Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Now showing 1 - 10 of 121
  • Conference Paper
    3d Printing-Assisted Fabrication of Microfluidic Pneumatic Valves
    (IEEE, 2023) Keleş, Şeyda; Karakuzu, Betül; Tekin, Hüseyin Cumhur
    Pneumatic valves have a crucial place in the fluidic control in microfluidic systems. Pneumatic valves containing polydimethylsiloxane (PDMS) membrane structures are used in microfluidic systems such as cell separation, and cell manipulation due to their flexible structure, and ease of production. This study demonstrates the rapid and straightforward fabrication of pneumatic valve structures using PDMS membranes, achieved through the utilization of 3D-printed molds. As a result of our experiments, we observed valve closure in a fluidic channel with a height of 150 μm. This closure was achieved by utilizing 400 μm × 800 μm PDMS membrane with a thickness of 66 μm positioned between the fluidic and control channels, while applying 1.5 bar of pressure to the control channel. When the pressure is removed, the opening time of the valve is only 0.02 s, and this response time allows rapid valving function. The presented valve fabrication strategy would allow easy and low-cost production of sophisticated microfluidic chips. © 2023 IEEE.
  • Conference Object
    Carrier Frequency Offset Based Shared Randomness for Secure Transmission in M-Psk Noma
    (IEEE, 2023) Göztepe, Caner; Karabulut Kurt, Güneş; Özbek, Berna
    Power domain non-orthogonal multiple access (NOMA) enhances spectral efficiency by superposing multiple users in the same time-frequency resource block at the expense of exposing the users' data. However, current approaches to improve the secrecy levels of users are limited to rate reduction. This paper proposes a secure NOMA system based on the shared randomness extracted from the reciprocal carrier frequency offsets (CFOs) between the transmitter-receiver pairs for M-ary phase-shift keying. As multiple users will have physically separated oscillators, it will result in independent CFOs among users. This randomness is used to introduce a constellation rotation in the transmitted symbols. We show that under ideal CFO estimates, the proposed approach achieves perfect secrecy among all NOMA users without introducing any rate reduction. We also demonstrate the practical applicability of the proposed approach by using a software-defined radio-based test bed. © 2023 IEEE.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Integrated Space Domain Awareness and Communication System
    (IEEE, 2023) Geçgel Çetin, Selen; Özbek, Berna; Karabulut Kurt, Güneş
    Space has been reforming and this evolution brings new threats that, together with technological developments and malicious intent, can pose a major challenge. Space domain awareness (SDA), a new conceptual idea, has come to the forefront. It aims sensing, detection, identification and countermeasures by providing autonomy, intelligence and flexibility against potential threats in space. In this study, we first present an insightful and clear view of the new space. Secondly, we propose an integrated SDA and communication (ISDAC) system for attacker detection. We assume that the attacker has advanced communication capabilities to vary attack scenarios, such as random attacks on some receiver antennas. To track random patterns and meet SDA requirements, a lightweight convolutional neural network architecture is developed. The proposed ISDAC system shows superior and robust performance under 12 different super-attacker configurations with a detection accuracy of over 97.8%. © 2023 IEEE.
  • Article
    Link Prediction for Completing Graphical Software Models Using Neural Networks
    (IEEE, 2023) Leblebici, Onur; Tuğlular, Tuğkan; Belli, Fevzi
    Deficiencies and inconsistencies introduced during the modeling of software systems may result in high costs and negatively impact the quality of all developments performed using these models. Therefore, developing more accurate models will aid software architects in developing software systems that match and exceed expectations. This paper proposes a graph neural network (GNN) method for predicting missing connections, or links, in graphical models, which are widely employed in modeling software systems. The proposed method utilizes graphs as allegedly incomplete, primitive graphical models of the system under consideration (SUC) as input and proposes links between its elements through the following steps: (i) transform the models into graph-structured data and extract features from the nodes, (ii) train the GNN model, and (iii) evaluate the performance of the trained model. Two GNN models based on SEAL and DeepLinker are evaluated using three performance metrics, namely cross-entropy loss, area under curve, and accuracy. Event sequence graphs (ESGs) are used as an example of applying the approach to an event-based behavioral modeling technique. Examining the results of experiments conducted on various datasets and variations of GNN reveals that missing connections between events in an ESG can be predicted even with relatively small datasets generated from ESG models. Author
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Görgül kip ayrıştırması kullanılarak optik faz kırınımında hassasiyet iyileştirilmesi
    (IEEE, 2023) Ataç, Enes; Dinleyici, Mehmet Salih
    Phase diffraction is a potent property used in transparent dielectric film characterization. The measured diffraction pattern on the camera is evaluated by matching numerically computed diffraction patterns to determine the optical properties of the ultra-thin films (refractive index, thickness, etc.). However, the obtained diffraction data is not only a nonlinear and non-stationary signal but also exhibits micron-scale variations, thus limiting the measurement accuracy. Therefore, it is challenging to identify shifts in minima and deviations in amplitude on diffraction data to extract information about the optical properties of phase objects. In this study, it is aimed to improve the thickness sensitivity of the system by applying Empirical Mode Decomposition (EMD) to plane wave-based near-field phase diffraction data. Since EMD is very sensitive to abrupt changes in the signal due to the spatial frequency components, the nanoscale variations in the film thickness become more observable and detectable. Experimental outputs and numerical simulations show that the decomposition increases the thickness sensitivity comparing the classical matching technique.
