architecture is a powerful signal analysis technique for non-stationary data. High speed implementation of this architecture is a challenging task. This paper proposes an efficient multi-input/multi-output VLSI architecture (MIMO) for two-dimensional lifting-based discrete wavelet transform (DWT

II. Continuous Wavelet Transform (CWT).....................................4

Abstract—The lifting scheme reduces the computational

of reconstructed image and the image browsing time reduces significantly. Index Terms — Discrete wavelet Transform (DWT),

Abstract—The wavelet transform is one of the most important method used in signal processing. In this study, we have introduced frequency-energy characteristics of local earthquakes using discrete wavelet transform. Frequency-energy characteristic was analyzed depend on difference between P

study on two techniques for analysis and visualization of voltage distortions with time-varying amplitudes. The techniques include the Discrete Wavelet Transform (DWT) and the Discrete Orthogonal S-Transform (DOST). Several power quality problems are analyzed using both the Discrete Wavelet Transform

The scientific community has invested a great deal of effort in the fields of discrete wavelet transform in the last few decades. Discrete wavelet transform (DWT) associated with the vector quantization has been proved to be a very useful tool for the compression of image. However, the DWT is very

on the fusion methods based on the Discrete Wavelet Transform (DWT), the most popular tool for image processing. Due to the numerous multi-scale transform, different fusion rules have been proposed for different purpose and applications.

using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet

many monitoring applications can tolerate some loss in the quality of the received image. DWT coding can decompose an image in data subbands, each one with different relevancies for the reconstruction of the original image at the receiver side. We propose an energy-efficient selective hop