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Information fusion is a key research area widely applied to various multimedia analysis tasks such as artificial intelligence, humancomputer interaction, robotics, distributed computing, financial systems and security/surveillance. Feature level fusion has been considered as the most promising fusion method due to the rich information presented at this level. A critical operation of feature level...
This paper aims at providing a general method for feature extraction and recognition. The most essential issues for pattern recognition include extracting discriminant features and improving recognition accuracy. Kernel Entropy Component Analysis (KECA), as a new method for data transformation and dimensionality reduction, has attracted more attentions. However, as KECA only reveals structure relating...
Motion compensation is very important for Inverse synthetic aperture radar (ISAR) imaging while the imaging quality is directly dependent on its performance. Range alignment (envelope alignment) is the key procedure of motion compensation. The nonextensive entropy, known as Tsallis entropy, is introduced into ISAR range alignment because of the correlation between adjacent one-dimensional range profile...
PCA-subspace method has been proposed for network-wide anomaly detection. Normal subspace contamination is still a great challenge for PCA although some methods are proposed to reduce the contamination. In this paper, we apply PCA-subspace method to six-month Origin-Destination (OD) flow data from the Abilene. The result shows that normal subspace contamination is mainly caused by anomalies from a...
In this paper, we present a statistical analysis of six traffic features based on entropy and distinct feature number at the packet level, and we find that, although these traffic features are unstable and show seasonal patterns like traffic volume for a long period, they are stable and consistent with Gaussian distribution in a short time period. However, this equilibrium property will be violated...
An improved method of motor detection based on symbolic time series analysis is proposed, and the method adaptively partition off the region which has the most symbols in the symbolic series into two new regions, which enhances the sensitive degree of symbols to the signal. Except that the fuzzy relative entropy is introduced in the paper to improve the reliability of the diagnosis results. Laboratory...
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