In this work, a dielectric grating/DBR heterostructure has been proposed to create a high-Q hybrid guided mode at nearinfrared (NIR) frequencies. It is found that this nontrivial guided mode can be excited in the dielectric grating/DBR structure, leading to a perfect light transmission with linewidth of about 0.1 nm and Q factor up to 1.3×104. More intriguingly, we show that bound states in the continuum (BIC) can emerge by properly choosing parameters in this resonant regime. The resonant wavelength and Q-factor of this optical mode can be tuned by varying the geometrical parameters, which is polarization-independent under the vertical incident of light. The ultra-narrow linewidth and tunable resonant transmission offered by this simple all-dielectric structure opens new opportunities for developing optical filtering, sensing, and light emitting devices with high-performance.
Particles trapped in optical beams can undergo orbital rotations at the micro/nano-scale and thus find applications in constructing micro-motors/machines, advancing the investigation of micro-rheology, etc. Rather than using the doughnut beams, here we report the orbital rotation of particles in a single Gaussian beam at the wavelength scale. By involving the transverse scattering forces of the light beam in the off-focal plane, the off-axis trapping of nanoparticles is possible and a rotation orbit has been built with small radius even less than the wavelength. Through focusing the incident Gaussian beam with circular polarization, there is the orbital angular momentum converted from the spin angular momentum, and the orbital angular momentum can then drive the particles to undergo orbital rotation. By changing the position of the offfocus plane, the rotation radius and speed can be tuned. The orbital rotation scheme with simple and flexible setup here can find applications in micro-motors and micro-machines.
KEYWORDS: Non line of sight propagation, Image restoration, Point spread functions, Reflection, Speckle, Reconstruction algorithms, Imaging systems, Neural networks, Data modeling, Mathematical optimization
Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.
KEYWORDS: Non line of sight propagation, Image restoration, Point spread functions, Reflection, Speckle, Reconstruction algorithms, Imaging systems, Neural networks, Data modeling, Mathematical optimization
Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.
Strong and narrow-linewidth circular dichroism (CD) spectroscopy promises potential applications in bio-chemical sensing and detection of the weak chirality in natural molecules. Here we proposed a chiral metasurface formed by the asymmetric metal double split ring resonator (DSRR) arrays, the circular dichroism (CD) of which has been investigated. The maximum CD for absorption response of the metasurface can reach 0.61 with an ultra-narrow spectral linewidth of 9.6 nm in the mid-infrared (MIR) band. Our calculation results show that the chiral metasurface can support two surface lattice resonance modes for the left circularly polarized (LCP) and right circularly polarized (RCP) light. The narrow linewidth of CD is enabled by the spin-selective high-Q resonance modes with a differential absorptivity for LCP and RCP light. Our findings shed light on the potential applications in spin-selective perfect optical absorption, high-sensitive polarization detection, and chirality sensing.
Light emitting diode (LED) is widely employed in industrial applications and scientific researches. With a spectrometer, the chromaticity of LED can be measured. However, chromaticity shift will occur due to the broadening effects of the spectrometer. In this paper, an approach is put forward to bandwidth correction for LED chromaticity based on Levenberg-Marquardt algorithm. We compare chromaticity of simulated LED spectra by using the proposed method and differential operator method to bandwidth correction. The experimental results show that the proposed approach achieves an excellent performance in bandwidth correction which proves the effectiveness of the approach. The method has also been tested on true blue LED spectra.
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