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Electroencephalogram (EEG) is a widely used signal for analyzing the activities of brain. It has extensively used for the diagnosis of different nervous system disorders such as Alzheimer's, Parkinson's, Seizures, Epilepsy, etc. Ocular activity creates significant artifacts in electroencephalogram recordings. These artifacts increase complexity in analyzing the EEG and obtaining the clinical information...
In this paper, a discrete wavelet packet transform algorithm is used for speech signal denoising. Both hard and soft thresholding are applied and noisy speech signal samples corrupted by white Gaussian noise from 0dB to +15dB are denoised in our experiments. Output SNR (Signal to Noise Ratio) values are calculated and compared with input SNR values using both types of thresholding methods. Soft thresholding...
In many real world applications, signals are nonstationary. One solution for processing non-stationary signals is the Wavelet Transform. Currently, there is tremendous focus on the application of Wavelet Transforms for real-time signal processing. This leads to the demand for efficient architectures for the implementation of Wavelet Transforms. In this paper, a Wavelet shrinkage technique is applied...
In this research, we proposed a new method for noise removal based on Dual Tree Complex Wavelet Transform (DTCWT) in order to maintain diagnostic information for ECG. DTCWT provides significant different levels of information about the nature of the data in terms of time and frequency. It also fights the problem of discrete wavelet transforms (DWT) variance. Signal Energy Contribution Efficiency (ECE)...
The noise-riding discharging/charging voltage (DCV) of the Li-Ion battery pack may result in erroneous equivalent circuit model (ECM)-based state-of-charge (SOC) estimation and state-of-health (SOH) prediction of the Li-Ion battery pack. Namely, the noisy DCV is intimately linked with a low battery management system (BMS) performance. Therefore, additional technique should be absolutely required for...
This paper presents a simple and novel approach for de-noising of the Audio Signals i.e. non-stationary signal using statistical distribution function at different sub-band level of coefficients. The performance of wavelets are analysed under various thresholding techniques. Nonstationary signals are continuous in nature consequently we use 1D Discrete Wavelet Transform which gives us a better time-...
In this paper, Denoising of ECG signal using thresholding criteria and wavelet decompositions. A modified threshold criteria proposed in the paper of Denoising. Here, an optimal wavelet selection are illustrated using signal retained energy (RE), percentage root mean square difference (PRD), signal-to-noise ratio (SNR) and mean square error (MSE) parameters. ECG signal contained the noise due to interference...
The ECG signal is used for various medical diagnoses. For diagnosis purposes, the ECG signal must be free from the noise and undesired disturbances. In the present work the best algorithm for de-noising the ECG signal is identified using Discrete Wavelet Transform. Statistical analysis and comparison has been made to find the best wavelet function, their decomposition level and threshold selection...
This recent technologies for recognizing fingerprints have proven as regards as reliable for security purposes. But, the efficient recognition is depending on the quality of fingerprint image and it is a complex computer problem while dealing with noisy and low quality images. The most common methods used to acquire the fingerprint images do not need expertise, but highly distorted images are still...
According to the characters of CE signal, improved wavelet thresholding with translation invariant was described to denoise for microchip capillary electrophoresis in this paper. The improved thresholding function has muti-derivative compared with the soft thresholding. The denoising method can not only remain the geometrical characteristics and keep the amplitudes of the original electrophoresis...
The use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions. However, the observed images are often corrupted by an additive Gaussian noise. In this paper, we are interested in multichannel image denoising based on a multiscale representation of the images. A multivariate statistical approach is adopted to take...
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