Pattern analysis techniques to process fermentation curves: Application to discrimination of enological alcoholic fermentations

2002 ◽  
Vol 79 (7) ◽  
pp. 804-815 ◽  
Author(s):  
Jean-Michel Roger ◽  
Jean-Marie Sablayrolles ◽  
Jean-Philippe Steyer ◽  
V�ronique Bellon-Maurel
Keyword(s):  
Pattern Analysis ◽  
Analysis Techniques

An Overview of Methods for Automatic Reassembly of Fragmented Objects

2011 ◽  
pp. 263-288
Author(s):  
Dimitris Arabadjis ◽  
Michael Exarhos ◽  
Fotios Giannopoulos ◽  
Solomon Zannos ◽  
Panayiotis Rousopoulos ◽  
...  
Keyword(s):  
Signal Processing ◽  
Pattern Analysis ◽  
Complete System ◽  
General Purpose ◽  
Wall Paintings ◽  
Analysis Techniques ◽  
Application Problem ◽  
Archaeological Objects

In this chapter the authors outline some research works characteristic for the application of Signal Processing and Pattern Analysis techniques to the automatic reconstruction / reassembly of fragmented archaeological objects. The studies described in the chapter cover in their application cases a variety of archaeological objects, ranging from documents and wall-paintings to pots and sculptures. Moreover there are distinct approaches in the treatment of these application cases, with some works focusing on the development of a reconstruction methodology of general purpose, while others aim to develop a complete system to treat a specific application problem. The methodologies developed in these studies are outlined in the chapter so as to retain the basic technical elements of each approach that compile the proposed reconstruction algorithmic scheme.

scholarly journals Layer histogram patterns in financial time series

2009 ◽  
Vol 6 (3) ◽  
pp. 137-146
Author(s):  
Verena Helen Van Zyl-Bulitta ◽  
R. Otte ◽  
JH Van Rooyen
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Financial Risk ◽  
Pattern Analysis ◽  
Financial Time Series ◽  
Data Set ◽  
Frequency Distributions ◽  
Analysis Techniques ◽  
Financial Time ◽  
Macroscopic Fluctuations

This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate and index data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from statistically insufficient samples of changes and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behavior of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study special emphasis is placed on the histogram pattern analysis of high frequency exchange rate data set. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings presented here widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics, more specifically the formation of discrete states.

Prediction of pork marbling scores using pattern analysis techniques

Food Control ◽  
2013 ◽  
Vol 31 (1) ◽  
pp. 224-229 ◽  
Author(s):  
H. Huang ◽  
L. Liu ◽  
M.O. Ngadi ◽  
C. Gariépy
Keyword(s):  
Pattern Analysis ◽  
Analysis Techniques

scholarly journals Examining Similarity Structure: Multidimensional Scaling and Related Approaches in Neuroimaging

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Svetlana V. Shinkareva ◽  
Jing Wang ◽  
Douglas H. Wedell
Keyword(s):  
Multidimensional Scaling ◽  
Pattern Analysis ◽  
Brain Regions ◽  
Multivariate Pattern Analysis ◽  
Imaging Data ◽  
Magnetic Resonance Imaging Data ◽  
Analysis Techniques ◽  
Similarity Structure ◽  
Brain Data ◽  
Multivariate Pattern

This paper covers similarity analyses, a subset of multivariate pattern analysis techniques that are based on similarity spaces defined by multivariate patterns. These techniques offer several advantages and complement other methods for brain data analyses, as they allow for comparison of representational structure across individuals, brain regions, and data acquisition methods. Particular attention is paid to multidimensional scaling and related approaches that yield spatial representations or provide methods for characterizing individual differences. We highlight unique contributions of these methods by reviewing recent applications to functional magnetic resonance imaging data and emphasize areas of caution in applying and interpreting similarity analysis methods.

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