The optimum use of energy in wireless sensor networks (WSNs) is very important. The recent researches show that organising the network nodes in some clusters leads to higher efficiency of energy and finally it increases the lifetime of the network. So, controlling the number and the location of the clusters head (CHs) and also the size of the clusters about the node number leads to a balance in...

Partial volume averaging (PVA) is present in nearly all practical imaging situations, medical imaging in particular. One method that has been used to account for the effects of PVA is the fuzzy c-means algorithm (FCM). We propose a new method for estimating the partial volume coefficient of each class at each voxel in a given image using a Bayesian statistical model. A prior probability on the ...

Field distributions generated by static Q ¯ Q and QQQ sources are calculated analytically in the framework of the Field Correlator Method (FCM) using Gaussian (bilocal) correlator. In both cases the string consists mostly of longitudinal color electric field, while transverse electric field contributes locally less then 3%, in agreement with earlier lattice studies. In the QQQ case the profile ...

A new robust clustering scheme based on fuzzy c-means is proposed and the concept of a fuzzy mega-cluster is introduced in this paper. The fuzzy mega-cluster is conceptually similar to the noise cluster, designed to group outliers in a separate cluster. This proposed scheme, called the mega-clustering algorithm is shown to be robust against outliers. Another interesting property is its ability ...

The well-known generalisation of hard Cmeans (HCM) clustering is fuzzy C-means (FCM) clustering where a weight exponent on each fuzzy membership is introduced as the degree of fuzziness. An alternative generalisation of HCM clustering is proposed in this paper. This is called fuzzy entropy (FE) clustering where a weight factor of the fuzzy entropy function is introduced as the degree of fuzzy e...

Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. However, it still suffers from two problems: one is insufficient robustness to image noise, and the other is the Euclidean distance in FCM, which is sensitive to outliers. In this paper, we propose two new algorithms, generalized FCM (GFCM) and hierarchical FCM (HFCM), to solve these two problems. Traditio...

The performance of diagnostic systems based on empirical models may vary in different zones of the training space. It is, thus, important to a-priori verify whether the model is working in a zone where the performance is expected to be satisfactory. In this respect, the objective of this work is to estimate the degree of confidence in the identification of nuclear transients by a diagnostic sys...

This paper presents the overview of an ongoing project which goal is to obtain and simulate the dynamics of qualitative systems through the combination of the properties of Fuzzy Boolean Networks and Fuzzy Rule Based Cognitive Maps.

Background removal is an application of image segmentation. There are many methods for image segmentation. In this paper, Fuzzy C-Means (FCM) is used for the image segmentation. In this paper, the clusters centroid is given as input from the histogram of the image. These inputs are updated and passed through FCM algorithm to get segmented images. The segmented images are added to remove the bac...