Free-space optical (FSO) communication is a promising key technology for large bandwidth, high data rate and cost effective data transmission. However, FSO systems experiences crucial challenges under atmospheric turbulence, pointing errors and eavesdropping threats. The proposed machine learning framework uses generative adversarial networks (GANs) for eavesdropping threats and malicious intrusions to improve the security. The GAN based framework influences a generative model to simulate attacks, such as eavesdropping and jamming, whereas the adversarial model learns to identify and mitigate these threats in real time. By continuously adapting these strategies, the GAN framework enhances the robustness of the FSO communication link. Experimental results show that the proposed framework minimizes interception threats.

In the era of modern technologies, developing effective crypt-coding systems is crucial when it comes to transmitting huge amounts of protected data quickly. Most encrypted image transmission systems do not sufficiently examine the effect of bit mistakes occurring during transmission. This problem is regarded as one that should be addressed by a competent coding scheme. In this paper, we have proposed an image encryption scheme based on generalized Reed–Muller (GRM) codes, QZ synthesis method, and Laguerre–Gaussian vortex beams (LGVB). In the proposed algorithm, GRM codes encode the image and add redundancy to it which increases its error-resistant quality. An encoded image is decomposed into two square images and each image is phase-encoded and modulated using random phase masks. The modulated image is then propagated through the Fourier domain. Vortex Fresnel array and the QZ decomposition operations are used to add security to generate the private keys. The proposed cryptosystem is robust against basic cryptographic attacks. The use of GRM codes adds on error correction capabilities in the cryptosystem. The correlation coefficient of the original and encrypted images is dropped to 0.3% , demonstrating the effectiveness of the encryption in randomizing the image data. System performance is tested by evaluating the mean-squared error, peak signal-to-noise ratio, structural similarity index measure and correlation coefficients.

In this study, we derived a theoretical formula to analyze the self-constructing properties of Hermite non-uniformly correlated (HNUC) beams when encountering an obstacle, focusing on intensity and coherence. Our results reveal that, despite an obstacle, the beam’s intensity distribution continues to display a significant self-focusing phenomenon. Within the focal length range, the lateral intensity distribution remains consistent with unobstructed propagation, regardless of the obstacle’s size. However, outside the self-focusing range, increasing the obstacle size leads to a decrease in the self-reconstructive capability of the intensity distribution, necessitating a longer propagation distance for recovery. Additionally, the presence of obstacles considerably impacts the coherence properties of the beams. When the obstacle is small, the degree of coherence (DOC) experiences a significant reconstructing effect. Our results have potential applications in optical tweezers, microscopy, and optical communication.

A compact plasmonic nanosensor has been described, which consists of a metal–insulator–metal (MIM) waveguide and two symmetric rectangular stubs coupled with a gammadion resonance cavity (GRC) with chiral properties. The transmission characteristics and magnetic field distribution of the MIM waveguide structure are simulated by employing the finite element method (FEM). Results revealed that a Fano resonance is excited in the designed whole waveguide system. The Fano resonance can be tuned by the structural parameters of the waveguide system. The MIM waveguide structure with optimized parameters can be applied in identify different concentrations of glucose solution. The sensitivity achieves up to 945 nm/RIU. This proposed structure has potential applications in the biosensors and optical communications.

To solve the problems of low contrast and less effective information of the low-light images, we proposed an effective information detection algorithm based on the visual perception characteristics of the human eye. First, the original image is double-sided filtered to reduce noise and edge preservation. Then, it is converted into the YUV color gamut, and the brightness mapping function is designed to map the brightness channel Y. During the specific mapping process, the overall dark image and the chiaroscuro image are classified according to the brightness variance of the whole image. Different brightness function mappings are designed for each image according to the visual perception characteristics of the human eye so as to realize adaptive brightness enhancement. Experimental results show that the proposed algorithm outperforms the existing algorithms in terms of subjective human eye experience, compared with histogram equalization and the MSR algorithm. It improves the PSNR (peak signal-to-noise ratio), SSIM (structural similarity index metric), and UQI (universal quality index) image quality evaluation indexes by 19.40, 1.17, and 0.77, respectively.

