Brainstem auditory evoked potentials (BAEP) are electrical potentials recorded in response to an auditory stimulus. Wavelet transform is adopted to extract the characteristic features of BAEP for interpretation and assessment. The results shows that there is significant difference (p<0.05) in the wavelet coefficients features in normal and abnormal BAEPs. Keywords— Brainstem Auditory Evoked pot...

The main objective of super-resolution (SR) imaging is to reconstruct a high-resolution (HR) image of a scene from one or more low-resolution images of the scene. In resolution enhancement of images, the main loss is on the high frequency components (edges) of the image. This is due to the smoothing caused by interpolation. Hence in order to enhance the quality of the super resolved image, pres...

The dual-tree complex wavelet transform is a useful tool in signal and image processing. In this paper, we propose a dual-tree complex wavelet transform (CWT) based algorithm for image inpainting problem. Our approach is based on Cai, Chan, Shen and Shen’s framelet-based algorithm. The complex wavelet transform outperforms the standard real wavelet transform in the sense of shift-invariance, di...

This paper analytically formulates both the optimal quantization noise allocation ratio and the coding gain of the two-dimensional morphological Haar wavelet transform. The two-dimensional morphological Haar wavelet transform has been proposed as a nonlinear wavelet transform. It has been anticipated for application to nonlinear transform coding. To utilize a transformation to transform coding,...

Decomposing the wavelet transform in lifting steps allows a simpler implementation of the transform filters and provides the flexibility necessary to satisfy other requirements, e.g., generating non-linear integer-to-integer wavelet transforms. The paper presents a flow-graph approach to the lifting factorization that gives a better insight to the main features of single-phase and two-phase wav...

Theory of wavelet transform is a young branch of applied mathematics extensively developing from early 1980's. Recently, the wavelet transform is studied increasingly with applications to digital signal processing and analysis including data compression. This paper briefly introduces theory of wavelet transform and shows several promising applications in cardiology. Also, examples of the use of...

Wavelet transforms have become increasingly important in image compression since wavelets allow both time and frequency analysis simultaneously. This paper investigates the fundamental concept behind the wavelet transform and provides an overview of some improved algorithms on the wavelet transform. The latter part of this paper emphasize on lifting scheme which is an improved technique based o...

An artificial neural network is one of the most important models for training features of voice conversion (VC) tasks. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as mel cepstral coefficients (MCC) which represent the spectrum features. However, a simple representation for fundamental frequency (F0) is not enough for neural networks to deal with an...