Speckle photography fringe analysis: assessment of current algorithms

Deep-learning density functionals for gradient descent optimization

Machine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via density functional theory. However, while they can easily be trained to accurately map ground-state density profiles to the corresponding energies, their functional derivatives often turn out to be too noisy, leading to instabilities in self-consistent iterations and in gradient-based searches of the ground-state density profile. We investigate how these instabilities occur when standard deep neural networks are adopted as regression models, and we show how to avoid them by using an ad hoc convolutional architecture featuring an interchannel averaging layer. The main testbed we consider is a realistic model for noninteracting atoms in optical speckle disorder. With the interchannel average, accurate and systematically improvable ground-state energies and density profiles are obtained via gradient-descent optimization, without instabilities nor violations of the variational principle.

Quantum Reservoir Computing for Speckle Disorder Potentials

Quantum reservoir computing is a machine learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the reservoir combined with a simple and fast training strategy. In this work, this technique is introduced with a quantum reservoir of spins and it is applied to find the ground state energy of an additional quantum system. The quantum reservoir computer is trained with a linear model to predict the lowest energy of a particle in the presence of different speckle disorder potentials. The performance of the task is analyzed with a focus on the observable quantities extracted from the reservoir and it is shown to be enhanced when two-qubit correlations are employed.

Speckle patterns formed by broadband terahertz radiation and their applications for ghost imaging

Speckle patterns can be very promising for many applications due to their unique properties. This paper presents the possibility of numerically and experimentally formation of speckle patterns using broadband THz radiation. Strong dependence of the statistical parameters of speckles, such as size and sharpness on the parameters of the diffuser are demonstrated: the correlation length and the mean square deviation of the phase surface inhomogeneity. As the surface correlation length is increasing, the speckle size also increases and its sharpness goes down. Alternatively, the magnification of the standard deviation of the surface height leads to the speckle size diminishing and growth of the speckle sharpness. The dimensions of the experimentally formed speckles correspond to the results of numerical simulation. The possibility of utilizing formed speckle patterns for the implementation of the ghost imaging technique has been demonstrated by methods of numerical modeling.

Scalable neural networks for the efficient learning of disordered quantum systems

Supervised machine learning is emerging as a powerful computational tool to predict the properties of complex quantum systems at a limited computational cost. In this article, we quantify how accurately deep neural networks can learn the properties of disordered quantum systems as a function of the system size. We implement a scalable convolutional network that can address arbitrary system sizes. This network is compared with a recently introduced extensive convolutional architecture [Mills et al., Chem. Sci. 10, 4129 (2019)2041-652010.1039/C8SC04578J] and with conventional dense networks with all-to-all connectivity. The networks are trained to predict the exact ground-state energies of various disordered systems, namely, a continuous-space single-particle Hamiltonian for cold-atoms in speckle disorder, and different setups of a quantum Ising chain with random couplings, including one with only short-range interactions and one augmented with a long-range term. In all testbeds we consider, the scalable network retains high accuracy as the system size increases. Furthermore, we demonstrate that the network scalability enables a transfer-learning protocol, whereby a pretraining performed on small systems drastically accelerates the learning of large-system properties, allowing reaching high accuracy with small training sets. In fact, with the scalable network one can even extrapolate to sizes larger than those included in the training set, accurately reproducing the results of state-of-the-art quantum Monte Carlo simulations.

Supervised machine learning of ultracold atoms with speckle disorder

We analyze how accurately supervised machine learning techniques can predict the lowest energy levels of one-dimensional noninteracting ultracold atoms subject to the correlated disorder due to an optical speckle field. Deep neural networks with different numbers of hidden layers and neurons per layer are trained on large sets of instances of the speckle field, whose energy levels have been preventively determined via a high-order finite difference technique. The Fourier components of the speckle field are used as the feature vector to represent the speckle-field instances. A comprehensive analysis of the details that determine the possible success of supervised machine learning tasks, namely the depth and the width of the neural network, the size of the training set, and the magnitude of the regularization parameter, is presented. It is found that ground state energies of previously unseen instances can be predicted with an essentially negligible error given a computationally feasible number of training instances. First and second excited state energies can be predicted too, albeit with slightly lower accuracy and using more layers of hidden neurons. We also find that a three-layer neural network is remarkably resilient to Gaussian noise added to the training-set data (up to 10% noise level), suggesting that cold-atom quantum simulators could be used to train artificial neural networks.

