研究目的
Designing efficient algorithms to process closed curves represented by basis functions or wavelets by introducing an inner-product calculus to evaluate correlations and L2 distances between such curves.
研究成果
The study concludes with the introduction of an inner-product calculus for evaluating correlations and L2 distances between closed curves, presenting formulas for the direct and exact evaluation of correlation matrices, and proposing a least-squares approximation scheme for resampling curves.
研究不足
Not explicitly mentioned in the abstract.
1：Experimental Design and Method Selection:
The study introduces an inner-product calculus for evaluating correlations and L2 distances between closed curves represented by basis functions or wavelets.
2：Sample Selection and Data Sources:
The study focuses on closed (periodic) parametric curves and periodic signals.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
The study presents formulas for the direct and exact evaluation of correlation matrices and proposes a least-squares approximation scheme for resampling curves.
5：Data Analysis Methods:
The study uses inner-product calculus for data analysis.
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