To reduce the impact of atmospheric turbulence on free space optical communication (FSOC) links and improve the performance of systems, an adaptive collaborative stochastic parallel gradient descent (AC-SPGD) optimization algorithm is proposed. In the iterative process of the AC-SPGD algorithm, the genetic algorithm (GA) is first used to find potential optimal parameter combinations, and then the particle swarm optimization (PSO) algorithm is used to quickly converge to the global optimal solution. Finally, the stochastic parallel gradient descent (SPGD) algorithm is used to make fine parameter adjustments, avoiding the problem of easy local extreme value convergence and slow iteration speed for SPGD. Simulation results show that the AC-SPGD algorithm can compensate for the wavefront error caused by strong turbulence in real time, achieving a good correction effect, and has faster convergence speed and higher correction accuracy than the traditional fixed gain optimization algorithm.

Owing to the chaotic time delay signature (TDS) of chaotic light, there is a "double-peak" phenomenon during the acquisition process of the chaotic Brillouin dynamic grating (BDG) system, which results in inaccurate measurements of the gain and reflection spectra of chaotic BDG systems under temperature variations. This research analyzes the principle behind the appearance of the double-peak in gain and reflection spectra, introduces the ratio of double-peak powers, and investigates the impact of TDS-induced sideband BDG systems on the gain and reflection spectra. The results indicate that the emergence of TDS significantly interferes with the sensing measurements of BDG systems, thereby degrading the measurement accuracy. Experimental results show that by altering the feedback cavity length and feedback strength of the chaotic light feedback loop, the number of sideband BDGs present in the optical fiber can be changed. The relationship between the feedback strength and the ratio of double-peak powers follows a quadratic polynomial rule, and the ratio of double-peak power also alters the influence range of TDS on the measurements. When the feedback strength increases from 0.01 to 0.09, the TDS decreases from 0.441 to 0.095, reducing the influence range of TDS on the gain spectrum by half. Further, the sensing error of the reflection spectrum is reduced to 30 MHz in the temperature range of 35?50 ℃, ensuring the accuracy of chaotic BDG system measurements.

In this study, we design, fabricate, and thoroughly test a fiber-optic biomimetic microelectromechanical system (MEMS) membrane-type acoustic. A silicon-based coupled-bridge double-diaphragm biomimetic membrane is developed by simulating the auditory organs of parasitic flies. The vibrations of the membrane are detected employing a Fabry-Pérot interferometer composed of optical fibers, thereby enabling the measurement of acoustic signals. The results demonstrate that the fabricated biomimetic membrane exhibits two distinct vibration modes. The packaged sensor demonstrates two resonant frequencies at approximately 810 and 1600 Hz, with minimum detectable sound pressures of 2.76 and 2.17 mPa/Hz1/2, respectively. In addition, the sensor exhibits a figure-eight directional response pattern. Within an incident angle range of 0°?60°, the amplitude of the sensor varies linearly with the angle. Thus, the results indicate that the proposed sensor holds potential for use in miniaturized soundsource target localization systems.

Aluminum-copper alloy is commonly used in aerospace lightweight structures because it is lightweight and possesses high strength. Laser welding has a high energy density and a small heat-affected zone, making it widely applicable in welding aluminum-copper alloys. However, due to the inherent characteristics of aluminum-copper alloys, problems such as porosity and cracks are prone to occur when using ordinary single-light-source laser welding. This study employs an infrared/blue light composite laser to weld 2A14T6/2A12T4 aluminum-copper alloy and studies the impact of process parameters on weld formation and joint microstructure. The results indicate that as welding heat input increases, weld seam width increases, while stability during welding decreases. When heat input decreases, porosity at the weld seam is reduced, but fusion on both sides of the weld seam becomes insufficient. The best weld forming effect is achieved when the infrared light power is 4500 W, the blue light power is 600 W, and the welding speed is 30 mm/s. The formation of the weld seam starts at the edge, gradually advances toward the center, and finally results in coarse columnar and fine equiaxed crystals. During welding, due to solidification imbalance, a large amount of strip-shaped θ-Al2Cu precipitates at the grain boundaries.

Compared with graphene (Gr) and tungsten disulfide (WS2), the Gr/WS2 heterojunction exhibits a greater modulation depth, owing to its strong interlayer coupling effect, and demonstrates excellent nonlinear optical properties. In this study, a Gr/WS2 composite material nano-film is prepared via the liquid-phase exfoliation method and coated onto the surface of an etched fiber via photodeposition method, thus forming an all-fiber saturable absorber device with advantages such as a high damage threshold, large modulation region, and simple structure. The proposed device is applied to an erbium-doped fiber continuous-wave laser, and traditional soliton mode-locking with a central wavelength of 1531.34 nm is achieved. Compared with single materials, the Gr/WS2 heterojunction under the same experimental conditions enables a narrow pulse width (5.6 ps) and higher output power (13.61 mW). These results indicate that the Gr/WS2 heterojunction is a promising candidate for pulse laser applications and provides a solid foundation for the development of high-performance ultrafast photonic devices.