Superfluid transition in a bose gas with correlated disorder

The superfluid transition of a three-dimensional gas of hard-sphere bosons in a disordered medium is studied using quantum Monte Carlo methods. Simulations are performed in continuous space both in the canonical and in the grand-canonical ensemble. At fixed density we calculate the shift of the transition temperature as a function of the disorder strength, while at fixed temperature we determine both the critical chemical potential and the critical density separating normal and superfluid phases. In the regime of strong disorder the normal phase extends up to large values of the degeneracy parameter, and the critical chemical potential exhibits a linear dependence in the intensity of the random potential. The role of interactions and disorder correlations is also discussed.

Quantum lattice Boltzmann simulation of expanding Bose-Einstein condensates in random potentials

The phenomenon of Anderson localization in expanding one-dimensional Bose-Einstein condensates is investigated by numerically solving the Gross-Pitaevskii equation with a random speckle potential. To this purpose, a quantum lattice Boltzmann (QLB) method is used, and compared with a standard Crank-Nicolson scheme. The QLB simulations show evidence of Anderson localization even for relatively low-energy condensates, with a healing length as large as one-tenth of the Thomas-Fermi length. Moreover, very long-time simulations, lasting up to 15 000 optical confinement periods, indicate that the Anderson localization degrades in time, although at a very slow pace. In particular, the inverse localization length is found to decay according to a t;{-1/3} law. This lends support to the idea that localized wave functions, although not strictly ground states, represent extremely long-lived metastable states of the expanding condensate.

High-speed dynamic speckle interferometry: phase errors due to intensity, velocity, and speckle decorrelation

The recently developed technique of high-speed phase-shifting speckle interferometry combined with temporal phase unwrapping allows dynamic displacement fields to be measured, even for objects containing global discontinuities such as cracks or boundaries. However, when local speckle averaging is included, small phase errors introduced at each time step are accumulated along the time axis, yielding total phase values that depend strongly on the speckle rereference rate. We present an analysis of the errors introduced in the phase evaluation by three sources: intensity errors, velocity errors, and speckle decorrelation. These errors are analyzed when they act both independently and together, for the most commonly used phase-shifting algorithms, with computer-generated speckle patterns. It is shown that, in a controlled out-of-plane geometry, errors in the unwrapped phase map that are due to speckle decorrelation rise as the time between rereferencing events is increased, whereas those due to intensity and velocity errors are reduced. It is also shown that speckle decorrelation errors are typically more important than the intensity and velocity errors. These results provide guidance as to the optimal speckle rereferencing rate in practical applications of the technique.

Three-dimensional deformation field measurement with digital speckle correlation

Digital speckle correlation is based on a detailed analysis of changes in speckle images that are recorded from laser-illuminated rough surfaces. The two in-plane components are obtained by cross-correlation of corresponding subimages, a method also known as digital speckle photography. The local gradient of the hitherto inaccessible out-of-plane component is determined from the characteristic dependence of the speckle correlation on the spatial frequency. A detailed experimental study is carried out to analyze the new technique for systematic and random measuring errors. For moderate decorrelation the accuracy of the out-of-plane measurement is better than lambda/10 and thus comparable with interferometric techniques. Yet the extremely simple and robust optical setup is suited to nondestructive-testing applications in harsh environments. The quality of the deformation maps is demonstrated in a practical application.

Laser speckle photography to measure surface displacements on cortical bone--verification of an automated analysis system

A non-contact strain measurement method that could be used in a physiological environment was adapted to determine accurately the mechanical properties of bone. The technique of laser speckle photography was applied to record in-plane surface displacements. An automated image analysis system, based on a Fast Fourier Transform algorithm, was developed for data acquisition and its accuracy and precision verified. It was established that the technique could be used at magnifications of up to 60x , providing an ultimate resolution of approximately 1 microm. Displacement measurements on cortical bone submerged in water were demonstrated, which is of great value in determining the true physiological properties of bone.

