Search Results (2)

A novel mode-locking method based on nonlinear multimode interference (NLMI) using a distributed large-core (105 μm) graded-index multimode fiber (GIMF)-based saturable absorber (SA) capable of generating four pulse modes is proposed. The distributed SA geometry consists of two GIMFs located at different positions in the resonant cavity. The coupling and joint operation not only facilitate resistance to pulse fragmentation but also provide a sophisticated and widely tunable transmission with saturable and reverse saturable absorption phenomena. Based on this, dissipative soliton (DS), dissipative soliton resonance (DSR), wedge-shaped, and staircase pulses are achieved without additional filters. The DS has accessible output power, pulse energy, bandwidth, and duration of up to 15.33 mW, 2.02 nJ, 22.63 nm, and ~1.68 ps. The DSR has an achievable pulse duration and energy of ~32.39 ns, 30.3 nJ. The dispersion range that allows DS operation is studied, and the dynamics of the evolution from DS to DSR are observed. The versatility, flexibility, and simplicity of the SA device, combined with the possibility of scaling the pulse energy, make it highly attractive for ultrafast optics and nonlinear dynamics. Full article

The use of Synthetic Aperture Sonar (SAS) in autonomous underwater vehicle (AUV) surveys has found applications in archaeological searches, underwater mine detection and wildlife monitoring. However, the easy confusability of natural objects with the target object leads to high false positive rates. To improve detection, the combination of SAS and optical images has recently attracted attention. While SAS data provides a large-scale survey, optical information can help contextualize it. This combination creates the need to match multimodal, optical–acoustic image pairs. The two images are not aligned, and are taken from different angles of view and at different times. As a result, challenges such as the different resolution, scaling and posture of the two sensors need to be overcome. In this research, motivated by the information gain when using both modalities, we turn to statistical exploration for feature analysis to investigate the relationship between the two modalities. In particular, we propose an entropic method for recognizing matching multimodal images of the same object and investigate the probabilistic dependency between the images of the two modalities based on their conditional probabilities. The results on a real dataset of SAS and optical images of the same and different objects on the seafloor confirm our assumption that the conditional probability of SAS images is different from the marginal probability given an optical image, and show a favorable trade-off between detection and false alarm rate that is higher than current benchmarks. For reproducibility, we share our database. Full article