Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
Browsing Scopus İndeksli Yayınlar Koleksiyonu by Scopus Q "N/A"
Now showing 1 - 20 of 336
- Results Per Page
- Sort Options
Conference Object Citation - WoS: 2Citation - Scopus: 194.8 Km-Range Direct Detection Fiber Optic Distributed Acoustic Sensor(Optica Publishing Group (Formerly OSA), 2019) Uyar, F.; Onat, T.; Unal, C.; Kartaloǧlu, T.; Ozdur, I.; Özbay, E.This work demonstrates an ultra-long range direct detection fiber optic distributed acoustic sensor which can detect vibrations at a distance of 94.8 km with 10 m resolution along the sensing fiber. © 2023 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 2Citation - Scopus: 394.8 Km-Range Direct Detection Fiber Optic Distributed Acoustic Sensor(Institute of Electrical and Electronics Engineers Inc., 2019) Uyar, Faruk; Onat, Talha; Unal, Canberk; Kartaloǧlu, Tolga; Ozdur, Ibrahim T.; Özbay, EkmelThis work demonstrates an ultra-long range direct detection fiber optic distributed acoustic sensor which can detect vibrations at a distance of 94.8 km with 10 m resolution along the sensing fiber. © 2019 Elsevier B.V., All rights reserved.Conference Object Absolute Phase Noise Analysis of a Harmonically Modelocked Semiconductor Laser(Optica Publishing Group (formerly OSA), 2017) Özharar, Sarper; Ozdur, Ibrahim T.We have designed and built a fiber coupled semiconductor laser at 1550 nm, which is harmonically mode-locked at 10 GHz by an external RF oscillator. The absolute phase noise of the laser is measured and discussed for two different cavity lengths. © 2023 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2Adaptive Reuse of Industrial Heritage: Resilience or Irreparable Loss(Docomomo, 2016) Baturayoğlu Yöney, Nilüfer; Asiliskender, Burak; Özer, Aysegul; Yoney, NiluferThe restoration and adaptive reuse of industrial heritage buildings and complexes, which present structurally and functionally resilient shells, provide us with an interesting dilemma in theory and practice: made of hard wearing materials to house straining functions and to last as long as possible, they are also flexible enough to adapt to almost any new purpose as a container. However, the presence of original machinery and equipment as well as designs based on machine-buildings may reduce the possibilities of adaptive reuse to a museum, where the buildings exhibit themselves, retaining the social, economic, historic and public aspects of cultural heritage as documents. Although originally built on the outskirts of urban settlements, today most industrial heritage complexes occupy central locations in the metropolitan sprawl of major cities. If disused, they are considered obsolete brownfields by local authorities and citizens despite personal and collective memories that may be attached to them. Their conversion into new uses presents major technical difficulties that require expertise in design and implementation. This paper discusses the theoretical and practical aspects of the adaptive reuse of industrial heritage and inherent problems, focusing on the case of the Sümerbank Kayseri Textile Factory (I. Nikolaev, Turkstroj, 1932-1935), which is being transformed into the campus of Abdullah GUI University. The restoration, renovation and adaptive reuse projects for different components of the complex follow similar principles of preservation and sustainability while they are modified to fit the architectural and technological characteristics of each building. Thus, although conversive and easily adaptable, the preservation of industrial architectural heritage becomes a dilemma between disruption and continuity, which the architects have to solve going beyond the possibilities of mere building stock on the one hand and that of the museum on the other. © 2017 Elsevier B.V., All rights reserved.Book Part Advanced Physicochemical Techniques for Wastewater Treatment(CRC Press, 2024) El Messaoudi, Noureddine; Georgin, Jordana; Cioğeroğlu, Zeynep; Şenol, Zeynep Mine; Kazan-Kaya, Emine Sena; Arslan, Dilek Şenol; Lacherai, AbdellahThis chapter provides an overview of advanced physicochemical techniques (APCTs) used in wastewater treatment, highlighting their principles, applications, and recent advancements. The chapter begins by discussing the APCTs involved in wastewater treatment, including membrane techniques, electrochemical methods, sonochemical treatment, microwave-assisted processes, hybrid processes, and green chemistry approaches. It explores the mechanisms by which these processes remove suspended solids, colloidal particles, and other contaminants from wastewater. The APCTs are versatile and can be applied to treat wastewater containing diverse pollutants, including heavy metals, organic compounds, and microorganisms. This versatility makes them suitable for various industrial and municipal wastewater streams. These methods are