Methods for performing DAF data filtering and padding
First Claim
1. A method for data padding and noise filtering implemented on a digital processing device, comprising the steps of:
- a. defining a total data set as the collection of all known and unknown data, where the unknown values may be interspersed among the known values, or concentrated in a region adjacent to the known values, or a combination of the two;
b. placing the unknown values of the data to be obtained by a padding procedure so that the total data set contains only equally space data—
the data has a constant sampling interval and the total data set represents a function;
c. calculating an appropriate difference between the true data and an approximation to the data for all known and unknown data values;
d. minimizing the difference with respect to the unknown data values by iteration or using the calculus of variations to obtain algebraic equations for the unknown values or solving the algebraic equation for the unknown data values.
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Abstract
A method for padding, filtering, denoising, image enhancing and increased time-frequency acquisition is described for digitized data of a data set is described where unknown data is estimated using real data by adding unknown data points in a manner that the padding routine can estimate the interior data set including known and unknown data to a given accuracy on the known data points. The method also provides filtering using non-interpolating, well-tempered distributed approximating functional (NIDAF)-low-band-pass filters. The method also provides for symmetric and/or anti-symmetric extension of the data set so that the data set may be better refined and can be filtered by Fourier and other type of low frequency or harmonic filters.
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Citations
13 Claims
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1. A method for data padding and noise filtering implemented on a digital processing device, comprising the steps of:
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a. defining a total data set as the collection of all known and unknown data, where the unknown values may be interspersed among the known values, or concentrated in a region adjacent to the known values, or a combination of the two;
b. placing the unknown values of the data to be obtained by a padding procedure so that the total data set contains only equally space data—
the data has a constant sampling interval and the total data set represents a function;
c. calculating an appropriate difference between the true data and an approximation to the data for all known and unknown data values;
d. minimizing the difference with respect to the unknown data values by iteration or using the calculus of variations to obtain algebraic equations for the unknown values or solving the algebraic equation for the unknown data values. - View Dependent Claims (2, 3, 4, 5, 9, 10, 11, 13)
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6. A method for data padding and noise filtering implemented on a digital processing device, comprising the steps of:
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a. defining a total data set as the collection of all known and unknown data, where the unknown values may be interspersed amongst the known values, or concentrated in a region adjacent to the known values, or a combination of the two;
b. placing the unknown values of the data to be obtained by a padding procedure so that the total data set contains only equally space data—
the data has a constant sampling interval and the total data set represents a function;
c. calculating an appropriate difference between the true data of the total data set and a DAF approximation to the data for all known and unknown data values; and
d. minimizing the difference with respect to the unknown data values by iteration or by using the calculus of variations to obtain algebraic equations for the unknown values or solving the algebraic equation for the unknown data values. - View Dependent Claims (7, 8)
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12. A method for data padding and noise filtering implemented on a digital processing device, comprising the steps of:
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a. defining a total data set as the collection of all known and unknown data, where the unknown values may be interspersed among the known values, or concentrated in a region adjacent to the known values, or a combination of the two;
b. placing the unknown values of the data to be obtained by a padding procedure so that the total data set contains only equally space data—
the data has a constant sampling interval and the total data set represents a function;
c. calculating an appropriate difference between the true data of the total data set and a DAF approximation to the data for all known and unknown data values;
d. solving the algebraic equation for the unknown data values.
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Specification