ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the field of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the num...

BP algorithm of neural net is used more in remote sensing data classification. One of drawbacks of BP algorithm is the overall low function when the net is training. To avoid this kind of problem, the paper introduces the tolerant rough set for classification-preprocessing the training data to reduce the influence elements of the training convergence in order to improve the net training success...

A routing scheme R in a network G = (V,E) is an algorithm that allows to send messages from one node to another in the network. We are first allowed a preprocessing phase in which we assign a unique label to each node p ∈ V and a routing table with additional information. After this preprocessing, the routing algorithm itself must be local (i.e., we can only use the information from the label o...

Study of different patterns on a local neighborhood of a texture plays an important role in characterization, and classification of the textures. Many preprocessing steps are used in the generation of textures for a better quality. The present paper studies how the percentage of occurrence factor of a typical pattern varies after applying various local preprocessing steps. For this eight simple...

We examine a computational geometric problem concerning the structure of polymers. We model a polymer as a polygonal chain in three dimensions. Each edge splits the polymer into two subchains, and a dihedral rotation rotates one of these subchains rigidly about the edge. The problem is to determine, given a chain, an edge, and an angle of rotation, if the motion can be performed without causing...

ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the eld of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the numbe...