ABSTRACT
The high measurement accuracy of the digital image correlation (DIC) method is derived from the sub-pixel registration algorithm, which interpolates the intensities at the sub-pixel position in the image. The displacement error caused by the interpolation is a systematic bias in the DIC method, known as the sinusoidal bias in the sub-pixel translation experiment. Although the interpolation bias has been well researched, there is a lack of a universal method to eliminate interpolation bias. In this work, we propose a universal method to eliminate the interpolation bias using a pre-deformed reference subset; pixel points in the pre-deformed subset are deviated from the integer-pixel location. The purpose of the adjustment is to set the deformed pixel points at a specific position, so that the interpolation bias of all deformed pixel points cancels each other out, close to zero. The adjustment of the pre-deformed reference subset is related with the subset size and subset deformation. Numerical experiments including DIC challenge data and a real uniaxial tensile test were conducted to verify the effectiveness and universality of the proposed method, contributing to improved measurement accuracy. Considering the effect of pixel point location on the interpolation bias, this work proposes a universal method to eliminate the interpolation bias and provides a perspective to study DIC errors.
ABSTRACT
Optical tweezers exert a strong trapping force on cells, making it crucial to analyze the movement of trapped cells. The rotation of cells plays a significant role in their swimming patterns, such as in sperm cells. We proposed a fast deep-learning-based method that can automatically determine the projection orientation of ellipsoidal-like cells without additional optical design. This method was utilized for analyzing the planar rotation of trapped sperm cells using an optical tweezer, demonstrating its feasibility in extracting the rotation of the cell head. Furthermore, we employed this method to investigate sperm cell activity by examining variations in sperm rotation rates under different conditions, including temperature and laser output power. Our findings provide evidence for the effectiveness of this method and the rotation analysis method developed may have clinical potential for sperm quality evaluation.
Subject(s)
Deep Learning , Male , Humans , Semen , Spermatozoa , Optical Tweezers , LasersABSTRACT
The division-of-focal-plane (DoFP) polarimeter can quickly and effectively obtain the polarization information of light in real time, where Stokes parameter reconstruction is a critical issue. Many reconstruction methods have been proposed to address this; however, their performance tends to degrade in the presence of noise. Thus, it is significant to clarify the noise-induced error in Stokes parameter reconstruction. In this work, we investigate the link between the noise-introduced error and the reconstruction method and develop a simple and effective way to evaluate the noise robustness of reconstruction methods. Furthermore, a novel experimental scheme of noise measurement, to the best of our knowledge, is designed to verify the theory. Based on the criterion, our scheme guides the selection of reconstruction methods and further promotes the practical application of the DoFP technique.
