Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite...

a cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. the cochlear implant is often referred to as a bionic ear. this paper presents an undecimated wavelet‑based speech coding strategy for cochlear implants, which gives a novel speech processor. the undecimated wavelet packet transform (uwpt) is computed like the wavelet ...

linear filters are used for a wide range of magnetic science including noise attenuation, spatial derivatives, upward and downward continuation and reduction to the pole. the majority of these filters are spatially-invariant, meaning that the filter has a constant wave number response over the whole signal. in contrast, many signals which encountered real problems such as magnetic signals, typi...

Diagnostics of rolling elements under varying operational conditions, where disturbances and other rotating elements have strong influence on correctness of analysis, requires engagement of advanced signal processing techniques. Extraction of signal components generated by bearing faults has been proven to be an exceptionally promising method for rolling element bearing fault detection. In this...

in this paper, we introduce an efficient method based on haar wavelet to approximate a solutionfor the two-dimensional linear stochastic fredholm integral equation. we also give an example to demonstrate the accuracy of the method.

Here we introduce our application of the wavelet analysis method to DNA sequences. In the signal processing field, Fourier transform is popular for analyzing wave data. However, although this method can process frequency information, it fails to handle locational data. In contrast, the wavelet method accommodates both locational and frequency information for wave analysis. The wavelet method is...

In the fluorescence detection system, noise will affect the sensitivity of the system. The method using Stationary Wavelet Transform to de-noise fluorescence signals based on adaptive wavelet entropy is studied. The de-noising effects of dbN families are compared, and then the db7 wavelet is chosen as the optimal wavelet. The noised fluorescence signal is decomposed at 5 levels via Discrete Wav...

Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improv...

recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. it is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. in this paper, f...