In this letter, we propose an indoor visible light positioning technique that combines deep neural network based on the Bayesian Regularization (BR-DNN) with sparse diagonal training data set. Unlike other neural networks, which require a large number of training data points to locate accurately, we realize the high precision positioning with only 20 training points in a $\rm 1.8m\times 1.8m\times 2.1m$ location area. Furthermore, we test a new optimization method of training data set which is the diagonal set. To verify our ideas, we experimentally demonstrate three different training data acquisition methods which contain the common choice of training points (even set), arbitrary selection (arbitrary set) and diagonal selection (diagonal set). Experimental results show that the average localization accuracy optimized by the BR-DNN is 3.40 cm with diagonal set, while the average localization accuracy is 4.35 cm for the arbitrary set and 4.58cm for the even set. In addition, the training time and positioning time are only 11.25ms and 8.66ms due to significant reduction of the sparse training data. All of above experimental results show that the algorithm and training data optimization we proposed provide a new solution for real-time and high accuracy positioning with neural network.

Tuneable retarder arrays, such as spatially patterned liquid crystal devices, have given rise to impressive photonic functionality, fuelling diverse applications ranging from microscopy and holography to encryption and communications. Presently these solutions are limited by the controllable degrees of freedom of structured matter, hindering applications that demand photonic systems with high flexibility and reconfigurable topologies. Here we demonstrate a compound modulator that implements a synthetic tuneable arbitrary retarder array as virtual pixels derived by cascading low functionality tuneable devices, realising full dynamic control of its arbitrary elliptical axis geometry, retardance value, and induced phase. Our approach offers unprecedented functionality that is user-defined and possesses high flexibility, allowing our modulator to act as a new beam generator, analyser, and corrector, opening an exciting path to tuneable topologies of light and matter.

While polarization sensing is vital in many areas of research, with applications spanning from microscopy to aerospace, traditional approaches are limited by method-related error amplification, accumulation, and pre-processing steps, constraining the performance of single-shot polarimetry. Here, we propose a measurement paradigm that circumvents these limitations, based on the use of a universal full Poincaré generator to map all polarization analyzer states into a single vectorially structured light field. All vector components are analyzed in a single shot, extracting the vectorial state through inference from a physical model of the resulting image, providing a single-step sensing procedure. To demonstrate the feasibility of our approach, we use a common graded index (GRIN) optic as our mapping device and show mean errors of < 1 % for each vector component. Our work paves the way for next-generation polarimetry, impacting a wide variety of applications that rely on vector measurement.

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

With the growing use of optical polarization in applications ranging from communications to medical diagnoses, adaptive correction of complex vectorial aberrations in optical systems has become an increasingly important area of research. However, research to date has focused primarily on phase and retardance aberrations, whereas another major source of aberration—diattenuation—remains largely unexplored. Unlike the others, diattenuation affects intensity in addition to phase and polarization, limiting the intrinsic correction capability of adaptive systems. In this work, we propose the use of optical skyrmions to probe diattenuation-aberrated systems and provide metrics that characterize the performance of vectorial adaptive optics (V-AO), with theoretical and experimental validations. Based on the probed results, we demonstrate V-AO correction under real-world aberrations for complex media imaging and analyze correction strategies to optimize measurements in aberrated polarimetric systems. This work paves the way for high-dimensional aberration correction, introduces a previously unidentified use of optical skyrmions, and provides insights that will aid the development of vectorial measurement systems.

Adaptive optics (AO) is a powerful tool employed across various research fields, from aerospace to microscopy. Traditionally, AO has focused on correcting optical phase aberrations, with recent advances extending to polarisation compensation. However, intensity errors are also prevalent in optical systems, yet effective correction methods are still in their infancy. Here, we introduce a novel AO approach, termed intensity adaptive optics (I-AO), which employs a dual-feedback loop mechanism to first address non-uniform intensity distribution and subsequently compensate for energy loss at the pupil plane. We demonstrate that I-AO can operate in both sensor-based and sensorless formats and validate its feasibility by quantitatively analysing the focus quality of an aberrated system. This technique expands the AO toolkit, paving the way for next-generation AO technology.