Horowitzbowden9254

In this letter, we proposed a deep learning wavefront sensing approach for the Shack-Hartmann sensors (SHWFS) to predict the wavefront from sub-aperture images without centroid calculation directly. This method can accurately reconstruct high spatial frequency wavefronts with fewer sub-apertures, breaking the limitation of d/r0 ≈ 1 (d is the diameter of sub-apertures and r0 is the atmospheric coherent length) when using SHWFS to detect atmospheric turbulence. Also, we used transfer learning to accelerate the training process, reducing training time by 98.4% compared to deep learning-based methods. Numerical simulations were employed to validate our approach, and the mean residual wavefront root-mean-square (RMS) is 0.08λ. The proposed method provides a new direction to detect atmospheric turbulence using SHWFS.Guided acoustic Brillouin (GAWBS) noise is measured using a novel, homodyne measurement technique for four commonly used fibers in long-distance optical transmission systems. The measurements are made with single spans and then shown to be consistent with separate multi-span long-distance measurements. The inverse dependence of the GAWBS noise on the fiber effective area is confirmed by comparing different fibers with the effective area varying between 80 µm2 and 150 µm2. The line broadening effect of the coating is observed, and the correlation between the width of the GAWBS peaks to the acoustic mode profile is confirmed. An extensive model of the GAWBS noise in long-distance fibers is presented, including corrections to some commonly repeated mistakes in previous reports. It is established through the model and verified with the measurements that the depolarized scattering caused by TR2m modes contributes twice as much to the optical noise in the orthogonal polarization to the original source, as it does to the noise in parallel polarization. Using this relationship, the polarized and depolarized contributions to the measured GAWBS noise is separated for the first time. As a result, a direct comparison between the theory and the measured GAWBS noise spectrum is shown for the first time with excellent agreement. It is confirmed that the total GAWBS noise can be calculated from fiber parameters under certain assumptions. It is predicted that the level of depolarized GAWBS noise created by the fiber may depend on the polarization diffusion length, and consequently, possible ways to reduce GAWBS noise are proposed. Using the developed theory, dependence of GAWBS noise on the location of the core is calculated to show that multi-core fibers would have a similar level of GAWBS noise no matter where their cores are positioned.Exceptional points (EPs) have revealed a lot of fundamental physics and promise many important applications. The effect of system nonlinearity on the property of EPs is yet to be well studied. Here, we propose an optical system with nonlinear dissipation to achieve a nonreciprocal EP. Our system consists of a linear whispering-gallery-mode microresonator (WGMR) coupling to a WGMR with nonlinear dissipation. In our system, the condition of EP appearance is dependent on the field intensity in the nonlinear WGMR. Due to the chirality of intracavity field intensity, the EPs and the transmission of the system can be nonreciprocal. Our work may pave the way to exploit nonreciprocal EP for optical information processing.Lossy-mode-resonance (LMR) is a surface plasmon resonance (SPR)-analogue optical phenomenon, which is sensitive to the surrounding environment variations and can be considered as an important detection signal in biochemical sensors. Compared with the SPR sensor which can only operate under transverse magnetic (TM)-polarized light, the LMR sensor shows a more excellent application prospect and can operate in both TM- and transverse electric (TE)-polarized light. In this work, a CH3NH3PbBr3-based LMR configuration is proposed to apply in optical sensors. When the incident light is in TM mode, the preferred way to improve the performance of the LMR sensor is optimizing the thickness of the matching layer, and the highest sensitivity of 11382 refractive index unit (RIU-1) is achieved, which is more than 200 times larger than that of the conventional Au-based SPR sensor; when the incident light is in TE mode, it is more advantageous to improve the properties of LMR sensor by optimizing the thickness of CH3NH3PbBr3 layer, and a high sensitivity of 21697 RIU-1 is obtained. With such high sensitivity, we believe that the CH3NH3PbBr3-based LMR sensor will find potential applications in biology, medicine, chemistry and other fields.Point spread function (PSF) of ghost imaging (GI) with pseudo-thermal light source doesn't satisfy the property of space translation invariance and existing GI linear reconstruction algorithms offer images with low quality when the measurement process doesn't reach ergodic. By modifying the intensity value of the speckle patterns recorded by the camera in the reference path, the property of PSF can be optimized and a linear reconstruction method called optimized ghost imaging (OGI) is proposed to stably recover the object's image even in the measurements below Nyquist limit. In comparison with existing GI linear reconstruction algorithms, both the simulated and experimental results demonstrate that the image's SNR can be significantly enhanced by OGI especially when the sampling ratio is larger than 0.68 and the detection SNR is greater than 20 dB.We theoretically propose the magneto-optically reorientation-induced image reconstruction in bulk nematic liquid crystals (NLCs). The underlying signals are reinforced and recovered at the expense of scattering noise under reorientation-induced self-focusing nonlinearity. The intensity perturbation gain is derived and the numerical results are presented to show the response of NLC molecules to the diffusive images. The nonlinear image recovery is influenced by the input light intensity, the magnetic field direction, and the correlation length. see more The results suggest an alternative approach to detect noisy images and promote the application of NLCs in image processing.