초록 ▼

Coin validation can be advantageously improved by transforming measured data values associated with coin characteristics from a first geometric space to a second geometric space, in which the transformed values in the second geometric space are preferably better adapted for discrimination between di

Coin validation can be advantageously improved by transforming measured data values associated with coin characteristics from a first geometric space to a second geometric space, in which the transformed values in the second geometric space are preferably better adapted for discrimination between different coin denominations than the corresponding values in the first geometric space. Preferably, principal component analysis is used to identify principal components that can be used as dimensions of the second geometric space, so that measured data values in the second geometric space are less correlated than in the first geometric space.

대표청구항 ▼

1. A method of manipulating data in relation to coin validation, the method including:transforming one or more first multivariate data values in a first geometric space to one or more respective second multivariate data values in a second geometric space, said first multivariate data values correspo

1. A method of manipulating data in relation to coin validation, the method including:transforming one or more first multivariate data values in a first geometric space to one or more respective second multivariate data values in a second geometric space, said first multivariate data values corresponding with data variables related to one or more coins;wherein at least one of the basis vectors of the dimensions of said second geometric space is different from any one of the basis vectors of the dimensions of said first geometric space; andwherein said second multivariate data values in said second geometric space are generally less correlated than said first multivariate data values in said first geometric space. 2. A method as claimed in claim 1, wherein said second multivariate data values in said second geometric space are generally uncorrelated. 3. A method as claimed in any one of claims 1 to 2, wherein the basis vectors of the dimensions of said second geometric space are determined with the assistance of principal component analysis on the basis of said first multivariate data values in said first geometric space. 4. A method as claimed in claim 1, wherein the number of the dimensions of said second geometric space is equal to or lower than the number of the dimensions of said first geometric space. 5. A method as claimed in claim 1, wherein said first geometric space has three dimensions, and said second geometric space has two dimensions. 6. A method as claimed in claim 1, further including: establishing one or more predetermined multivariate sets of said second multivariate data values in said second geometric space, wherein said predetermined multivariate data sets can be used to assess whether a coin is of a coin denomination respectively corresponding with one of said one or more predetermined multivariate sets. 7. A method as claimed in claim 6, wherein at least one of said one or more predetermined multivariate sets arm determined from average values of a plurality of said first multivariate data values, after said transformation from said first geometric space to said second geometric space. 8. A method as claimed in claim 1, further including:sampling variables associated with one or more coins to derive said first multivariate data values. 9. A method as claimed in claim 1, further including:comparing one of said second multivariate data values in said second geometric space with one or more predetermined multivariate sets in said second geometric space. 10. A method as claimed in claim 9, further including assessing, on the basis of said comparison of said one or more second multivariate data values with said predetermined multivariate data sets, whether said one or more second multivariate data values correspond with one of said predetermined multivariate sets and hence a respective coin denomination. 11. A method as claimed in claim 1, wherein said comparison is performed for a plurality of said second multivariate data values in respective said second geometric spaces, and each of said second geometric spaces is different from each other. 12. A method of manipulating data in relation to coin validation, the method including:sampling variables associated with one or more coins to derive one or more first multivariate data values in a first geometric space;transforming one or more first multivariate data values in a first geometric space to one or more respective second multivariate data values in a second geometric space, said first multivariate data values corresponding with data variables related to one or more coins;determining one or more predetermined multivariate sets of said second multivariate data values in said second geometric space, wherein each of said one or more predetermined multivariate sets can be used to determine whether any of said one or more second multivariate data values correspond with respective coin denominations;wherein at least one of the basis vectors of the dimensions of sa id second geometric space is different from any one of the basis vectors of the dimensions of said first geometric space; andwherein said second multivariate data values in said second geometric space are generally less correlated than said first multivariate data values in said first geometric space. 13. A method of manipulating data in relation to validation, the method including:sampling variables associated with one or more coins to derive one or more first multivariate data values in a first geometric space;transforming said one or more first multivariate data values in said first geometric space to one or more respective second multivariate data values in a second geometric space, said first multivariate data values corresponding with data variables related to one or more coins;comparing one of said second multivariate data values in said second geometric space with one or more predetermined multivariate sets in said second geometric space, wherein each of said one or more predetermined multivariate sets correspond with respective coin denominations;assessing, on the basis of said comparison of said one or more second multivariate data values with said predetermined multivariate data sets, whether said one or more second multivariate data values correspond with one of said predetermined multivariate sets and hence said respective coin denominations;wherein at least one of the basis vectors of the dimensions of said second geometric space is different from any one of the basis vectors of the dimensions of said first geometric space; andwherein said second multivariate data values in said second geometric space are generally less correlated than said first multivariate data values in said first geometric space.