Basing on the momentum mismatching together with the crystal rotatory property, we investigate the diffraction performance of an acousto-optic tunable filter (AOTF). The relationship between diffraction efficiency, momentum mismatching, incident optical wave vector, and ultrasound wave vector is analyzed. The correlation between the frequency tuning relation and the incident angle of light is demonstrated, which is usually ignored in the AOTF design. The diffracted wavelength can be decided by a particular acoustic frequency only when the incident angle of light is fixed. Theoretical and experimental analysis indicates that we can acquire a narrowband spectrum and big angular aperture with a proper incident angle.

The modeling of compliant bridge-type displacement amplification mechanisms has challenges due to the intrinsic coupling of kinematic and mechanical behaviors. A structure load performance integrated model method for the bridge-type displacement amplification mechanism is presented. The established modeling based on Castigliano’s second theorem considers the deformations of all members, the effect of external load and the nonlinear shear effect. Compared to the finite element model (FEM) and existing models, the established modeling precisely predicts significant nonlinearity of the displacement amplification ratio (DAR) with the driving force, strong sensitivity of DAR to the external load and corresponding relationships of structural parameters with DAR, which is the closest to the FEM result over existing models. The variance-based sensitivities of structural parameters to DAR are thoroughly analyzed, indicating that sensitive structure parameters need to be focused on. Modeling applications further prove the reliability and expandability of the proposed model method. The proposed model method can provide support for the design, optimization and control of compliant systems with bridge-type displacement amplification mechanisms.

In hyperspectral images, ghost image residuals exceeding a certain threshold not only reduce the recognition accuracy of the imaging detection system but also decrease the target identification rate. Ghost image residuals affect both the recognition accuracy of the detection system and the accuracy of spectral calibration, thereby influencing qualitative and quantitative inversion. Conventional ghost image residual correction methods can significantly affect both the relative and absolute calibration accuracy of hyperspectral images. To minimize the impact on spectral calibration accuracy during ghost image residual correction, we propose a ghost image degradation model and an iterative optimization algorithm. In the proposed approach, a ghost image residual degradation model is constructed based on the point spread function (PSF) of ghost image residuals and their energy distribution characteristics. Using the proportion of ghost image residuals and the accuracy of hyperspectral image calibration as constraints, we iteratively optimized typical regional target ghost image residuals across different spectral channels, achieving automated correction of ghost image residuals in various spectral bands. The experimental results show that the energy proportion of ghost image residuals at different wavelengths decreased from 4.6% to 0.3%, the variations in spectral curves before and after correction were less than 0.8%, and the change in absolute radiometric calibration accuracy was below 0.06%.