Cylindrical surfaces with complex parameters (CSCP), including off-axis, aspheric, and other properties, constitute fundamental components within complex optical systems. Two-dimensional pseudo lateral shearing interferometry (2DPLSI) is a non-null and generalized method for CSCP. It can eliminate wavefront error of components within systematic and retrace error, thereby achieving high-precision measurement. However, the accuracy of measurement is influenced by factors such as the parameters of the measurement system, rendering the analysis of measurement precision of 2DPLSI to be important. The sources of error in 2DPLSI are discussed in this paper; their effects are simulated using the Monte Carlo (MC) method. Furthermore, a wavefront construction method based on power spectral density (PSD) is proposed, which simulates actual wavefronts more effectively. In addition, experiments are conducted to validate the optimized measurement system parameters derived from the simulation results. Experimental results show that the optimized measurement system parameters effectively improve measurement accuracy, retain low-mid spatial frequency information of wavefront, and eliminate the influence of gridding artifacts.

Different optical reconstruction methods have stringent requirements on the material or shape of the objects, and have poor generalization ability. To address this issue, this article proposes a stable and reliable target contour recognition algorithm based on omnidirectional illumination using shape from silhouette (SFS). It solves the problem of low signal-to-noise ratio of light signals reflected by materials with high reflectivity or transparency, and achieves performance that is comparable to the state-of-the-art deep learning networks. Moreover, this article solves the blind spot problem of the SFS algorithm in single-image fast reconstruction, and proposes a targeted growth visual hull (VH) method, which further enhances the reconstruction speed of the traditional SFS method while ensuring reconstruction accuracy. This article takes the reconstruction measurement of supermarket products as an example to demonstrate the value of the proposed method in practical applications.

Cylindrical surfaces with complex parameters (CSCPs) have off-axis and aspheric properties. High-precision measurement of cylindrical surfaces is a key research focus in optical metrology. Two-dimensional pseudo lateral shearing interferometry (2DPLSI) enables non-null generalized interferometry for cylindrical surfaces. However, due to the non-rotational symmetry of cylindrical surfaces with complex parameters, measuring them using two-dimensional pseudo lateral shearing interferometry inevitably introduces misalignment aberrations, degrading the accuracy of cylindrical surface reconstruction. To address this issue, we propose a novel non-null testing method: the cylindrical surface is translated in the orthogonal directions to carry out the shearing process, and wavefront errors are eliminated through second-order differencing. Furthermore, a reconstruction algorithm in one direction is proposed. Using only the partial derivative in the x direction, the wavefront error of misalignment aberrations can be reconstructed, enabling high-precision recovery of the cylindrical surface. Experimental results using a Fizeau interferometer demonstrate that the proposed method effectively reconstructs misalignment aberrations. The reconstructed cylindrical surface achieves a peak-to-valley (PV) value of 0.45λ (λ = 632.8 nm) and a root-mean-square (RMS) value of 0.12λ, comparable to the 0.37λ PV and 0.09λ RMS obtained via null testing. The repeatability of the proposed method is superior to λ/1000 RMS.

The phenomenon of bio-electrophysiology has made significant progress in today's social sciences. The detection of electrical signals in medical biosciences has already become a workable method for plant electrophysiology, an invasive measurement tool for describing plant thinking. We used a electrophysiological sensor, which can present the autonomous device of plant thinking, which can transmit potential data of plants and convert them into signals of plant self-consciousness. Plant sensing can measure some aspects of the internal state of natural plants. T2 preparation experimental results show that 200%~240% of improved to electrophysiological signal on Tomato Sapling.