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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Anja Obradov Bogdan Pavković Departmant of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350349153

ISBN: (纸本)9798350349160

Functional safety is an important aspect in the development of safety-critical software for the automotive industry, which can be challenging to achieve due to integration of software components of different criticality on one platform. The AU-TOSAR architecture supports the development of safety-critical systems by providing safety measures and mechanisms, including mechanisms in the memory domain. This paper proposes a tool for ensuring Freedom from Interference between software components of different ASIL while verifying AUTOSAR safety mechanisms in memory domain. Tool proposed in this paper is used to analyze and verify the correct implementation of memory partitioning in a system consisting of mixed-criticality software components. By verifying the correct implementation of the memory partitioning and memory mapping mechanisms, we contribute to the correct functionality of memory partitioning mechanism and memory protection unit.

关键词： Industries Technological innovation Interference Software Robustness Resource management Zinc

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Graph-Based Decentralized Exploration and Semantic Inspection for Multi-Robot Systems

Graph-Based Decentralized Exploration and Semantic Inspectio...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Nada E. Abbas Tamara Petrovic Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper presents a decentralized graph-based exploration and inspection framework for Multi-Robot Systems, designed to address challenges in subterranean and largescale environments. Unlike prior works that focus solely on exploration or inspection, this framework integrates volumetric exploration, semantic inspection, and dynamic task allocation into a unified decentralized system. A key novelty of this work is the seamless integration of these modules in a multi-robot setting, allowing UAVs to autonomously coordinate their tasks without relying on centralized control. The framework employs a hierarchical graph structure, utilizing a dense local graph for immediate navigation and a sparse global graph for long-term planning and repositioning. Extensive simulations in large-scale complex-shaped environments demonstrate that the proposed approach improves the completeness of the generated maps, reduces inconsistencies in the constructed mesh, and accelerates the overall exploration-inspection process compared to existing decentralized strategies.

关键词： Semantics Inspection Prediction algorithms Dynamic scheduling Aircraft navigation Robustness Planning Multi-robot systems Resource management Centralized control

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Person re-identification with transformers and image stacking

Person re-identification with transformers and image stackin...

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Telecommunications Forum (TELFOR)

作者： Dimitrije Stojanović Bogdan Pavković Nenad Četić Departmant of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

Person re-identification (ReID) is increasingly important due to the expansion of surveillance cameras. ReID can effectively operate in various conditions, making it suitable for security, retail analytics, and smart city applications. We propose a transformer-based model, DeepChangeVIT-ReID, fine-tuned with triplet loss, using the DeepChange dataset. We address long-term ReID challenges, including pose variations, camera angle differences, and clothing alterations. DeepChangeVIT-ReID achieves state-of-the-art performance, significantly improving Rank-1 accuracy compared to existing methods on the DeepChange dataset.

关键词： Accuracy Smart cities Surveillance Scalability Stacking Transformers Cameras Telecommunications Security Identification of persons

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Prescriptive Procedure for Manual Code Smell Annotation

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SSRN 2024年

作者： Prokić, Simona Luburić, Nikola Slivka, Jelena Kovačević, Aleksandar Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

Code smells are structures in code that present potential software maintainability issues. Manually constructing high-quality datasets to train ML models for code smell detection is challenging. Inconsistent annotations, small size, non-realistic smell-to-non-smell ratio, and poor smell coverage hinder the dataset quality. These issues arise mainly due to the time-consuming nature of manual annotation and annotators’ disagreements caused by ambiguous and vague smell *** address challenges related to building high-quality datasets suitable for training ML models for smell detection, we designed a prescriptive procedure for manual code smell annotation. The proposed procedure represents an extension of our previous work, aiming to support the annotation of any smell defined by Fowler. We validated the procedure by employing three annotators to annotate smells following the proposed annotation *** main contribution of this paper is a prescriptive annotation procedure that benefits the following stakeholders: annotators building high-quality smell datasets that can be used to train ML models, ML researchers building ML models for smell detection, and software engineers employing ML models to enhance the software maintainability. Secondary contributions are the code smell dataset containing Data Class, Feature Envy, and Refused Bequest, and DataSet Explorer tool which supports annotators during the annotation procedure. © 2024, The Authors. All rights reserved.