To ensure the stable operation of unmanned aerial vehicle water depth detection LiDAR under different working conditions, a LiDAR heat dissipation structure designed by combining optical, mechanical, and thermal integration analysis is proposed. The design helps achieve temperature control under different working conditions. The heat dissipation structure is designed such that it fully integrates the environmental conditions during the operation of the drone. Firstl, a thermal analysis model for the laser radar system is established using Icepak. Then, Ansys is used to perform thermal mechanical coupling analysis on the optical structure. Next, the optical system structure deformation is imported into Zemax to calculate the modulation transfer function (MTF) curve of the system through the optical mechanical interface Sigfit. Finally, the heat dissipation structure is further optimized based on the imaging quality. The experimental analysis results indicate that the temperature of each module remains within the allowable range under different operating conditions. After optimizing the heat dissipation structure, it is observed that the MTF value of the optical system is greater than 0.285 at an ambient temperature of 40 ℃, and the MTF of the optical system is greater than 0.25 at normal operating temperatures, indicating good imaging performance of the system. The results of the field experiments verify the accuracy of the optical mechanical thermal integration analysis and also indicated that the receiving optical system can achieve echo reception. In summary, the heat dissipation structure designed in this article can integrated in unmanned aerial vehicle water depth measurement LiDAR systems to ensure the stable operation of the system.

A suitable denoising method is explored to reduce the influence of noise on atmospheric Rayleigh lidar echo signal. Combined with simulated and measured echo signals, the denoising effects of moving average, Hanning window sliding-window, wavelet transform (WT), ensemble empirical mode decomposition (EEMD), WT-EEMD-LOWESS, and EEMD-VMD-IMWOA methods are compared with respect to their influence on photon number profile, atmospheric fluctuation information, and temperature retrieval accuracy. The analysis results show that the temperature retrieval results processed by the Hanning window sliding-window and the EEMD-VMD-IMWOA methods have high accuracy and effectively retain the atmospheric fluctuation information, surpassing the other methods. However, EEMD-VMD-IMWOA method has better robustness, and the Hanning window sliding-window method is not suitable for processing high signal-to-noise ratio signals. If the signal-to-noise ratio of the measured signal is in the range of 0?200, the Hanning window sliding-window method can be selected for denoising; otherwise, EEMD-VMD-IMWOA method should be preferred to improve retrieval accuracy.

To address the challenges of cumbersome operation and spatiotemporal discontinuities in traditional laboratory-based water quality testing methods, this study proposes a total nitrogen inversion method based on hyperspectral imaging. Taking the Shiwuli River in the Chaohu Basin as the research object, near ground hyperspectral data serve as the data source. The hyperspectral data of the water samples are resampled to match the resolution of unmanned aerial vehicle (UAV) hyperspectral data. The random frog (RF) algorithm is used to extract the characteristic bands of the total nitrogen mass concentration in the water samples. The particle swarm optimization (PSO)-backpropagation neural network (BPNN) algorithm is then used to construct a total nitrogen inversion model, enabling the inversion of total nitrogen mass concentration in water bodies using UAV hyperspectral images. These results indicate that the feature bands 465.1, 495.2, 756.2, 830.1 nm, and 847.7 nm, extracted using the RF algorithm, align with the sensitive band range of total nitrogen. The established PSO-BPNN inversion model has a prediction coefficient of determination (R2) of 0.862 and a root mean square error (RMSE) of 0.405 mg/L for the training set, while the test set yields a prediction R2 of 0.711 and RMSE of 0.640 mg/L. The RMSE of the test set is significantly reduced compared with those by the BPNN and partial least squares models. Applying this model to UAV hyperspectral imaging enables rapid inversion of the spatial distribution characteristics of total nitrogen, with the relative deviation between the inversion values at verification points and the measured values remaining below 5.50%. These findings demonstrate that the model exhibits a certain degree of generalization and strong practical applicability.

NOx is a major pollutant in exhaust gas and primarily comprises NO and NO2, with NO being the main component. To meet the stringent requirements for high-precision detection of NO concentration, this study proposes an NO concentration inversion method based on ultraviolet (UV) differential spectroscopy. This method employs an empirical wavelet transform (EWT) to decompose the UV spectrum of NO and applies an adaptive Savitzky-Golay (ASG) filter to process the decomposed signal. The filtered signal is reconstructed using the inverse EWT (IEWT). Finally, a long short-term memory (LSTM) neural network performs the inversion to determine the NO concentration. The spectral signal of NO is first processed via the EWT-ASG method and subsequently analyzed using the LSTM neural network. Results demonstrate that the predicted NO concentration has maximum and minimum errors of approximately 6% and 0.01%, respectively, when compared to the true values. Moreover, the root mean square error of the inversion accuracy improves by 35.86% compared to the unfiltered state. Thus, the proposed method provides strong technical support for the concentration inversion of NO and other components of exhaust gas.