Measuring Thickness and Pretilt in Reflective Vertically Aligned Nematic Liquid Crystal Displays

Pretilt angle is a parameter of the utmost importance in the ultimate performance of vertically-aligned negative nematic LC displays. When these devices work in reflective mode, as is the LCOS microdisplays, accurate measurement of pretilt angles becomes a difficult problem, since usual experimental setups based on retardation of the polarization components of the impinging light are proportional to the product effective birefringence (neff - no) times thickness, and any attempt to separate these variables is cancelled out by symmetry. This work shows a relatively simple method capable of separating both variables. An experimental setup specifically aimed at vertically aligned reflective cells has been prepared. At the same time, a simulation model has been developed taking into account the properties of actual reflective displays. Comparison between experimental and theoretical results shows some discrepancies that can be explained assuming that the LC profile contains a residual twist. Including that twist in the model, an excellent agreement between theory and experiment has been achieved. Matching of simulations and measurements yields to the separate determination of pretilt angle and thickness and gives good estimates for the residual twist angle

La versión p-adaptable del Método de los Elementos Finitos y del Método de los Elementos de Contorno: aplicaciones en elasticidad bidimensional

Este trabajo discute la bondad de las técnicas numéricas p-autoadaptables, comparando el Método de los Elementos Finitos (MEF) y el Método de los Elementos de Contorno(MEC). Se presenta un breve resumen de las herramientas matemáticas necesarias para gobernar el proceso de refinamiento en ambos métodos. Finalmente, se presenta un ejemplo ilustrativo de relevancia práctica en ingeniería, el cual pone de manifiesto la potencia y versatilidad de las técnicas p-adaptables frente a situaciones reales

Evaluation of accurate eye corner detection methods for gaze estimation

Accurate detection of iris center and eye corners appears to be a promising approach for low cost gaze estimation. In this paper we propose novel eye inner corner detection methods. Appearance and feature based segmentation approaches are suggested. All these methods are exhaustively tested on a realistic dataset containing images of subjects gazing at different points on a screen. We have demonstrated that a method based on a neural network presents the best performance even in light changing scenarios. In addition to this method, algorithms based on AAM and Harris corner detector present better accuracies than recent high performance face points tracking methods such as Intraface

3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders

Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 ± 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 ± 0.23, p = 2.2 · 10 -16 )

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging

Simplified qualitative discrete numerical model to determine cracking pattern in brittle materials by means of finite element method

This paper presents the formulation, implementation, and validation of a simplified qualitative model to determine the crack path of solids considering static loads, infinitesimal strain, and plane stress condition. This model is based on finite element method with a special meshing technique, where nonlinear link elements are included between the faces of the linear triangular elements. The stiffness loss of some link elements represents the crack opening. Three experimental tests of bending beams are simulated, where the cracking pattern calculated with the proposed numerical model is similar to experimental result. The advantages of the proposed model compared to discrete crack approaches with interface elements can be the implementation simplicity, the numerical stability, and the very low computational cost. The simulation with greater values of the initial stiffness of the link elements does not affect the discontinuity path and the stability of the numerical solution. The exploded mesh procedure presented in this model avoids a complex nonlinear analysis and regenerative or adaptive meshes.Peer ReviewedPostprint (published version