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Author: Kara,A.Scopus Q: Q2

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    Citation - Scopus: 3
    An Industry Sponsored Undergraduate Research (ur) Experience: Preliminary Study on Fulfillment of Program Outcomes and Industry Requirements
    (IEEE Computer Society, 2014) Kapusuz,K.Y.; Kara, Ali; Kara,A.; Kara, Ali; Department of Electrical & Electronics Engineering; Department of Electrical & Electronics Engineering
    This study presents educational results of an industry sponsored undergraduate research (UR) project. The aim of the study is to show how such project works contribute to students in acquiring qualifications or skills necessary by the industry, and abilities regarding with program outcomes of accreditation organisations. The study is based on quantitative (surveys) and qualitative (self descriptions) data collected from senior students who worked in 9 months UR project sponsored by a company in Radio frequency (RF) and Communications domain. The preliminary results showed that an industry sponsored undergraduate research project may serve to both short term (industry requirements) and longer term (program outcomes) expectations in undergraduate curriculum of engineering departments. © 2014 IEEE.
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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.