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Author: Dalveren,Y.Subject: deep learning

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    Article
    Citation - WoS: 3
    Citation - Scopus: 5
    Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Maiga,B.; Dalveren,Y.; Kara,A.; Derawi,M.
    Vehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture. © 2023 by the authors.
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    Citation - Scopus: 6
    A Mini-Review on Radio Frequency Fingerprinting Localization in Outdoor Environments: Recent Advances and Challenges
    (Institute of Electrical and Electronics Engineers Inc., 2022) Dogan,D.; Dalveren,Y.; Kara,A.
    A considerable growth in demand for locating the source of emissions in outdoor environments has led to the rapid development of various localization methods. Among these, RF fingerprinting (RFF) localization has become one of the most promising method due to its unique advantages resulted from the recent developments in machine learning techniques. In this short review, it is aimed to assess the existing RFF methods in the literature for outdoor localization. For this purpose, firstly, the current state of RFF localization methods in outdoor environments are overviewed. Then, the main research challenges in the development of RFF localization are highlighted. This is followed by a brief discussion on the open issues in order to give future research directions. Furthermore, the research efforts currently undertaken by the authors are briefly addressed. © 2022 IEEE.