Spatial transverse optical vortices (STOVs), characterized by possessing transverse orbital angular momentum, offer new degrees of freedom for optical manipulation. However, since the vortex phase circulation of STOVs is the spatiotemporal plane (x-t plane), traditional phase modulation methods based on the x-y transverse plane are no longer applicable. Therefore, exploring new methods for generating STOVs has become a significant challenge. Here, a 4π tight focusing system with overlapped phase from two counter-propagating radially polarized beams is proposed to generate STOVs. First, by independently designing the phase wavefronts of counter-propagating beams, uniform nano-scale spherical spots are generated in the focal region. Then, treating spherical spots as spatial trajectory units and superimposing a time-dependent helical phase factor exp(–ilφ<sub>st</sub>), precise control over the spot’s spatial position and motion trajectory is achieved. When the temporal step is sufficiently small, i.e., under high temporal resolution, smooth STOVs emerge. Through simultaneous modulation of temporal and spatial factor, STOVs with distinct radii r can be generated. This approach ultimately enables precise customization of STOVs in size, smoothness, and geometry, facilitating high-precision advancements in fields such as super-resolution microscopy, quantum manipulation and subwavelength nonlinear optical effects.

To achieve non-contact quantitative testing of various sugar-like concentrations in sugar-sweetened beverages, we built an infrared spectroscopy testing system for sugar-sweetened beverages, and proposed a multi-characteristic spectral weighting algorithm. The system uses the infrared laser to irradiate sugar-sweetened beverages and analyzes their absorption characteristics in the reflected spectrum. We selected multi-characteristic wavelengths for combined analysis based on the characteristic wavelength distribution of the sugar. In the calibration experiment, it obtained the near-infrared characteristic spectrum distributions of glucose, sucrose, and fructose. During the process of changing sugar concentration from 1.0% to 5.0%, the absorbance increased from 0.045 to 0.215. The linearity of its growth rate is 0.997. The test results for comparing algorithms show that in condition 1, the SCW method’s test mean is very different from the real value. It cannot tell the difference between different types of sugar concentrations in mixed sugar solutions. The test mean of the DWD method and this algorithm are 19.1% and 6.3%, respectively. In condition 2, the test mean values of SCW method, DWD method, and this algorithm are 15.0%, 7.6%, and 4.2%, respectively. It verifies that this algorithm has better accuracy in sugar concentration inversion.

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.

Carbon fiber reinforced plastic (CFRP) is widely used in fields such as aircraft and construction due to its advantages of light weight, high hardness, wear and corrosion resistance. To quantitatively detect internal defects in CFRP panels, a THz reflective defect detection system was built. A defect analysis algorithm based on multi-layer gradient threshold is proposed. Based on the manufacturing process of CFRP, a THz wave echo function model for multi-layer CFRP structures was derived. A CFRP plate containing debonding defects and crack defects was trial produced, to obtain its THz time-domain spectrum. The experiment completed the reconstruction of THz two-dimensional images of CFRP plates. The test results show that three different depths of test data can be detected, and the calculated diameters of the debonding defect areas are 9.12, 9.86, and 9.93 mm, respectively, with a relative error mean of 3.63%. Crack defects can also be effectively identified, but the quality of their test images will decrease with increasing pre-embedded depth. The linearity between defect depth and testing frequency is 0.98, which verifies the possibility of quantitative calculation. The test results of power spectral density and defect depth show that it has good uniformity.

In optical communication systems, mitigating transmission errors caused by channel noise remains a critical challenge. This paper proposes bit repetition coding as a simple and effective method to enhance signal fidelity at the receiver. Our approach achieves a significant reduction in the bit error rate (BER), with simulations showing a decrease from 10<sup>–3</sup> to 10<sup>–10</sup> as the repetition count increases from 3 to 7. Furthermore, the clarity of the received signal improves consistently, with errors approaching zero at optimal repetition levels. This balance between simplicity and performance demonstrates the potential of repetition coding as a robust solution to transmission noise. Beyond its practical benefits, the study opens new pathways for designing resilient optical communication systems and highlights the relevance of repetition coding in improving both system reliability and efficiency. Future research is encouraged to explore its integration into next-generation optical networks.

With the rapid development of optical fiber communication technology, the requirement for high-quality splicing has also increased. The accuracy of optical fiber alignment is the most crucial factor in determining splicing quality. The imaging quality of the microscope objective and the accuracy of the optical fiber alignment algorithm together determine the accuracy of optical fiber alignment. This paper proposes a new alignment method based on the black line at the interface between optical fiber core and cladding. The imaging characteristics of the black line are analyzed, and the initial structure of the microscope objective is deduced based on the theory of apochromatism, and finally a high-precision optical fiber fusion splicing microscope objective with a waveband from 460 to 660 nm is designed. An experimental device is set up to obtain optical fiber images, and the results indicate that the microscope objective plays a significant role in magnifying black lines. The horizontal and vertical offsets of optical fiber can be accurately identified through image processing. This paper considers the lens design theory and feature recognition algorithm comprehensively to improve the accuracy of optical fiber alignment, ultimately achieving high-quality optical fiber fusion splicing.

To measure the state of ethanol differentiation process, a fiber option sensing based process state monitoring system is designed. It includes a laser, demo module, PC processing module, fiber option sensing network, and state feedback control unit. A data mining algorithm for multi-parameter demo is proposed to accurately achieve temperature and strain field classification and combine accuracy to the different weights of temperature and strain on different positions, improving the correlation between wavelengths offset and state parameters. The experiment compared four common abnormal situations, The experiment adopts the method of collecting the temperature field inside the tank and the stress field outside the tank, with strain testing of 100–5000 με and temperature testing range of 0–120 °C. The results showed that the average temperature sensitivity after calibration was 0.0102 nm / °C, and the linearity was 0.9959. The average strain sensitivity is 0.499 pm/με, and the linearity is 0.9982. Feedback control has the ability to adjust state fluctuations online, and the feedback time varies for different types of anomalies. The temperature and strain wavelength deviations after correction for all four cases are less than ±1 °C and ±50 με.

In recent years, with the expansion of information, artificial intelligence technology has been developed and used in various fields. Among them, optical neural network provides a new type of special neural network accelerator chip solution, which has the advantages of high speed, high bandwidth, and low power consumption. In this paper, we construct an optical neural network based on Mach–Zehnder interferometer. The experimental results on the image classification of MNIST handwritten digitals show that the optical neural network has high accuracy, fast convergence and good scalability.

The article considers a mathematical model of an incoherent optical system, into which defocusing is introduced as an aberration. When modeling, it is assumed that if non-coherent lighting is used to illuminate an object, then the transforming optical system should be considered as linear with respect to the intensity of the light. An analysis of the optical system of a general form was carried out, and relations were obtained for a system with a circular pupil, who allowed obtaining defocused images at the output of the optical system, and the defocusing value was rather simply adjustable. The proposed model can be used either as a low-pass filter for preprocessing of interferograms, or as a method for extracting informative image fragments, using which it is possible to synthesize the original image.

In recent years, optical neural networks have attracted widespread attention, due to their advantages of high speed, high parallelism, high bandwidth, and low power consumption. Photonic unitary neural network is a kind of neural networks that utilize the principles of unitary matrices and photonics to perform computations. In this paper, we design a photonic unitary neural network based on Mach–Zehnder interferometer arrays. The results show that the network has a good performance on both triangular and circular binary classification datasets, where most of the data points are correctly classified. The accuracies achieve 97% and 95% for triangular and circular datasets, with the loss function values of 0.023 and 0.046, respectively.