We investigate the localization of two incoherent point sources with arbitrary angular and axial separations in the paraxial approximation. By using quantum metrology techniques, we show that a simultaneous estimation of the two separations is achievable by a single quantum measurement, with a precision saturating the ultimate limit stemming from the quantum Cramér-Rao bound. Such a precision is not degraded in the subwavelength regime, thus overcoming the traditional limitations of classical direct imaging derived from Rayleigh’s criterion. Our results are qualitatively independent of the point spread function of the imaging system, and quantitatively illustrated in detail for the Gaussian instance. This analysis may have relevant applications in three-dimensional surface measurements.

Precision object handling and manipulation require the accurate positioning of industrial robots. A common practice for performing end effector positioning is to read joint angles and use industrial robot forward kinematics (FKs). However, industrial robot FKs rely on the robot Denavit–Hartenberg (DH) parameter values, which include uncertainties. Sources of uncertainty associated with industrial robot FKs include mechanical wear, manufacturing and assembly tolerances, and robot calibration errors. It is therefore necessary to increase the accuracy of DH parameter values to reduce the impact of uncertainties on industrial robot FKs. In this paper, we use differential evolution, particle swarm optimization, an artificial bee colony, and a gravitational search algorithm to calibrate industrial robot DH parameters. A laser tracker system, Leica AT960-MR, is utilized to register accurate positional measurements. The nominal accuracy of this non-contact metrology equipment is less than 3 μm/m. Metaheuristic optimization approaches such as differential evolution, particle swarm optimization, an artificial bee colony and a gravitational search algorithm are used as optimization methods to perform the calibration using laser tracker position data. It is observed that, using the proposed approach with an artificial bee colony optimization algorithm, the accuracy of industrial robot FKs in terms of mean absolute errors of static and near-static motion over all three dimensions for the test data decreases from its measured value of 75.4 μm to 60.1 μm (a 20.3% improvement).

This literature review investigates the integration of machine learning (ML) into optical metrology, unveiling enhancements in both efficiency and effectiveness of measurement processes. With a focus on phase demodulation, unwrapping, and phase-to-height conversion, the review highlights how ML algorithms have transformed traditional optical metrology techniques, offering improved speed, accuracy, and data processing capabilities. Efficiency improvements are underscored by advancements in data generation, intelligent sampling, and processing strategies, where ML algorithms have accelerated the metrological evaluations. Effectiveness is enhanced in measurement precision, with ML providing robust solutions to complex pattern recognition and noise reduction challenges. Additionally, the role of parallel computing using graphics processing units and field programmable gate arrays is emphasised, showcasing their importance in supporting the computationally intensive ML algorithms for real-time processing. This review culminates in identifying future research directions, emphasising the potential of advanced ML models and broader applications within optical metrology. Through this investigation, the review articulates a future where optical metrology, empowered by ML, achieves improved levels of operational efficiency and effectiveness.

We have performed point-contact spectroscopy measurements on a sample constituted by a metallic ferromagnetic oxide $\left({\text{SrRuO}}_3\right)$ bottom electrode and a tunnel ferrimagnetic $\left({\text{CoFe}}_2{\text{O}}_4\right)$ barrier. Andreev reflection is observed across the tunnel barrier. From the comparison of Andreev reflection in ${\text{SrRuO}}_3$ and across the ${\text{CoFe}}_2{\text{O}}_4$ barrier we infer that the ferrimagnetic barrier has a spin filter efficiency not larger than $+13\mathrm{\%}$. The observation of a moderate and positive spin filtering is discussed in the context of the microstructure of the barriers and symmetry-related spin-filtering effects.

Additive manufactured parts have complex geometries featuring high slope angles and occlusions that can be difficult or even impossible to measure; in this scenario, photogrammetry presents itself as an attractive, low-cost candidate technology to acquire digital form data. In this paper, we propose a pipeline to optimise, automate and accelerate the photogrammetric measurement process. The first step is to detect the optimum camera positions which maximise surface coverage and measurement quality, while minimising the total number of images required. This is achieved through a global optimisation approach using a genetic algorithm. In parallel to the view optimisation, a convolutional neural network (CNN) is trained on rendered images of the CAD data of the part to predict the pose of the object relative to the camera from a single image. Once trained, the CNN can be used to find the initial alignment between object and camera allowing full automation of the optimised measurement procedure. These techniques are verified on a sample part showing good coverage of the object and accurate pose estimation. The procedure presented in this work simplifies the measurement process and represents a step towards a fully automated measurement and inspection pipeline.

We study a system represented by a Bose-Einstein condensate interacting with a cavity field in presence of a strong off-resonant pumping laser. This system can be described by a three-mode Gaussian state, where two are the atomic modes corresponding to atoms populating upper and lower momentum sidebands and the third mode describes the scattered cavity field light. We show that, as a consequence of the collective atomic recoil instability, these modes possess a genuine tripartite entanglement that increases unboundedly with the evolution time and is larger than the bipartite entanglement in any reduced two-mode bipartition. We further show that the state of the system exhibits genuine tripartite nonlocality, which can be revealed by a robust violation of the Svetlichny inequality when performing displaced parity measurements. Our exact results are obtained by exploiting the powerful machinery of phase-space informational measures for Gaussian states, which we briefly review in the opening sections of the paper.