This paper proposed and demonstrated a scheme to generate frequency-doubled triangular-shaped waveforms based on external modulation and polarization control. After optical carrier suppression (OCS) modulation in a dual-electrode Mach-Zehnder modulator (De-MZM) and optical double sideband (ODSB) modulation in a dual-parallel Mach-Zehnder modulator (DP-MZM), we obtain the optical spectrum with four main sidebands with equal frequency intervals, while maintaining a consistent 9.5 dB amplitude difference between the inner and outer sidebands. Subsequently, polarization control uses an optical interleaver (OI) and a time delay line (TDL) to eliminate the coherent interference of different sidebands. Finally, by applying a 10 GHz radio frequency (RF) signal, a 20 GHz triangular waveform is obtained. The tunability and feasibility of the triangular waveform generation have been also investigated. This proposal provides a feasible method to generate triangular waveform with high repetition rate.

Silicon substrates were plasma treated before being coated by plasma-polymerized hexamethyldisilazane (pp-HMDSN) thin films. The pretreatments included oxygen (O₂), SF₆ and argon (Ar) plasmas. The effect of these pretreatments on the properties of deposited thin films was studied, including film thickness, morphology and photoluminescence (PL). The deposition of pp-HMDSN thin films was performed using plasma enhanced chemical vapor deposition (PECVD). It was found that the substrate pretreatment induces an increase of film thickness, and the morphology of the deposited thin film follows that of the treated substrate, while the intensity of PL increases due to change of nanostructure accompanied with roughness and thin film thickness increase.

In this paper, Cd_xZn_1−xS thin films were deposited on glass substrates by chemical bath deposition (CBD), and the effects of different concentrations of ammonia water on the morphology, structure and optical properties of the films were studied. Cd_xZn_1−xS thin films have hexagonal crystal structure, the transmittance is above 75% in the visible range, and the optical band gap is between 2.6 eV and 2.9 eV The influence of the experimental group of deposited thin films on the performance parameters of the Cu(In,Ga)Se₂ (CIGS) battery was studied by simulation with solar cell capacitance simulator (SCAPS-1D) software, and the cell efficiency reached 22.42%. It is introduced that the buffer layer prepared in this experiment is feasible in CIGS solar cells.

This paper considers the physical layer security (PLS) performance analysis of an unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) communication system over Fisher-Snedecor (F) distributed turbulence fading channel. We consider the combined effects of atmospheric turbulence, pointing error (PE), atmospheric attenuation, and angle-of-arrival (AOA) fluctuations on the channel. The communication between two legitimate peers in the presence of an external eavesdropper is studied from the perspective of communication theory security. Specifically, under two different eavesdropping scenarios where an eavesdropper is located close to the legitimate transmitter or receiver, we derive the exact closed-form expressions of the secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC), respectively. These analytical results are validated through Monte Carlo simulations. Furthermore, we also analyze the impact of various link parameters for both the main channel and the eavesdropping channel on the system performance.

To address the considerable complexity of the successive cancellation list flip (SCLF) decoding algorithm for polar codes, a partitioned parity check (PC)-aided SCLF decoding algorithm for polar codes based on error distribution of the critical set (CS) (ED-PC-SCLF decoding algorithm) is proposed. The algorithm segments polar codes into several partitions, considering the cumulative likelihood of the initial erroneous occurrence within the CS. For each partition, PC codes are employed to detect and flip the erroneous non-frozen bits. To improve the competitiveness of the correct path, path pruning during decoding is incorporated to only retain the best path per partition, thus the bit-flipping accuracy is enhanced. Additionally, a new partition of the flip set is designed according to re-decoding iterations. The experimental results reveal that the proposed ED-PC-SCLF decoding algorithm is superior to the dynamic SCLF (D-SCLF) decoding algorithm and the SCLF based on distributed parity check codes (DPC-SCLF) decoding algorithm in both the error correction performance and the complexity.

Online monitoring of end-tidal carbon dioxide (EtCO₂) concentration held substantial clinical diagnostic value, as it provided insight into a patient’s respiratory and metabolic status. The wavelength modulation spectroscopy (WMS) method, due to real-time capability, high precision, and excellent gas selectivity, was widely used for the measurement of EtCO₂ concentration. The Beer-Lambert law was approximated using a first-order Taylor series in traditional WMS methods, resulting in a strong linear relationship between gas concentration and the second harmonic amplitude. However, the measuring errors increased with higher gas sample concentrations, particularly when the concentrations exceeded 10%. Therefore, in this study, an optimized WMS method was proposed, which was innovative in that it adjusted the phase of the lock-in signal to account for the phase shift caused by laser linear modulation. This approach eliminated the reliance on the first-order Taylor series approximation of the Beer-Lambert law and mitigated the influence of the laser linear modulation coefficient. Subsequently, a series of CO₂ concentration gradients (≤20%) were used for detection experiments employing the second harmonic method, the 2f/1f method, and the optimized WMS method. The experimental results demonstrated a clear linear relationship between gas concentration and second harmonic amplitude. For the lower gas concentration range (1% to 5%), the 2f/1f method demonstrated the highest measurement accuracy, with errors less than 0.1%, while the optimized WMS method also performed well, with errors not exceeding 0.2%. However, for higher concentrations (5% to 20%), the optimized WMS method exhibited significantly smaller errors and remained stable at around 0.2%, while the errors of the other methods increased substantially. Therefore, the optimized WMS method achieved high measurement accuracy even in high-absorbance EtCO₂ measurements, highlighting its superiority in wide-range gas concentration detection.