关键词： Maintainability

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Detection of Hyperbolic Reflections on Real GPR Data Using YOLO Model Trained with Synthetic Data

Detection of Hyperbolic Reflections on Real GPR Data Using Y...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Željko Bugarinović Milan Gavrilović Aleksandar Ristić Milka Šarkanović Bugarinović Aleksandra Radulović Miro Govedarica Department for computing and control engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This paper analyzes the application of the YOLO algorithm to automatically recognize hyperbolic reflections on radargrams. A specific shape of hyperbolic reflection on radargrams occurs with objects of circular cross-section that are scanned perpendicular to their axis, such as underground pipes. Requirements for processing a large number of radargrams are increasingly being imposed in practice. Therefore, the development of algorithms for the automated finding of hyperbolic reflections on radargrams has been intensified in recent years. Conventional methods take a lot of time and are more susceptible to noise than algorithms based on deep learning. One of such algorithms is also applied in this paper. The model was trained with 2000 synthetic radargrams generated using gprMax software. The basic idea is reflected in the analysis of the successful detection of hyperbolic reflections on real radargrams using a model trained only with synthetic radargrams. Experimental results show a high level of success.

关键词： YOLO Training Software algorithms Noise Signal processing algorithms Reflection Data models

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs

User Input Search - Custom Motion Estimation Algorithm Optim...

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Proceedings of the International Convention MIPRO

作者： J. Benjak D. Hofman Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data – the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested.

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Power Line Following Based on Measurements of the Magnetic Field

Power Line Following Based on Measurements of the Magnetic F...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Goran Vasiljević Dean Martinović Stjepan Bogdan Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Unska 3 University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a method to control the UAV to follow the power line with a specific position and orientation based only on the measurement of the magnetic field generated by the current flow in the power line. In this way, it is possible to localize the UAV with respect to the power line without the need for additional sensors, even in poor visibility conditions. The measurements from four magnetometers attached to the UAV are used to solve an optimization problem that involves determining the relative pose of the UAV with respect to the power line. Based on the relative pose, the UAV is controlled to follow the power line in a predefined position and orientation. Experiments in a test setup have confirmed that the method is applicable in a realistic environment.

关键词： Location awareness Visualization Power measurement Magnetic field measurement Magnetometers Current measurement Position measurement Autonomous aerial vehicles Magnetic fields Optimization

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Advanced UVM-Based Verification Methodology for Consumer Electronics HW Design Evaluation and Validation

Advanced UVM-Based Verification Methodology for Consumer Ele...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Marko Savić Nebojša Pjevalica Computing and Control Engineering Dept. Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350349153

ISBN: (纸本)9798350349160

The rapid evolution of consumer electronics demands increasingly complex hardware devices supporting a wide range of consumer needs. Designing and verifying such solutions is becoming more challenging. This paper introduces an automated approach for assessing the efficacy of implemented digital systems. Leveraging these insights, the proposed methodology aims to guide HDL designers, enabling them to identify and rectify errors more effectively. The proposed methodology is tested on the example of a relatively simple digital design on a population of twenty designers. The achieved results expose a significant increase in performance and quality in a relatively short time interval.

关键词： Technological innovation Digital systems Sociology Hardware Hardware design languages Statistics Consumer electronics

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AI-Enhanced Structural Health Monitoring with a Multi-Rotor Aerial Vehicle

AI-Enhanced Structural Health Monitoring with a Multi-Rotor ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Filip Zorić Ana Milas Tamara Petrović Zdenko Kovačić Matko Orsag Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Roads, bridges, tunnels, railways and canals are crucial for the transportation of goods and people in modern society. Modern infrastructure is subject to damage and requires therefore regular inspection and maintenance to remain safe and functional. While this can be done manually, large parts of the mentioned infrastructure are hardly accessible, which is a main motivational factor for the use of multi-rotor aerial vehicles (MAV s) for inspection and maintenance tasks. MAV s, paired with novel deep learning computer vision techniques, such as instance segmentation, are emerging as an obvious choice for automating certain parts of structural health monitoring (SHM). In this manuscript, we provide an overview of available SHM datasets and how they can be utilized for the task of autonomous crack detection. We use available datasets to train and compare two neural network architectures for instance segmentation. The segmented crack instances are then localized in a global coordinate frame to perform autonomous mapping of the potentially dangerous infrastructure defects. Experimental studies demonstrate the effectiveness of the proposed SHM approach, which results in precisely localized infrastructure defects marked on the global map.

关键词： Instance segmentation Irrigation Structural panels Neural networks Transportation Inspection Rail transportation

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