Mode-locking and wavelength-tuning of a NPR fiber laser based on optical speckle.

Passively mode-locked fiber lasers based on nonlinear polarization rotation (NPR) have been widely used due to their ability to produce short pulses with high peak power. Nevertheless, environmental perturbations can influence the mode-locked state, making it a challenge for the practical implementation. Therefore, researchers are searching for assessment criteria to quickly assist and maintain mode-locking of NPR fiber lasers. Speckle patterns containing spectral information can be generated when the laser transmits through a scattering medium, which can serve as indicators of the mode-locked state. The mode-locked regions are confined to the area close to the minimum texture contrast of speckle patterns. Based on these characteristics, we manually simulate the automatic mode-locking (AML). In addition, we utilize convolutional neural networks (CNNs) to recognize speckle patterns of wavelength tunable lasers and determine the center wavelength.

Yb-doped mode-locked fiber laser with a hybrid structure of NPR and a Lyot filter as the saturable absorber.

Passively mode-locked fiber lasers based on a nonlinear polarization rotation (NPR) have attracted much attention due to their ability to generate short pulses with wide spectra and high peak power. However, environmental perturbations can easily cause the lasers to lose the mode-locked state and make it a challenge for practical application. The aim of this research is to improve the laser stability by inserting a Lyot filter into the mode-locked laser cavity. The experimental results indicate that the mode-locked state can be maintained when the radius of the fiber loop is changed from 7.5 to 1.5 cm, while the signal-to-noise ratio of the fundamental frequency remains almost the same. The tunability of the output power can be achieved by adding a half-wave plate (HWP) in the laser cavity without changing the pump power, while the mode-locked state remains stable. By adjusting the angle of the HWP2, the output power can be adjusted from 3.36 to 66.5 mW at repetition rate of 29.7 MHz.

Ultrafast vortex arrays generated from a mode-locked oscillator with dispersion management.

Herein, we demonstrate the generation of optical vortex arrays pulses using a Sagnac common-path interferometric vortex generator. Hermite-Gaussian (HG) modes with different orders are initially obtained from a SESAM mode-locked laser in the positive dispersion regime. Then, in the interferometric vortex generator, by controlling the phase difference and sheering displacement between two HG modes, optical vortex pulses with different numbers of phase singularities are generated through superposition. The generated HG10 mode has a pulse width of 2 ps and maximum energy of 0.75 nJ. One-dimensional vortex arrays and triangular vortex arrays are also generated, which are formed by HGm0 and HG0n modes, respectively. This work has potential applications in the massive manipulation of microparticles, optical communication, and so on.

1024-ary composite OAM shift keying for free-space optical communication system decoded by a two-step neural network.

The demand for high-dimensional encoding techniques for communication systems is increasing. Vortex beams carrying orbital angular momentum (OAM) provide new degrees of freedom for optical communication. In this study, we propose an approach for increasing the channel capacity of free-space optical communication systems by integrating superimposed orbital angular momentum (OAM) states and deep learning techniques. We generate composite vortex beams with topological charges ranging from -4 to 8 and radial coefficients ranging from 0 to 3. A phase difference among each OAM state is introduced to significantly increase the number of available superimposed states, achieving up to 1024-ary codes with distinct features. To accurately decode the high-dimensional codes, we propose a two-step convolutional neural network (CNN). The first step is to make a coarse classification of the codes, while the second step is to finely identify the code and achieve decoding. Our proposed method demonstrates 100% accuracy achieved for the coarse classification after 7 epochs, 100% accuracy achieved for the fine identification after 12 epochs, and 99.84% accuracy achieved for testing, which is much faster and more accurate than one-step decoding. To demonstrate the feasibility of our method, we successfully transmitted a 24-bit true-color Peppers image once with a resolution of 64 × 64 in the laboratory, yielding a bit error rate of 0.

Light scattering control with the two-step focusing method based on neural networks and multi-pixel coding.

Focusing light through scattering media is essential for high-resolution optical imaging and deep penetration. Here, a two-step focusing method based on neural networks (NNs) and multi-pixel coding is proposed to achieve high-quality focusing with theoretical maximum enhancement. In the first step, a single-layer neural network (SLNN) is used to obtain the initial mask, which can be used to focus with a moderate enhancement. In the second step, we use multi-pixel coding to encode the initial mask. The coded masks and their corresponding speckle patterns are used to train another SLNN to get the final mask and achieve high-quality focusing. In this experiment, for a mask of 16 × 16 modulation units, in the case of using 8 pixels in a modulation unit, focus with the enhancement of 40.3 (only 0.44 less than the theoretical value) has been achieved with 3000 pictures (1000 pictures in the first step and 2000 pictures in the second step). Compared with the case of employing only the initial mask and the direct multi-pixel encoded mask, the enhancement is increased by 220% and 24%. The proposed method provides a new idea for improving the focusing effect through the scattering media using NNs.

Common Sense Model program on illness perceptions in patients with impaired awareness of hypoglycemia.

Background: Illness perceptions are important for patients with insulin-treated type 2 diabetes mellitus (T2DM) and impaired awareness of hypoglycemia (IAH), as they determine health-related behaviors and motivations. Patients with IAH in many countries have poor illness perception, and there is a paucity of research exploring the effectiveness of Common Sense Model (CSM)-based interventions in this population.Objective: To investigate the effects of a CSM-based intervention program on perceptions of illness in patients with insulin-treated T2DM and IAH.Design: Quasi-randomized controlled trial.Methods: 78 patients with IAH receiving routine care were included. The intervention group (n = 39) participated in a CSM-based program, whereas the control group (n = 39) did not. Illness perceptions, coping styles, hypoglycemia fear, and awareness of hypoglycemia at baseline, 1, and 3 months were analyzed and compared between the two groups.Results: The intervention group exhibited significant improvements in consequences (ß = -1.615, P = 0.032); personal control (ß = -1.897, P = 0.006); treatment control (ß = -1.274, P = 0.046); and positive coping style (ß = 4.872, P = 0.002) at the 3-month follow-up, and timeline (ß = 2.769, P = 0.004) at the 1-month follow-up. Hypoglycemia fear and awareness were not significantly improved in the intervention group compared with the control group. No intervention-related adverse events were observed.Conclusions: A CSM-based intervention program can modify illness perceptions to an extent and improve the positive coping style in patients with IAH.Impact statementNurses should conduct a CSM-based intervention program to help patients with IAH improve illness perceptions.