Accuracy in electronic speckle photography

Electronic speckle photography is an accurate, easy-to-use, video-based technique for the analysis of two- and three-dimensional deformation fields and in-plane strain fields, based on numerical cross correlation. Through the use of statistical optics, simulated speckle patterns, and experiments the accuracy in electronic speckle photography was found to depend on correlation, speckle size, window size, and correlation filter. The estimated correlation was found to be the combined effect of three mutually competing factors because of classical speckle correlation, subimage overlap, and displacement gradients. In many applications white-light speckle patterns provide a more accurate estimate of the displacement field than do laser speckle patterns.

Iteration of phase window correlation and least-squares fit for Young's fringe pattern processing

Correlation by means of phase windows that artificially create phase shifts is combined with the least-squares fit to provide a processing algorithm. A Young's pattern is correlated by means of the phase windows to generate four patterns with phase shifts of 0 degrees, 90 degrees, 180 degrees, and 270 degrees. The phase of the Young's pattern is derived from the four patterns. Then the derived phase is least-squares fitted to a phase plane. The above steps are repeated once to improve accuracy. Speckle noise is suppressed by the correlation operations, as well as a loop including phase calculation, fringe reconstruction, and smoothing. The magnitude and direction of the displacement associated with Young's pattern are determined from the phase plane. The algorithm takes less calculation time than the fast Fourier transform method and does so with improved accuracy. Software has been developed and is used in the experiment.

Fourier-transformation, phase-iteration, and least-square-fit image processing for Young's fringe pattern

The fast Fourier transform, phase iteration, and the least-square fit are combined into an automated processing technique for the analysis of Young's fringe patterns. A Young's fringe pattern is first fast-Fourier-transform filtered to get an initial phase, phase iteration is carried out to improve the phase if necessary, and then the phase is least-square fitted to a phase plane. The magnitude and the direction of the displacement associated with the Young's pattern are determined from the phase plane.

Electronic speckle photography: increased accuracy by nonintegral pixel shifting

Electronic speckle photography offers a simple and fast technique for measuring in-plane displacement fields in solid and fluid mechanics. An improved algorithm is presented and analyzed by use of both computer-simulated speckle patterns and real experiments. The idea of the improved algorithm is to maximize the correlation between correlated subimages from different images by shifting one of them by nonintegral pixel values. The improved algorithm was found to determine displacement components with an uncertainty of less than 1% of a pixel and with negligible systematic errors in ideal experimental conditions.

Phase-correlation techniques for quasi real-time measurement of deformations with digital speckle interferometry

A new approach of in-plane speckle-displacement analysis by binary phase-correlation evaluation is described. An optical implementation of the method based on digital speckle-pattern interferometry and one-focal-length correlator architecture that uses a liquid-crystal light valve is proposed.

Particle image velocimetry: high-speed transparency scanning and correlation-peak location in optical processing systems

In order to exploit the full potential offered by optical correlation processing in the analysis of transparencies obtained by particle image velocimetry one needs to advance the transparency and to track the position of correlation peaks at great speed. In the following, this procedure is discussed with reference to an optical analysis system based on a high-speed ferroelectric optically addressed spatial light modulator. We present a system that uses scanning optics both to scan through the input transparency and to locate the correlation peaks in the resulting optical output distributions as a practical solution to the problem. The feasibility of this approach is demonstrated at a processing speed of ~500 autocorrelations/s when a one-dimensional acousto-optic output scanning device is used.

Approach to automatic analysis of Young's fringes in speckle photography

A new flexible automatic method for analysis of Young's fringes that allows software, electronic hardware, and optoelectronic realizations is proposed. A displacement vector is sought in the two-dimensional spatial frequency domain, the separate slices of which are obtained through multiple use of the one-dimensional Fourier transform of the Radon-transformed fringe image. An estimate of fringe angle and spacing is found from a projection with the principal Fourier spectrum peak. Afterward its position is repeatedly refined until the desired accuracy is reached. Although the method is relevant mainly to speckle photography, our approach is also applicable to fringe patterns that are generated by a computer. The problem of fringe visibility is widely discussed, and a new estimate is proposed.