  • Conference Object
    Citation - Scopus: 2
    Parkinson hastalığı sınıflandırmasına yönelik ivmeölçer tabanlı zamanlama analizi
    (IEEE, 2023) Karaçalı, Bilge; Onay, Fatih
    Parkinson's disease is a neurodegenerative disorder caused by dopamine deficiency in the basal ganglia, resulting in cognitive and motor impairments. In this study, accelerometer signals were used to estimate the delay time between the command to start pedaling and the actual movement onset in three groups: healthy individuals (n=13), Parkinson's disease patients (n=13), and patients with freezing of gait symptoms (n=13). Features were extracted from the delay time distributions for each participant and subjected to a triple classification. Linear support vector machine achieved a classification accuracy of 69.2% for all participants. Notably, the average time to start pedaling was found to be significantly different among the three groups, and accelerometer-based timing analysis could be used as a diagnostic tool to assist clinical tests.
  • Conference Object
    Dalgacık gürültü giderme yöntemiyle mikrodalga bileşen karakterizasyonunun iyileştirilmesi
    (IEEE, 2023) Karatay, Anıl; Olcay, Bilal Orkan; Yaman, Fatih
    In this study, an efficient approach is presented to improve the characterization of various microwave components commonly used in communication and radar applications, such as antennas and power dividers. The components were initially simulated and then fabricated using the Computer Simulation Technology (CST) software. Vector Network Analyzer (VNA) measurements of the fabricated components were performed using a low-cost but noisy coaxial cable, and the measurement results were processed using a wavelet-based noise reduction method. For comparison purposes, the Haar and Daubechies-4 (DB4) wavelet functions were applied separately, and the results were examined. It was observed that the correlation and root mean square error between the ideal and measurement results improved in a positive direction with the noise reduction application. This approach provides significant cost and labor advantages, particularly when expensive elements such as gold and silver are used in coaxial cables that are physically free from noise. The experimental and numerical results show good agreement between the ideal simulation results and the filtered measurement results.
  • Conference Object
    Kurt saldırıları için sentetik irislerde örnek seçilimi
    (IEEE, 2023) Akdeniz, Eyüp Kaan; Erdoğmuş, Nesli
    In this study, samples with higher potential to succeed in wolf attacks are picked among synthetically generated iris images, and the composed subset is shown to pose a more significant threat toward an iris recognition system backed by a Presentation Attack Detection (PAD) module with respect to randomly selected samples. Iris images generated by Deep Convolutional Generative Adversarial Networks (DCGAN) are firstly filtered by rejection sampling on PAD score distribution of real iris image PAD scores. Next, the probability of zero success in all attack attempts is calculated for each synthetic iris image, using real iris images in the training set, and match and non-match score distributions are calculated on those. Synthetic images with the lowest probabilities of zero success are included in the final set. Our hypothesis that this set would be more successful in wolf attacks is tested by comparing its spoofing performances with randomly selected sample sets.
  • Conference Object
    Algıda gecikme ve kısa-ömürlü senkronizasyon temelli yeni bir hayali motor aktivite tanıma yaklaşımı
    (IEEE, 2023) Olcay, B. Orkan; Karaçalı, Bilge
    This study proposes a novel approach for investigating a brain-computer interface that considers the temporal organization of brain activity, explicitly accounting for perception latency. To this end, we aligned the onset of task periods with the concurrence of left parietal and parieto-occipital electrodes to obtain the timings of perception latencies. Then, activity-specific synchronization timings between channel pairs were calculated using the time-aligned task periods. The perception latency and activity-specific synchronization timings were subsequently used for feature extraction and classification. The proposed approach achieved significantly better performance when comparing the proposed approach with the method that did not account for the perception latency
  • Conference Object
    Citation - Scopus: 2
    Emerging Concept of Human Centric Lighting in Literature Review
    (IEEE, 2023) Kazanasmaz, Zehra Tuğçe; Köse, Fatma Büşra; Tayfur, Gökmen
    Human centric lighting is an umbrella concept which covers human health and well-being in general. As the conventional lighting techniques are based on horizontal workplane illuminance, it drives from the vertical eye level illuminance and its spectral distribution triggering the non-visual effects on humans. That is named as melanopic illuminance consequently. Its metrics have taken their place in lighting design literature and applications, with emergence of related standards subsequently. This literature overview contributes about the understanding the meaning human centric lighting due to transition from visual to non-visual effects of light, and how they direct recent research through light's impacts on human performance, emotions health and well-being, and relations to energy saving even. The shift from the concept of human centric lighting to circadian lighting design is obvious in very current studies. © 2023 IEEE.