designed to target specific contaminants, resulting in the effective treatment and purification of wastewater. These techniques can be integrated with conventional wastewater treatment processes, enhancing overall treatment efficiency. Ongoing research and development in APCTs contribute to continuous innovation, leading to the discovery of new and improved methods for wastewater treatment. The chapter presents case studies and discusses the advantages, limitations, and future prospects of APCTs. © 2024 Elsevier B.V., All rights reserved.Book Part Advances in the Computation of NMR Parameters for Inorganic Nuclides(Elsevier, 2023) Holmes, Sean T.; Alkan, Fahri; Dybowski, Cecil R.In this article, we discuss practical aspects of the computation of NMR parameters of inorganic nuclides, as well as insights afforded by such calculations into the characterization of molecular-level structure and dynamics and the validation of theoretical models. An emphasis is placed on calculation of the magnetic shielding tensors of solids using cluster-based models that account for intermolecular interactions. In particular, the use of valence modification of terminal atoms using bond valence theory (VMTA/BV), which reduces net charges on clusters through terminal pseudoatoms with nonstandard nuclear charges, is demonstrated to be a robust technique for calculations on nuclei in network solids. Cluster-based calculations, including those that employ the VMTA/BV method, afford a unique opportunity to calculate magnetic shielding tensors for nuclei in solids by using density functional theory approximations beyond the generalized gradient approximation and by incorporating relativistic effects at the spin-orbit level. These developments are spurred by use of the zeroth-order regular approximation (ZORA), which provides a robust method of accounting for relativistic effects (up to the spin-orbit level) experienced by valence electrons. Calculations of NMR parameters are discussed for fluorine, cadmium, tin, tellurium, mercury, lead, and platinum, all of which have seen significant advances in recent years. These examples highlight the importance of such factors as coordination geometry, oxidation state, relativistic effects, and density functional approximations on computed magnetic shielding tensors. © 2023 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Air Gun With Water Bullet(Eindhoven University of Technology, 2023) Bozkuş, Zafer; Dinçer, A. Ersin; Tijsseling, A. S.; van de Ven, Alphons A.F.; van de Ven, FonsThe gun is a 12 m long inclined pipe of 0.1 m diameter which is connected to a charge of compressed air contained in a 0.5 m3 vessel. The bullet is a slug of water sitting in the upstream lower end of the pipe. The trigger is a hand-operated valve. The target is an elbow at the upstream higher end of the pipe. The smoking gun effect is created by a mist of water coming out of the pipe after each shot. The apparatus is not a toy but meant for serious research. When steam lines are out of operation and/or lack thermal insulation, liquid water collects in the lower parts of the system. System restart may accelerate the water slugs to velocities as high as 50 m/s, and subsequent slug impacts on elbows and orifices may cause pressure peaks with magnitudes only encountered in water-hammer events. The experimental programme consists of water slugs fired towards an elbow with an open end, a closed end, and an orifice end. The varied parameters are air pressure, water mass, outlet condition (open, closed, orifice). Upstream driving pressure and downstream impact pressure are measured in each experimental run. Pressure peaks up to 50 bar have been observed. Experimental results are compared with preliminary predictions from basic one-dimensional models. © 2024 Elsevier B.V., All rights reserved.Conference Object Akciğer Tümörlü Hastaların PET ve BT Görüntülerinin Çakıştırılıp Birleştirilmesi(IEEE, 2015) Ayyildiz, Oguzhan; Yilmaz, Bulent; Karacavus, Seyhan; Kayaalti, Omer; Icer, Semra; Eset, Kubra; Kaya, EserImage fusion attracts attention in medical field due to complementary behavior and application such as diagnosis and treatment planning. In this study, first positron emission tomography (PET) and computed tomography (CT) images coming from 8 nonsmall cell lung cancer were registered then wavelet and principal component analysis methods were applied to fuse images. According to mutual information metric and nuclear medicine expert wavelet method gave better results when compared to PCA.Conference Object Citation - Scopus: 2Akıllı Şebeke Uygulamaları için Pille Çalışan Duyarga Düğümlerinin Yaşam Süresi Analizi(Institute of Electrical and Electronics Engineers Inc., 2016) Eris, Cigdem; Güngör, Vehbi Çağrı; Boluk, Pinar SarisarayWireless Sensor Networks (WSNs) enable smart grids where sensor nodes monitor and control the important