Parallel processing system for rapid analysis of speckle-photography and particle-image-velocimetry data

An automated system has been constructed to process double-exposure speckle-photography and particle-image-velocimetry images. A 3 × 3 array of laser beams probes the photograph, forming nine fringe patterns in parallel; these are then analyzed sequentially by digital computer and the use of a two-dimensional Fourier-transform method. Results are presented showing that the random errors in the measured displacements from such a system approach the expected speckle-noise-limited performance, with a total analysis time per displacement vector of 160 ms.

Electronic speckle photography: analysis of an algorithm giving the displacement with subpixel accuracy

Replacing photographic recording by electronic processing has some obvious advantages. An algorithm used for electronic speckle pattern photography is presented, and the reliability and accuracy is analyzed by using computer-generated speckle patterns. The algorithm is based on a two-dimensional discrete cross correlation between subimages from different images. Subpixel accuracy is obtained by a Fourier series expansion of the discrete correlation surface. The accuracy of the algorithm was found to vary in proportion to sigma/n(1 - delta)(2), where sigma is the speckle size, n is the subimage size, and delta is the amount of decorrelation, with negligible systematic errors. For typical values the uncertainty in the displacement is approximately 0.05 pixels. The uncertainty is found to increase with increased displacement gradients.

Maximum-likelihood analysis of speckle photography fringe patterns

A new method for analyzing the Young's fringe patterns from a double-exposure speckle photograph is proposed based on maximum-likelihood estimation. Unlike previous linear algorithms, which rely on Fourier spectral analysis, the method permits knowledge of the speckle-noise statistics (in particular, that the noise is multiplicative rather than additive) to be incorporated in a systematic way. As a result, random errors in the measured displacement components are reduced, in the case of good visibility fringe patterns by a factor of up to 6. The proposed method is also applicable to the general problem of measuring the spatial frequency components of a two-dimensional sinusoid in the presence of signaldependent noise.

Digital processing of dual-plate speckle photography data

Dual-plate speckle photography provides a technique for canceling rigid body displacements of an object by recording the images that correspond to the two deformation states on separate photographic plates. When both plates are positioned with a glass base between the emulsions and illuminated by a laser beam, a pattern of circular fringes is formed. Speckle displacement can be determined by measuring the distance between the fringe pattern center and the undiffracted laser beam. The performance of an algorithm for automatic analysis of these fringes is evaluated by using computer-generated fringe patterns. Accuracy is determined for different values of speckle displacements and fringe visibilities.

Automatic specklegram fringe analysis by using symmetry evaluation of the Theta-scanning function

New approaches are applied to overcome the difficulty of accurate angle measurements in specklegram information processing by introducing a prism rotator for compensation and by using symmetry evaluation. Together with slit scanning and the fast Fourier transform procedure, speckle random noise has been effectively suppressed, and accurate measurements of Theta and s are achieved. The method we present makes it possible to process the specklegram information automatically. The experimental setup is described in detail. The principles and experimental curves are given.

Speckle photography fringe analysis: effect of imaging geometry on displacement errors

Random errors in the measured displacement components from double-exposure speckle photographs recorded through a rectangular aperture are considered both by numerical simulations and by simple dimensional analysis.

Fluorescent particle image velocimetry: application to flow measurement in refractive index-matched porous media

This paper presents results in which particle image velocimetry (PIV) is used in conjunction with refractive index matching to measure fluid flow velocities within complex, multiphase systems. This application required the adaptation of PIV for use with fluorescent, rather than scattering, seed particles; we refer to the technique as fluorescent PIV (FPIV). We applied index-matched FPIV to the measurement of low flow velocities (tens of microns per second) at high spatial resolution (tens of microns) in a porous medium. We produced clear images of flowing particles in heterogeneous porous media and obtained reliable velocity vectors by a point-by-point interrogation of these images. We also found evidence of the intrapore mixing of porous media flow.