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    A Case Study on the Relationship between Water Quality Parameters: Bursa
    (Sakarya University, 2022) Gumus, Ergun
    Monitoring the quality of mains water in residential areas where industrialization is intense is of vital importance in terms of human health. For this purpose, quality parameters expressing the physical, chemical and biological properties of water are periodically observed through laboratory tests. During the evaluation of water quality, these parameters can be assessed individually or as a group by considering their interrelations. In this context, by using water quality reports of Bursa province which is an industrial city, answers to two questions were sought. The first of these questions is, getting evaluated on a group basis, which groups of water quality parameters are found to be highly correlated. The second question is whether the correlation between these interrelated parameter groups can be maintained in different measurement periods. For these purposes, analyzes were made using an approach which utilizes canonical correlation analysis, exhaustive scanning, and sliding window methods. As a result of these analyzes, it was observed that used approach gave successful results in terms of determining interrelated parameter groups and the differences in terms of interrelations between the measurement periods over these groups.
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    A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions
    (Bursa Teknik Üniversitesi, 2022) Akman, Ceren; Gumus, Ergun
    Until today, various approaches have been proposed in order to create Augmented Reality (AR) environment where virtual-real integration takes place. One of these approaches is vision-based model, and it is divided into two branches as Marker-Based Augmented Reality (MBAR) and Markerless Augmented Reality (MAR). In the use of MBAR model, a reference image is introduced to the system before, and when this image enters the camera view, an AR environment is created. However, in MAR model, no image is introduced to the system before. Instead, it uses natural  characteristics present in the image, such as edges, corners, and geometrical shapes to create an AR environment. In order to use MAR model, it is necessary to use algorithms which require high processing power and memory capacity. Within the scope of this study, MAR model was chosen as reference and an evaluation on combinations of descriptive extractors (such as ORB, SIFT, and SURF) and matchers (such as Bruteforce, Bruteforce-Hamming, and Flannbase) was presented. In this context, it was aimed that we could obtain knowledge about i) the number of key points and detection time with the use of different descriptor extractors, and ii) the number of matching key points and the amount of positional deviation of a virtual object placed on a real world scene with the use of different matchers. In line with this goal, analyses were made, using different image scales and brightness levels on both PC and mobile platforms. Results showed that, for both platforms, combinations using ORB method could work faster with less deviation than the combinations using other methods in all conditions. In addition, RANSAC algorithm was also used to reduce the total mean deviation ratio, and it was seen that the rate could be reduced from 70% to 4.5%..