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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 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 A Holistic Methodology for Digital Governance Transition and Measuring Its Effect on Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansDigital transformation has become one of the most emphasized issues for organizations today. Organizations have a long way to go in this difficult road. First of all, they should develop and measure their governance skills, and then they need to create the necessary studies in both technological and managerial terms for IT systems to become a value. While doing all these, they should also create a risk management structure with a correct approach by performing consultancy, supervision, and evaluation services completely. In line with this whole process, there are different standards and practices in every field they need to follow. This paper presents a methodology proposition in a general perspective that can be applied end-to-end in the field of digitalization, starting from the governance structure to the IT value-adding strategy. Thanks to this new methodology, it is possible to see many processes such as governance, consultancy, digital transformation, auditing, internal control, which are disconnected from each other, in the big picture and arrange all activities accordingly. With long-term follow-up, the strategic and tactical perspectives of institutions can be created and their progress over time can be followed by this new methodology. This paper performs both qualitative and quantitative measurements of the applications of the new methodology to compare them with existing ones. This methodology introduces a holistic methodology lacking in the market.Publication Metadata only A Research for Measuring the Effects of COVID-19 on Digital Transformation within Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansPandemics are not only medical phenomenon, but they also influence people and society in many respects. It has an effect on almost all markets all over the world. COVID-19 pandemic, expressed as change, empowerment, or post-traumatic growth, with several negative consequences as well as positive consequences. It also has the potential for opportunities. Years of change in the way companies do business have resulted from the COVID 19 crisis across all industries and regions. The aim of this research is to examine the relationship between different demographic variables, COVID-19's impact on digital transformation and post-traumatic effects. The article reflects in practical terms on whether and how the COVID-19 emergence in organizations accelerates digital transformation. This study is a descriptive quantitative approach research based on general survey model.Publication Metadata only ASD-EVNet: An Ensemble Vision Network based on Facial Expression for Autism Spectrum Disorder Recognition(IEEE, 2023) Jaby, Assil; Islam, Md Baharul; Ahad, Md Atiqur Rahman; Bahcesehir University; University of East LondonAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects individuals' social interaction, communication, and behavior. Early diagnosis and intervention are critical for the well-being and development of children with ASD. Available methods for diagnosing ASD are unpredictable (or with limited accuracy) or require significant time and resources. We aim to enhance the precision of ASD diagnosis by utilizing facial expressions, a readily accessible and limited time-consuming approach. This paper presents ASD Ensemble Vision Network (ASD-EVNet) for recognizing ASD based on facial expressions. The model utilizes three Vision Transformer (ViT) architectures, pre-trained on imageNet-21K and fine-tuned on the ASD dataset. We also develop an extensive collection of facial expression-based ASD dataset for children (FADC). The ensemble learning model was then created by combining the predictions of the three ViT models and feeding it to a classifier. Our experiments demonstrate that the proposed ensemble learning model outperforms and achieves state-of-the-art results in detecting ASD based on facial expressions.Publication Metadata only Multivariate Spatio-temporal Cellular Traffic Prediction with Handover Based Clustering(IEEE, 2022) Tuna, Evren; Soysal, Alkan; Turkcell Turkey; Bahcesehir University; Virginia Polytechnic Institute & State UniversityWe consider an RNN-based traffic volume prediction, which is a critical problem for network slice management and resource allocation in slicing-enabled next generation cellular networks. We propose to use a novel cost function that takes SLA violations into account. Our approach is multivariate and spatio-temporal in three aspects. First, we consider the effects of several other RAN features in a cell besides the traffic volume. Second, we introduce feature vectors based on peak hours of the day and days of the week. Third, we introduce feature vectors based on incoming handover statistics from the neighboring cells. Our results show about 60% improvement over MAE-based univariate LSTM models and about 20% improvement over SLA-based univariate models.Publication Metadata only Detection of Human Breathing Using Signal Processing Techniques Under Debris(IEEE, 2024) Erenoglu, Mehmet Z.; Aldirmaz-Colak, Sultan; Tokan, Nurhan Turker; Doncov, NS; Kostov, M; Dimitrov, KL; Bahcesehir University; Kocaeli University; Yildiz Technical UniversityIt is of paramount importance to rescue a human trapped in a collapsed building quickly and effectively. In such search and rescue operations, radar technology is an effective tool used to detect living humans beneath the debris. In this work, we have applied time-frequency domain techniques, namely, Short-Time Fourier Transform (STFT), Wavelet Transform (WT) to the radar signal for detecting the movement of conscious or unconscious human beings that are trapped in a building debris. To mitigate noise effect on the measured signal, denoising techniques are performed. The performance of the methods is experimentally verified by using the debris model that was created in the laboratory. The signal processing techniques provide critical information and enable rapid detection of the human trapped under the debris.Publication Metadata only BinoVFAR: An Efficient Binocular Visual Field Assessment Method using Augmented Reality Glasses(ASSOC COMPUTING MACHINERY, 2021) Islam, Md Baharul; Sadeghzadeh, Arezoo; Bahcesehir University; Bahcesehir UniversityVirtual Reality (VR)-based Visual Field Assessment (VFA) methods completely isolate the users from the real world, which results in nausea, eye strain, and lack of concentration and patience for the time-consuming test. In this paper, a robust binocular visual field assessment method based on novel Augmented Reality (AR) glasses is presented, namely, BinoVFAR that can simultaneously find the VF of both eyes. In this method, 60 stimuli in an arrangement of 6 rows and 10 columns randomly appear on a white background on the display of the AR glasses. These stimuli are displayed for 2 seconds that continuously change the intensities from light gray to black. Wearing the AR glasses and focusing on the central fixation point, the users are asked to click the clicker by seen a stimulus. The visible stimuli's intensities and positions are recorded in a 6 x 10 matrix based on the users' responses. A bi-cubic interpolation is applied to compute the binocular visual field map (as a 600 x 1000 matrix). A set of experiments (with an average accuracy of 99.93%), including repeatability and reproducibility tests (with an average Intra-class correlation coefficient (ICC) of 99.72%), are conducted to evaluate the BinoVFAR method.Publication Metadata only Low-Powered Agriculture IoT Systems with LoRa(IEEE, 2020) Kokten, Esma; Caliskan, Bahadir Can; Karamzadeh, Saeid; Soyak, Ece Gelal; Aboltins, A; Litvinenko, A; Bahcesehir University; Bahcesehir UniversityMonitoring is key to increase the efficiency of food storage in the open field in terms of cost, logistics and quality of the crops. For the long range data transmission in such environments, mobile technologies are not suitable, as the end devices are generally battery-limited. In this work, a prototype has been developed for monitoring goods in storage. The battery life time of this prototype is analysed in terms of calculations as well as measurements, on LoRa technology. Our results show that (i) while sleeping current has the smallest percentage, it has the greatest impact in increasing battery life, (ii) monitoring node shall have low self discharge battery for long battery life, and (iii) sensors are the main power sink that deplete battery.