The development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are ‘unlabeled’, i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology...

Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties. Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image...

The thesis of this work is that programmable graphics hardware can be used to implement complex iterative algorithms. This is achieved by manipulating the graphics processing pipeline, which was design specifically for the graphical rendering process, to perform more general purpose applications. In doing so, the parallelism of the graphics hardware can be to achieve performance gains compared ...

Within condition based maintenance (CBM), the whole aspect of prognostics is composed of various tasks from multidimensional data to remaining useful life (RUL) of the equipment. Apart from data acquisition phase, data-driven prognostics is achieved in three main steps: features extraction and selection, features prediction, and health-state classification. The main aim of this paper is to prop...

The ability to cluster data accurately is essential to applications such as image segmentation. Therefore, techniques that enhance accuracy are of keen interest. One such technique involves applying a quantum mechanical system model, such as that of the quantum bit, to generate probabilistic numerical output to be used as variable input for clustering algorithms. This work demonstrates that app...

In this paper, the modification of conditional Fuzzy C-Means (CFCM) aimed at estimation of unknown desired channel states is accomplished for Bayesian blind channel equalizer under the presence of heavy additive Gaussian noise (AWGN). For the modification of CFCM to search the optimal channel states of a heavy noise-corrupted communication channel, a Gaussian weighted partition matrix, along wi...

Most of the clustering methods used in the clustering of chemical structures such as Ward’s, Group Average, Kmeans and Jarvis-Patrick, are known as hard or crisp as they partition a dataset into strictly disjoint subsets; and thus are not suitable for the clustering of chemical structures exhibiting more than one activity. Although, fuzzy clustering algorithms such as fuzzy cmeans provides an i...

Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like th...

Many conventional contrast enhancement techniques adopt a global approach to enhance all the brightness level of the image. However, it is usually difficult to enhance all land cover classes appearing in the satellite images, because local contrast information and details may be lost in the dark and bright areas. In this study, a fuzzy-based image enhancement method is developed to partition th...