Yongqiang Yang

The ground-airborne frequency-domain electromagnetic (GAFEM) method has the potential to detect underground anomalies at large depth ranges in areas with complex terrain. However, its specific configuration, which involves ground-based transmitting and airborne signal acquisition, inevitably introduces attitude noise into the measured magnetic field vector data. The presence of this attitude noise alters the characteristics of the measured data, consequently compromising the accuracy of imaging methods that rely on vector data. To eliminate the influence of attitude on GAFEM, this study proposes a rapid imaging technique for GAFEM based on the scalar magnetic field. This method utilizes the total magnetic field magnitude at each measurement point along the survey line as the input for imaging parameter calculation, thereby circumventing the effects of attitude noise and enabling high-resolution detection of underground anomalies. This study begins by analyzing the mechanism through which flight attitude affects GAFEM. Using a previously published GAFEM imaging method based on vector magnetic fields, it is demonstrated that flight attitude severely degrades the accuracy of such methods. Furthermore, a new imaging approach is proposed that employs the scalar magnetic field, which is immune to variations in flight attitude. A detailed description of the method’s principles, physical basis, and computational procedures is provided. The feasibility of the method is validated using a synthetic GAFEM model. The results indicate that the proposed method can achieve high-resolution detection of underground anomalies without being affected by attitude noise. Finally, the performance of the method is further tested using a simulation model constructed from actual geological data. The results confirm that the proposed method possesses the capability to identify underground anomalies in a complex model.

Distortion is a key parameter affecting the imaging performance of lenses. In this study, we propose a testing method based on detector Nyquist sampling of image data to achieve high-precision measurements of the distortion distribution of lenses. The distribution patterns of distortions in horizontal and vertical directions can be obtained by analyzing the distribution patterns of Moiré fringes in images under Nyquist sampling conditions and using phase-shift algorithms. The distortion-distribution characteristics of the lens are then calculated using distortion formulas. This method is characterized by high testing accuracy and sampling resolution. The image-plane distortion distribution exhibited a consistent linear trend when the object-plane position varied within a limited spatial range. Furthermore, the proposed method achieved a magnification deviation factor repeatability accuracy of approximately ±108 nm/cm and third-order distortion-measurement accuracy of approximately ±108 nm/cm³. This method enables a high-precision distortion evaluation of conventional industrial imaging lenses.

With the increasing acquisition speed of image sensors, it has become necessary to provide image sources with higher frame rates and grayscale in order to meet testing requirements. In the field of semi-physical simulation projection, digital micromirror devices are often chosen for their high resolution, uniformity, response speed, and energy concentration. In this study, we utilized digital micromirror devices to construct a high-frame-rate and large-grayscale simulation projection system. To achieve our experiment goals, we employed two digital micromirror devices. One DMD was used to modulate the light intensity of the light source, while the other generated images with different bit planes. By projecting the target images onto the image sensor, we were able to achieve a frame rate of 1611 hz for the projected 12-bit image. This system meets the requirements for our experiment design and ensures the accurate testing of image sensors.

Quantifying free-form surfaces using differential confocal microscopy can be challenging, as it requires balancing accuracy and efficiency. When the axial scanning mechanism involves sloshing and the measured surface has a finite slope, traditional linear fitting can introduce significant errors. This study introduces a compensation strategy based on Pearson’s correlation coefficient to effectively reduce measurement errors. Additionally, a fast-matching algorithm based on peak clustering was proposed to meet real-time requirements for non-contact probes. To validate the effectiveness of the compensation strategy and matching algorithm, detailed simulations and physical experiments were conducted. The results showed that for a numerical aperture of 0.4 and a depth of slope < 12°, the measurement error was <10 nm, improving the speed of the traditional algorithm system by 83.37%. Furthermore, repeatability and anti-disturbance experiments demonstrated that the proposed compensation strategy is simple, efficient, and robust. Overall, the proposed method has significant potential for application in the realization of high-speed measurements of free-form surfaces.

A new sensor type is proposed to accurately detect the surface profiles of three-dimensional (3D) free-form surfaces. This sensor is based on the single-exposure, zero-crossing method and is used to measure position and angle simultaneously. First, the field intensity distribution in the posterior focal plane of the confocal microscope’s objective was modeled accurately. Second, because the camera needs to trigger acquisition when the surface (to be measured) reaches the focal position of the sensor, a zero-crossing prediction method based on a sliding window was proposed. Third, a fast, spatially convergent, peak-extraction algorithm was proposed to improve the accuracy and efficiency of peak extraction. This scheme reduces system installation and adjustment difficulties, and the single-exposure, zero-crossing method achieves high-speed, real-time image acquisitions. The experimental results indicate that the average error of the zero-crossing prediction system was 17.63 nm, the average error of the tilt degree measurement was 0.011° in the range of 0–8°, and the prediction error of the tilt direction measurement was 0.089° in the range of 0–360°. The sensor can measure the slope and can be potentially used for 3D surface precision detection.

To improve the accuracy of three-dimensional (3D) surface contour measurements of freeform optics, a two-dimensional (2D) tilt measurement sensor for confocal microscopy (CM) systems is proposed based on a double-cylindrical mirror structure. First, the proposed system is accurately modeled. Second, we introduce a modified mean–shift-based peak-extraction algorithm with a novel kernel function (MSN) because the reflectivity of the measured object and fluctuation of the light source affect the measurement accuracy. Third, a partition fitting (PF) strategy is proposed to reduce the fitting error and improve the measurement accuracy. Simulations and experiments reveal that the robustness, speed, and angular prediction accuracy of the system effectively improved as a function of MSN and PF. The developed sensor can measure the 2D tilt, where each tilt is a composition of two separate dimensions, and the mean prediction errors in the 2D plane from −10°–+10° are 0.0134° (0.067% full scale (F.S)) and 0.0142° (0.071% F.S). The sensor enables the optical probe of a traditional CM to obtain accurate and simultaneous estimates of the 2D inclination angle and spatial position coordinates of the measured surface. The proposed sensor has potential in 3D topographic reconstruction and dynamic sampling rate optimization for 3D contour detection.