WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
Browse
2 results
Search Results
Article Citation - WoS: 2Citation - Scopus: 2Investigation of the Structural and Magnetic Properties of Rapidly Solidified Nd-Fe Alloys(Springer, 2024-07) Aytekin, Orkun; Kurban, Rifat; Durmus, Ali; Colak, Hakan; Karakose, ErcanThis study introduces the first literature report of rapidly solidified Nd-Fe-B-Ce alloys fabricated using the melt-spinning technique at varying disc rotation speeds. The resulting alloy images are then analyzed using various image processing techniques, and their structural and magnetic characteristics are described. The alloys are characterized using a variety of methods, including x-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), energy-dispersive x-ray spectroscopy (EDX), differential thermal analysis (DTA), vibrating sample magnetometry (VSM), and Vickers microhardness tests. By using XRD, the tetragonal hard magnetic Nd2Fe14B phase is detected in the Nd30Fe65B0.9Ce5 alloy. The FE-SEM microstructure analysis shows that the grain structure of the ingot alloy is indistinct, and the tetragonal symmetric structure begins to appear at disc rotation speeds of 20 m/s and 40 m/s. The analysis of FE-SEM images using histogram analysis, the image segmentation technique, and VSM method reveals that the coercivity values of the sample produced at the 80 m/s solidification speed increased by approximately 34% when compared to the ingot alloy.Article Citation - WoS: 16Citation - Scopus: 26Gaussian of Differences: A Simple and Efficient General Image Fusion Method(MDPI, 2023-08-15) Kurban, RifatThe separate analysis of images obtained from a single source using different camera settings or spectral bands, whether from one or more than one sensor, is quite difficult. To solve this problem, a single image containing all of the distinctive pieces of information in each source image is generally created by combining the images, a process called image fusion. In this paper, a simple and efficient, pixel-based image fusion method is proposed that relies on weighting the edge information associated with each pixel of all of the source images proportional to the distance from their neighbors by employing a Gaussian filter. The proposed method, Gaussian of differences (GD), was evaluated using multi-modal medical images, multi-sensor visible and infrared images, multi-focus images, and multi-exposure images, and was compared to existing state-of-the-art fusion methods by utilizing objective fusion quality metrics. The parameters of the GD method are further enhanced by employing the pattern search (PS) algorithm, resulting in an adaptive optimization strategy. Extensive experiments illustrated that the proposed GD fusion method ranked better on average than others in terms of objective quality metrics and CPU time consumption.