parameters of power grid components. However, energy-aware communication protocols should be developed to extend network lifetime of WSNs in smart grid environments. In this study, the lifetime of wireless sensor nodes has been analyzed for various smart grid environments, such as 500 kV substation, main power control room, and underground network transformer vaults. In addition, the effects of different operation modes of sensor nodes on node lifetime have been reviewed. © 2017 Elsevier B.V., All rights reserved.Article Citation - Scopus: 4Alliances to Acquisitions: A Road Map to Advance the Field of Strategic Management(Emerald Group Publishing Ltd. Howard House Wagon Lane, Bingley BD16 1WA, 2017) Zakaria, Rimi; Genç, Omer FarukAlthough primarily treated as two distinct research streams, strategic alliances and mergers and acquisitions together occupy much of the strategic management discourse. Alliances, in many cases, end in acquisitions as firms use alliances as intermediate strategic options to eventually acquire a partner. As the discipline of strategy matures and the frequency and the volume of inter-firm cooperation continue to rise, it is imperative to integrate these two research streams for a holistic understanding of the theory of the firm. The purpose of this conceptual piece is threefold. First, we review the extant studies that combine these two governance modes: alliance and acquisitions. Second, drawing on the dominant strategic management theories, we highlight how prior inter-firm alliances inform future acquisitions in terms of (a) pre-combination decisions, (b) post-deal integration processes, (c) alternatives and strategies, and (d) performance outcomes. Finally, in view of the emerging trends and evocative gaps, we offer a conceptual road map to encourage future theoretical development and empirical research. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 7Citation - Scopus: 12Ambient Energy Harvesting for Low Powered Wireless Sensor Network Based Smart Grid Applications(Institute of Electrical and Electronics Engineers Inc., 2019) Faheem, Muhammed Yasir; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Raza, Basit; Ngadi, M. A.; Güngör, Vehbi ÇağrıLimited battery lifetime is one of the most critical issues for wireless sensor networks (WSNs)-based smart grid (SG) applications. Recently, ambient energy harvesting (AEH) has been considered to significantly improve the network lifetime of the WSNs-based SG applications. However, extracting a significant amount of energy from the ambient energy resource due to time varying links quality affected by power grid environments is the main issue for WSNs-based applications in SG. In this paper, we propose a novel multi-source energy harvesting mechanisms for WSNs-based SG applications. The propose hybrid ambient energy harvesting framework through the designed circuitry successfully harvests massive power density by capturing the radial electric field (EF) and ambient radio frequency WiFi 2.4GHz band signals present in the vicinity of 500kV power grid station. The design energy harvesting schemes have been implemented on the recently developed routing protocol for SG applications. The experiments using EstiNet9.0, demonstrate that the designed framework is efficient in terms of energy harvesting capabilities to enable a long-lasting lifetime of the WSNs-based smart grid applications. © 2020 Elsevier B.V., All rights reserved.Article An Ultra-Low Fabric Capacitive Glove for Real-Time Motion Tracking and Human–computer Interaction(Institute of Physics, 2025) Başıbüyük, Y.; Mutluç, M.N.; Şavur, Ö.; İçöz, K.This study presents the development of a wearable glove system that integrates ultra-low-cost, fabric-based capacitive sensors for motion detection and human–computer interaction. The system combines touch and bend sensors fabricated from commercially available silver-coated fabric and silicone acrylic tape, enabling real-time tracking of finger movements via measurable capacitance changes. The glove translates physical gestures into digital commands, facilitating intuitive control in virtual environments. Experimental evaluation demonstrated stable operation across a wide pressure range (10–200 g, equivalent to 1.25–25 kPa), with an unnormalized sensitivity of ∼0.00504 pF g−1 (∼0.0040 pF kPa−1), corresponding to a normalized sensitivity of ∼0.0067 kPa−1 when referenced to the baseline capacitance (C0 ≈ 6 pF). The device exhibited high repeatability over 4000 loading cycles, and minimal signal variation (coefficient of variation, CV < 0.005). Integration with a Unity-based interface enabled low-latency gesture tracking in real time. Each sensor was fabricated for less than $0.05 using simple, scalable methods, without nanomaterials or cleanroom processing. Owing to its affordability, fabrication simplicity, and mechanical robustness, the proposed glove system provides a practical and scalable platform for wearable motion tracking, with strong potential in rehabilitation, assistive technologies, and interactive systems. