Single crystal CVD diamond growth strategy by the use of a 3D geometrical model: Growth on (113) oriented substrates

The quality of single crystal diamond obtained by microwave CVD processes has been drastically improved in the last 5 years thanks to surface pretreatment of the substrates [A. Tallaire, J. Achard, F. Silva, R.S. Sussmann, A. Gicquel, E. Rzepka, Physica Status Solidi (A) 201, 2419-2424 (2004); G. Bogdan, M. Nesladek, J. D'Haen, J. Maes, V.V. Moshchalkov, K. Haenen, M. D'Olieslaeger, Physica Status Solidi (A) 202, 2066-2072 (2005); M. Yamamoto, T. Teraji, T. Ito, Journal of Crystal Growth 285, 130-136 (2005)]. Additionally, recent results have unambiguously shown the occurrence of (110) faces on crystal edges and (113) faces on crystal corners [F. Silva, J. Achard, X. Bonnin, A. Michau, A. Tallaire, O. Brinza, A. Gicquel, Physica Status Solidi (A) 203, 3049-3055 (2006)]. We have developed a 3D geometrical growth model to account for the final crystal morphology. The basic parameters of this growth model are the relative displacement speeds of (111), (110) and (113) faces normalized to that of the (100) faces, respectively alpha, beta, and gamma. This model predicts both the final equilibrium shape of the crystal (i.e. after infinite growth time) and the crystal morphology as a function of alpha, beta, gamma, and deposition time.

An optimized operating point, deduced from the model, has been validated experimentally by measuring the growth rate in (100), (111), (110), and (113) orientations. Furthermore, the evolution of alpha, beta, gamma as a function of methane concentration in the gas discharge has been established. From these results, crystal growth strategies can be proposed in order, for example, to enlarge the deposition area. In particular, we will show, using the growth model, that the only possibility to significantly increase the deposition area is, for our growth conditions, to use a (113) oriented substrate. A comparison between the grown crystal and the model results will be discussed and characterizations of the grown film (Photoluminescence spectroscopy, EPR, SEM) will be presented. (C) 2008 Elsevier B.V. All rights reserved.

Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.

Validation of a 3D damage model for predicting the response of composite structures under crushing loads

A 3D intralaminar continuum damage mechanics based material model, combining damage mode interaction and material nonlinearity, was developed to predict the damage response of composite structures undergoing crush loading. This model captures the structural response without the need for calibration of experimentally determined material parameters. When used in the design of energy absorbing composite structures, it can reduce the dependence on physical testing. This paper validates this model against experimental data obtained from the literature and in-house testing. Results show that the model can predict the force response of the crushed composite structures with good accuracy. The simulated energy absorption in each test case was within 12% of the experimental value. Post-crush deformation and the damage morphologies, such as ply splitting, splaying and breakage, were also accurately reproduced. This study establishes the capability of this damage model for predicting the responses of composite structures under crushing loads.

An adaptive motion model for person tracking with instantaneous head-pose features

This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous `intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.

A 3-D well-differentiated model of pediatric bronchial epithelium demonstrates unstimulated morphological differences between asthmatic and nonasthmatic cells

There is a need for reproducible and effective models of pediatric bronchial epithelium to study disease states such as asthma. We aimed to develop, characterize, and differentiate an effective, an efficient, and a reliable three-dimensional model of pediatric bronchial epithelium to test the hypothesis that children with asthma differ in their epithelial morphologic phenotype when compared with nonasthmatic children. Primary cell cultures from both asthmatic and nonasthmatic children were grown and differentiated at the air-liquid interface for 28 d. Tight junction formation, MUC5AC secretion, IL-8, IL-6, prostaglandin E2 production, and the percentage of goblet and ciliated cells in culture were assessed. Well-differentiated, multilayered, columnar epithelium containing both ciliated and goblet cells from asthmatic and nonasthmatic subjects were generated. All cultures demonstrated tight junction formation at the apical surface and exhibited mucus production and secretion. Asthmatic and nonasthmatic cultures secreted similar quantities of IL-8, IL-6, and prostaglandin E2. Cultures developed from asthmatic children contained considerably more goblet cells and fewer ciliated cells compared with those from nonasthmatic children. A well-differentiated model of pediatric epithelium has been developed that will be useful for more in vivo like study of the mechanisms at play during asthma.

