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Now showing 1 - 10 of 38
  • Article
    W-Band RCS Prediction of Small Objects: Comparing Two Widely Used Methods with Experimental Validation
    (Gazi University, 2025) Kara, Ali; Aydın, Elif; Yardım, Funda Ergün; Sezgin, Deniz
    This paper compares the accuracy of Shooting and Bouncing Rays and Electric Field Integral Equation methods for Radar Cross Section prediction of small objects at 77-81 GHz band. Existing studies on RCS prediction methods often lack comprehensive comparisons between computational and experimental results, particularly for small objects measured with a 77 GHz radar. This study addresses this gap by presenting an in-depth analysis of both simulation and measurement data. In this work, three targets with varying geometries and materials were measured with a frequency modulated continuous wave radar and simulated using Ansys HFSS and CST Studio Suite. The measurements were performed with a commercial off-the-shelf (COTS) frequency modulated continuous wave radar operating at 77–81 GHz. This study aims to emphasize the importance of considering both efficiency and accuracy when opting for an RCS prediction method. Overall, the outcomes of both methods have largely demonstrated good alignment. It has been noted that, while Shooting and Bouncing Rays method offers promising time-saving advantages, Electric Field Integral Equation method remains a valuable tool for complex geometries where precise results are crucial.
  • Article
    Citation - WoS: 31
    Citation - Scopus: 40
    Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices
    (Ieee-inst Electrical Electronics Engineers inc, 2019) Aghnaiya, Alghannai; Ali, Aysha M.; Kara, Ali
    Radio frequency fingerprinting (RFF) is based on identification of unique features of RF transient signals emitted by radio devices. RF transient signals of radio devices are short in duration, non-stationary and nonlinear time series. This paper evaluates the performance of RF fingerprinting method based on variational mode decomposition (VMD). For this purpose, VMD is used to decompose Bluetooth (BT) transient signals into a series of band-limited modes, and then, the transient signal is reconstructed from the modes. Higher order statistical (HOS) features are extracted from the complex form of reconstructed transients. Then, Linear Support Vector Machine (LVM) classifier is used to identify BT devices. The method has been tested experimentally with BT devices of different brands, models and series. The classification performance shows that VMD based RF fingerprinting method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as Hilbert-Huang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lower (2-3 dB) SNR levels.
  • Conference Object
    Citation - WoS: 17
    Citation - Scopus: 23
    Maintenance, Sustainability and Extendibility in Virtual and Remote Laboratories
    (Elsevier Science Bv, 2011) Kara, Ali; Ozbek, Mehmet Efe; Cagiltay, Nergiz Ercil; Aydin, Elif
    This study presents discussions on sustainability of Virtual and Remote Laboratories (VRL), and provides challenges toward maintenance of VRLs. Technical and pedagogical issues in extension and sustenance of VRLs are discussed with the experiences of the authors gained in the development of a VRL system, European Remote Radio Laboratory (ERRL) platform. Moreover, the study presents actions to be taken in sustenance plan and expendability of VRL system with the advances in Information and Communication Technologies (ICT) and educational technologies along with the needs of educators and learners in formal education. (C) 2011 Published by Elsevier Ltd.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 10
    Quality of Service Assessment: a Case Study on Performance Benchmarking of Cellular Network Operators in Turkey
    (Tubitak Scientific & Technological Research Council Turkey, 2015) Kadioglu, Rana; Dalveren, Yaser; Dalveren, Yaser; Kara, Ali; Kara, Ali; Dalveren, Yaser; Kara, Ali; Department of Electrical & Electronics Engineering; Department of Electrical & Electronics Engineering
    This paper presents findings on performance benchmarking of cellular network operators in Turkey. Benchmarking is based on measurements of standard key performance indicators (KPIs) in one of the metropolitan cities of Turkey, Ankara. Performance benchmarking is formulated by incorporating customer perception by conducting surveys on how important KPIs are from the user's point of view. KPIs are measured, with standard test equipment, by drive test method on specified routes. According to the performance benchmarking results, the GSM and UMTS network operators achieving the best performance were determined in Ankara. Speech qualities of network operators, as the most popular service, were also evaluated by several statistical methods including pdf/cdf analysis and chi-square and Fisher's exact tests. The network operator providing the highest speech quality in Ankara was determined with the methods applied. Overall, the results and approaches on benchmarking of cellular networks in Turkey are reported for the first time in this paper. The approaches proposed in this paper could be adapted to wide-scale benchmarking of services in cellular networks.
