Single-shot areal profilometry using hyperspectral interferometry with a microlens array

We describe a single-shot technique to measure areal profiles on optically smooth and stepped surfaces for applications where rapid data acquisition in non-cooperative environments is essential. It is based on hyperspectral interferometry (HSI), a technique in which the output of a white-light interferometer provides the input to a hyperspectral imaging system. Previous HSI implementations suffered from inefficient utilisation of the available pixels which limited the number of measured coordinates and/or unambiguous depth range. In the current paper a >20-fold increase in pixel utilisation is achieved through the use of a 2-D microlens array, that leads to a 35×35 channel system with an unambiguous depth range of 0.88 mm

Depth-resolved phase imaging

Traditional full-field interferometric techniques (speckle, moiré, holography etc) encode the surface deformation state of the object under test in the form of 2-D phase images. Over the past 10 years, a family of related techniques (Wavelength Scanning Interferometry, Phase Contrast Spectral Optical Coherence Tomography (OCT), Tilt Scanning Interferometry and Hyperspectral Interferometry) has emerged that allows one to measure the volume deformation state within weakly-scattering objects. The techniques can be thought of as combining the phase-sensing capabilities of Phase Shifting Interferometry and the depth-sensing capabilities of OCT. This paper provides an overview of the techniques, and describes a theoretical framework based on the Ewald sphere construction that allows key parameters such as depth resolution and displacement sensitivity to be calculated straightforwardly for any given optical geometry and wavelength scan range. Finally, the related issue of robust phase unwrapping of noisy 3-D wrapped phase volumes is also described

Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features

Object recognition and pose estimation is a fundamental problem in automated quality control and assembly in the manufacturing industry. Real world objects present in a manufacturing engineering setting tend to contain more smooth surfaces and edges than unique key points, making state-of-the-art algorithms that are mainly based on key-point detection, and key-point description with RANSAC and Hough based correspondence aggregators, unsuitable. An alternative approach using maximum likelihood has recently been proposed in which surface patches are regarded as the features of interest1. In the current study, the results of extending this algorithm to include curved features are presented. The proposed algorithm that combines both surfaces and curves improved the pose estimation by a factor up to 3×, compared to surfaces alone, and reduced the overall misalignment error down to 0.61 mm

Object recognition and localisation from 3D point clouds by maximum likelihood estimation

We present an algorithm based on maximum likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike ‘interest point’ based algorithms which normally discard such data. Compared to the 6D Hough transform it has negligible memory requirements, and is computationally efficient compared to iterative closest point (ICP) algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degree of freedom (DOF) example is given, followed by a full 6 DOF analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an rms alignment error as low as 0:3 mm

High-throughput single-shot hyperspectral interferometer for areal profilometry based on microlens array integral field unit

A single-shot technique to measure areal profiles on optically smooth and rough surfaces and for applications in non-cooperative environments is presented. It is based on hyperspectral interferometry (HSI), a technique in which the output of a white-light interferometer provides the input to a hyperspectral imaging system. Previous HSI implementations suffered from inefficient utilisation of the available pixels which limited the number of measured coordinates and/or unambiguous depth range. In this paper a >20-fold increase in pixel utilization is achieved through the use of a 2-D microlens array as proposed for integral field units in astronomy applications. This leads to a 35×35 channel system with an unambiguous depth range of 0.88 mm

Comparative Analysis of the Saccharomyces cerevisiae and Caenorhabditis elegans Protein Interaction Network

Protein interaction networks aim to summarize the complex interplay of proteins in an organism. Early studies suggested that the position of a protein in the network determines its evolutionary rate but there has been considerable disagreement as to what extent other factors, such as protein abundance, modify this reported dependence. We compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans with those of closely related species to elucidate the recent evolutionary history of their respective protein interaction networks. Interaction and expression data are studied in the light of a detailed phylogenetic analysis. The underlying network structure is incorporated explicitly into the statistical analysis. The increased phylogenetic resolution, paired with high-quality interaction data, allows us to resolve the way in which protein interaction network structure and abundance of proteins affect the evolutionary rate. We find that expression levels are better predictors of the evolutionary rate than a protein's connectivity. Detailed analysis of the two organisms also shows that the evolutionary rates of interacting proteins are not sufficiently similar to be mutually predictive. It appears that meaningful inferences about the evolution of protein interaction networks require comparative analysis of reasonably closely related species. The signature of protein evolution is shaped by a protein's abundance in the organism and its function and the biological process it is involved in. Its position in the interaction networks and its connectivity may modulate this but they appear to have only minor influence on a protein's evolutionary rate.Comment: Accepted for publication in BMC Evolutionary Biolog

Under Pressure: A Case Study of the Effects of External Pressure on MLB Players using Twitter Sentiment Analysis

Performance under pressure and psychological momentum are well-documented topics in sports psychology, but most research focuses on “in-game” pressure. This study views pressure more broadly to examine how the external pressure of fans, quantified using the sentiment of tweets mentioning the players, can affect how MLB players perform. Although external pressure is intangible, it can impact a player’s psyche and performance. This investigation focuses on players Chris Sale and David Price. A new process was developed leveraging the Vader package in Python that can generate tweet sentiment to compare to several performance metrics from Baseball Reference. Results proved to be promising with correlation analysis pointing to some association between sentiment and performance. There was also an observed difference in how both players handled the pressure depending on whether they played for a small or large market team. An anecdotal study of the 2018 season showed even more interesting differences between Sale’s and Price’s performance and Twitter sentiment. Price’s performance and Twitter’s sentiment moved in a cyclical manner throughout the season whereas Sale’s results were much more consistent and less sensitive to change. Finally, a study focused on the impact of both pressure and past performance on future outings showed results consistent with past studies on the subject. For example, Sale was most likely to perform well under pressure if he preceded the start with a very good or bad outing rather than an average outing. Information like this could be useful for front offices and managers. More analysis should be conducted to confirm and expand on the findings of this project. However, this case study can be used as a foundation for a new and innovative approach to player evaluation, ultimately complementing existing methods and informing decisions regarding otherwise intangible factors