K. Martin’s scientific contributions

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (9)


The CMake build manager
  • Article

January 2003

·

80 Reads

·

12 Citations

Doctor Dobbs Journal

W. Hoffman

·

K. Martin

A study is performed on the CMake build manager. CMake is an open-source, cross-platform C/C++ build manager that lets the user specify build parameters in a text file. It also includes integrated support for regression testing. It provides the ability to determine the byte order and other hardware-specific characteristics, when developing the cross-platform software.




Large-scale data visualization using parallel data streaming

August 2001

·

142 Reads

·

105 Citations

IEEE Computer Graphics and Applications

J. Ahrens

·

K. Brislawn

·

K. Martin

·

[...]

·

We present an architectural approach based on parallel data streaming to enable visualizations on a parallel cluster. Our approach requires less memory than other visualizations while achieving high code reuse. We implemented our architecture within the Visualization Toolkit (VTK). It includes specific additions to support message passing interfaces (MPIs); memory limit-based streaming of both implicit and explicit topologies; translation of streaming requests between topologies; and passing data and pipeline control between shared, distributed, and mixed memory configurations. The architecture directly supports both sort-first and sort-last parallel rendering



Multiple Modality Biomagnetic Analysis System

January 2000

·

12 Reads

·

4 Citations

As an emerging new modality, biomagnetic analysis presents exciting opportunities while raising some difficult issues. During the past four years researchers at General Electric, The University of Ulm and The University of Chieti have created a state of the art biomagnetic analysis system, and have addressed many of these difficulties. One of the critical aspects of a biomagnetic analysis system is how it handles different types of data. Many of the clinical requirements for a successful system require fast, efficient and flexible data management. This paper describes what those requirements are and how we addressed them. We start by providing a high level overview of our system. We then list some of the pertinent clinical requirements, present our solutions and describe how well they have worked. Finally we discuss future directions.




Citations (8)


... The process of transforming data into sensory stimuli and visual images is called data visualisation (Schroeder et al., 2003). Powerful charts, diagrams or maps provide solutions to explore, analyse, and present data. ...

Reference:

MARK-AGE data management: Cleaning, exploration and visualization of data
The visualisation toolkit
  • Citing Article
  • January 2003

... With an echo time T E of about 15 ms and a repetition time T R > 800 ms, according to heart beat gating a total measurement time of about 15 min can be reached. At the beginning the Biomagnetic Analysis Console (BAC) software was used for MCG signal analysis [3]. Now, the standard analysis tool is the Open Magnetic and Electric Graphic Analysis (OMEGA) software [4]. ...

Multiple Modality Biomagnetic Analysis System
  • Citing Chapter
  • January 2000

... We extend these memory resources to cover heterogeneous memory, and demonstrate how they can be applied to heterogeneous programming to provide a more comfortable and productive experience to C++ programmers. The project also provides the CMake [8] build infrastructure for making use of the supported heterogeneous programming languages on all platforms that those languages themselves support. ...

The CMake build manager
  • Citing Article
  • January 2003

Doctor Dobbs Journal

... A multitude of methods have been proposed to generate transfer functions. They can be broadly classified into four categories which are described in detail by Pfister et al. [11] . The first are trial and error methods where the user tweaks parameters to achieve the desired result. ...

Visualization viewpoints: The transfer function bake-off
  • Citing Article
  • January 2001

... (i) scanning subjects to obtain image stacks; (ii) creating individual 3D models from the stacks; (iii) cutting each model to generate a sub-model of the user's interest; (iv) making image stacks that contain only the information pertaining to the sub-models; (v) iteratively registering the corresponding new 2D image stacks from the previous step; (vi) averaging the newly created sub-models based on intensity to produce the generic model from all the individual sub-models. All the algorithms are implemented using Java and C++ based on functionalities from open source toolkits VTK (Visualization Toolkit [17] ), ITK (Insight Segmentation and Registration Toolkit [13]) and ImageJ [18]. Both volumetric data and surface model for the generic 3D model are created at the final step. ...

THE VISUALIZATION TOOLKIT PRENTICE HALL PTR
  • Citing Article

... It is applied for scientific purposes in various fields of research (Joshi, Scheinost, Vives, Spencer, Staib & Papademetris 2008), (Sustersic, Kandemir, Phoha & Schmiedekamp 2008), (Badesa, Pinto, Sabater, Azorin, Sofrony & Cardenas 2009). This software package is capable of scalable parallel processing (Ahrens, Law, Schroeder, Martin, Inc & Papka 2000) and is also used for supercomputing visualizations (Ahrens, Lo, Nouanesengsy, Patchett & McPherson 2008). The working principle of VTK is based on visualization pipelines Fig. 1(a). ...

A parallel approach for e ciently visualizing extremely large
  • Citing Article

... The elastic rarefaction wave can be calculated after determining the density of a compressed material with a given stress 1 from the initial condition (25) and the condition that the material is at the elasticplastic limit in compression. The change in the deviatoric part of the stresses in an elastic rarefaction wave is twice as large as in an elastic precursor and, therefore, the material density is calculated after a similar application of the equation for the change in deviatoric stress (26): ...

Large-scale data visualization using parallel data streaming
  • Citing Article
  • August 2001

IEEE Computer Graphics and Applications