  • Article
    Citation - WoS: 17
    Citation - Scopus: 31
    Deep Learning-Based Vehicle Classification for Low Quality Images
    (Mdpi, 2022) Tas, Sumeyra; Sari, Ozgen; Dalveren, Yaser; Pazar, Senol; Kara, Ali; Derawi, Mohammad
    This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 x 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines
    (Springer, 2022) Coruk, Remziye Busra; Gokdogan, Bengisu Yalcinkaya; Benzaghta, Mohamed; Kara, Ali
    The recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Various Automatic Modulation Classification (AMC) algorithms have been developed for this purpose in the literature. However, classification with low computational complexity as well as reasonable processing time is still a challenge. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higher-order moments and cumulants. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for testing the proposed method at 0-20 dB SNR. The overall accuracy for quadratic Support Vector Machine (SVM) is found to be as high as 98% at 10 dB. The comparison of the results with other AMC papers published in the literature indicates that the proposed method present higher accuracy, especially for realistic channel induced OTA dataset.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Modelling and Design of Pre-Equalizers for a Fully Operational Visible Light Communication System
    (Mdpi, 2023) Bostanoglu, Murat; Dalveren, Yaser; Catak, Ferhat Ozgur; Kara, Ali
    Nowadays, Visible Light Communication (VLC) has gained much attention due to the significant advancements in Light Emitting Diode (LED) technology. However, the bandwidth of LEDs is one of the important concerns that limits the transmission rates in a VLC system. In order to eliminate this limitation, various types of equalization methods are employed. Among these, using digital pre-equalizers can be a good choice because of their simple and reusable structure. Therefore, several digital pre-equalizer methods have been proposed for VLC systems in the literature. Yet, there is no study in the literature that examines the implementation of digital pre-equalizers in a realistic VLC system based on the IEEE 802.15.13 standard. Hence, the purpose of this study is to propose digital pre-equalizers for VLC systems based on the IEEE 802.15.13 standard. For this purpose, firstly, a realistic channel model is built by collecting the signal recordings from a real 802.15.13-compliant VLC system. Then, the channel model is integrated into a VLC system modeled in MATLAB. This is followed by the design of two different digital pre-equalizers. Next, simulations are conducted to evaluate their feasibility in terms of the system's BER performance under bandwidth-efficient modulation schemes, such as 64-QAM and 256-QAM. Results show that, although the second pre-equalizer provides lower BERs, its design and implementation might be costly. Nevertheless, the first design can be selected as a low-cost alternative to be used in the VLC system.
  • Article
    Mükemmel İletken Silindir Modeli ile 28 Ghz’de İç Mekân Linklerini Bloke Eden İnsanların Etkilerinin İrdelenmesi
    (2020) Dalveren, Yaser; Kara, Ali
    Literatürde, kısa mesafe iç mekân haberleşme linklerinde insan vücudu blokajının sebep olduğu kaybıntahmininde matematiksel olarak sade bir yapıya sahip olması sebebiyle Çift Bıçak Kenarlı Kırınım (ÇBKK)modeli sıklıkla kullanılmaktadır. Fakat modelde insan vücudu benzetimi için kullanılan dikdörtgensel ekran,insan vücudu fiziğini temsil etmek için yeterli olmayabilir. Bu durum, özellikle çoklu insan vücudu blokajıolması durumunda, modelin tahmin doğruluğunu olumsuz etkileyebilir. Öte yandan, insan vücudu benzetimindeGeometrik Kırınım Teorisi (GKT) temelli mükemmel iletken silindir modeli, literatürde sıklıkla kullan bir diğermodeldir. Ancak bu modelin, çoklu insan vücudu blokajı durumunda, yayılım kaybını tahmin etmedeki etkisihenüz çalışılmamıştır. Bu nedenle, sunulan bu kısa çalışmadaki amaç, iletken silindir modelinin, 5G için tahsisedilmesi en muhtemel frekans bantlarından biri olan 28 GHz’de, çoklu insan vücudu blokajının neden olduğukısa mesafe iç mekân linklerindeki yayılım kaybını tahmin etmedeki doğruluğunu irdelemektir. Bu amaçla,öncelikle, kısa mesafe iç mekân linki bir insan vücudu ile tamamen bloklanmış; aynı anda, link yakınındakibaşka bir insan vücudu linke yaklaştırılarak ölçümler yapılmıştır. Sonrasında, yayılım kaybını tahmin etmek içinGKT ve ÇBKK modelinden faydalanılmıştır. Tahmin doğruluğu analizi için simülasyon ve ölçüm sonuçlarıkarşılaştırılmıştır. Sonuç olarak deneysel çalışmalar ile literatürde ilk defa, çoklu insan vücudu blokajının GKTmodeli ile tahmin doğruluğunun arttığı gözlemlenmiştir.
  • Article
    ISAR Imaging of Drone Swarms at 77 GHz
    (Tubitak Scientific & Technological Research Council Turkey, 2025) Coruk, Remziye Busra; Kara, Ali; Aydin, Elif
    The proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense antidrone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, sizes, and payload configurations by employing ANSYS simulations. To enhance image quality, different window functions are evaluated, and the Hamming window is selected due to its highest peak signal-to-noise ratio (PSNR) (16.3645) and structural similarity (SSIM) (0.9067) values, ensuring superior noise reduction and structural preservation. The results demonstrate that the effectiveness of high-resolution ISAR imaging in accurately detecting and characterizing drone swarms pave the way for enhanced airspace security measures.
  • Article
    Citation - WoS: 29
    Citation - Scopus: 33
    On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices
    (Mdpi, 2020) Aghnaiya, Alghannai; Dalveren, Yaser; Kara, Ali
    Radio frequency fingerprinting (RFF) is one of the communication network's security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (similar to 4% higher) at lower SNR levels (-5-5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.