Applied optics最新文献

This work proposes an optical pumping strategy based on a broadband laser to overcome the problem of atomic spin polarization gradients and sensitivity to optical frequency noise in NMR gyroscopes. A band-limited Gaussian white noise radiofrequency signal is applied to a lithium niobate electro-optic phase modulator to do this. A narrow linewidth laser maintains its frequency stability while uniformly broadening its spectrum to the hundreds of MHz range. Compared to unmodulated pumping, the proposed scheme enhances the uniformity of rubidium spin polarization by approximately 10%, reaching a measured value of 92.8%. Additionally, the sensitivity of the inert gas precession signal to optical frequency noise is reduced by half. This scheme can be extended to other atomic sensors requiring higher uniform spin polarization distributions and provides a promising way to achieve high-stability, low-noise NMR gyroscope systems.

The fundamental conflict between infrared stealth and thermal management, where suppressing thermal emission for camouflage inevitably causes detrimental heat accumulation, poses a long-standing challenge in modern military technology. This work resolves this paradox through a bottom-up design of a particle composite coating, where complex spectral selectivity is engineered at the single-particle level. We computationally designed and validated a multilayer spherical particle, consisting of a CaMg(CO₃)₂ shell, a VO₂ inner shell, and a Ge core, embedded within a polyethylene (PE) binder. The synergistic roles of the materials allow for precise spectral control: CaMg(CO₃)₂ provides a primary emission peak in the 6-7 µm range, VO₂ broadens this non-atmospheric window for enhanced heat dissipation, and the Ge layer simultaneously shields absorption in the infrared stealth bands and boosts absorption in the VIS-NIR spectrum. The optimized coating achieves a high average emissivity of 0.6471 in the VIS-NIR and 0.5091 in the 5-8 µm band for effective thermal radiation, while maintaining exceptionally low emissivity in the atmospheric window bands (SWIR: 0.2326, MWIR: 0.3208, and LWIR: 0.0915). Simulated thermal imaging demonstrates superior stealth performance. This coating offers a scalable and effective strategy for developing next-generation materials compatible with both multiband stealth and heat dissipation requirements.

Phosphorus is an essential macronutrient for plant development, and its availability in soil directly influences agricultural productivity. However, traditional laboratory quantification of phosphorus is costly, slow, and destructive. This study introduces a system for automated quantification of total phosphorus (TP) using hyperspectral analysis on soil samples enriched with phosphorus fertilizer (P₂O₅). A previously developed acquisition protocol by the authors was employed, involving the design, development, and construction of a platform equipped with a Bayspec OCI-F camera. The lighting system was designed to ensure adequate spectral response in the visible (VIS) and near-infrared (NIR) regions, covering the range from 420 to 1000 nm. A total of 152 soil samples with varying phosphorus concentrations were prepared. From the hyperspectral images (HSI), the spectral response of each sample was extracted. The data were divided into 80% for training and 20% for validation. Partial least squares regression (PLSR) was used to estimate total phosphorus (TP), and variable importance in projection (VIP) analysis reduced the spectral bands from 145 to 78. Subsequently, a forward propagation artificial neural network (ANN) was trained to predict TP content in new samples. The system achieved a coefficient of determination (R²) of 0.99401, a ratio of performance to deviation (RPD) of 9.1, and a ratio of performance to interquartile range (RPIQ) of 13.9, indicating a good fit. Additionally, it achieved a mean absolute percentage error (MAPE) of 12.1% and a root-mean-square error (RMSE) of 7426 ppm, demonstrating reliable estimation of total phosphorus in soils.

This paper introduces a novel, to our knowledge, wavefront reconstruction algorithm that significantly improves the accuracy in boundary regions, a common source of error in traditional approaches. The proposed method enhances the edge performance by correcting wavefront slopes using Taylor's theorem and redefining the phase point integration process. Extensive numerical simulations were conducted using Zernike polynomial models and varying levels of Gaussian noise to assess both the accuracy and robustness of the proposed method. The results demonstrate that the method consistently outperforms conventional techniques, especially with regard to high-order aberrations and boundary areas while maintaining good noise resilience. Experimental validation involving the deflectometry measurements of a deformable mirror further confirms the method's practical effectiveness and applicability.

Conventional camera calibration typically requires acquiring clear and focused target images for accurate feature detection. Defocused target images may reduce the feature detection accuracy and even lead to failure in estimating camera parameters. To address this issue, this paper employs the crossed fringe as a calibration pattern and develops an effective phase regression network (Phase-ReNet) for wrapped phase calibration, from which feature points can be extracted with high precision. Unlike traditional phase-shifting methods, which require multiple patterns, our method recovers horizontal and vertical phase maps using just a single pattern, significantly improving calibration efficiency. Experimental results demonstrate that this method can achieve feature detection accuracy comparable to traditional phase-shifting methods, and the mean reprojection errors of the defocused camera are only 0.0552 pixels. These results highlight that our method is suitable for defocused camera calibration tasks.

