Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a large extent within the time frame of signals. Although previous studies have focused on the issues, their results have not been satisfactory. Therefore, we pre...

Principal component analysis (PCA) guided clustering approach is widely used in high dimensional data to improve the efficiency of Kmeans cluster solutions. Typically, Pearson correlation is used in PCA to provide an eigenanalysis to obtain the associated components that account for most of the variations in the data. However, PCA based Pearson correlation can be sensitive on non-Gaussian distr...

A new model for neuro-fuzzy (NF) classification systems is proposed. The motivation is to utilize the feature-wise degree of belonging of patterns to all classes that are obtained through a fuzzification process. A fuzzification process generates a membership matrix having total number of elements equal to the product of the number of features and classes present in the data set. These matrix e...

A multi-step Expectation Maximization based (EM-based) algorithm is proposed to solve piecewise surface regression problem which has typical applications in market segmentation research, identification of consumer behavior patterns, weather patterns in meteorological research, and so on. The multiple steps involved are local regression on each data point of the training data set and a small set...

Collisions between pairs of galaxies usually end in the coalescence (merger) of the two galaxies. Collisions and mergers are rare phenomena, yet they may signal the ultimate fate of most galaxies, including our own Milky Way. With the onset of massive collection of astronomical data, a computerized and automated method will be necessary for identifying those colliding galaxies worthy of more de...

Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization...

We introduce a novel method for assessing the robustness of clusters found by partitioning algorithms. First, we show how the stability of individual objects can be estimated based on repeated runs of the K -means and K -medoids algorithms. The quality of the resulting clusterings, expressed by the popular Calinski–Harabasz, Silhouette, Dunn and Davies–Bouldin cluster validity indices, is taken...

This paper describes a comparative study of color image segmentation for various color spaces such as RGB, YUV, XYZ, Lab, HSV, YCC and CMYK using Dynamic Histogram based Rough Fuzzy C Means (DHRFCM). The proposed algorithm DHRFCM is based on modified Rough Fuzzy C Means (RFCM), which is further divided into three stages. In the pre-processing stage, convert RGB into required color space and the...

The task of speaker diarization is to answer the question "who spoke when?" In this paper, we present different clustering approaches which consist of Evolutionary Computation Algorithms (ECAs) such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, and Differential Evolution (DE) algorithm as well as Teaching-Learning-Based Optimization (TLBO) technique as a new optimizati...

In this paper a new pattern recognition methodology is described for the classification of the daily chronological load curves of power systems, in order to estimate their respective representative daily load profiles, which can be mainly used for load forecasting and feasibility studies of demand side management programs. It is based on pattern recognition methods, such as k-means, adaptive ve...