Traditional wireless positioning techniques often suffer from accuracy degradation in indoor environments due to multipath effects and occlusion. To address this issue, this paper proposes an indoor positioning method for visible light communication (VLC) combined with intelligent reflective surface (IRS) assistance to improve the positioning accuracy and stability in complex environments. This work proposes the concepts of a virtual source and virtual receiver based on IRS and conducts positioning optimization by combining the measurements of the time difference of arrival (TDOA) and angle of arrival (AOA). The research adopts a semi-positive definite relaxation (SDR) optimization method to efficiently solve the nonlinear optimization problem, ensuring the global convergence and accuracy of the algorithm. Meanwhile, the weights of the positioning results of the virtual light source and the real light source are dynamically adjusted by using the distance residual, thereby reducing the influence of measurement noise. Monte Carlo simulation experiments demonstrate the advantages of the proposed method in terms of the positioning error and robustness compared with traditional positioning algorithms in the environment of large noise interference. The experimental results demonstrate the efficacy of the method in addressing multipath and occlusion issues, while also exhibiting notable adaptability and stability across diverse hardware configurations.

Vortex beams are unique in that they have annular spatial profiles and carry orbital angular momentum. This has led to their use in applications including laser processing, microparticle manipulation and signal transmission. Off-axis vortex beams, which may be considered a subset of vortex beams, display a broader spectrum of physical characteristics in comparison with their conventional (integer-order) counterparts. In this work, we derive the equations which describe the intensity distribution of off-axis vortex beams and use these to theoretically model their spatial profile. These models are supported by experimental generation of both integer and off-axis vortex beams, and the presence of orbital angular momentum is investigated through the use of the cylindrical lens transformation method.

Laser-induced breakdown spectroscopy (LIBS), limited by matrix effects, self-absorption in complex samples, and ambient atmospheric influences, still requires further improvement in detection sensitivity and signal stability. In this work, the excitation beam of LIBS is modulated into an optical vortex by an optical phase element, and optical vortex-induced LIBS is used to detect and analyze coal samples. Building on the uniform annular intensity distribution and orbital angular momentum (OAM) carried by the optical vortex, it is anticipated that spectral signal intensity can be enhanced by improving plasma ablation efficiency, reducing shielding effects, and increasing electron collision frequency, thereby reducing signal uncertainty and enhancing LIBS analytical performance. For the first time, a classification model combining principal component analysis (PCA) and support vector machine (SVM) is developed, integrating optical vortex-induced LIBS technology with machine learning. Using the PCA-SVM model, optical vortex-based LIBS attained a coal classification accuracy of 95%, significantly higher than the 88% achieved with Gaussian beams, thereby markedly improving classification performance for complex matrix samples. These results demonstrate that optical vortex-induced LIBS possesses strong potential for efficient detection of samples with complex matrices.