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Öğe Detection of Driver Distraction using YOLOv5 Network(Institute of Electrical and Electronics Engineers Inc., 2021) Atas, Kubilay; Vural, Revna AcarThe impact of deaths and injuries on families and society is increasing the willingness of the authorities to investigate the cause of the increasing traffic accidents day by day. It is important to emphasize that the majority of traffic accidents are attributed to driver distraction. The role of mobile phones and cigarette usage on driver distraction is a well-known fact. In this study, the authors focus on detecting mobile phone usage and smoking in-vehicle environment. The images collected with a mobile phone docked on the windshield and a yolov5s network is trained with manually labeled images. As a consequence of the study, the authors achieved to distinguish drivers and passengers. Also, they investigate to improve detecting other labeled classes such as 'DriverHand', 'PassengerHand', 'DriverHandWithPhone', etc. © 2021 IEEE.Öğe Implementation of CNN based COVID-19 classification model from CT images(IEEE, 2021) Kaya, Atakan; Atas, Kubilay; Myderrizi, IndritThe number of COVID-19 patients around the globe is increasing day by day. Statistics show that even after almost 10 months from outbreak, number of the total patients has not reached to its peak value yet. Easy spreading of the virus among people causes high number of patients at the same time. Accelerating the reduction in spread is of vital importance. In order to achieve this reduction, early diagnosis of the disease and the number of tests and scans to be performed frequently becomes important. In this paper, a comprehensive model examination is made to overcome COVID-19 diagnosing problem. Using CT images, data augmentation technique is applied first in the pre-processing section and then pre-trained deep CNN networks perform the classification. The model is tested using various networks and high accuracy results of 96.5% and 97.9% are obtained for VGG-16 and EfficientNetB3 networks, respectively.Öğe Investigation of Temperature, Humidity and Force-Sensitive Sensors for Future Smart Bed Pads(SPIE, 2024) Zeynep Aydin, S.; Nur Beken, G.; Atas, Kubilay; Akgol, Volkan; Erkmen, Burcu; Vural, Revna A.In this study, the performance of temperature, humidity, and pressure sensors that can be used in smart mattress pads has been investigated. The measurement accuracy of pressure sensors has been compared for different sponge thicknesses and weights. Among 9 different test cases, the RP-S40-ST force sensor yielded the best results. As for temperature sensors, heat transfer measurements were compared for specific temperatures using different sponge thicknesses. The AHT15 and DS18B20 temperature sensors successfully measured 74% of the applied heat on a 2 cm thick viscoelastic sponge, and 52.4% on a thicker 4 cm sponge. Additionally, the humidity sensor's performance in measuring humidity on the mattress pad was evaluated. Based on these assessments, the sensors that will provide the best results for the design of the mattress pad have been proposed. The influence of the sponge, an essential component of the mattress pad, on the functioning of these sensors was also observed. Moreover, apart from their measurement capabilities, the impact of these sensors on the patient's comfort was also taken into consideration. © 2024 SPIE.Öğe LIDAR-AIDED TOTAL VARIATION REGULARIZED NONNEGATIVE TENSOR FACTORIZATION FOR HYPERSPECTRAL UNMIXING(Institute of Electrical and Electronics Engineers Inc., 2021) Kaya, Atakan; Atas, Kubilay; Kahraman, SevcanHyperspectral unmixing (HU) is an important research field in hyperspectral image processing. In recent years, Nonnegative Tensor Factorization (NTF)-based methods have gained great importance in remote sensing imagery, especially hyperspectral unmixing, regardless of any information loss. Nevertheless, NTF has some disadvantages, such as signal-to-noise ratio (SNR) and noncovexity conditions. Mentioned problem can be solved by introducing some spatial regularizations. On the other hand, LiDAR data provides Digital Surface Model (DSM) information gives accurate elevation information about the observed scene. Moreover, total variation (TV)-based regularization provides piecewise smoothness and it preserve edge structure information in the abundance maps. However, this property could be inappropriate for pixels located in edges. LiDAR-DSM alleviates this problem by contributing neighboring objects pixels differently. In this paper, we proposed a simple yet efficient HU framework that incorporates LiDAR data with TV regularized matrix-vector NTF method (LiMVNTF-TV). Experimental studies are carried out on simulation data sets and demonstrate that the proposed framework can provide better abundance estimation maps. © 2021 IEEE.