Abstract. We describe an extension to the "best-basis" method to select an orthonormal basis suitable for signal/image classification problems from a large collection of orthonormal bases consisting of wavelet packets or local trigonometric bases. The original best-basis algorithm selects a basis minimizing entropy from such a "library of orthonormal bases" whereas the proposed algorithm selects a basis maximizing a certain discriminant measure (e.g., relative entropy) among classes. Once such a basis is selected, a small number of most significant coordinates (features) are fed into a traditional classifier such as Linear Discriminant Analysis (LDA) or Classification and Regression Tree (CARTTM). The performance of these statistical methods is enhanced since the proposed methods reduce the dimensionality of the problem at hand without losing important information for that problem. Here, the basis functions which are well-localized in the time-frequency plane are used as feature extractors. We applied our method to two signal classification problems and an image texture classification problem. These experiments show the superiority of our method over the direct application of these classifiers on the input signals. As a further application, we also describe a method to extract signal component from data consisting of signal and textured background.
Methyl jasmonate (MeJA) as well as abscisic acid (ABA) induces stomatal closure with their signal crosstalk. We investigated the function of a regulatory A subunit of protein phosphatase 2A, RCN1, in MeJA signaling. Both MeJA and ABA failed to induce stomatal closure in Arabidopsis rcn1 knockout mutants unlike in wild-type plants. Neither MeJA nor ABA induced reactive oxygen species (ROS) production and suppressed inward-rectifying potassium channel activities in rcn1 mutants but not in wild-type plants. These results suggest that RCN1 functions upstream of ROS production and downstream of the branch point of MeJA signaling and ABA signaling in Arabidopsis guard cells.
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