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.Conference Object Citation - WoS: 3Citation - Scopus: 4Analytical Design of Linear Variable Capacitors With Shaped-Finger Comb-Drive Actuators(Institute of Electrical and Electronics Engineers Inc., 2018) Hah, DooyoungVariable capacitors have a broad usage in radio frequency (RF) circuits. Microelectromechanical systems (MEMS) technology can provide variable capacitors with high quality factor and wide tuning range characteristics. One of the design goals for MEMS varactors has been linear capacitance- voltage (C-V) characteristics. To design a linear C-V varactor, a shaped-finger comb-drive actuator is proposed in this paper. The shaped-finger design method, originally developed to obtain linear wavelength-voltage relationships in a tunable optical filter, is modified in this work for a linear C-V varactor, which involves development of a new governing equation. Moreover, conformal mapping is employed in calculation of capacitances, making the whole design process almost all-analytical with the minimum usage of numerical analysis methods. Variable capacitors with the shaped-finger design show linearity factor (LF) - defined as the maximum deviation from the perfect linear relationship - as low as 0.4%, tremendously improved from that of the conventional constant-finger-gap devices (LF: 49.9%). The characteristics of the designed variable capacitor are further investigated through 3-D numerical analysis, and show LF better than 11.5% for the finger thickness in the range between 1 and 10 micrometers. Versatility of the design method is further demonstrated by design of a varactor with linear resonant frequency-voltage (f-V) characteristics for voltage-controlled oscillator (VCO) applications. The developed analytical design method with shaped fingers can find a wide range of applications where comb-drive actuators are used. © 2018 Elsevier B.V., All rights reserved.Conference Object Arrays of Multi-Color Emitting Cesium Lead Halide Perovskite Nanocrystals and Efficient White Light Generation by Tailored Anion Exchange Reactions and Electrohydrodynamic Jet Printing(Optica Publishing Group (Formerly OSA), 2018) Altıntas, Yemliha; Torun, Ilker; Yazici, Ahmet F.; Beskazak, Emre; Onses, Mustafa Serdar; Mutlugün, EvrenWe employ highly efficient and narrow band emitter Cesium-lead-halide perovskite nanocrystals, optimized by the anion exchange method, for efficient white light generation by patterning multiple lines of different colors via proposed electrohydrodynamic jet printing. © 2023 Elsevier B.V., All rights reserved.Article Citation - WoS: 7Citation - Scopus: 4Artificial Neural Network Modeling and Simulation of In-Vitro Nanoparticle-Cell Interactions(Amer Scientific Publishers, 2014) Cenk, Neslihan; Budak, Gurer; Dayanik, Savas; Sabuncuoglu, IhsanIn this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA), all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.Conference Object Citation - Scopus: 21Assessing Employee Attrition Using Classifications Algorithms(Association for Computing Machinery, 2020) Ozdemir, Fatma; Cos¸kun, Mustafa; Gezer, Cengiz; Güngör, Vehbi Çağrı; Coskun, Mustafa; Cagri Gungor, V.Employees leave an organization when other organizations offer better opportunities than their current organizations. Continuity and sustenance and even completion of jobs are crucial issues for the companies not to suffer financial losses. Especially if the talented employees, who are at critical positions in the companies, leave the job, it becomes difficult for the organizations to maintain their businesses. Today, organizations would like to predict attrition of their employees and plan and prepare for it. However, the HR departments of organizations are not advanced enough to make such predictions in a handcrafted manner. For this reason, organizations are looking for new systems or methods that automatize the prediction of employee attrition utilizing data mining methods. In this study, we use IBM HR data set and apply different classification methods, such as Support Vector Machine (SVM), Random Forest, J48, LogitBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes, Bagging, AdaBoost, Logistic Regression, to predict the employee attrition. Different from exiting studies, we systematically evaluate our findings with various classification metrics, such as F-measure, Area Under Curve, accuracy, sensitivity, and specificity. We observe that data mining methods can be useful for predicting the employee attrition. