With progress in radar systems, a number of methods have been developed for signal processing and detection in radars. A number of modern radar signal processing methods use time-frequency transforms, especially the wavelet transform (WT) which is a well-known linear transform. The interference canceling is one of the most important applications of the wavelet transform. In Ad-hoc detection met...

The Fourier transform (FT) nicely represents a wiggling, sinelike data set with only a few peaks in the frequency domain. But while the FT is appropriate for simple stationary data like harmonic waves, complicated signals or images require more elaborate transforms. These transforms must catch fast local variations, detect blurred object contours, differentiate textures, and capture different s...

With progress in radar systems, a number of methods have been developed for signal processing and detection in radars. A number of modern radar signal processing methods use time-frequency transforms, especially the wavelet transform (WT) which is a well-known linear transform. The interference canceling is one of the most important applications of the wavelet transform. In Ad-hoc detection met...

An algorithm is presented for image blur detection with the 2D Haar Wavelet transform (2D HWT). The algorithm classifies an image as blurred or sharp by splitting it into N x N tiles, applying several iterations of the 2D HWT to each tile, and grouping horizontally, vertically, and diagonally connected tiles with pronounced changes into tile clusters. Images with large tile clusters are classif...

In this paper, we discuss about a simple and lossless compression method for compression of medical images. Method is based on wavelet transform of medical application. Wavelets provide a mathematical way of encoding information in such a way that it is layered according to level of detail. These approximations can be stored using a lot less space than the original data. Here a low complex 2D i...

Wavelet-based analysis versus Gaussian smoothing in statistical parametric mapping (SPM) for detecting and analyzing brain activity from functional magnetic resonance imaging (fMRI) data is presented. Detection of activation in fMRI data can be performed in the wavelet domain by a coefficient-wise statistical t-test. The link between the wavelet analysis and SPM is based on two observations: (i...

We propose deterministic sampling strategies for compressive imaging based on Delsarte-Goethals frames. We show that these sampling strategies result in multi-scale measurements which can be related to the 2D Haar wavelet transform. We demonstrate the effectiveness of our proposed strategies through numerical experiments.