The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda (λ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.
This review paper is surveyed in different concerns. It has been conducted to know about designing of adaptive filter and also to know where the adaptive algorithms are used in the different applications. The main goal of this review paper is to study and performance of different adaptive filter algorithms on the basis of literature survey.
Underwater images suffer from less visibility problem due to principal of light refraction under water environment. Therefore, this paper address the problem of enhancing the contrast of the underwater images captured under different lightning conditions and depths. This paper presenting the comparison of the two distinct popular enhancement methods and it concluded that this different method generates two different image sets from original low contrast image. These sets can be further utilized for object segmentation using fuzzy based clustering methods efficiently. Paper presents enhancement using contrast limit adaptive histogram equalization (CLAHE) and, global contrast adjustment method.. The CLAHE method is used to the images so that contrast of output images is improved also gives better information. It is concluded that using the different contrast enhancement method different image data set can be created for same input image which can be utilized for higher level processing task and features extractions.
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