (SoC) for evaluating the measurement data and an extension of the sensor module to include safety-related elements [2], [3]. By integrating the sensor at IC level, a compact overall solution is to be made possible. After the first successful tests with the safety SoC, further examinations of the measuring method with regard to functional safety are now necessary. © 2023 IEEE.

关键词： System-on-chip

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A Practical Approach for Reliability Prediction of Safety Critical Software Using Multi-Model Ensemble Techniques 7

A Practical Approach for Reliability Prediction of Safety Cr...

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7th International Conference on System Reliability and Safety, ICSRS 2023

作者： Ouertani, Aymen Krini, Ossmane Börcsök, Habil. Josef Institute of Functional Safety & Cyber Security Baden-Wuerttemberg Cooperative State University Lörrach Germany Computer Architecture und System Programming University of Kassel Kassel Germany

ISBN: (纸本)9798350306057

This paper introduces a novel and comprehensive approach for estimating the reliability of safety critical software components in autonomous vehicle motion systems. The proposed approach in this paper presents a combination software reliability model (CSRM) that integrates multiple non-homogeneous Poisson process (NHPP) software reliability growth models (SRGMs) to achieve a reasonable compromise between accuracy, the trade-off between the goodness of fit and the simplicity, and stability. By using machine learning techniques, the CSRM effectively combines the strengths of individual SRGMs while mitigating their weaknesses through suitable evaluation and calibration techniques. The developed CSRM has been successfully applied and validated to facilitate a smoother and more efficient evaluation of reliability targets for software components in autonomous robotic wheelchair (ARW). Based on the validation results, the new CSRM has significantly enhanced the efficiency of the process for evaluating whether the defined reliability goals were being achieved. Furthermore, has facilitated a more accurate assessment of the need for further test executions and better planning of the required verification and validation session. This new approach provides valuable insights into the reliability of the developed software, particularly for software developers lacking extensive experience in identifying and applying appropriate SGRM. © 2023 IEEE.

关键词： Economic and social effects

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Method of Assessing the Impact of External Factors on Geopositioning System Operation Using Android GPS API 16

Method of Assessing the Impact of External Factors on Geopos...

引用

16th IEEE International Conference on computer Science and Information Technologies, CSIT 2021

作者： Hovorushchenko, Tetiana Pavlova, Olga Avsiyevych, Volodymyr Computer Engineering and System Programming Department Khmelnytskyi Ukraine

ISBN: (纸本)9781665442572

The main methods of analysis the accuracy of the geolocation system were studied. The aspects of technology for obtaining geographical coordinates using Google geolocation system (GPS) were considered. The best method of obtaining data from satellites was searched. Today the geolocation system has become widely used in many industries, such as military, aviation, maritime, navigation of ordinary users. Also, a significant spread of this technology is noticed in many geographical areas, such as cartography, geodesy and is an area of interests of many scientists. A mobile app based on Android platform for conducting the experiments to determine the accuracy of geolocation services in the form of Web Quests was developed. © 2021 IEEE.

关键词： Global positioning system

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Neuromorphic Neurons and Networks for Artificial Intelligence Built Using Temporal Space Calculations 32

Neuromorphic Neurons and Networks for Artificial Intelligenc...

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32nd International Scientific Conference Electronics, ET 2023

作者： Trendafilov, Ianislav Gancheva, Veska Neuromorphica Ltd. 8 Kliment Ohridski blvd. Sofia1335 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350302004

We deigned a spiking neural network that computes network weights in the temporal dimension. Such a network can be used for artificial intelligence and deep learning. We demonstrate circuits implementing blocks for building such a network and then a training model. This enables creation of efficient Hessenstein-Reichardt detectors observed in motion detection networks in the nature. The proposed network allows reconfiguration across network layers algorithmically. © 2023 IEEE.

关键词： Neural networks

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Advantages of Fog Computing: A Comparative Analysis with Cloud Computing for Enhanced Edge Computing Capabilities 4

Advantages of Fog Computing: A Comparative Analysis with Clo...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Hunko, Mykhailo Tkachov, Vitelii Kuchuk, Heorhii Kovalenko, Andny Department of Electronic Computers Kharkiv National University of Radio Electonics Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350395532

Fog computing extends the capabilities of cloud computing by enabling computing at the edge of the network, involving devices such as mobile collaborative devices or fixed nodes with integrated storage, computing, and communication capabilities. Fog computing offers benefits such as improved efficiency, increased security, savings in network bandwidth, and increased flexibility/in order to provide a complete understanding of Fog computing, this paper presents its salient features and highlights the differences from cloud computing research. Cloud computing is an emerging technology that offers computing resources on a pay-per-use basis. It provides three service models, and the cloud offers cost-effective centrally managed resources for reliable computing for specific tasks. This document presents a comparison between Fog computing and cloud computing, highlighting the differences in design, deployment, services, and tools available to organizations and users. By explicating the distinctiveness of fog computing and its contrast to cloud computing, we contribute to the field by shedding light on the unparalleled potential of fog computing in conserving resources while delivering robust computational solutions at the network periphery. Our study offers a new perspective that challenges the prevailing practices and extends the discourse on optimizing distributed computing architectures. © 2023 IEEE.

