In this paper, we propose a collective self-supervised learning method to be deployed in acoustic sensor arrays. We describe a series of experiments on the automated classification of tropical bird species and bird individuals from their songs by a classifier ensemble. Simulation results showed that accurate classification can be achieved using the proposed model.

Due to the different properties of the dynamical simulation of multi-body systems, it is important to introduce a classification that allows for the right selection of a simulation type according to the user’s needs. The proposed classification divides simulations with respect to the main characteristics, such as speed, accuracy, processing power, preparation work, resulting in the need for ver...

This paper presents a method for classification and location of transmission line faults based on Artificial Neural Network (ANN). Although for fault classification prefault and postfault three phase currents samples taken at one end of transmission line are used as ANN inputs, for location of faults post fault samples of both current and voltages of three phases are required . Simulation studi...

This paper presents the preliminary results of an effort to develop a simplified simulation-based tool for designing K-5 high performance schools in hot and humid climates. As a first step of the research, a survey to define the dominant school building shape was conducted in an independent school district in Central Texas. This survey used satellite views of the K-5 schools, where each school ...

In this paper we present the capability of the Rank M-Type Radial Basis Function (RMRBF) neural network in the classification of Pap smear microscopic images. From simulation results we observe that the RMRBF neural network has better classification capabilities in comparison with other RBF based algorithms.

We review compressive sensing and its extension to classification and joint signal recovery. We present an overview of compressed sensing, followed by some simulation results on perfect reconstruction for sparse signals. We review previous work on compressed signal classification and discuss relations between the two earlier papers. Finally, we discuss joint signal reconstruction for compressed...

Clustering method for remote sensing satellite image classification based on Messy Genetic Algorithm: GA is proposed. Through simulation study and experiments with real remote sensing satellite images, the proposed method is validated in comparison to the conventional simple GA. It is also found that the proposed clustering method is useful for preprocessing of the classifications. Keywordsclus...

This paper discusses the recognition of raised alphanumeric markings on rubber tires for their classification. The main subject of this paper is the finding, separating, and recognizing the characters raised on rubber tires, which is different from the recognition of printed characters. The complter simulation result shows that the proposed system can be successfully applied to the industrial a...

For the learning of Classification Tree, many researchers have used different splitting criteria, in which most commonly impurity-based criteria are: Gini index, Entropy function and Exponent-based index. By comparing Misclassification rates, none of the splitting criterion can be declared as providing best results in every situation. In this study, a new Mean Deviation based index has been pro...

This paper summarizes the results of a simulation study of single processors architectures for graphics. The entire work is done from point-of-view of RISC/CISC style classification. A comparison between various categories is evaluated using HDL description of representative processors, as well as simulations based on ENDOT simulation tools.