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Comparison of non-linear palette usage for gamma-ray tomography imaging

100%

Budzisz J. , Mosorov V. , Biedron S.

Image Processing & Communications

2013

tom Vol. 18, no. 2-3

119--125

Gamma-ray tomography is used for non-invasive studying of objects. To enable correct interpretation of such measurements, they need to be presented in analysis-friendly way. One method is to use ILST (Iterative Least Squares Technique) algorithm to visualize 1D detector data on a 2D grid, so that gammaray attenuation is visible with a resemblance to cross-section structure. However algorithm imperfections and thresholding do not always allow inferring shapes of the structure correctly. To remedy this, the analyses of the whole range of reconstructed values are to be used with a non-linear transformation function to visualize and emphasize density gradient.

On Visual Assessment of Software Quality

100%

Bartoszuk C. , Timoszuk G. , Dabrowski R. , Stencel K.

e-Informatica Software Engineering Journal

2014

tom Vol. 8, nr 1

7--26

Development and maintenance of understandable and modifiable software is very challenging. Good system design and implementation requires strict discipline. The architecture of a project can sometimes be exceptionally difficult to grasp by developers. A project’s documentation gets outdated in a matter of days. These problems can be addressed using software analysis and visualization tools. Incorporating such tools into the process of continuous integration provides a constant up-to-date view of the project as a whole and helps keeping track of what is going on in the project. In this article we describe an innovative method of software analysis and visualization using graph-based approach. The benefits of this approach are shown through experimental evaluation in visual assessment of software quality using a proof-of-concept implementation — the Magnify tool.

Statistical and visualization data mining tools for foundry poduction

63%

Perzyk M.

Archives of Foundry Engineering

2007

tom Vol. 7, iss. 3

111-116

In recent years a rapid development of a new, interdisciplinary knowledge area, called data mining, is observed. Its main task is extracting useful information from previously collected large amount of data. The main possibilities and potential applications of data mining in manufacturing industry are characterized. The main types of data mining techniques are briefly discussed, including statistical, artificial intelligence, data base and visualization tools. The statistical methods and visualization methods are presented in more detail, showing their general possibilities, advantages as well as characteristic examples of applications in foundry production. Results of the author's research are presented, aimed at validation of selected statistical tools which can be easily and effectively used in manufacturing industry. A performance analysis of ANOVA and contingency tables based methods, dedicated for determination of the most significant process parameters as well as for detection of possible interactions among them, has been made. Several numerical tests have been performed using simulated data sets, with assumed hidden relationships as well some real data, related to the strength of ductile cast iron, collected in a foundry. It is concluded that the statistical methods offer relatively easy and fairly reliable tools for extraction of that type of knowledge about foundry manufacturing processes. However, further research is needed, aimed at explanation of some imperfections of the investigated tools as well assessment of their validity for more complex tasks.