Audio watermarking has become an important technology for the intellectual property protection. For numerous applications, such as cinema and concert situation, watermarks surviving noisy analog environments are better suited or even required. This paper discusses the robustness of digital audio watermarking algorithms against digital-to-analogue (D/A) and analogue-to-digital (A/D) conversions. Based on discrete wavelet decomposition, the chirp signal is introduced as watermarking bits and the radon transform of time-frequency distribution of wavelet coefficients is also considered. Then the relationship of statistical radon moment in two adjacent wavelet sub-bands is utilized to embed watermarking bits. Experimental results show that the proposed algorithm can resist on the attack of DA and AD conversion with lower bit error and higher capacity. The proposed method could also be against common signal processing and MPEG compression attack, which show its good prospect in real application.
Functional magnetic resonance imaging (fMRI) has become one of the important tools of functional connectivity studies of the human brain. Fuzzy clustering method (FCM) is a common method for analysis of FMRI data. Traditional FCA methods measure the similarity between the BOLD time course of a centroid and the ones of all other voxels in the brain on the basis of Pearson correlation coefficient. This article puts forward a multi-voxel-based similarity measure, an improved RV (IRV) measure, which takes the hypothesis into account that the function homogeneous voxels of brain volume are spatially clustered within a local region. Experimental validation is presented through four visual fMRI data sets which shows that the IRVFCA method not only has improved the speed of FCA, but has comparatively raised the accuracy of the method.
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