Optical Review最新文献

We investigate the photoelectron momentum distributions (PEMDs) of hydrogen atoms ionized by a parallel two-color chirped laser field, employing the semi-classical two-step model (SCTS). By modulating the chirp parameter and adjusting the ionization time offset on the attosecond timescale, we demonstrate the ability to control electronic interference across different orbitals. We present PEMDs images obtained over a single time window to explore the electron momentum dynamics, and analyze the classical trajectories to uncover the underlying interference mechanisms. Furthermore, we show the significant impact of the carrier-envelope phase (CEP) on the PEMDs. Offering a novel approach for controlling electron dynamics with attosecond precision, these findings have potential applications in attosecond imaging, ultrafast spectroscopy, and quantum control. In particular, the possibility of manipulating electron interference is opened, which paves the way for investigating electron dynamics in atoms and molecules on ultrafast timescales, with far-reaching implications for quantum information processing, ultrafast chemistry, and advanced spectroscopic techniques.

A rigorous treatment of the reflection and transmission of an electromagnetic wave by a superconductor in its ground state is reported. Starting from Maxwell’s equations, a calculation on the reflection and refraction at the interface between a metallic and superconducting plate is made. For the metal, a complex conductivity (sigma ^{textrm{Re}} + isigma ^{textrm{Im}}) derived from the Drude-Zener treatment of the conduction electrons, while for the superconductor the London equation is incorporated into the equations. Thereafter, we consider the case when the first medium is either helium atmosphere or vacuum by setting the dielectric constant and magnetic permeability to unity, and by taking the limit (sigma ^{textrm{Re}} rightarrow 0) and (sigma ^{textrm{Im}} rightarrow 0). The reflection and transmission coefficients thus obtained are, 1.00 and 2.00, respectively over a wide frequency range from 10 GHz to 1 THz for the case when the magnetic field of the wave ({varvec{H}}) is perpendicular to the plane of incidence. The coefficients when ({varvec{H}}) lies in the plane of incidence are also calculated. These findings are discussed in relation to total reflection.

In this paper, an all-dielectric metasurface refractive index sensor based on Fano resonance is proposed. The unit structure consists of two silicon ellipsoidal columns with rectangular holes deposited on a silicon dioxide substrate. Based on the theory of bound states in the continuum (BIC), the transition from BIC to quasi-bound states in the continuum (QBIC) can be achieved by modifying the tilting angle of the elliptic column, thereby enabling the excitation of the sharp Fano resonance at 1183.7 nm and 1298.54 nm wavelengths, which in turn yields a maximum quality factor (Q-factor) of 15,575. The near-field distribution indicates that both QBICs are predominantly governed by magnetic dipole (MD) modes. Furthermore, the refractive index sensing performance of the metasurface structure is analyzed by varying the refractive index of the surrounding ambient medium, resulting in a maximum sensitivity (S) of 563 nm/RIU. The sensing characteristics of the structure for seawater temperature and salinity are subsequently investigated, and the maximum sensitivities are determined to be 52 pm/K and 0.265 nm/‰, which demonstrated excellent sensing performance. The proposed metasurface structure has a wide range of potential applications in fields, such as biosensing, optical switching, marine environment monitoring and sensing, and more.

Infrared sensors are widely used in human action recognition because of their low light influence and excellent privacy protection. However, the traditional deep learning networks and training or testing methods tend to fall into the trap of local optimum because of the similarity between infrared image classes and the lack of discriminative features such as texture and depth, and thus obtain poor recognition results. To address this issue, we propose a novel human action recognition method based on similarity evaluation. This method innovatively transforms the traditional training and testing (verification) mode. First, we use a feature-to-feature training method to make the network pay more attention to the behavioral information that distinguishes the classes. Second, we design a Integrate Channel Attention Module(ICA) to enable Siamese network to focus on the areas of interest. Finally, we propose the Multimodal Similarity Evaluation Module (MSE). The module aims to address the fuzzy matching problem of feature areas. The contrast experiment results show that our method outperforms existing mainstream methods on several benchmark datasets. The excellent accuracy provides an innovative method for addressing various problems related to high similarity between classes.

Turbulence effect is an important factor leading to performance degradation of underwater wireless optical communication (UWOC) systems. In order to improve the reliability of the communication system, this paper adopts a UWOC system based on low-density parity-check (LDPC) code and pulse position modulation (PPM) to overcome the effects caused by turbulence, and proposes an improved Offset Min-Sum algorithm to combat turbulent channel interference. The generalized gamma distribution (GGD) distribution is used as the channel model, and the relationship between BER, SNR, different of LDPC decoding algorithms, and PPM order is quantified by simulation for different turbulence intensities.The results show that the performance of the improved OMS algorithm is optimal under weak, moderate, and strong turbulence; at ({sigma }_{I}^{2})=2.0399 and BER = 10^–6, the improved OMS algorithm with 4-PPM, 8-PPM, and 16-PPM modulation has a coding gain of 0.16 dB, 1.2 dB, and 4 dB, respectively, compared with the OMS algorithm.

