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Publication Metadata only Characterizing Attenuation of Bumper Modifications: A Comparative Study in the E-Band(IEEE, 2024) Neubauer, Michael; Hirschmugl, Michael; Petanjek, David; Kiebach, Helge; Karamzadeh, Saeid; Kolosovs, D; Anstalt fur Verbrennungskraftmaschinen List; Bahcesehir UniversityHaving knowledge about the attenuation of the materials surrounding a radar sensor is of great importance in the automotive sector. Only then can the manufacturer guarantee the desired functionality of the sensor. Due to the fact that a lot of automotive manufacturers also started integrating radar sensors behind rear bumpers for improved visibility to the back of a car, it is vital to know how those bumpers and possible repair scenarios done to them can affect the radar sensor. This paper proposes a way to measure the attenuation in the E-band of not only square slab samples representing various types of paint and repair scenarios on automotive bumpers, but also actual rear bumpers with different types of repair scenarios. The results are then presented and compared relative to each other.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 SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(IEEE, 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Daffodil International University; Khulna University of Engineering & Technology (KUET); Bahcesehir University; Daffodil International UniversitySkin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.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 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 Natural Language Processing-Based Product Category Classification Model for E-Commerce(IEEE, 2022) Koksal, Deniz; Alacan, M. Mert; Olgun, Ecesu; Sakar, C. Okan; Bahcesehir UniversityIn scope of the great dynamism that the world of e-commerce gained with the post-pandemic changes on customer behavior, extending the customer's time on site has become much more valuable. With regards to creating a better understanding of the online shopper's intent on site, an effective search engine is the best tool for improving the user experience. A useful search engine needs to produce fast results and do intent analysis of customers correctly to present successful recommendations. In this study, BERT, ELECTRA and RoBERTa which are language models that give successful results in similar problems and pre-training libraries in Turkish languages are used for developing text classification and customer intent analysis models based on e-commerce product categories. The results of these deep learning models, which were optimized using the product description and comment libraries of the selected e-commerce brand, are shared in a comparative way, and the results of our e-commerce end-user intent analysis model, which was tested on user search history, were detailed on the basis of product category. The developed model can be used for e-commerce product prioritization in Turkish language, as well as creating an infrastructure for the development of intent analysis models that are able to predict as far as the main product that the user wants to display in searches containing multiple products.Publication Metadata only Consumer Preference Estimation Using EEG Signals and Deep Learning(IEEE, 2024) Ceylan, Burak; Cekic, Yalcin; Akan, Aydin; Bahcesehir University; Izmir Ekonomi UniversitesiEmotion estimation is an extremely critical and current research topic for human-computer interaction. In this study, a liking estimation method using electroencephalogram (EEG) signals is proposed to be used in neuromarketing studies. EEG data recorded while participants watch the advertisement videos of two different automobile brands are processed with deep learning techniques to estimate their liking status. After watching the videos, participants were presented with selected image sections from the advertisements (front view, console, side view, rear view, stop lamp, brand logo and front grille) and were asked to rate their liking by scoring from 1 to 5. EEG signals corresponding to these regions were converted into a two dimensional and RGB colored image using the short-time Fourier transform (STFT) method, and liking status was estimated using Deep Learning. The successful results obtained show that the proposed method can be used in neuromarketing studies.Publication Metadata only Neural Network-Based Human Detection Using Raw UWB Radar Data(IEEE, 2024) Dogan, Emine Berjin; Yousefi, Mohammad; Soyak, Ece Gelal; Karamzadeh, Saeid; Kolosovs, D; Bahcesehir University; Bahcesehir University; Bahcesehir University; Bahcesehir UniversityUltra-Wideband (UWB) radar technology is a widely used technology for human detection and tracking through walls, because of its effectiveness in low-visibility situations. This study demonstrates a neural network-based identification of human presence using raw data obtained directly from the UWB radar. First, measurements have been collected with different human subjects at different positions relative to the UWB radar. A convolutional neural network (CNN) model has been trained on this dataset, to detect the presence of a human. Next, the algorithm effectiveness is deeply investigated using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, and the observations on detected presence are discussed.