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Dalveren, Yaser
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Name Variants
Dalveren, Y.
Y., Dalveren
Dalveren,Y.
Y.,Dalveren
D.,Yaser
Dalveren, Yaser
Yaser, Dalveren
D., Yaser
Y., Dalveren
Dalveren,Y.
Y.,Dalveren
D.,Yaser
Dalveren, Yaser
Yaser, Dalveren
D., Yaser
Job Title
Doçent Doktor
Email Address
yaser.dalveren@atilim.edu.tr
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
62
Articles
33
Citation Count
255
Supervised Theses
11
61 results
Scholarly Output Search Results
Now showing 1 - 10 of 61
Article Citation Count: 31Variational Mode Decomposition-Based Threat Classification for Fiber Optic Distributed Acoustic Sensing(Ieee-inst Electrical Electronics Engineers inc, 2020) Dalveren, Yaser; Kara, Ali; Aghnaiya, Alghannai; Kara, Ali; Department of Electrical & Electronics EngineeringIn this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signal-to-Noise Ratio (SNR) levels (& x2212;4 to & x2212;18 dB). It has observed that the classification accuracy at high/moderate (& x2212;4 to & x2212;8 dB) and low (& x2212;8 to & x2212;18 dB) SNR levels are 79.5 & x0025; and 75.2 & x0025;, respectively. To the best of authors & x2019; knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems.Data Paper Citation Count: 26A Database for the Radio Frequency Fingerprinting of Bluetooth Devices(Mdpi, 2020) Uzundurukan, Emre; Dalveren, Yaser; Kara, Ali; Department of Electrical & Electronics Engineering; Airframe and Powerplant MaintenanceRadio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices.Article Advancing Mmwave Altimetry for Unmanned Aerial Systems: a Signal Processing Framework for Optimized Waveform Design(Mdpi, 2024) Dalveren, Yaser; Kara, Ali; Kara, Ali; Derawi, Mohammad; Department of Electrical & Electronics EngineeringThis research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input-multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77-81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.Article Citation Count: 4On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection(Mdpi, 2022) Dalveren, Yaser; Kara, Ali; Catak, Ferhat Ozgur; Kara, Ali; Department of Electrical & Electronics EngineeringIn the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC- a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set ofWi-Fi signals captured from variousWi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signalto-noise ratio (SNR) values defined as low (3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.Article Citation Count: 7Multipath Exploitation in Emitter Localization for Irregular Terrains(Spolecnost Pro Radioelektronicke inzenyrstvi, 2019) Dalveren, Yaser; Kara, Ali; Department of Electrical & Electronics EngineeringElectronic Support Measures (ESM) systems have many operational challenges while locating radar emitter's position around irregular terrains such as islands due to multipath scattering. To overcome these challenges, this paper addresses exploiting multipath scattering in passive localization of radar emitters around irregular terrains. The idea is based on the use of multipath scattered signals as virtual sensor through Geographical Information System (GIS). In this way, it is presented that single receiver (ESM receiver) passive localization can be achieved for radar emitters. The study is initiated with estimating candidate multipath scattering centers over irregular terrain. To do this, ESM receivers' Angle of Arrival (AOA) and Time of Arrival (TOA) information are required for directly received radar pulses along with multipath scattered pulses. The problem then turns out to be multiple-sensor localization problem for which Time Difference of Arrival (TDOA)-based techniques can easily be applied. However, there is high degree of uncertainty in location of candidate multipath scattering centers as the multipath scattering involves diffuse components over irregular terrain. Apparently, this causes large localization errors in TDOA. To reduce this error, a reliability based weighting method is proposed. Simulation results regarding with a simplified 3D model are also presented.Conference Object Citation Count: 2A Mini-Review on Radio Frequency Fingerprinting Localization in Outdoor Environments: Recent Advances and Challenges(Institute of Electrical and Electronics Engineers Inc., 2022) Dalveren, Yaser; Kara, Ali; Kara,A.; Doğan, Deren; Department of Electrical & Electronics Engineering; Mechatronics EngineeringA 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.Article Citation Count: 6Deep Learning-Based Vehicle Classification for Low Quality Images(Mdpi, 2022) Dalveren, Yaser; Kara, Ali; Dalveren, Yaser; Pazar, Senol; Kara, Ali; Derawi, Mohammad; Department of Electrical & Electronics EngineeringThis 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 Count: 1Distributed denial-of-service attack mitigation in network functions virtualization-based 5G networks using management and orchestration(Wiley, 2021) Dalveren, Yaser; Kara, Ali; Maiga, Bamoye; Kara, Ali; Department of Electrical & Electronics EngineeringThe fifth generation (5G) technology is expected to allow connectivity to billions of devices, known as Internet of Things (IoT). However, IoT devices will inevitably be the main target of various cyberattack types. The most common one is known as distributed denial-of-service (DDoS) attack. In order to mitigate such attacks, network functions virtualization (NFV) has a great potential to provide the benefit of elasticity and low-cost solutions for protecting 5G networks. In this context, this study proposes a new mechanism developed to mitigate DDoS attacks in 5G NFV networks. The proposed mechanism utilizes intrusion prevention system's (IPS) virtual machines (VMs) to intercept the queries. Based on the volume of DDoS traffic, IPS's VMs are dynamically deployed by means of management and orchestration (MANO) in order to balance the load. To evaluate the effectiveness of the mechanism, experiments are conducted in a real 5G NFV environment built by using 5G NFV environment tools. To our best knowledge, this is the first time that NFV-based mechanism is experimentally tested in a real 5G NFV environment for mitigating DDoS attacks in 5G networks. The experimental results verify that the proposed mechanism can mitigate DDoS attacks effectively.Article Citation Count: 24On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices(Mdpi, 2020) Dalveren, Yaser; Kara, Ali; Kara, Ali; Department of Electrical & Electronics EngineeringRadio 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.Article Citation Count: 1Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Dalveren, Yaser; Kara, Ali; Kara,A.; Derawi,M.; Department of Electrical & Electronics EngineeringVehicle 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.
