De-noising of EEG Signals With Shift Based Cycle Spinning on Wave Atoms

To predict our brain disorders, EEG signals need to be analyzed. However, most EEG signals were affected by different kinds of noise during their acquision. So, signal analysis become most difficult due to the contamination of various noise. An appropriate technique is necessary to remove the noise from the signal. Wavelet Transform is one the most widely used technique for removing the noise from EEG signals. Wave atom is one of the new multiscale-multidirectional transforms, which is better than both wavelet as well as curvelet transforms. This wave atom transform has good orientation characteristic by which it preserves the edges in an efficient manner. This paper introduced a new method for denoising of EEG signal by shift-based cycle spinning on wave atom transform. Cycle spinning is a technique can be used to enhance the capability of wave atoms. An original EEG signals from public EEG database were used for this experiment. The results are analysed based on the performance measurements like SNR and MSE. The experimental results show that cycle spinning technique with appropriate shifts could be the better choice to denoise an EEG signals.

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.