In response to the demand for real-time non-destructive detection of delamination defects in curved structure carbon fiber–reinforced polymer (CFRP), an anisotropic CFRP model was constructed based on the laser thermal elastic effect, and the generation and propagation of ultrasonic waves inside CFPR were analyzed. On this basis, the orientation information of the delamination defect is obtained by calculating the time it takes for the ultrasonic transverse wave to reach the CFRP surface probe after encountering the delamination defect. When the delamination defect with the size of 1×0.2 mm is located between the first and second layers, the detection errors at different probe positions are 0.48% and 0.64%, respectively. Moreover, when the defect is located between the second and third layers, the detection error can also be controlled within 1%.

We proposed and demonstrated a tunable injection-locked laser, which can be locked in a wide range. It can also be used in cascaded injection locking lasers to decrease the influence of the temperature drift. Based on this technology, a photonic scanning receiver is proposed to implement wide-range microwave frequency measurement. The receiver applies optical frequency scanning to simplify the channelization. In the experiment, the receiver can measure weak microwave signals within the frequency of 16.5 GHz.

A machine learning method designing flat broadband erbium-doped fiber amplifier (EDFA) + Raman hybrid amplifier was demonstrated. First, we trained a neural network (NN) using data consisting of Raman amplifier pump parameters and gains arranged in descending, ascending, and random order. This trained NN is utilized to calculate pump parameters based on the target gain of the hybrid amplifier. Then, the gain accuracy is optimized through the application of fitting equations that establish a relationship between the adjustment power of Raman’s pump and the gain ratio of the EDFA/Raman amplifier. The optimized results show that when using descending datasets, the error of output power is minimized, with means of root-mean-square-error and maximum error of 0.24 and 0.54 mW, respectively. Its average flatness is not significantly different from the results obtained using ascending and randomly arranged training data. Second, we divided the training data into five parts according to gain ratio of EDFA and Raman amplifier. The accuracy deteriorates when training the NN with a portion of the data, but the accuracy could be enhanced by repeatedly training the NN with the same data. Our results contribute to the construction of intelligent fiber optic transmission networks.

By fully considering display characteristics, an innovative multi-primary-color light-emitting diode (LED) display scheme is proposed aimed at achieving wider color gamut coverage (CGC) and lower blue light hazard efficiency of radiation (BLHER) while meeting the requirements for melanopic efficacy of luminous radiation (MELR). The multi-verse optimizer algorithm is employed to optimize the peak wavelengths and luminous proportions of each primary color LEDs. Results show that when MELR is below 1.5, the BLHER is as low as 0.08, and the CGC exceeds 0.98 under the Rec.2020 standard for the multi-primary-color LED display, which is significantly outperforming BLHER of 0.16 and CGC of 89% for the three-primary-color LED display. Furthermore, it is demonstrated the four-primary-color LED display has slightly better performance in BLHER at night compared with the five-primary-color LED display scheme. As a result, when the number of primary colors reaches 4, the increase in the number of LEDs cannot significantly improve the display performance. Therefore, if the gamut area is not pursued very much, the four-primary-color LED display is the best display scheme with high-cost performance.

Underwater visible light communication (UVLC) has the advantages of high speed, low latency, and high confidentiality. However, the signal transmission is susceptible to light-emitting diode (LED) modulation bandwidth, non-linear effects of LEDs, and underwater channels. Neural networks, capable of addressing complex nonlinear problems, are increasingly applied to signal equalization in visible light communication. It is found that multilayer perceptron (MLP) is capable of extracting spectral features and nonlinear relationships, and gated recurrent unit (GRU) is capable of handling timing correlation and channel fading problems. Therefore, we propose a GRU-MLP model as a post-equalizer for the UVLC system and experiment using orthogonal frequency division multiplexing modulated signals on a 60-cm underwater experimental platform. The results show that the GRU-MLP equalizer can extend the system transmission bandwidth by 47% higher than the bidirectional gated recurrent unit (BIGRU) and long short-term memory (LSTM) equalizer when only limited by the LED bandwidth; under the influence of the underwater optical channel, the performance of GRU-MLP is similar to that of BIGRU and LSTM. The bit error rate of the GRU-MLP algorithm is significantly lower than other algorithms under the combined effect of two factors. In summary, GRU-MLP demonstrates superior equalization performance in bandwidth-constrained complex channel environments.