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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 Channel estimation and adaptive M-QAM in cognitive radio links(IEEE, 2008) Soysal, Alkan; Ulukus, Sennur; Clancy, Charles; Bahcesehir University; University System of Maryland; University of Maryland College Park; United States Department of DefenseCognitive radios have the ability to sense their RF environment and adapt their transmission parameters to perform optimally in any situation. Part of this involves selecting the best modulation type for a particular channel. In this paper we consider a variable-rate, variable-power, adaptive, M-ary Quadrature Amplitude Modulation (M-QAM) scheme in a single-user communication scenario. The channel between the transmitter and receiver is assumed to be a Rayleigh block-fading channel. Each block is divided into training and data phases. During the training phase, the receiver estimates the channel and feeds the estimate back to the transmitter. During the data phase, the transmitter sends its message by adapting the size of the M-QAM constellation. We first find a closed-form expression that relates the Bit Error Rate (BER) to the constellation size of the M-QAM, and therefore to the data rate of our system. Then, for a given target BER, we maximize the data rate over the training parameters, which are the training signal, the training duration, and the training power. When these optimum parameters are used in a MATLAB implementation, we find that the target BER is matched to within an order of magnitude, and the resulting data rate is close to the theoretical limit.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 MIMO Multiple Access Channels with Noisy Channel Estimation and Partial CSI Feedback(IEEE, 2008) Soysal, Alkan; Ulukus, Sennur; Bahcesehir University; University System of Maryland; University of Maryland College ParkWe consider correlated MIMO multiple access channels with block fading, where each block is divided into training and data transmission phases. We find the channel estimation and data transmission parameters that jointly optimize the achievable data rate of the system. Our results for the training phase are particularly interesting, where we show that the optimum training signals of the users should be non-overlapping in time. For the data transmission phase, we propose an iterative algorithm that updates the parameters of the users in a round-robin fashion. In particular, the algorithm updates the training and data transmission parameters of a user, when those of the rest of the users are fixed, in a way to maximize the achievable sum-rate in a multiple access channel, and iterates over users in a round-robin fashion.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.