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Fingerprints are one of the most important scientific proof instruments in solving forensic cases. Identification in fingerprints consists of three levels based on the flow direction of the papillary lines at the first level, the minutiae points at the second level, and the pores at the third level. The inadequacy of existing imaging systems in detecting fingerprints and the lack of pore details at the desired level limit the widespread use of third-level identification. The fact that fingerprints with images based on pores in the unsolved database are not subjected to any evaluation criteria and remain in the database reveals the importance of the study to be carried out. In this study, different from classical fingerprint identification methods, a direct pore-based identification system for fingerprint matching is proposed with the dataset created by using the Docucenter Nirvis device and Projectina Image Acquisition-7000 software as a hyperspectral imaging system where pores were visualized more clearly. Although difficult from an operational perspective, the pores in the 800 fingerprints in the database were manually marked for the accuracy of the results. Next, by using a scoring based on iterative closest point algorithm, latent fingerprints were found. Results suggest that the higher the number of pores examined and the more accurately the pores were marked, the higher the hit score. At the same time, query results showed that the scores of other sequential fingerprints in the database which came after the matching fingerprint were even lower. [ABSTRACT FROM AUTHOR]

In this paper, an online learning framework called adaptive decision fusion (ADF) is employed for short-term wind speed and turbulence intensity forecasting by use of wind speed data for each season for the city ofİzmit, located in the northwest of Turkey. Fixed-weight (FW) linear combination is derived and used for comparison with ADF. Wind speeds and turbulence intensities are predicted from the existing wind speed data and computed turbulence intensities, respectively, using the ADF and FW methods. Simulations are carried out for each season and the results are tested on mean absolute percentage error criterion. It is shown that the proposed model captured the system dynamic behavior and made accurate predictions based on the seasonal wind speed characteristics of the site. The procedure described here can be used to estimate the local velocity and turbulence intensity in a wind power plant during a storm. [ABSTRACT FROM AUTHOR]