In this paper, we report the theory of a self-coupling laser sensor array system for improving the signal-frequency inversion problem and the results of velocity measurements with this system. A self-coupling laser sensor is an interferometer that uses optical beats produced by the interference between the light returned from the laser target and the light in the active layer of the laser diode. Using wavelength modulation, this system can simultaneously measure multiple metrological quantities, such as the absolute distance to a target and velocity of a target. However, in a self-coupling laser sensor using wavelength modulation, the signal frequency is inverted and becomes negative if the Doppler shift of the returned light owing to the movement of the target is larger than the signal frequency when the target is stopped. In this case, it is impossible to detect the positive or negative value of the signal directly, resulting in a large measurement error. This has been regarded as a problem that limits the measurement dynamic range of the modulated self-coupling laser sensors. In this study, we propose a system to accurately detect the signal-frequency inversion and improve the measurement dynamic range. The proposed system detects the positive or negative value of the signal frequency from the relationship between the velocity and signal frequency obtained by irradiating multiple beams with different modulation frequencies and then recalculates the accurate measurement value. The measurement results reveal that this system can accurately measure the moving velocity of a target, even when the signal frequency is inverted.

In this study, we present a through-focus re-radiation simulation aimed at detecting scattering from semiconductor structures. We employ the beam synthesis propagation (BSP) module within the finite-difference time-domain (FDTD) method, optimizing the simulation of optical systems by reducing time and computational resources typically required for imaging and illumination. To validate the approach, we simulated scattering from Silicon nitride (Si₃N₄) lines on a silicon (Si) substrate with various defect sizes and types at a 193 nm wavelength. The results demonstrated the detection of specific defect signals and identified the limitations of detectable defect sizes. These findings are intended to serve as pre-processing data for predicting outcomes in through-focus scanning optical microscopy (TSOM) imaging.

Injection-molded lenses have an inhomogeneous stress-induced birefringence that can degrade optical performance. This paper presents a new approach for measuring and analyzing inhomogeneous anisotropic samples. The birefringence distribution is characterized by 3D index ellipsoids, and a tomographic reconstruction of this 3D distribution is developed from a linear line projection relationship between the spatially varying index ellipsoids and tomographic polarimetry. This forward representation enables a tensor-valued backprojection for reconstructing the birefringence distribution of an inhomogeneous anisotropic sample. In this approach, each index ellipsoid is represented by a Hermitian matrix, and the 3D birefringence distribution is defined as the distribution of these matrices. This paper is centered on the introduction of the fundamental algorithm and the presentation of a general solution by applying the Radon transform and the backprojection to a tensor field, without requiring specific parameters such as stress fields. Consequently, the computational approach presented in this paper demonstrates that, using 60 tomographic views, reconstruction errors for parameters that characterize spatially varying index ellipsoids remain less than 5%. Here, the error is defined as the ratio of reconstruction variation to the respective maximum values of the original distributions.

The demand for fast, accurate, and cost-effective methods for three-dimensional shape and color measurements has been increasing. Ideally, both the shape and color of an object should be obtained in a single shot. Color fringe projection profilometry allows single-shot 3D shape measurement; however, it faces challenges when applied to colored objects. The fringe patterns are attenuated, leading to inaccuracies in shape measurement, and the fringes obscure the object's color information. This study proposes a novel approach to address these challenges by using a deep learning-based ResUNet model. Our method uses two independently trained ResUNets to correct fringe distortions for improved shape measurement accuracy and to remove fringe patterns for color information extraction from the same captured images. The simulation and experimental results demonstrate the effectiveness and applicability of this approach for single-shot 3D shape and color measurements.

In various sports, the motion information of athletes is often measured for monitoring and evaluation, and the direct use of common optical equipment to determine the position and orientation of moving objects according to geometric information in a scene has become an important research topic in image understanding. As many sports grounds have a center circle and halfway line, we propose an algorithm that first obtains constraints on the image of the circle center by using homography based on the geometric properties of the circle perimeter and corresponding circumferential angle. Then, the vanishing line is obtained from the image of the circle center and the complete circle image based on the pole-polar relation with respect to the camera internal parameters. By decomposing the circle image, the camera external parameters are obtained to determine the homography matrix from a spatial point to an image point. The camera at the edge of the moving field is calibrated according to the duality of the conic and homography matrices. Using the homography matrix between a point on the moving ground plane and the corresponding image point, the coordinates of the measured point can be recovered to estimate the pose (i.e., position and orientation) of a moving target.