In this study, a detection system based on Polarization-Sensitive Optical Coherence Tomography (PS-OCT) using Mueller Matrix Optical Coherence Tomography (MM-OCT) was developed. By employing PS-OCT technology, the system was able to fully detect all sixteen elements of the Mueller matrix. By comparing the intensity element M00 among the sixteen elements of the Mueller matrix, the texture structure of the pearl layers could be observed. This allowed for differentiation between freshwater and saltwater pearls, identification of genuine and fake pearls, detection of internal flaws in pearls, and differentiation between nucleated and non-nucleated pearls. The study also involved the labeling of connected regions in binary images, where pixels within the same connected region were assigned the same label. The labeled images were displayed to facilitate more intuitive qualitative analysis, and quantitative analysis was performed using gray-level co-occurrence matrices. Subsequently, pearl layer pixels were extracted from multiple angles in the images, and the thickness of the pearl layer was calculated using the extracted pixels and axial resolution. Finally, detection and classification of unknown pearls were conducted, yielding results consistent with the actual outcomes. The measured thickness results after sectioning matched the calculated results, providing evidence for the feasibility of the experimental method proposed in this study.
Terahertz waves are increasingly used in fields such as information and communication technology, homeland security, and biomedical engineering. Optical Coherence Tomography (OCT) is a non-invasive, high-resolution imaging technique that can image within a depth of 1mm under the skin, and it has the characteristics of fast imaging speed and high detection sensitivity. Using OCT technology to study human skin, it was found that the human skin sweat ducts are helical structures. When the sweat ducts of the helical structure are filled with sweat composed of conductive electrolytes, combined with the morphological and dielectric properties of the skin, the sweat ducts can act as low Q-factor helical antennas and have electromagnetic effects in the Sub-Terahertz band. In this study, based on the morphological structure of sweat ducts in the skin, we established a basic sweat duct equivalent model, which consists of spiral sweat ducts and three skin layers (stratum corneum, epidermis, and dermis). In this work, we investigate the frequency points of the stronger radiation of the sweat duct model at different frequencies and compare the effects of the turning direction of the helical sweat duct and changing the length of the sweat duct on its radiation variation at specific frequencies. The results show that there are significant differences in the magnitude and direction of planar radiation for different lengths of sweat ducts, and the differences in the turning direction of the helical sweat ducts also affect the angle of sweat duct radiation. The research on the electromagnetic radiation characteristics of sweat tubes in this study is of great guidance to the IC design research of human skin sweat tubes.
Among the biometric identification methods, fingerprint identification is one of the most widely researched and applied biometric identification technologies. However, the traditional fingerprint identification system is vulnerable to attacks with the use of fake fingerprints, causing security problems. At the same time, when the skin of the finger is worn, wet, stained the efficiency of fingerprint identification will suffer. Optical Coherence Tomography is a non-invasive high resolution imaging technology that can image the subcutaneous depth of 1mm. Therefore, OCT can be used to obtain fingerprints inside the finger to effectively solve the security problem of fingerprint recognition, and at the same time solve the problem of the reduction in the recognition performance when the finger epidermis is damaged by external factors. In this research, OCT technology is used to collect the data of the three-dimensional structure of the fingertip by the aid of the deep learning U-net, SIFT and FLANN algorithm to ensure the reconstruction and recognition of internal fingerprints. The results show that U-net can extract the contour of the subcutaneous papilla layer and reconstruct the 2D internal fingerprint. Then we use Sift algorithm to match and splice the feature points of the internal fingerprints collected by multiple overlapping and establish a large area of internal finger template library. Finally, the FLANN algorithm library is used to extract the minutiae of the tested internal fingerprint and match the fingerprint template to achieve identity recognition. Compared with the traditional algorithm, this method is difficult to imitate and has high security.
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