This manuscript proposes a multi-image security algorithm based on phase truncation and pixel scrambling in the gyrator domain. The algorithm utilizes chaotic maps to improve key strength and phase truncation to provide asymmetric nature to the proposed algorithm. These features make proposed algorithm secure encryption algorithms for multiple images simultaneously. The algorithm utilized affine chaotic map for pixel scrambling. The proposed algorithm is tested with different types of images. The efficacy and robustness of proposed algorithm is tested through statistical and visual attacks such as correlation coefficient, information entropy, mean squared error, peak signal-to-noise ratio, histogram, mesh and correlation distribution plots. Efficacy of proposed algorithm is also analysed using the contamination and cryptographic attack such as known plaintext, chosen plaintext, and brute force attack. Results show that proposed algorithm is robust and secure to use in real-time applications.

We present a novel image encryption approach utilizing a unique combination of Yang–Gu mixing amplitude-phase retrieval and a nonlinear discrete chaotic umbrella map in the Fresnel domain. Our suggested method involves the algorithm of a deterministic phase mask prior to applying the Fresnel transform to the input image. To address the limitations and vulnerabilities of single chaotic map systems, we employ umbrella maps to scramble the modified image’s pixels. This not only expands the range of available controls but also improves the key size. We first encode the scrambled picture, then apply a Fresnel transform, followed by a second application of the phase mask. The resulting image’s phase-truncated component undergoes Yang–Gu mixture amplitude-phase retrieval algorithm in the Fresnel domain. The encryption process employs one-way binary modulation, ensuring easier storage and transmission of the image. Extensive statistical and attack analyses have been performed to evaluate the strength of proposed technique. Moreover, the sensitivity analysis of the scheme has been thoroughly investigated, confirming its effectiveness and reliability of encryption scheme.

This work presents a novel approach for pathogen identification using a one-dimensional annular photonic crystal (APC). The APC structure consists of alternating silicon layers with varying porosities arranged in a periodic pattern. A defect layer has been incorporated for the detection and monitoring of <i>E. Coli</i> bacteria. The optical characteristics of the APC are analyzed by solving Maxwell’s equations and employing the modified transfer matrix method in Cartesian coordinates. The proposed APC design, which features a configuration of (layer a/layer b)<sup>N</sup>/defect layer/(layer a/layer b)<sup>N</sup>, demonstrates promising potential for pathogen detection when using water and <i>E. Coli</i> samples in the defect layer alongside porous silicon in a multilayer stack. All design parameters are meticulously optimized to achieve peak sensor performance. Various performance metrics, including resonant peak position, quality factor, figure of merit, and sensitivity, are calculated. Numerical simulations and theoretical analyses provide insight into the interaction between pathogenic microorganisms and the APC. This research contributes to the development of sensitive and effective optical sensing technologies for pathogen detection across various applications. Our design achieves a sensitivity of 231.4 nm/RIU and a quality factor of 667.5.

To accurately predict the strain field distribution at the assembly position and achieve online calibration of the assembly process, built on an online monitoring system for assembly status based on FBGs network. The installation positions of various FBG sensors were designed through strain field simulation analysis. A strain field prediction model based on support vector machine (SVM) algorithm was designed. It uses FBGs data to predict the strain field distribution at the assembly position for correcting the assembly position and posture. In the calibration experiment, the strain response curves of six FBG sensors were tested, and their linearity was above 0.98. The maximum sensitivity was 2.67 pm/N and the minimum sensitivity was 1.12 pm/N. Three typical assembly anomalies were compared. For different types of assembly anomalies, the response wavelength values and distribution characteristics of each FBG sensor have significant differences. For the same type of assembly anomaly, there is a good linear relationship between the response wavelength values and strain values. In the prediction experiment, the maximum error of strain prediction for the validation set was 4.37 με, and the average error was 2.54 με. The predicted results are close to the training set results.

On the basis of the vector diffraction theory, this article investigates the transverse energy flow distributions of azimuthally polarized Lorentz–Gaussian beams modulated by power order space-variant phase modulation. The findings of the study show that the distribution of transverse energy flow is significantly affected by variations in the power order of the space-variant phase <i>n</i>. We obtained circular distribution, two zone distribution, bullet-shaped distribution, and reverse <i>Z</i>-distribution. Furthermore, it can be observed that the variation of phase change parameter <i>C</i> will affect the transverse energy flow distributions, while the variation of topological charge <i>m</i> will lead to the diffusion of energy. These phenomena may assist in capturing specific particles.