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2ATGRUVAE: Reducing Noise and Improving Forecasting Performance in Stock Data(Institute of Electrical and Electronics Engineers Inc., 2024) Akkaş, Huseyin; Kolukisa, Burak; Bakir-Güngör, BurcuNowadays, to maximize their income, investors and researchers try to predict the future prices of stocks in the market using artificial intelligence algorithms. However, noise in stock price fluctuations negatively a ffects t he accuracy of the forecasts. To this end, Attention Based Variational Autoencoders with Gated Recurrent Units (ATGRUVAE) method is developed to remove the noise in stock price fluctuations a nd compared with variational, basic and noise removing autoencoders. Exper-iments are conducted using historical stock prices of well-known companies such as Apple, Google and Amazon and 9 different indicator values derived from these stock prices. The noise cleaned stocks are then trained and tested on Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Linear Regression (LR) models. The results show that the proposed ATGRUVAE model outperforms all three models and demonstrates its ability to capture complex patterns in stock market data. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 6Citation - Scopus: 12Autonomous UAV Navigation via Deep Reinforcement Learning Using PPO(IEEE, 2022) Kabas, BilalIn this paper, a computer vision-based navigation system is proposed for autonomous unmanned aerial vehicles (UAV). The proposed navigation system is based on a deep reinforcement learning-based high-level controller. In this paper, proximal policy optimization (PPO), which is a deep reinforcement learning method, is used to train the artificial neural network in an end-to-end way using a continuous reward function. The proposed method has been tested on images obtained from different modalities (RGB and depth) in simulation environments that are created using Unreal Engine and Microsoft AirSim. For the navigation problem that this work is concerned with, a success rate of 96% has been obtained by using RGB cameras. Since RGB cameras are lighter than depth cameras and the trained artificial neural network has a parameter number less than 170.000, the proposed method is suitable to be deployed in micro aerial vehicles. Code is publicly available*.Conference Object Benchmarking CNN Architectures for Eye Disease Detection With Transfer Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2025) Keles, Tolgahan; Aykanat, Muhammet Ali; Kurban, RifatIn this study, convolutional neural networks (CNN)-based approaches were compared to classify eye diseases using transfer learning techniques. A series of data augmentation strategies, including random rotation, shifting, shearing, zooming, and horizontal flipping, were applied to increase the training data's robustness and diversity. Several state-of-the-art CNNs, including ResNet50, VGG19, EfficientNetB0, Xception, InceptionV3, DenseNet121, MobileNetV2, NASNetMobile, and ConvNeXtBase, were fine-tuned through transfer learning. During training, models were evaluated based on their accuracy, training time, and validation performance, while early stopping mechanisms were employed to prevent overfitting. Experimental results demonstrated that DenseNet121 achieved the highest validation accuracy (72%) during the training phase and the best test set performance with an accuracy of 68% and an AUC-ROC of 0.93. MobileNetV2, on the other hand, provided a strong balance between classification accuracy (65%) and low inference time (7.28 ms), making it appropriate for real-time uses. The findings highlight the importance of selecting appropriate architectures by considering both predictive performance and computational efficiency, particularly in the context of medical imaging, where real-world deployment constraints are critical. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2Beyin Bilgisayar Arayüzü Uygulamalari için Dinlenme, Harekete Niyet ve Hareket Ayırma(Institute of Electrical and Electronics Engineers Inc., 2018) Oztürk, Nedime; Yilmaz, BulentBrain-computer interface (BCI) is a system that provides a means to control prosthesis, wheelchair, or similar devices using brain waves without direct motor nervous system involvement. For this purpose, brain waves obtained from multiple electrodes placed on the scalp (EEG, Electroencephalogram) are used. Emotiv Epoc used to obtain EEG signals is a low-cost device and has real-time applications. The aim of this study is the detection of rest, imagination and real movement using EEG signals obtained by Emotiv Epoc headset. As a result, As a result, the data obtained from 39 trials from a female subject were classified resting, motion imagination and movement, according to 97.4% accuracy by using the statistical features of distortion, logarithm energy entropy, energy, Shannon entropy and kurtosis. In this study, it has been shown that this system can be remarkably successful for BCI applications. © 2019 Elsevier B.V., All rights reserved.
