A near-group fusion category is a fusion category C where all but 1 simple objects are invertible. Examples of these include the Tambara-Yamagami categories and the even sectors of the D (1) 5 and E6 subfactors, though there are infinitely many others. We classify the near-group fusion categories, and compute their doubles and the modular data relevant to conformal field theory. Among other thi...

In this paper the behavior of three combinational rules for temporal/sequential attribute data fusion for target type estimation are analyzed. The comparative analysis is based on: Dempster’s fusion rule proposed in Dempster-Shafer Theory; Proportional Conflict Redistribution rule no. 5 (PCR5), proposed in Dezert-Smarandache Theory and one alternative class fusion rule, connecting the combinati...

For solving the uncertain problem in the process of WEB page classification, a general fusion classification model and algorithm are proposed, which based on model theory of information fusion. In the model, the hidden classification information is extracted from the WEB page, pre-processed firstly, then the processed data are input into the fusion mode, which deals with the different data with...

Fusion frame theory is an emerging mathematical theory that provides a natural framework for performing hierarchical data processing. A fusion frame is a frame-like collection of subspaces in a Hilbert space, thereby generalizing the concept of a frame for signal representation. In this paper, we study the existence and construction of fusion frames. We first present a complete characterization...

In recent years, with the development of compressive sensing (CS) theory, it has been widely applied to each field including image fusion, and obtained better fusion effect. And CS can reduce dimensions and the amount of data characteristics as well as the large amount and high computation complex. Therefore, this paper proposes a novel medical image fusion method based on compressive sensing t...

We propose a new way to initialize the mass function of the Dempster-Shafer theory of evidence. The new initialization process is based on a fuzzy statistical approach and uses the FSEM algorithm (Fuzzy Statistical Estimation Maximization). This allows to classify image in "pure" and "fuzzy" regions, and thus enable an optimal estimation of the inaccuracy and uncertainty of the classification. ...

The basic quantity to be estimated in the Bayesian approach to data fusion is the conditional probability density function (CPDF). In recent times, computationally efficient particle filtering approaches are gaining growing importance in estimating these CPDF. In this approach, i.i.d samples are used to represent the conditional probability densities. However, their application in data fusion i...