Clusters of microcalcifications in mammograms are an important early sign of breast cancer in women. Comparing with microcalcifications, the breast background tissues have high local self-similarity, which is the basic property of fractal objects. A fast fractal modeling method of mammograms for detecting the presence of microcalcifications is proposed in this paper. The conventional fractal modeling method consumes too much computation time. In the proposed method, the image is divided into shade (homogeneous) and non-shade blocks based on the dynamic range and only the non-shade blocks are

In the last few years the gene expression microarray technology has become a central tool in the field of functional genomics in which the expression levels of thousands of genes in a biological sample are determined in a single experiment. Several clustering and biclustering methods have been introduced to analyze the gene expression data by identifying the similar patterns and grouping genes into subsets that share biological significance. However, it is not clear how the different methods compare with each other with respect to the biological relevance of the biclusters and clusters as well

Stimulated echo acquisition mode (STEAM) is a generic imaging technique that lies at the core of many magnetic resonance imaging (MRI) techniques such MRI tagging, displacement encoded MRI, black-blood cardiac imaging. Nevertheless, tissue deformation causes frequency shift of the MR signal and leads to severe signal attenuation. In this work, a maximum likelihood estimator for the signal amplitude is proposed and used to correct the image artifacts. Numerical simulation and real MR data are used to test and validate the proposed method. © 2011 IEEE.

In the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier (KNN), Fuzzy K-nearest neighbor classifier and Multi-layer Perceptron (MLP) and then compare the results with Fuzzy Integral, Decision Templates, Weighted Majority, Majority Naïve Bayes, Maximum

Brain-Computer Interfaces (BCI) is a one kind of communication system that enables control of devices or communication with others only through brain signal activities without using motor activities. The main application for BCI is to provide an alternative channel for helping disabled persons, hereafter mentioned as subjects, to communicate with the external world. This paper tries to demonstrate the performance of different machine learning algorithms based on classification accuracy. Performance has been evaluated on dataset II from BCI Competition III for the year 2004 for two subjects 'A'

The main idea to correct sight disorders using lasers is to modify corneal curvature by applying laser to specific layers of the cornea. Intrastromal Photorefractive keratectomy is a laser technique used to correct sight disorders by evaporating corneal tissue, which results in small cavities that may coincide to form a larger cavity that will collapse to deform the curvature of the cornea. In this work, a 3D finite element model of the cornea was designed with typical parameters to simulate the procedure. The model outcome was compared with an earlier 2D model used for the same purpose, so as

Soft labels allow a pattern to belong to multiple classes with different degrees. In many real world applications the association of a pattern to multiple classes is more realistic; to describe overlap and uncertainties in class belongingness. The objective of this work is to develop a fuzzy Gaussian process model for classification of soft labeled data. Gaussian process models have gained popularity in the recent years in classification and regression problems and are example of a flexible, probabilistic, non-parametric model with uncertainty predictions. Here we derive a fuzzy Gaussian model

Mobile Ad hoc Networks (MANETs) have lots of applications. Due to the features of open medium, absence of infrastructure, dynamic changing network topology, cooperative algorithms, lack of centralized monitoring and management point, resource constraints and lack of a clear line of defense, these networks are vulnerable to attacks. A vital problem that must be solved in order to realize these applications is that concerning the security aspects of such networks. Solving these problems combined with the widespread availability of devices such as PDAs, laptops, small fixtures on buildings and

Brain-Computer Interfaces (BCI) is a one kind of communication system that enables control of devices or communication with others only through brain signal activities without using motor activities. P300 Speller is a BCI paradigm that helps disabled subjects to spell words by means of their brain signal activities. This paper tries to demonstrate the performance of different machine learning algorithms based on classification accuracy. Performance has been evaluated on the data sets acquired using BC12000's P300 Speller Paradigm provided by BCI competitions II (2003) & III (2004) organizers

Object localization has become a necessary module in many radiofrequency identification (RFID) systems that require tracking features besides the conventional identification feature. A number of techniques exists in literature that uses the RFID signal information to locate the tagged objects, i.e. objects wearing RFID tags. Nevertheless, in many applications, it is required to track objects that do not carry a tag (whether intentionally or unintentionally). In this work, we propose a technique for tag-less object localization. The technique is based on reconstructing the attenuation map of