The number of features that can be computed over an image is, for practical purposes, limitless. Unfortunately, the number of features that can be computed and exploited by most computer vision systems is considerably less. As a result, it is important to develop techniques for selecting features from very large data sets that include many irrelevant or redundant features. This work addresses t...

An harmonic lattice theory is used, in conjunction with Mura’s theory of eigendistorsions, to study the structure and energetics of nascent dislocation loops in face-centredcubic (FCC) crystals. An analytical expression for the activation energies of such loops is derived. The results obtained herein indicate that thermal activation of small dislocation loops is possible at high stress levels s...

[email protected] Gunma Astronomical Observatory, 6860-86 Nakayama, Takayama-mura, Agatsuma-gun, Gunma 377-0702 Gunma Study Center, The University of the Air, 1-13-2 Wakamiya-cho, Maebashi, Gunma 371-0032 Liberal Arts Education Center, Tokai University, 1117 Kitakaname, Hiratsuka, Kanagawa 259-1292 Department of Physics, Faculty of Science, Graduate School of Tokai University, 1117 Kitakaname,

Fast multiplication can be constructed by combining the tree structure of multiplication's addition, i.e. Wallace tree, with the parallel on-they conversion to have a multiplication without using any carry propagation adder at the end. This parallel on-they conversion speeds up the conversion of redundant binary to conventional binary. With the delay of n 4 , we can have the conversion from red...

We introduce a scheme to simplify a multi-valued network using redundancy removal techniques. Recent methods [1], [2] for binary redundancy removal avoid the use of state traversal. Additionally, [2] finds multiple compatible redundancies simultaneously. We extend these powerful advances in the field of binary redundancy removal to perform redundancy removal for multi-valued networks. First we ...

Classification problems have a large number of features in datasets, but not all them are useful for classification. Irrelevant and redundant features reduce the performance. These features may be considered as noisy. In order to solve this problem we perform a feature selection process. It is a preprocessing technique for solving classification problem. Feature Selection aims to choose relevan...

Data acquisition, storage and management have been improved, while the key factors of many phenomena are not well known. Consequently, irrelevant and redundant features artificially increase the size of datasets, which complicates learning tasks, such as regression. To address this problem, feature selection methods have been proposed. This research introduces a new supervised filter based on t...

Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features. We can use density-ba...