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Publication Metadata only SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir UniversityThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.Publication Metadata only Automatic melodic segmentation of Turkish makam music scores(IEEE, 2014) Bozkurt, Baris; Karaali, Bilge; Karaosmanoglu, M. Kemal; Unal, Erdem; Bahcesehir University; Izmir Institute of Technology; Yildiz Technical University; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)Automatic melodic segmentation is one of the important steps in computational analysis of melodic content from symbolic data This widely studied research problem has been very rarely considered for Turkish makam music. In this paper we first present test results for state-of-the-art techniques from literature on Turkish makam music data Then, we present a statistical classification-based segmentation system that exploits the link between makant melodies and usul and makam scale hierarchies together with the well-known features in literature. We show through tests on a large dataset that the proposed system has a higher accuracy.Publication Metadata only Dendritic Spine Shape Classification from Two-Photon Microscopy Images(IEEE, 2015) Ghani, Muhammad Usman; Kanik, Sumeyra Demir; Argunsah, Ali Ozgur; Tasdizen, Tolga; Unay, Devrim; Cetin, Mujdat; Sabanci University; Fundacao Champalimaud; Utah System of Higher Education; University of Utah; Bahcesehir UniversityFunctional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly.Publication Metadata only Throughput-maximizing OFDMA Scheduler for IEEE 802.11ax Networks(IEEE, 2020) Kuran, Mehmet Sukru; Dilmac, A.; Topal, Omer; Yamansavascilar, Baris; Avallone, Stefano; Tugcu, Tuna; Bahcesehir University; Bogazici University; University of Naples Federico IIIn this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client's load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.Publication Metadata only Performance Comparison of Oral, Laryngeal and Thoracic Sounds in the Detection of COVID-19 by Employing Machine Learning Techniques(IEEE, 2022) Gozuacik, Necip; Serbes, Gorkem; Kara, Eyup; Atar, Eren; Sakar, C. Okan; Yener, H. Murat; Borekci, Sermin; Korkmazer, Bora; Karaali, Ridvan; Kara, Halide; Gulmez, Zuleyha; Cogen, Talha; Atas, Ahmet; Bahcesehir University; Yildiz Technical University; Istanbul University; Istanbul University - CerrahpasaCOVID-19 can directly or indirectly cause lung involvements by crossing the upper airways. It is essential to quickly detect the lung involvement condition and to follow up and treat these patients by early hospitalization. In recent COVID-19 diagnosis procedure, PCR testing is applied to the samples taken from the patients and a quarantine period is applied to the patient until the test results are received. As a complement to PCR tests and for faster diagnosis, thin-section lung computed tomography (CT) imaging is used in COVID-19 patients. In this study, it is aimed to develop a method that is as reliable as CT, and compared to CT, less risky, more accessible, and less costly for the diagnosis of COVID-19 disease. For this purpose, first speech and cough sounds from the oral, laryngeal and thoracic regions of COVID-19 patients and healthy individuals were obtained with the multi-channel voice recording system we proposed, the obtained data were processed with machine learning methods and their accuracies in COVID-19 diagnosis were presented comparatively. In our study, the best results were obtained with the features extracted from the cough sounds taken from the oral region.Publication Metadata only Assessing Automotive Bumper Materials: A Comparative Study on Attenuation at 77GHz(IEEE, 2024) Neubauer, Michael; Petanjek, David; Hirschmugl, Michael; Kiebach, Helge; Karamzadeh, Saeid; Bahcesehir UniversityHaving information about the materials surrounding the radar sensors is essential in the automotive sector, in order to guarantee good functionality of the sensor. Since automotive manufacturers also start integrating radar sensors behind bumpers, it is essential to know the characteristics of the materials used in bumpers and how a repair of a bumper would alter those characteristics. This paper shows, how the damping characteristics of different samples were measured at 77GHz using a scalar measurement setup and compares the attenuation for different types of paints and modifications.Publication Metadata only Design and analysis of a label-free water-soluble glucose sensor based on suspended hybrid plasmonic micro-ring resonator(IEEE, 2022) Moeinimaleki, Kaveh; Moeinimaleki, Babak; Zarean, Rana; Abedashtiani, Alireza; Karamzadeh, Saeid; Akdeniz University; Bahcesehir UniversityIn this paper, the design and analysis of a label-free water-soluble glucose sensor based on a suspended hybrid plasmonic micro-ring resonator with an external radius of 940 nm in the third telecommunication window to sense the concentration of water-soluble glucose is presented. The simulation results obtained using three-dimensional FDTD show that the sensitivity and the figure of merit values obtained for glucose measurements with concentrations of 0 to 50% in water solution are 218.5 (nm/RIU) and 16.11 (1/RIU), respectively. Comparing the results of the proposed sensor with the previous work, which has four times more footprint, shows that the structure's sensitivity has increased by 17 percent.Publication Metadata only Coupled Shape Priors for Dynamic Segmentation of Dendritic Spines(IEEE, 2017) Atabakilachini, Naeimeh; Erdil, Ertunc; Argunsah, A. Ozgur; Rada, Lavdie; Unay, Devrim; Cetin, Mujdat; Sabanci University; University of Zurich; Bahcesehir University; Izmir Ekonomi UniversitesiSegmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points to segment a spine in the current time point. In particular, using a training set consisting of spines in two consecutive time points to construct coupled shape priors, and given the segmentation in the previous time point, we can improve the segmentation process of the spine in the current time point. Our approach has been evaluated on 2-photon microscopy images of dendritic spines and its effectiveness has been demonstrated by both visual and quantitative results.Publication Metadata only Electricity Price Prediction Using Encoder-Decoder Recurrent Neural Networks in Turkish Dayahead Market(IEEE, 2020) Gunduz, Salih; Ugurlu, Umut; Oksuz, Ilkay; Ministry of National Education - Turkey; Bahcesehir University; Istanbul Technical UniversityForecasting electricity prices accurately is an essential requirement for the players in the market. The sequential data of electricity poses many challenges such as seasonality and high volatility. Recently, recurrent neural network based methods have showed great success in forecasting time series type problems. In this paper, we propose to use an encoder-decoder recurrent neural network for forecasting hourly electricity prices in the Turkish Day-ahead Market. The approach involves two recurrent neural networks, one to encode the source sequence, called the encoder, and a second to decode the encoded source sequence into the target sequence, called the decoder. We trained and tested our framework on the Turkish electricity price data from 2013 to 2016 and report the accuracy of various recurrent neural network models in terms of mean absolute error. The encoder-decoder recurrent neural networks achieved better accuracy compared to classical recurrent neural networks.Publication Metadata only From Asia to Europe: Short-Term Traffic Flow Prediction Between Continents(IEEE, 2014) Kaya, Sevgi; Kilic, Necati; Kocak, Taskin; Gungor, Cagri; Swiss Federal Institutes of Technology Domain; ETH Zurich; Bahcesehir UniversityModelling the traffic flow and predicting the near-future traffic status are two challenging problems of the smart transportation on roads. The difficulty is particularly pronounced in forecasting the complex non-linear dynamics of flow. Most of the state-of-the-art work on traffic flow prediction determine the parameters based on the fundamental relationship between flow, density and speed without considering its influence to the consecutive one. However, these approaches tend to fail in real life scenarios due to the negligence of the spatio-temporal dependence of parameters within road segments. In this paper, we propose a new traffic flow model to predict the arterial travel time using probe data. We then evaluate our model under various traffic conditions to determine its feasibility for near-future traffic flow prediction. The proposed method presents promising results by outperforming the state-of-the-art in predicting near-future traffic flow on roads in case of sparse data and high flow density.