Automated mixed dimensional modelling from 2D and 3D CAD models

The motivation for this paper is to present procedures for automatically creating idealised finite element models from the 3D CAD solid geometry of a component. The procedures produce an accurate and efficient analysis model with little effort on the part of the user. The technique is applicable to thin walled components with local complex features and automatically creates analysis models where 3D elements representing the complex regions in the component are embedded in an efficient shell mesh representing the mid-faces of the thin sheet regions. As the resulting models contain elements of more than one dimension, they are referred to as mixed dimensional models. Although these models are computationally more expensive than some of the idealisation techniques currently employed in industry, they do allow the structural behaviour of the model to be analysed more accurately, which is essential if appropriate design decisions are to be made. Also, using these procedures, analysis models can be created automatically whereas the current idealisation techniques are mostly manual, have long preparation times, and are based on engineering judgement. In the paper the idealisation approach is first applied to 2D models that are used to approximate axisymmetric components for analysis. For these models 2D elements representing the complex regions are embedded in a 1D mesh representing the midline of the cross section of the thin sheet regions. Also discussed is the coupling, which is necessary to link the elements of different dimensionality together. Analysis results from a 3D mixed dimensional model created using the techniques in this paper are compared to those from a stiffened shell model and a 3D solid model to demonstrate the improved accuracy of the new approach. At the end of the paper a quantitative analysis of the reduction in computational cost due to shell meshing thin sheet regions demonstrates that the reduction in degrees of freedom is proportional to the square of the aspect ratio of the region, and for long slender solids, the reduction can be proportional to the aspect ratio of the region if appropriate meshing algorithms are used.

A spatio-temporal 2D-models framework for human pose recovery in monocular sequences

This paper addresses the pose recovery problem of a particular articulated object: the human body. In this model-based approach, the 2D-shape is associated to the corresponding stick figure allowing the joint segmentation and pose recovery of the subject observed in the scene. The main disadvantage of 2D-models is their restriction to the viewpoint. To cope with this limitation, local spatio-temporal 2D-models corresponding to many views of the same sequences are trained, concatenated and sorted in a global framework. Temporal and spatial constraints are then considered to build the probabilistic transition matrix (PTM) that gives a frame to frame estimation of the most probable local models to use during the fitting procedure, thus limiting the feature space. This approach takes advantage of 3D information avoiding the use of a complex 3D human model. The experiments carried out on both indoor and outdoor sequences have demonstrated the ability of this approach to adequately segment pedestrians and estimate their poses independently of the direction of motion during the sequence. (c) 2008 Elsevier Ltd. All rights reserved.

3-D micromechanical model for predicting the elastic behaviour of woven laminates

This paper presents an analytical model for the prediction of the elastic behaviour of plain-weave fabric composites. The fabric is a hybrid plain-weave with different materials and undulations in the warp and weft directions. The derivation of the effective material properties is based on classical laminate theory (CLT).

The theoretical predictions have been compared with experimental results and predictions using alternative models available in the literature. Composite laminates were manufactured using the resin infusion under flexible tooling (RIFT) process and tested under tension and in-plane shear loading to validate the model. A good correlation between theoretical and experimental results for the prediction of in-plane properties was obtained. The limitations of the existing theoretical models based on classical laminate theory (CLT) for predicting the out-of-plane mechanical properties are presented and discussed. 

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.
Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.

Target Detection and Tracking With Heterogeneous Sensors

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on time difference of arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multiband generalized cross correlation (GCC) analysis. The estimated directions of arrival are then postprocessed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a weighted probabilistic data association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multiband GCC, KF, and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.