Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Serum Creatinine Detection in a Microfluidic Chip Using a Smartphone Camera(Chemical and Biological Microsystems Society, 2022) Karakuzu, B.; Tarim, E.A.; Tekin, H.C.We present a microfluidic chip platform to detect serum creatinine levels using the enzyme-linked immunosorbent assay (ELISA) principle. In the platform, surface modified microfluidic channel sensitively captured target molecules from the serum sample, and then ELISA protocol was applied inside the channels. Afterward, the blue color formed as a result of the enzymatic reaction was measured via a smartphone camera. The proposed strategy allows the detection of creatinine rapidly in a minute amount of the serum samples without the need for expensive equipment. Thus, chronic kidney disease (CKD) could be monitored easily at point-of-care settings via the proposed creatinine detection strategy. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.Conference Object Citation - WoS: 7Citation - Scopus: 6Semantic Pose Verification for Outdoor Visual Localization With Self-Supervised Contrastive Learning(IEEE, 2022) Guerrero, Jose J.; Orhan, Semih; Baştanlar, YalınAny city-scale visual localization system has to overcome long-term appearance changes, such as varying illumination conditions or seasonal changes between query and database images. Since semantic content is more robust to such changes, we exploit semantic information to improve visual localization. In our scenario, the database consists of gnomonic views generated from panoramic images (e.g. Google Street View) and query images are collected with a standard field-of-view camera at a different time. To improve localization, we check the semantic similarity between query and database images, which is not trivial since the position and viewpoint of the cameras do not exactly match. To learn similarity, we propose training a CNN in a self-supervised fashion with contrastive learning on a dataset of semantically segmented images. With experiments we showed that this semantic similarity estimation approach works better than measuring the similarity at pixel-level. Finally, we used the semantic similarity scores to verify the retrievals obtained by a state-of-the-art visual localization method and observed that contrastive learning-based pose verification increases top-1 recall value to 0.90 which corresponds to a 2% improvement.