The laser detection technology based on orbital angular momentum (OAM) beam has been rapidly developed, but it is restricted by many factors from being applied in practice. One of the most significant issues is that it is greatly affected by environmental conditions. When it is applied in the fog environment, the signal-to-noise ratio (SNR) of the echo signal will be seriously reduced due to the interference of environmental noise. A comprehensive study on the forward propagation and backscattering characteristic evolution theory of OAM transmission through the atmospheric fog environment is essential to formulate corresponding strategies to improve its environmental adaptability. In this work, we proposed a light field initialization method based on the acceptance-rejection method (ARM) which can convert the light field to photon flow. By combining this method with the electric Monte-Carlo (EMC), we established a propagation dynamics analysis model of OAM beam in a fog environment to reveal the propagation and evolution process of OAM beam in a complex environment. This work provides the theoretical and technical support for improving the applicability and detection accuracy of OAM laser detection technology in a complex environment. Furthermore, by combining with other models, this model can be updated for analysis of the transmission dynamics of multidimensional modulated light field under more complex environment conditions, such as foggy, smoky, and rainy environments, which can help to improve the performance of free space optical communication, Lidar, and laser energy delivery systems.

This study proposes the enhanced line-detection adaptive you only look once (ELA-YOLO), an enhanced YOLOv8-based object detection algorithm, to improve the identification and classification of critical power components. By integrating efficient multi-scale attention (EMA) into redesigned cross stage partial feature fusion (C2f) modules (C2f_EMA), the backbone network achieves dynamic multi-scale feature fusion. The neck network is further optimized through asymmetric padding convolution (APConv) in C2f_AP modules, enhancing spatial feature integration. Additionally, the large selective kernel (LSK) attention mechanism strengthens context-aware feature extraction capabilities. Experimental results demonstrate that ELA-YOLO outperforms YOLOv8s with a 2.8% improvement in mean average precision at intersection over union threshold 0.50 (mAP₅₀) while incurring only a 4.5% computational overhead, establishing an optimal balance between detection accuracy and operational efficiency for real-world power equipment inspection scenarios.

To address the limitations of single-source localization methods in complex indoor environments, such as insufficient accuracy and stability, this paper proposes an indoor localization method based on multi-modal information fusion of Wi-Fi channel state information (CSI) fingerprint images and ZigBee received signal strength indication (RSSI). First, Hampel filtering is applied to preprocess CSI signals, and both amplitude and phase information of CSI are combined to form high-resolution image fingerprint data. For RSSI signals, data packets collected by ZigBee sensor networks are processed through outlier removal and matrix transformation to generate corresponding fingerprint data. Inspired by image classification tasks, a lightweight efficient channel attention convolutional neural network (ECA-CNN) is designed to extract and train features from CSI fingerprint images, while a transformer network is utilized to train RSSI fingerprint data. Finally, a soft voting method integrates the fingerprint databases from both models to produce classification outputs. Experimental results demonstrate that this method significantly improves localization accuracy and robustness in indoor environments, effectively overcoming the limitations of single-source localization.

To address the low accuracy of traditional stereo matching methods in depth-discontinuous and weak-texture regions, we propose an improved algorithm based on dynamic multi-feature fusion and dual-branch adaptive aggregation. A nonlinear weighting function dynamically integrates noise-resistant Re-Census, multi-directional gradient and Lab-color costs. Distinct arm extension rules are applied to weak texture and depth-discontinuous areas, enabling a dual-branch adaptive aggregation that adapts to local scene characteristics. Disparity estimation follows a winner takes all (WTA) strategy. The ultimate disparity map is generated through a region-based disparity optimization module and multiple optimization processes. Experimental results on the Middlebury dataset indicate a 16.1% relative decrease in the mismatch rate compared to the baseline. Evaluations on the KITTI dataset and real-world scenes captured with a ZED 2i camera further demonstrate the robustness of the proposed algorithm.

This study establishes a coupled fuselage-nozzle-plume model based on thermal-mechanical coupling mechanisms to analyze jet aircraft infrared characteristics. Numerical simulations reveal that skin temperature under non-afterburner flight conditions increases nonlinearly with Mach number and decreases gradiently with altitude, while nozzle thermal disturbance creates jet-like plume temperature attenuation. The 3–5 µm radiation primarily originates from high-temperature nozzle components, whereas 8–14 µm band exhibits stronger sensitivity to skin temperature variations, demonstrating superior omnidirectional detection potential. Information entropy analysis shows long-wave imaging exceeds medium-wave by two orders of magnitude, attributed to enhanced grayscale uniformity. These findings provide critical theoretical support for infrared signature analysis and detection technology development under complex operational conditions.