关键词： Fog computing

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Application of IoT in the study of biogas release parameters 33

Application of IoT in the study of biogas release parameters

引用

33rd International Scientific Conference Electronics, ET 2024

作者： Nikolov, Neven K. Tzanova, Anelia I. Koparanov, Kiril A. Antonova, Elena V. Minkovska, Daniela V. Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Department of Chemistry 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350376449

This paper describes the development of an IoT device prototype for measuring biogas release parameters. Through the device, measurements are made, and statistics are accumulated based on which calculations are performed. Two types of samples were tested, the obtained results were displayed graphically, and the data was analyzed. © 2024 IEEE.

关键词： biogas ESP32 IoT measurement MQTT

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Optimization of Neural Network Calculations Using Integer Arithmetic

Optimization of Neural Network Calculations Using Integer Ar...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Oleksandr Zakovorotnyi Andrii Khulap Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

Most of the sensor devices in the Internet of Things systems are based on energy-efficient microcontrollers, the computing resources of which are limited, as well as the amount of available memory. Increasing the security of the use of such devices with the help of neural networks is an important and urgent problem. The article describes the possibility of using artificial neural networks in small microcontrollers with limited resources. The purpose of this work is to check the possibility of calculating neural networks based on integer arithmetic to reduce the time of calculating a neural network and eliminate data normalization operations, as well as to evaluate the feasibility of using such neural networks in the field of security of the Internet of Things in comparison with traditional methods, such as black lists and white lists. The following results were obtained: when switching to integer arithmetic, compared to floating point, the accuracy of the result calculations is within the permissible error of neural network training, that is, it has not changed. Execution time decreased by $30-96 \%$ , depending on the architecture of the microcontroller. The program size is reduced by $22-48 \%$ , also depending on the microcontroller architecture. Conclusions: the possibility and expediency of using neural networks optimized for microcontrollers with limited resources was proved. This will increase the security of Internet of Things systems, especially against device authentication threats and intrusion detection. Prospects for further research are determined.

关键词： Training Microcontrollers Neural networks Memory management Intrusion detection Switches Security Internet of Things Optimization Arithmetic

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Object Specialization to Partially Reduce Polymorphism of Attributes 23

Object Specialization to Partially Reduce Polymorphism of At...

引用

Proceedings of the 2023 7th International Conference on computer Science and Artificial Intelligence

作者： Alena Vasileva Yegor Bugayenko Faculty of Computer Science HSE University Russia System Programming Laboratory Huawei RRI Russia

ISBN: (纸本)9798400708688

In object-oriented programming languages, objects with polymorphic attributes can negatively impact performance and hinder static analysis. These attributes require dynamic dispatch, which is slower than static binding, and complicate the analysis process. We propose a novel algorithm for object specialization that addresses this issue by replacing polymorphic attributes with monomorphic ones, resulting in improved performance and simplified static analysis. Our algorithm is a new approach compared to existing function specialization algorithms. We provide a proof of the algorithm’s soundness and correctness, and present an implementation of the algorithm as a software tool. Empirical evaluation shows that our approach achieves significant improvements in performance and simplifies the static analysis process. Our algorithm can be applied to a variety of object-oriented languages such as Java and Python.

关键词： Object-Oriented programming Polymorphism Specialization

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Model Based Development Methodology in Compliance with ISO 26262 for Safe Automotive ECU

Model Based Development Methodology in Compliance with ISO 2...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Muhammad Ikram Hafiz Department of Computer Architecture and System Programming University of Kassel Kassel Germany

This work presents a practical approach of designing a functionally safe ECU for automotive application by implementing the Model Based Development (MBD) methodology. Functional safety (FuSa) in automotive can be achieved by strictly following the processes of electrical and electronic systems development as given in the ISO 26262. The scope of FuSa encompasses the risks emerging from systematic failures and random hardware failures. One approach to mitigate these failures is the Model Based Development (MBD) on which the ISO 26262 provides guidance. This approach enables to model an ECU and accelerates an IP creation by automatic RTL code generation for rapid prototyping on an FPGA level. In this work the MBD is implemented in accordance to the ISO 26262 to design and test a hardware model of an embedded ECU. The embedded Design under Test (DUT) is simulated and its output verified in Matlab/Simulink. Upon successful results, the RTL codes of the DUT are generated for a target FPGA. The RTL codes are simulated and the functionality of the DUT is verified at the FPGA level.

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Detection computer Network Intrusion Using Deep Neural Networks

Detection Computer Network Intrusion Using Deep Neural Netwo...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Vadym Poltoratskyi Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

In this work, the effectiveness of using classical machine learning methods and modern deep neural network models for intrusion detection in computer networks has been investigated. The purpose of this work is to develop a model for detecting intrusions into computer networks based on the Transformer model using tabular input data. In this work, the UNSW-NB15 dataset is used as the source data. This dataset contains information about normal network behaviour as well as during synthetic intrusions. Models for intrusion detection in computer networks based on machine learning methods were investigated: Decision Tree, KNN, Logistic Regression, SVM, Gradient Boosting, Random Forest. A method of converting tabular data into images was developed, which made it possible to build intrusion detection models based on Vision Transformer and Vision Transformer for small-size datasets on modern Transformer architecture. The research results showed that developed model based on Vision Transformer and Vision Transformer for small-size datasets improves the quality of identification, and eliminates the need for a preprocessing step such as dimensionality reduction.

关键词： Support vector machines computer vision Computational modeling Intrusion detection Artificial neural networks Forestry Transformers Data models computer networks Regression tree analysis

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