A dual-wavelength ring-cavity erbium-doped fiber (EDF) laser is designed based on two polarization beam splitters (PBSs) and a polarization controller (PC) performing gain equalization and polarization hole burning (PHB) effect. At room temperature, a stable dual-wavelength laser and a multi-output port laser which can simultaneously emit single-wavelength lasing and dual-wavelength lasing are obtained. The signal-to-noise ratios (SNRs) for single-wavelength outputs were 54.70 dB and 57.10 dB, with power fluctuations less than 0.038 mW and 0.029 mW, respectively. For dual-wavelength lasing, the SNRs were 59.63 dB and 59.25 dB, with power fluctuations less than 0.018 mW and 0.008 mW, respectively. The center wavelength drift was less than 0.006 nm for both single-wavelength and dual-wavelength outputs.

The large dynamic range and high performance of temperature and humidity profile lidar made it a popular tool for monitoring the atmospheric environment. In this study, we carried out an accurate analysis of the key components of the lidar system, including lasers, the emitting and receiving light paths, and photodetectors. We combined the validation of simulations with experimental testing, and then the applicability indicators and necessary conditions in accordance were suggested. For the frequency stability of the laser, when the wavelength shift is less than 0.15%, the measurement accuracy of the system can be guaranteed to be less than 5%. The degree of near-field signal distortion will be significantly impacted by the size of the geometric factor’s transition zone. The introduced measurement error is less than 2% when the deviation angle of the emission axis is less than 0.1 mrad. It has been tested that selecting a low-sensitivity detector can help to improve the sensitivity of temperature detection since this channel is sensitive to the detector’s nonlinearity. To enhance lidar’s detection capabilities and direct the lidar system design process, it is beneficial to analyze the precision of the key components.

At present, the naked-eye three-dimensional (3D) display technology still has some drawbacks, such as low brightness uniformity, high crosstalk, low light efficiency, short viewing distance, and the manufacturing is difficulty. Based on the principle of naked-eye 3D display and the Fresnel optical theory, this paper designs a Fresnel lens array and the star-shaped liquid crystal display (LCD) switch of unit LCD screen to achieve low-crosstalk and high brightness uniformity for the autostereoscopic 3D display. The unit parameters of a 139.7 cm 4K model autostereoscopic 3D displayer are provided and they are optimized by the TracePro software. The results show that when the pitch of the Fresnel lens on the exit surface is 0.304 mm, the width of each serration of Fresnel lens is 0.023 4 mm, the length of the Fresnel lens is 2.87 mm, and the center height of star-shaped LCD switch is 0.030 mm, the center length is 0.040 mm, the width of star-shaped LCD switch is 0.050 mm, and the image crosstalk is less than 2% when the viewing distance is 2.50 m. The problem on the brightness of the image in different positions is improved.

Cu₂ZnSnSSe₄ (CZTSSe) thin film solar cells, with adjustable bandgap and rich elemental content, hold promise in next-gen photovoltaics. Crystalline quality is pivotal for efficient light absorption and carrier transport. During the post-selenization process, understanding crystal growth mechanisms, and improving layer quality are essential. We explored the effects of ramp rate and annealing temperature on CZTSSe films, using X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscope (SEM), and ultraviolet-visual spectrophotometry (UV-Vis). The optimal performance occurred at 25.25 °C/min ramp rate and 530 °C annealing. This led to smoother surfaces, higher density, and larger grains. This condition produced a single-layer structure with large grains, no secondary phases, and a 1.14 eV bandgap, making it promising for photovoltaic applications. The study has highlighted the effect of selenization conditions on the characteristics of the CZTSSe absorber layer and has provided valuable information for developing CZTSSe thin film solar cells.

This paper reports the preparation of yttrium oxide (Y₂O₃) doped with various concentrations of Er³⁺ using high temperature synthesis method. Photoluminescence (PL) emission spectra of the samples were recorded at an excitation of 980 nm laser source. Two prominent peaks centered at 484 nm and 574 nm were found and attributed to the ²P_3/2→⁴I_11/2 and ⁴S_3/2→⁴I_15/2, respectively. The sample with 2.5 mol% of Er³⁺ provided the optimum intensity in emission spectra. The sample with optimum PL emission was investigated for its thermoluminescence (TL) glow curve exhibited the second order kinetics. The peak TL intensity was found around 236 °C, i.e., towards high temperature which supports the fact of formation of deeper traps. Therefore, the material taken may be regarded as a good candidate for light emitting diode (LED) applications.

To improve the quality of the illumination distribution, one novel indoor visible light communication (VLC) system, which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflection-intelligent reflecting surface (STAR-IRS), has been proposed in this work. A Harris Hawks optimizer algorithm (HHOA)-based two-stage alternating iteration algorithm (TSAIA) is presented to jointly optimize the magnitude and uniformity of the received optical power. Besides, to demonstrate the superiority of the proposed strategy, several benchmark schemes are simulated and compared. Results showed that compared to other optimization strategies, the TSAIA scheme is more capable of balancing the average value and variance of the received optical power, when the maximal ratio combining (MRC) strategy is adopted at the receiver. Moreover, as the number of the STAR-IRS elements increases, the optical power variance of the system optimized by TSAIA scheme would become smaller while the average optical power would get larger. This study will benefit the design of received optical power distribution for indoor VLC systems.

Quantization noise caused by analog-to-digital converter (ADC) gives rise to the reliability performance degradation of communication systems. In this paper, a quantized non-Hermitian symmetry (NHS) orthogonal frequency-division multiplexing-based visible light communication (OFDM-VLC) system is presented. In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC, a quantized mathematical model of NHS OFDM-VLC is established. Based on the proposed quantized model, a closed-form bit error rate (BER) expression is derived. The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC. In addition, channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC.

In order to identify the tilt direction of the self-mixing signals under weak feedback regime interfered by noise, a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural network can identify the direction of the self-mixing fringes accurately and quickly. In the process of measurement, the measurement signal can be normalized and then the neural network can be used to discriminate the direction. Simulation and experimental results show that the proposed method is suitable for self-mixing interference signals with noise in the whole weak feedback regime, and can maintain a high discrimination accuracy for signals interfered by 5 dB large noise. Combined with fringe counting method, accurate and rapid displacement reconstruction can be realized.

Because methane is flammable and explosive, the detection process is time-consuming and dangerous, and it is difficult to obtain labeled data. In order to reduce the dependence on marker data when detecting methane concentration using tunable diode laser absorption spectroscopy (TDLAS) technology, this paper designs a methane gas acquisition platform based on TDLAS and proposes a methane gas concentration detection model based on semi-supervised learning. Firstly, the methane gas is feature extracted, and then semi-supervised learning is introduced to select the optimal feature combination; subsequently, the traditional whale optimization algorithm is improved to optimize the parameters of the random forest to detect the methane gas concentration. The results show that the model is not only able to select the optimal feature combination under limited labeled data, but also has an accuracy of 94.25%, which is better than the traditional model, and is robust in terms of parameter optimization.

The total nitrogen (TN) is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality. Accurate and rapid methods are crucial for determining the TN content in water. Herein, a fast, highly sensitive, and pollution-free approach is proposed, which combines ultraviolet (UV) absorption spectroscopy with Bayesian optimized least squares support vector machine (LSSVM) for detecting TN content in water. Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values. The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM. To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model, the Bayesian optimization (BO) algorithm was used to optimize the parameters of the LSSVM. Results show that the prediction model performs well in predicting TN concentration, with a high coefficient of prediction determination (R²=0.941 3) and a low root mean square error of prediction (RMSE=0.077 9 mg/L). Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance.