Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 27Citation - Scopus: 34Volume Fraction, Thickness, and Permeability of the Sealing Layer in Microbial Self-Healing Concrete Containing Biogranules(Frontiers Media S.A., 2018-11-23) Ersan, Yusuf Cagatay; Palin, Damian; Tasdemir, Sena Busra Yengec; Tasdemir, Kasim; Jonkers, Henk M.; Boon, Nico; De Belie, Nele; Yengec Tasdemir, Sena BusraAutonomous repair systems in construction materials have become a promising alternative to current unsustainable and labor-intensive maintenance methods. Biomineralization is a popular route that has been applied to enhance the self-healing capacity of concrete. Various axenic microbial cultures were coupled with protective carriers, and their combination appears to be useful for the development of healing agents for realizing self-healing concrete. The advantageous traits of non-axenic cultures, such as economic feasibility, self-protection, and high specific activity have been neglected so far, and thus the number of studies investigating their performance as healing agents is scarce. Here we present the self-healing performance of a mortar containing a healing agent consisting of non-axenic biogranules with a denitrifying core. Mortar specimens with a defined crack width of 400 mu m were used in the experiments and treated with tap water for 28 days. Self-healing was quantified in terms of the crack volume reduction, the thickness of the sealing layer along the crack depth and water permeability under 0.1 bar pressure. Complete visual crack closure was achieved in the bio-based specimens in 28 days, the thickness of the calcite layer was recorded as 10 mm and the healed crack volume was detected as 6%. Upon self-sealing of the specimens, the water permeability decreased by 83%. Overall, non-axenic biogranules with a denitrifying core shows great potential for development of self-healing bioconcrete.Conference Object Citation - Scopus: 3Mixture of Learners for Cancer Stem Cell Detection Using Cd13 and H&E Stained Images(SPIE - The International Society for Optics and Photonics, 2016-03-23) Oguz, Oguzhan; Akbas, Cem Emre; Mallah, Maen; Tasdemir, Kasim; Guzelcan, Ece Akhan; Muenzenmayer, Christian; Atalay, Rengul Cetin; Akhan Güzelcan, Ece; Tagdemir, KaslmIn this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H&E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H&E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using HSLE stained microscopic tissue images.Article Citation - WoS: 15Citation - Scopus: 22Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Altinisik, Enes; Tasdemir, Kasim; Sencar, Husrev TahaThe photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos. Tests on a public dataset also verify that the proposed method improves the attribution performance by increasing the accuracy and allowing the use of smaller length videos to perform attribution.Article Citation - WoS: 6Citation - Scopus: 7Improved Senescent Cell Segmentation on Bright-Field Microscopy Images Exploiting Representation Level Contrastive Learning(Wiley, 2024-03) Celebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Tasdemir, Kasim; Icoz, KutayMesenchymal stem cells (MSCs) are stromal cells which have multi-lineage differentiation and self-renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright-field microscope is time-consuming and needs an expert operator. In this study, the senescence cells were segmented and counted automatically by deep learning algorithms. However, well-performing deep learning algorithms require large numbers of labeled datasets. The manual labeling is time consuming and needs an expert. This makes deep learning-based automated counting process impractically expensive. To address this challenge, self-supervised learning based approach was implemented. The approach incorporates representation level contrastive learning component into the instance segmentation algorithm for efficient senescent cell segmentation with limited labeled data. Test results showed that the proposed model improves mean average precision and mean average recall of downstream segmentation task by 8.3% and 3.4% compared to original segmentation model.Article Citation - WoS: 6Citation - Scopus: 7BLSTM Based Night-Time Wildfire Detection From Video(Public Library Science, 2022-06-03) Agirman, Ahmet K.; Tasdemir, KasimDistinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. This makes the analysis of temporal behavior of night-time fire indispensable for classification. To this end, a BLSTM based night-time wildfire event detection from a video algorithm is proposed. It is shown in the experiments that the proposed algorithm attains 95.15% of accuracy when tested against a wide variety of actual recordings of night-time wildfire incidents and 23.7 ms per frame detection time. Moreover, to pave the way for more targeted solutions to this challenging problem, experiment-based thorough investigations of possible sources of incorrect predictions and discussion of the unique nature of night-time wildfire videos are presented in the paper.