Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., “HMML-BP”, based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring.

To address the stringent performance requirements of space-based gravitational-wave detection telescopes in terms of superior wavefront quality, a high beam–compression ratio, and exceptional thermal stability, this study proposes a 1064 nm off-axis four-mirror infrared telescope system. Ultra-low-expansion glass (ULE), 4J36 Invar, and T800-based carbon-fiber-reinforced polymer (T800 CFRP) are integrated to achieve optimal thermal-expansion compatibility among the principal structural components, while a three-point symmetric bipod flexure support is specifically developed for the primary mirror. A compliance model is formulated based on Castigliano’s second theorem, followed by systematic optimization of the key structural parameters. Furthermore, coupled analyses of gravitational loading and orbital thermal effects are conducted in conjunction with a staged assembly and alignment procedure. The results demonstrate that, under a 1 g gravitational load, the maximum displacement of the primary mirror is approximately 1.72 μm, while the surface-figure error over the effective reflective aperture is maintained below λ/100. Across an orbital temperature range of −18.16 to 63.45 °C, the maximum root-mean-square and peak-to-valley surface-figure errors of the primary mirror are limited to 3.61 nm and 14.97 nm, respectively. These numerical results indicate favorable static optomechanical and thermomechanical stability under the investigated gravitational and orbital thermal conditions, thereby providing a technical reference for the development of the TianQin space-based gravitational-wave telescope.

This work presents a biomimetic curved compound-eye imaging system (BCCEIS) engineered for extended-range depth mapping. The system is designed to emulate an apposition-type compound eye and comprises three key components: a hemispherical array of lenslets forming a curved multi-aperture imaging surface, an optical relay subsystem that transforms the curved focal plane into a flat image plane compatible with a commercial CMOS sensor, and a high-resolution CMOS detector. Comprehensive optical analysis confirms effective aberration correction, with the root-mean-square (RMS) spot radii across the field of view (FOV) remaining smaller than the radius of the Airy disk. The fabricated prototype achieves an angular resolution of 2.5 mrad within an ultra-wide 97.4° FOV. Furthermore, the system demonstrates accurate depth reconstruction within the entire FOV at distances up to approximately 2 m, exhibiting errors below 2%. Owing to its compact form, wide FOV, and robust depth-sensing performance, the BCCEIS shows strong potential as a payload for unmanned aerial vehicles in applications such as security surveillance and obstacle avoidance.

Spectro-polarimetric imaging systems can simultaneously acquire spatial, spectral, and polarimetric information during remote sensing, yet the multimodal fusion data are often constrained in practical applications by insufficient exploitation of complementary information across different modalities. To address this issue, we propose a multimodal image fusion method based on a Dual-Branch Cross-Attention Synergistic Transformer (DBCS-T). In our method, three complementary feature components are independently extracted, i.e., a Characteristic Polarization Image (CPI), a Characteristic Spectral Image (CSI) and a Characteristic Intensity Image (CII). For CPI, it is derived from Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP) inputs via DBCS-T, which integrates a Cross-Channel Transposed Attention (CCTA) module for cross-modal interaction and a Multi-Scale Polarization Feature Adaptive Modulation (MPAM) module for local feature enhancement. CSI is obtained by leveraging maximum-divergence spectral band differences guided by prior spectral radiance curves. CII is computed from the Stokes parameter $S_0$. These three components are then fused via Principal Component Analysis (PCA) into a Multimodal Fusion Image (MFI). To demonstrate the effectiveness of our method, an experiment was conducted on a scene that contains real vegetation, artificial foliage, and same-color metallic objects. The experimental results show that the proposed method achieves effective semantic segmentation of all three target categories. Furthermore, quantitative evaluation demonstrates that the MFI attains the lowest Kullback–Leibler (KL) and Jensen–Shannon (JS) Divergence values among all evaluated modalities, with image entropy exceeding that of individual source inputs. These results validate the complementarity of the extracted multimodal features, significantly enhance the interpretation performance for complex scenes, and demonstrate the broad application potential of the proposed fusion framework in remote sensing and multidimensional imaging.