Informatica

Fuzzy c-means (FCM) is a well-known and widely applied fuzzy clustering method. Although there have been considerable studies which focused on the selection of better fuzzifier values in FCM, there is still not one widely accepted criterion. Also, in practical applications, the distributions of many data sets are not uniform. Hence, it is necessary to understand the impact of cluster size distribution on the selection of fuzzifier value. In this paper, the coefficient of variation (CV) is used to measure the variation of cluster sizes in a data set, and the difference of coefficient of variation (DCV) is the change of variation in cluster sizes after FCM clustering. Then, considering that the fuzzifier value with which FCM clustering produces minor change in cluster variation is better, a criterion for fuzzifier selection in FCM is presented from cluster size distribution perspective, followed by a fuzzifier selection algorithm called CSD-m (cluster size distribution for fuzzifier selection) algorithm. Also, we developed an indicator called Influence Coefficient of Fuzzifier ($\mathit{ICF}$) to measure the influence of fuzzifier values on FCM clustering results. Finally, experimental results on 8 synthetic data sets and 4 real-world data sets illustrate the effectiveness of the proposed criterion and CSD-m algorithm. The results also demonstrate that the widely used fuzzifier value $m=2$ is not optimal for many data sets with large variation in cluster sizes. Based on the relationship between ${\mathit{CV}_{0}}$ and $\mathit{ICF}$, we further found that there is a linear correlation between the extent of fuzzifier value influence and the original cluster size distributions.

Basic motion structures of crowd aggregation and crowd dispersion are defined and a novel method for identifying these crowd behaviours is proposed. Based on integral optical flow, background and foreground are separated and intensive motion region is obtained. Crowd motion is analysed at pixel-level statistically for each frame to obtain quantity of pixels moving toward or away from each position and their comprehensive motion at each position. Regional motion indicators are computed and regional motion maps are formed to describe motions at region-level. Crowd behaviours are identified by threshold segmentation of regional motion maps.

This paper describes a method how to represent and build a reusable VHDL component. By that component we can, for example, describe a family of the relative VHDL models. To represent the component, we use external functions as a mechanism to support a pre-processing and perform the instantiation of the component. A user interface, the constituent of the reusable component, serves for transferring parameters for the instantiation. We deliver a formal syntax of the functions and examples of their semantics. We describe the design of the reusable component as a procedure of transferring of: a) the intrinsic characteristics for a given family of domain objects and b) features from a given VHDL model(s). Those features require to be re-coded and extended with new ones by means of the external functions introduced. To test a reusable component, we use pre-processing and modelling.

A practical method for segmentation and estimation of model parameters of processes is proposed in this paper. A pseudo-stationary random process with instantly changing properties is divided into stationary segments. Every segment is described by an autoregressive model. A maximum likehood method is used for segmentation of the random process and estimation of unknown model parameters. An example with simulated data is presented.