In tunable diode laser absorption spectroscopy (TDLAS), the multipass cell (MPC) that comprises two identical and coaxial spherical mirrors can provide long optical path length (OPL) and reduce the limit of system detection. We proposed an intelligent method to design MPC with long optical path length (OPL) and small size using parallel multi-population genetic algorithm (PMPGA). The developed MPCs presented the maximum mirror utilization in the same size of spherical mirrors with the diameter of 25.4 mm and 40 mm. The performance of the developed MPCs were compared with other teams. We verified the results of PMPGA and obtained a micro MPC design with an OPL of 6.3 m. The diameter of the mirror was 25.4 mm, and the distance between the two mirrors was only 30.5 mm. And the developed MPC was verified by using TDLAS, which can improve the development of portable gas sensors.

Multipass cell (MPC), with a long effective optical path length (OPL), is an effective method to improve measurement sensitivity in laser absorption spectroscopy for trace gas sensing. We present a space equation (SE) method to design MPC, which can solve inaccuracy caused by using paraxial-matrix (PM) method. The location and dimension of light spot pattern can be calculated accurately and intuitively through SE method. Meanwhile, actual effective OPL can be accurately calculated, SE method can reduce the interference effect, the direction of each ray can be obtained accurately, and, hence, designing the angle of outgoing ray is simple. By adjusting the mirror curvature, mirror space, incident angle, and incident position, we realized four dense spot patterns with different multiple reflections in MATLAB simulation. We calculated the shape changes of light spot after multiple reflection and its influence on the design of outgoing ray hole size accurately. We also considered the evolution of dense pattern and found a method easy to obtain multiple ring pattern. We verified the validity of the proposed model and built a compact MPC with seven rings dense spot pattern. Such MPC offers an effective OPL of 36.48 m with 198 times of reflection. We verified the tunable diode laser absorption spectroscopy (TDLAS) technology to measure 610-ppmv standard methane. In contrast to PM method, the improvement of the signal-to-noise ratio (SNR) of 1.36 times is achieved when the volume of MPC is reduced by 3 times. In addition, the SNR can be improved to 6.6 times, while the volume of MPC is the same. Results validated the ability of the SE method to design MPC.

In recent years, visible light positioning (VLP) techniques have been gaining popularity in research. Among them, the scheme of using a camera as a receiver provides a low-cost, high-precision positioning capability and easy integration with existing multimedia devices and robots. However, the pose changes of the receiver can lead to image distortion and light displacement, significantly increasing positioning errors. Addressing these errors is crucial for enhancing the accuracy of VLP. Most current solutions rely on gyroscopes or Inertial Measurement Units (IMUs) for error optimization, but these approaches often add complexity and cost to the system. To overcome these limitations, we propose a 3D positioning algorithm based on an attention mechanism convolutional neural network (CNN) aimed at reducing the errors caused by angles. We designed experiments and comparisons within a rotation angle range of ±15 degrees. The results demonstrate that the average error for 2D positioning is within 5.74 cm and the height error is within 3.92 cm, and the average error for 3D positioning is within 6.8 cm. Among the four groups of experiments for 3D positioning, compared to the traditional algorithm, the improvements were 7.931 cm, 15.569 cm, 6.004 cm, and 16.506 cm. The experiments indicate that it can be applied to high-precision visible light positioning for single-light imaging.

A compact and high-sensitivity sensing system is required for the exploration of natural gas hydrates (NGHs) to measure dissolved CO ₂ in seawater. In this study, a mid-infrared (MIR) CO ₂ -sensing system was investigated by wavelength modulation spectroscopy. The system contained an interband cascade laser operating in MIR wavelength, an optical multipass gas cell, and an MIR mercury cadmium telluride detector. A combination of three absorption lines was utilized to achieve measurement with a wide CO ₂ -concentration range. Additionally, we proposed and introduced in detail a method of optimizing the operating temperature, pressure, and modulation depth. The optimum minimum detection limit of 11 ppbv was achieved when the integration time was 145 s. In the northern South China Sea, a deep-sea test was performed, and the test results validated the satisfactory performance of the sensing system for deep-sea NGH exploration.

Underwater Optical Camera Communication (UOCC) has emerged as a promising paradigm for short-range, high-bandwidth, and secure data exchange in autonomous underwater vehicles (AUVs). UOCC performance strongly depends on exposure time and International Standards Organization (ISO) sensitivity—two parameters that govern photon capture, contrast, and bit detection fidelity. However, optical propagation in aquatic environments is highly susceptible to turbidity, scattering, and illumination variability, which severely degrade image clarity and signal-to-noise ratio (SNR). Conventional systems with fixed imaging settings cannot adapt to time-varying conditions, limiting communication reliability. While validating the feasibility of deep learning for exposure prediction, this baseline lacked environmental awareness and generalization to dynamic scenarios. To overcome these limitations, we introduce a Real-to-Sim-to-Deployment framework that couples a physically calibrated emulation platform with a Hybrid CNN-MLP Model (HCMM). By fusing optical images, environmental states, and camera configurations, the HCMM achieves substantially improved parameter prediction accuracy, reducing RMSE to 0.23–0.33. When deployed on embedded hardware, it enables real-time adaptive reconfiguration and delivers up to 8.5 dB SNR gain, surpassing both static-parameter systems and the prior CNN baseline. These results demonstrate that environment-aware multimodal learning, supported by reproducible optical channel emulation, provides a scalable and robust solution for practical UOCC deployment in positioning, inspection, and laser-based underwater communication.