A hierarchical structure for automatic meshing and adaptive FEM analysis

A new algorithm for generating automatically, from solid models of mechanical parts, finite element meshes that are organized as spatially addressable quaternary trees (for 2-D work) or octal trees (for 3-D work) is discussed. Because such meshes are inherently hierarchical as well as spatially addressable, they permit efficient substructuring techniques to be used for both global analysis and incremental remeshing and reanalysis. The global and incremental techniques are summarized and some results from an experimental closed loop 2-D system in which meshing, analysis, error evaluation, and remeshing and reanalysis are done automatically and adaptively are presented. The implementation of 3-D work is briefly discussed

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full 2D image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields and other sample-dependent properties. However, extracting this information requires complex analysis pipelines, from data wrangling to calibration to analysis to visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail, and present results from several experimental datasets. We have also implemented a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open source HDF5 standard. We hope this tool will benefit the research community, helps to move the developing standards for data and computational methods in electron microscopy, and invite the community to contribute to this ongoing, fully open-source project

Automated framework for robust content-based verification of print-scan degraded text documents

Fraudulent documents frequently cause severe financial damages and impose security breaches to civil and government organizations. The rapid advances in technology and the widespread availability of personal computers has not reduced the use of printed documents. While digital documents can be verified by many robust and secure methods such as digital signatures and digital watermarks, verification of printed documents still relies on manual inspection of embedded physical security mechanisms.The objective of this thesis is to propose an efficient automated framework for robust content-based verification of printed documents. The principal issue is to achieve robustness with respect to the degradations and increased levels of noise that occur from multiple cycles of printing and scanning. It is shown that classic OCR systems fail under such conditions, moreover OCR systems typically rely heavily on the use of high level linguistic structures to improve recognition rates. However inferring knowledge about the contents of the document image from a-priori statistics is contrary to the nature of document verification. Instead a system is proposed that utilizes specific knowledge of the document to perform highly accurate content verification based on a Print-Scan degradation model and character shape recognition. Such specific knowledge of the document is a reasonable choice for the verification domain since the document contents are already known in order to verify them.The system analyses digital multi font PDF documents to generate a descriptive summary of the document, referred to as \Document Description Map" (DDM). The DDM is later used for verifying the content of printed and scanned copies of the original documents. The system utilizes 2-D Discrete Cosine Transform based features and an adaptive hierarchical classifier trained with synthetic data generated by a Print-Scan degradation model. The system is tested with varying degrees of Print-Scan Channel corruption on a variety of documents with corruption produced by repetitive printing and scanning of the test documents. Results show the approach achieves excellent accuracy and robustness despite the high level of noise

Optimization of support structures for selective laser melting.

Additive Manufacturing (AM) users rely on experiments and experience to predict the performance of AM processes. This trial and error approach for qualifying AM parts takes significant time and money. Simulation tools are an urgent need for today’s AM industry. One area of need is the automatic generation of optimized support structures based upon the scan patterns used to produce those structures. This research seeks to develop support structure generation tools based on real scan pattern and thermo-mechanical simulation tools for Selective Laser Melting (SLM) developed at the University of Louisville and being commercialized by 3DSIM, LLC. In order to benchmark the 3DSIM thermo-mechanical simulation tool, a thermal finite element model has been developed in ANSYS which uses the similar multi-scale meshing strategies as 3DSIM. The use of the sub-modeling approach for dynamic meshing was verified by comparing it against a uniform fine mesh model. The results of the two models match within an acceptable tolerance. Also, a mesh sensitivity analysis was carried out in order to show solution convergence as a function of increasing mesh density. The results of this analysis were also validated using experiments to show a match between experimental and simulated melt pools. Finally, the ANSYS solution was compared with 3DSIM results. The result of 3DSIM for a simple represented model is validated compared against the ANSYS model. What is more, it was significantly faster than their ANSYS counterparts for solving problems using a dynamic mesh. A scan pattern generation tool has been implemented to enable the input of real scan patterns as it is used in fabrication. The scan pattern is arbitrarily varied using user-defined parameters including hatching space, orientation angle, scanning start point, etc. Several types of scan patterns such as traditional S and chessboard are included in the tool. A simplified representation of the thermomechanical properties of support structures in order to accelerate the simulation of supports has been formulated. The effective thermal properties of support structures are represented using thermal homogenization. The effective thermal properties of the support structures have been found to be a function of their geometry, anisotropy and constituent independent thermal properties. The results from this study have been compared against standard models and a good match has been found. A novel framework for a support structure generation and optimization tool has been developed to overcome the difficulty of dealing with support structures in SLM. Supports are optimized and designed based on the thermal stress accumulated in parts as they are made as well as geometrical rules. The support structure is designed to be withstand the thermal stress at locations where it could cause damage to the part and support structure, while minimizing the overall need for support structure materials. The support structure is designed with non-uniform parameters so as to make it flexible to alter based upon thermal stress. Experiments were conducted to explore the threshold for block support structure parameters and results were applied to adjust and verify the tool

On-Line Dependability Enhancement of Multiprocessor SoCs by Resource Management

This paper describes a new approach towards dependable design of homogeneous multi-processor SoCs in an example satellite-navigation application. First, the NoC dependability is functionally verified via embedded software. Then the Xentium processor tiles are periodically verified via on-line self-testing techniques, by using a new IIP Dependability Manager. Based on the Dependability Manager results, faulty tiles are electronically excluded and replaced by fault-free spare tiles via on-line resource management. This integrated approach enables fast electronic fault detection/diagnosis and repair, and hence a high system availability. The dependability application runs in parallel with the actual application, resulting in a very dependable system. All parts have been verified by simulation

IEEE Standard 1500 Compliance Verification for Embedded Cores

Core-based design and reuse are the two key elements for an efficient system-on-chip (SoC) development. Unfortunately, they also introduce new challenges in SoC testing, such as core test reuse and the need of a common test infrastructure working with cores originating from different vendors. The IEEE 1500 Standard for Embedded Core Testing addresses these issues by proposing a flexible hardware test wrapper architecture for embedded cores, together with a core test language (CTL) used to describe the implemented wrapper functionalities. Several intellectual property providers have already announced IEEE Standard 1500 compliance in both existing and future design blocks. In this paper, we address the problem of guaranteeing the compliance of a wrapper architecture and its CTL description to the IEEE Standard 1500. This step is mandatory to fully trust the wrapper functionalities in applying the test sequences to the core. We present a systematic methodology to build a verification framework for IEEE Standard 1500 compliant cores, allowing core providers and/or integrators to verify the compliance of their products (sold or purchased) to the standar