FLEXAN (version 2.0) user's guide

The FLEXAN (Flexible Animation) computer program, Version 2.0 is described. FLEXAN animates 3-D wireframe structural dynamics on the Evans and Sutherland PS300 graphics workstation with a VAX/VMS host computer. Animation options include: unconstrained vibrational modes, mode time histories (multiple modes), delta time histories (modal and/or nonmodal deformations), color time histories (elements of the structure change colors through time), and rotational time histories (parts of the structure rotate through time). Concurrent color, mode, delta, and rotation, time history animations are supported. FLEXAN does not model structures or calculate the dynamics of structures; it only animates data from other computer programs. FLEXAN was developed to aid in the study of the structural dynamics of spacecraft

NASTRAN data generation of helicopter fuselages using interactive graphics

The development and implementation of a preprocessor system for the finite element analysis of helicopter fuselages is described. The system utilizes interactive graphics for the generation, display, and editing of NASTRAN data for fuselage models. It is operated from an IBM 2250 cathode ray tube (CRT) console driven by an IBM 370/145 computer. Real time interaction plus automatic data generation reduces the nominal 6 to 10 week time for manual generation and checking of data to a few days. The interactive graphics system consists of a series of satellite programs operated from a central NASTRAN Systems Monitor. Fuselage structural models including the outer shell and internal structure may be rapidly generated. All numbering systems are automatically assigned. Hard copy plots of the model labeled with GRID or elements ID's are also available. General purpose programs for displaying and editing NASTRAN data are included in the system. Utilization of the NASTRAN interactive graphics system has made possible the multiple finite element analysis of complex helicopter fuselage structures within design schedules

Surface networks

© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou

Graphics Processing Unit Bloom Filters: Classical and Probabilistic

Graphics Processing Units (GPUs) have been used to enhance the speed and efficiency of both data structures and algorithms alike. A common data structure used in Computer Science is the Bloom Filter, which is used in many types of applications including databases and security logging. The Bloom Filter is a lossy data structure that uses several hash functions to store keys into a bit array. A novel, new Bloom Filter meant for use in internet traffic detection called the Probabilistic Bloom Filter has recently been developed. In practice, this new Bloom Filter typically makes use of more hash functions than its classical counterpart. Because both of these data structures contain information that can be inserted in independent batch operations, this makes each data structure a prime target to be parallelized on a Graphics Processing Unit. This paper develops a scalable, optimized Graphics Processing Unit implementation of the classical and Probabilistic Bloom Filters. The results of processing the Bloom Filter on the Graphics Processing Unit (GPU) are compared to processing the same Bloom Filter on the Central Processing Unit (CPU). By processing the data structures on Graphics Processing Units, a substantial decrease in processing time was observed and recorded. For most cases, the decrease in time was linearly proportional to the number of keys inserted and the number of hash functions used

BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes

We present BrainPainter, a software that automatically generates images of highlighted brain structures given a list of numbers corresponding to the output colours of each region. Compared to existing visualisation software (i.e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e.g. propagation of pathology on the brain. We highlight three use cases where BrainPainter was used in existing neuroimaging studies: (1) visualisation of the degree of atrophy through interpolation along a user-defined gradient of colours, (2) visualisation of the progression of pathology in Alzheimer's disease as well as (3) visualisation of pathology in subcortical regions in Huntington's disease. Moreover, through the design of BrainPainter we demonstrate the possibility of using a powerful 3D computer graphics engine such as Blender to generate brain visualisations for the neuroscience community. Blender's capabilities, e.g. particle simulations, motion graphics, UV unwrapping, raster graphics editing, raytracing and illumination effects, open a wealth of possibilities for brain visualisation not available in current neuroimaging software. BrainPainter is customisable, easy to use, and can run straight from the web browser: https://brainpainter.csail.mit.edu , as well as from source-code packaged in a docker container: https://github.com/mrazvan22/brain-coloring . It can be used to visualise biomarker data from any brain imaging modality, or simply to highlight a particular brain structure for e.g. anatomy courses.Comment: Accepted at the MICCAI Multimodal Brain Imaging Analysis (MBIA) workshop, 201

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Basic and the personal computer

BASIC and the Personal Computer starts you thinking about the uses of a personal computer, then shows you how to make these ideas become a reality by using the power of interactive computer programming. It features an outstanding presentation of BASIC and extended BASIC, together with detailed examples that cover the full range of applications possible with personal computers. The topics covered included microcomputer hardware, microcomputer programming in BASIC and extended BASIC, computer graphics, word processing, data structures, sorting algorithms, computer games, computer art, simulations, business applications, color graphics, and the use of special interface devices for futuristic applications that are just strating to appear. The book is written in a friendly and informal tone, and requires no previous experience with computing. The text is integrated with a large number of original illustrations that clarify both beginning and advanced concepts. The result is a presentation that reviewers have described as “beautifully organized” – “extraordinarily thorough”-“just plain excellent”-“delight.