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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(More)
We introduce a randomized procedure that, given an m × n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on applying a structured l × m random matrix R to each column of A, where l is an integer near to, but greater than, k. The structure of R allows us to apply it to an arbitrary m × 1 vector at a cost(More)
We describe an extension to the \best-basis" method to construct an orthonormal basis which maximizes a class separability for signal classiication problems. This algorithm reduces the dimen-sionality of these problems by using basis functions which are well localized in time-frequency plane as feature extractors. We tested our method using two synthetic(More)
—In this paper we describe a new technique for detecting and characterizing ellipsoidal shapes automatically from any type of image. This technique is a single pass algorithm which can extract any group of ellipse parameters or characteristics which can be computed from those parameters without having to detect all five parameters for each ellipsoidal(More)
Fig. IO. The noisy image for Example 4. obtained from the noi\y projec-tion\ of the Shepp-Logan phantom Fig. I I. MMSE image obtained by constraining the wavelet coefficients of the noisy image: Example 4. threshold is used to set the finest-scale coefficients to zero. The resulting MMSE image is shown in Fig. I 1 The noise power has been reduced by 20.3%(More)
The authors previously developed the so-called local discriminant basis (LDB) method for signal and image classiÿcation problems. The original LDB method relies on diierences in the time–frequency energy distribution of each class: it selects the subspaces where these energy distributions are well separated by some measure such as the Kullback–Leibler(More)
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(More)
We propose a new method to analyze and represent data recorded on a domain of general shape in R d by computing the eigenfunctions of Laplacian defined over there and expanding the data into these eigenfunctions. Instead of directly solving the eigenvalue problem on such a domain via the Helmholtz equation (which can be quite complicated and costly), we(More)
We describe extensions to the \best-basis" method to select orthonormal bases suitable for signal classiica-tion and regression problems from a large collection of orthonormal bases. For classiication problems, we select the basis which maximizes relative entropy of time-frequency energy distributions among classes. For regression problems, we select the(More)
Intracellular components in methyl jasmonate (MeJA) signaling remain largely unknown, to compare those in well-understood abscisic acid (ABA) signaling. We have reported that nitric oxide (NO) is a signaling component in MeJA-induced stomatal closure, as well as ABA-induced stomatal closure in the previous study. To gain further information about the role(More)