An Analysis of netCDF-FastBit Integration and Primitive Spatial-Temporal Operations

A process allowing for the intuitive use of SQL queries on dense multidimensional data stored in Network Common Data Format (netCDF) files is developed using advanced bitmap indexing provided by the FastBit bitmap indexing tool. A method for netCDF data extraction and FastBit index creation is presented and a geospatial Range and pseudo-KNN search based on the haversine function is implemented via SQL. A two step filtering algorithm is shown to greatly enhance the speed of these geospatial queries, allowing for extremely efficient processing of the netCDF data in bitmap indexed form

Gerber File Parsing for Conversion to Bitmap Image–The VINCI7D Case Study

The technological market is increasingly evolving as evidenced by the innovative and streamlined manufacturing processes. Printed Circuit Boards (PCB) are widely employed in the electronics fabrication industry, resorting to the Gerber open standard format to transfer the manufacturing data. The Gerber format describes not only metadata related to the manufacturing process but also the PCB image. To be able to map the electronic circuit pattern to be printed, a parser to convert Gerber files into a bitmap image is required. The current literature as well as available Gerber viewers and libraries showed limitations mainly in the Gerber format support, focusing only on a subset of commands. In this work, the development of a recursive descent approach for parsing Gerber files is described, outlining its interpretation and the renderization of 2D bitmap images. All the defined commands in the specification based on Gerber X2 generation were successfully rendered, unlike the tested commercial parsers used in the experiments. Moreover, the obtained results were comparable to those parsers regarding the commands they can execute as well as the ground-truth, emphasizing the accuracy of the proposed approach. Its top-down and recursive architecture allows easy integration with other software regardless of the platform, highlighting its potential inclusion and integration in the production of electronic circuits.info:eu-repo/semantics/publishedVersio

Satellite Data Processing System (SDPS) users manual V1.0

SDPS is a menu driven interactive program designed to facilitate the display and output of image and line-based data sets common to telemetry, modeling and remote sensing. This program can be used to display up to four separate raster images and overlay line-based data such as coastlines, ship tracks and velocity vectors. The program uses multiple windows to communicate information with the user. At any given time, the program may have up to four image display windows as well as auxiliary windows containing information about each image displayed. SDPS is not a commercial program. It does not contain complete type checking or error diagnostics which may allow the program to crash. Known anomalies will be mentioned in the appropriate section as notes or cautions. SDPS was designed to be used on Sun Microsystems Workstations running SunView1 (Sun Visual/Integrated Environment for Workstations). It was primarily designed to be used on workstations equipped with color monitors, but most of the line-based functions and several of the raster-based functions can be used with monochrome monitors. The program currently runs on Sun 3 series workstations running Sun OS 4.0 and should port easily to Sun 4 and Sun 386 series workstations with SunView1. Users should also be familiar with UNIX, Sun workstations and the SunView window system

Image processing mini manual

The intent is to provide an introduction to the image processing capabilities available at the Langley Research Center (LaRC) Central Scientific Computing Complex (CSCC). Various image processing software components are described. Information is given concerning the use of these components in the Data Visualization and Animation Laboratory at LaRC

Waltz User Manual

This Document describes relevant information to understand and control the Waltz Visualization System. Waltz is a tool to visualize three dimensional data and reads special reference files containing details of the data file, path name, dimensions and aspect ratios of the data. Waltz (as the name suggests) contains three parts: Generalization, Specialization and Abstraction. The Generalization Process splits the data into spatially connected groups. A specialization is formed from a subset (selection) of these groups. The results are displayed in multiple abstract views of the same data. These abstractions are formed by losing or augmenting the data to facilitate in the understanding of the data

Embedding interactive, three-dimensional content in portable document format to deliver gross anatomy information and knowledge

The Portable Document Format (PDF) is likely the most widely used digital file format for scholarly and scientific electronic publishing. Since format specification version 1.6, three-dimensional (3D) models in Universal 3D (U3D) format can be embedded into PDF files. The present study demonstrates a repertoire of graphic strategies and modes of presentation that exploit the potentials of 3D models embedded in PDF to deliver anatomical information and knowledge. Three-dimensional models and scenes representing anatomical structures generated by 3D surface scanning or by segmentation from either clinical imaging data or cadaver sectional images were converted into U3D format and then embedded into PDF files using both freely and commercially available software. The relevant steps and required software tools are described. Built-in tools in Adobe Acrobat and JavaScript scripting both were used to pre-configure user interaction with 3D contents. Eight successive proof-of-concept examples of increasing complexity are presented and provided as supplementary material, including both unannotated and annotated 3D specimens, use of bitmap-textures, guided navigation through predetermined 3D scenes, 3D animation, and interactive navigation through tri-planar sectional human cadaver images. Three-dimensional contents embedded in PDF files are generally comparable to multimedia and dedicated 3D software in terms of quality, flexibility, and convenience, and offer new unprecedented opportunities to deliver anatomical information and knowledg

Finding Top-k Dominance on Incomplete Big Data Using Map-Reduce Framework

Incomplete data is one major kind of multi-dimensional dataset that has random-distributed missing nodes in its dimensions. It is very difficult to retrieve information from this type of dataset when it becomes huge. Finding top-k dominant values in this type of dataset is a challenging procedure. Some algorithms are present to enhance this process but are mostly efficient only when dealing with a small-size incomplete data. One of the algorithms that make the application of TKD query possible is the Bitmap Index Guided (BIG) algorithm. This algorithm strongly improves the performance for incomplete data, but it is not originally capable of finding top-k dominant values in incomplete big data, nor is it designed to do so. Several other algorithms have been proposed to find the TKD query, such as Skyband Based and Upper Bound Based algorithms, but their performance is also questionable. Algorithms developed previously were among the first attempts to apply TKD query on incomplete data; however, all these had weak performances or were not compatible with the incomplete data. This thesis proposes MapReduced Enhanced Bitmap Index Guided Algorithm (MRBIG) for dealing with the aforementioned issues. MRBIG uses the MapReduce framework to enhance the performance of applying top-k dominance queries on huge incomplete datasets. The proposed approach uses the MapReduce parallel computing approach using multiple computing nodes. The framework separates the tasks between several computing nodes that independently and simultaneously work to find the result. This method has achieved up to two times faster processing time in finding the TKD query result in comparison to previously presented algorithms