Wavefront coding technology employs a phase mask to modulate the phase of incident light, thereby dispersing the laser spot on the detector and achieving laser protection for optical systems. Current research has predominantly concentrated on validating laser damage at a single imaging distance, neglecting the evolution of protective capability across varying distances in the wavefront coding imaging system. To address this limitation, this study establishes a wavefront coding imaging system based on a cubic phase function and experimentally elucidates the variation of laser suppression capacity with transmission distance. Under conditions of pulsed laser-induced point damage in the visible spectrum, a strong correlation is observed between the laser suppression ratio and the laser damage threshold improvement value. Additionally, the NAFNet model is utilized to restore encoded images, resulting in high-fidelity reconstruction. The PSNR for both simulated and experimentally decoded images consistently surpasses 23 dB. Furthermore, under laser irradiation conditions, the model adeptly eliminates laser artifacts and recovers image content. This study possesses considerable practical value for the design and implementation of laser protection mechanisms in optical systems.

In this paper, we report a mode-interference-based approach for the efficient and reliable diameter measurement of micro/nanofibers (MNFs), enabling the in situ monitoring of MNFs fabricated from both single-mode fibers (SMFs) and multimode fibers (MMFs). The proposed method integrates automated signal processing with parameter-corrected flame-brush models, establishing a real-time closed-loop feedback mechanism during the fabrication process. Within the 524–1778 nm range, measurement accuracies better than 8 nm (< 1.25%) for SMF and 5 nm (< 0.78%) for MMF are demonstrated. Furthermore, to address the challenge of reconstructing complex taper profiles, we introduce a one-dimensional convolutional neural network (1D-CNN). Trained on a physics-enhanced data set, this network enables the end-to-end precision measurement of taper morphology. Within the diameter range of 1.9–10 µm, the maximum relative error is maintained below 0.35%, with a maximum absolute error of less than 9 nm. This method demonstrates broad applicability, offering a reliable solution for the fabrication of high-performance MNF-based photonic devices.

This paper proposes a robust design for a free-space optical (FSO) system assisted by an unmanned aerial vehicle (UAV) equipped with an intelligent reflecting surface (IRS), operating under probabilistic malicious jamming. The UAV-carried IRS establishes an auxiliary link when the direct path is blocked. The system experiences composite fading (saturated turbulence, pointing errors, and angle-of-arrival fluctuations), and the jammer's intermittent activity is modeled by a Bernoulli process. We derive a closed-form expression for the average outage probability (OP) under this unified channel-and-jamming model. To address the critical performance-cost trade-off, a bi-objective optimization problem is formulated to jointly minimize the OP and the hardware deployment cost. An alternating optimization (AO) algorithm is proposed to solve the resulting mixed-integer nonlinear programming problem by decoupling it into discrete (number of IRS elements) and continuous (power, angles, apertures) subproblems, which are efficiently handled via integer search and particle swarm optimization, respectively. Simulation results demonstrate that the proposed AO algorithm converges within 15 iterations, substantially faster than genetic algorithm and random search, and achieves a near-optimal trade-off, reducing outage probability by an order of magnitude compared to non-optimized benchmarks while keeping hardware cost within budget.