Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing. However, their effectiveness is often compromised by frequency-selective responses caused by fabrication, transmission, and environmental factors. To mitigate these issues, this study introduces a deep reinforcement learning calibration (DRC) method inspired by the deep deterministic policy gradient training strategy. This method continuously and autonomously learns from the system, effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods. In systems based on dispersion compensating fiber, micro-ring resonator array, and Mach-Zehnder interferometer array that use multi-wavelength optical carriers as the light source, the DRC method enables the completion of the corresponding signal processing functions within 21 iterations. This method provides efficient and accurate control, making it suitable for applications such as optical convolution computation acceleration, microwave photonic signal processing, and optical network routing.

Hyperspectral imaging (HSI) technology is able to provide fine spectral and spatial information of objects. It has the ability to discriminate materials and thereby has been used in a wide range of areas. However, traditional HSI strongly depends on the sunlight and hence is restricted to daytime. In this paper, a visible/near-infrared active HSI classification method illuminated by a visible/near-infrared supercontinuum laser is developed for spectra detection and objects imaging in the dark. Besides, a deep-learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active HSI classification. By applying the method to a selection of objects in the dark successfully, we demonstrate that with the active HSI classification method, it is possible to detect objects of interest in practical applications. Correct active HSI classification of different objects further supports the viability of the method for camouflage detection, biomedical alteration detection, cave painting mapping and so on.

Fourier Neural Operators (FNOs) have gained increasing attention for their effectiveness in extracting frequencydomain features and efficiently approximating functions, making them wellsuited for classification tasks. However, the absence of specialized photonic hardware has limited the acceleration of FNO inference. In this study, we introduce what we believe is the first photonic hardware framework dedicated to speeding up the Fourier layer of an FNO. Our approach employs a frequency domain convolutional photonic chip and a micro-ring array chip, achieving 5-bit quantization precision in the inference process. On the Radio ML 2016.10b dataset, our Fourier convolutional neural network achieves a peak identification accuracy of 95.50%, outperforming standard convolution-based networks. These findings highlight the transformative potential of co-designing software and hardware, demonstrating how photonic computing can deliver specialized acceleration for critical AI components and substantially improve inference efficiency. Ultimately, this work lays a foundation for integrating photonic technologies into next-generation AI accelerators, pointing to a promising direction for further research and development in optoelectronic hybrid computing.