The uniform magnetic field of various strength was applied to the perpendicularly and in-plane demagnetized media, and the change in each magnetic cluster state was investigated as the fundamental investigation of the influence of demagnetization method on noise during signal recording on the stacked perpendicular recording media. The results showed that the in-plane demagnetization can achieve lower noise level if the recording field is not very high. In other words, the in-plane demagnetization is an effective way to achieve lower noise in transition area, near track edge of recorded bit, and in high-density bit. In addition, the simulation clarified that this noise reduction can be explained using the idea of sub-domain structure in the in-plane demagnetized media.

This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.