Auto-term detection using time-frequency array processing
The problem of nonstationary signal detection using antenna arrays is considered. A method for detecting source signal auto-term regions in the time-frequency plane is presented, based on spatial time-frequency distribution (STFD) matrices. A general signal detection framework when using arbitrary time-frequency kernels is proposed and the trace of a whitened STFD matrix is used to form an appropriate test statistic. The expressions for the mean and variance of the statistic, necessary to evaluate the test, are provided. The detector performance using the Wigner-Ville distribution is investigated via simulated and theoretical results.
We study the scalability problem of a priori-like algorithms that are used in mining association rules. We show how a priori suffers from performance deterioration for large-scale problems and propose alternative data structures and operations that can be used to apply the a priori-trick optimization method in large-scale problems. In the proposed method, the database is transformed into a more efficient structure that is used along with the intersection operation, to find the frequent itemsets in the database. The performance evaluation shows that, with a minor increase in the storage requirement, the proposed technique outperforms significantly the existing algorithms especially in large-scale problems.