This paper reviews two different uses of the continuous wavelet transform for modal identification purposes. The properties of the wavelet transform, mainly energetic, allow to emphasize or filter the main information within measured signals and thus facilitate the modal parameter identification especially when mechanical systems exhibit modal coupling and/or relatively strong damping.

Let Mn,m be the space of real n × m matrices which can be identified with the Euclidean space R. We introduce continuous wavelet transforms on Mn,m with a multivalued scaling parameter represented by a positive definite symmetric matrix. These transforms agree with the polar decomposition on Mn,m and coincide with classical ones in the rank-one case m = 1. We prove an analog of Calderón’s repro...

We examine the outer Galactic HI disk for deviations from the b = 0 plane by constructing maps of disk surface density, mean height, and thickness. We find that the Galactic warp is well described by a vertical offset plus two Fourier modes of frequency 1 and 2, all of which grow with Galactocentric radius. Adding the m = 2 mode accounts for the large asymmetry between the northern and southern...

This paper reviews two different uses of the continuous wavelet transform for modal identification purposes. The properties of the wavelet transform, mainly energetic, allow to emphasize or filter the main information within measured signals and thus facilitate the modal parameter identification especially when mechanical systems exhibit modal coupling and/or relatively strong damping.

Object tracking in the video sequence is a challenging task because of its various applications

This paper describes the fingerprint verification based on wavelet transform and the local dominant orientation. Daubechies wavelet is use to decompose the fingerprint image. The local dominant orientation is computed using the coherence. The trainee and test fingerprint images are aligned around core point, in this paper the core point is detected as a maximum curvature point. We received 85 p...

The Haar wavelet transform is easier to describe than the orthonormal basis itself. An image is broken up into 2N−1× 2N−1 blocks of 2× 2 pixels. Let’s focus on a single block at index (i, j), and label its pixels p1, p2, p3, p4. Now consider another 2 N × 2N grid divided into four quadrants. The (i, j) entry of the upper-left quadrant will take the value 1 4(p1 + p2 + p3 + p4), and the (i, j)-e...