Diffractive optical element-based background-oriented schlieren (BOS) is a popular technique for quantitative flow visualization. This technique relies on encoding spatial density variations of the test medium in the form of an optical fringe pattern; and hence, its accuracy is directly influenced by the quality of fringe pattern demodulation. We introduce a robust deep learning-assisted subspace method, which enables reliable fringe pattern demodulation even in the presence of severe noise and uneven fringe distortions in recorded BOS fringe patterns. The method's effectiveness in handling fringe pattern artifacts is demonstrated via rigorous numerical simulations. Furthermore, the method's practical applicability is experimentally validated using real-world BOS images obtained from a liquid diffusion process.

Non-uniform angular luminance distribution and limited viewing-angle control remain key challenges in MicroLED display modules. In this paper, we propose an optical packaging structure and a design method based on a freeform microlens array (MLA) to achieve prescribed angular luminance distributions. While freeform optics have been extensively studied for general illumination, their application in MicroLED packaging for directional luminance control has been rarely explored. Here, we extend an integrable ray mapping framework into the luminance domain and apply it to the design of MicroLED packaging optics, enabling compact, high-efficiency, and high-uniformity beam shaping. A lossless energy transformation strategy is first employed to convert the target luminance distribution into a uniformly sampled angular domain, addressing sampling imbalances at large viewing angles. An extended integrable ray mapping method is then used to compute a mapping between the MicroLED source domain and the angular domain, from which manufacturable single-surface freeform lenses and MLAs are derived. Two design examples-under symmetric and non-rotationally symmetric viewing conditions-are presented to validate the proposed approach. Simulation results demonstrate that the method achieves accurate angular luminance control and high uniformity across the desired viewing cone, confirming its practicality for real-world MicroLED display applications.

Quantum key distribution (QKD) ensures unconditional security for public key encryption by utilizing the principles of quantum mechanics. This study designs and fabricates a QKD chip based on the silicon-on-insulator (SOI) platform, employing a time-bin encoding scheme with decoy states. In the design, we integrate a slow thermo-optic phase modulator with a carrier-depletion modulator to ensure high fidelity of quantum states and high-speed encoding capabilities, enabling precise and flexible time-bin encoding. We achieve the encoding and decoding of four BB84 quantum states at a repetition rate of 100 MHz. In the experiment, the visibility of the interference fringes for the phase state |+⟩ is 93.66%, and for the phase state |-⟩, it is 92.36%. The extinction ratios for the time states |0⟩ and |1⟩ are 19.33 and 18.72 dB, respectively. The experimental results demonstrate that the chip has efficient quantum state preparation capability, providing significant support for the practical implementation of quantum key distribution technology. Additionally, we propose a new chip structure, to our knowledge, using SOI and Si₃N₄ heterogeneous integration. The SOI waveguide is used for high-speed modulation encoding, while the delay line is composed of Si₃N₄ waveguides. This structure is designed to address the stability issues of the chip caused by temperature variations.

The semantic segmentation is a critical task in LiDAR point cloud processing. Leveraging temporal information to provide contextual data for regions with low visibility or sparse observations has recently become a popular research direction, especially in autonomous driving. Existing methods, however, are often over-reliant on past frames, leading to cumulative errors (drift) caused by unconstrained frame-by-frame stacking. This paper proposes a dynamic alignment of historical frame memory information to ensure consistency with the observations of the current frame, reducing deviations caused by viewpoint changes or object movements and ensuring more accurate capture of current frame features. In addition, a new multi-scale feature fusion method, to the best of our knowledge, was introduced using the spatiotemporal (ST) method to extract the ST features, which reduces the inconsistencies between 2D range image coordinates and 3D Cartesian outputs. This approach enhances feature representation by optimizing and fusing the aligned channel features. This method was evaluated on the SemanticKITTI and SensatUrban datasets. The experimental results showed that it outperforms existing state-of-the-art methods regarding accuracy.

To successfully translate optical miniature probes, such as the endoscopic miniprobe, catheter probe, and capsular probe, into clinical practice, it is essential to accurately characterize their performance and thereby precisely define the final specifications of the related device outcomes in various aspects such as biochemical, electrical, physical, mechanical, and optical aspects. Although a variety of related device concepts have been introduced in the biophotonics field in the past three decades, all the previous studies, to the best of our knowledge, focused on showcasing their biomedical potential or applications rather than developing or solidifying related metrologies to objectively characterize their performances. In this study, we developed a metrological system that can measure the beam-intensity rotational uniformity of a side-scanning optical miniature probe, regardless of its beam firing angle to the probe axis. Moreover, by applying the developed system to our photoacoustic endoscopic probe prior to conducting in vivo imaging experiments, we were able to confirm the beam uniformity far more accurately than was previously possible by visual inspection. In this paper, we introduce the basic concept and operating principles of the developed system and discuss the importance of characterizing the relevant beam-intensity uniformity.