We introduced a reference interferometer in linear frequency modulation (LFM)-based heterodyne-type interferometric fiber-optic hydrophone system, and use an adaptive filtering technique to suppress the light source phase noise and improve system tolerance for laser linewidth.
Orbital Angular Momentum (OAM) beam is a vortex beam with helical phase term. The OAM multiplexing can make the beam carry more information. However, in practical communication, the scattering effect of the channel may lead to the distortion of the phase of the optical field. In this paper, an optimal spectral method based on the scaling is proposed for the inversion of the scattered light field, and the BFGS algorithm is used to iteratively optimize the initialized retrieved light field. Simulation results show that when the sampling rate is 4, the correlation of optical field inversion increases to more than 0.9999. The inversion error is reduced by $10^{-10}$ times compared with the method combining the speckle-correlation scattering matrix (SSM) and Gerchberg-Saxton (GS) algorithm, achieving a high-fidelity phase recovery.
Photonic neural networks use photons instead of electrons to perform artificial intelligence (AI) tasks, with the advantage of high-speed, low-power information processing. However, existing architectures are designed for a single task, which cannot reuse multiple tasks in parallel in a single system because the competition among different tasks will degrade the model performance. In this paper, a novel optical multi-task learning system is proposed by designing a multi-wavelength diffractive photonic neural network (DPNN) using a joint optimization method. By encoding the input of multiple tasks into multi-wavelength channels, the system can significantly reduce the competition to execute multi-tasks in parallel with high precision. We design the two-task and four-task DPNNs with two and four spectral channels respectively, for classifying different inputs from the EMNIST, KMNIST, FMNIST, and MNIST databases. Numerical evaluations show that for multitask learning, multi-wavelength DPNNs achieve significantly higher classification accuracies than single-wavelength DPNNs under the same network size. Moreover, as the network size increases, the classification accuracy of multi-wavelength DPNNs is comparable to that of individually training multiple single-wavelength DPNNs to perform multiple tasks separately. Our work provides a proven technical solution for developing high-throughput neuromorphic photonic computing and more general artificial intelligence systems to perform multiple tasks in parallel.
A fragmentation-aware lightpath provisioning model, which suppresses the fragmentation caused by allocating spectrum slots to lightpaths and due to inter-core crosstalk, is proposed. The proposed model suppresses the blocking probability in spectrally-spatially elastic optical networks.
We studied the time domain characteristic in whispering-gallery mode laser and whispering-gallery Fabry-Perot hybrid laser. In the hybrid cavity laser, mode competition at mode hopping position is intense, and the time domain signals of the competition modes oscillate alternately and complementarily. However, the time domain signals of whispering-gallery mode laser are relatively stable.
