Abstract: Provided are two-dimensional autofocus methods in a synthetic aperture radar (SAR) system which include: (1) two-dimensional pulse pair product algorithm including shear PGA, eigenvector phase history (“EPH”), shear PGA/EPH); (2) two-dimensional optimization algorithms including parametric one-dimensional estimate/two-dimensional correction, parametric two dimensional estimate/two-dimensional correction, unconstrained two-dimensional nonparametric and constrained two-dimensional nonparametric methods; (3) a two-dimensional geometry filter algorithm; (4) a two-dimensional prominent point processing algorithm; (5) a one-dimensional phase estimate of higher order two dimensional phase errors; and, (6) a fast SHARP parametric autofocus algorithm.

Abstract: Provided are two-dimensional autofocus methods in a synthetic aperture radar (SAR) system which include: (1) two-dimensional pulse pair product algorithm including shear PGA, eigenvector phase history (“EPH”), shear PGA/EPH); (2) two-dimensional optimization algorithms including parametric one-dimensional estimate/two-dimensional correction, parametric two dimensional estimate/two-dimensional correction, unconstrained two-dimensional nonparametric and constrained two-dimensional nonparametric methods; (3) a two-dimensional geometry filter algorithm; (4) a two-dimensional prominent point processing algorithm; (5) a one-dimensional phase estimate of higher order two dimensional phase errors; and, (6) a fast SHARP parametric autofocus algorithm.

Abstract: Provided are two-dimensional autofocus methods in a synthetic aperture radar (SAR) system which include: (1) two-dimensional pulse pair product algorithm including shear PGA, eigenvector phase history (“EPH”), shear PGA/EPH); (2) two-dimensional optimization algorithms including parametric one-dimensional estimate/two-dimensional correction, parametric two dimensional estimate/two-dimensional correction, unconstrained two-dimensional nonparametric and constrained two-dimensional nonparametric methods; (3) a two-dimensional geometry filter algorithm; (4) a two-dimensional prominent point processing algorithm; (5) a one-dimensional phase estimate of higher order two dimensional phase errors; and, (6) a fast SHARP parametric autofocus algorithm.

Abstract: Provided are two-dimensional autofocus methods in a synthetic aperture radar (SAR) system which include: (1) two-dimensional pulse pair product algorithm including shear PGA, eigenvector phase history (“EPH”), shear PGA/EPH); (2) two-dimensional optimization algorithms including parametric one-dimensional estimate/two-dimensional correction, parametric two dimensional estimate/two-dimensional correction, unconstrained two-dimensional nonparametric and constrained two-dimensional nonparametric methods; (3) a two-dimensional geometry filter algorithm; (4) a two-dimensional prominent point processing algorithm; (5) a one-dimensional phase estimate of higher order two dimensional phase errors; and, (6) a fast SHARP parametric autofocus algorithm.

Abstract: Provided are two-dimensional autofocus methods in a synthetic aperture radar (SAR) system which include: (1) two-dimensional pulse pair product algorithm including shear PGA, eigenvector phase history (“EPH”), shear PGA/EPH); (2) two-dimensional optimization algorithms including parametric one-dimensional estimate/two-dimensional correction, parametric two dimensional estimate/two-dimensional correction, unconstrained two-dimensional nonparametric and constrained two-dimensional nonparametric methods; (3) a two-dimensional geometry filter algorithm; (4) a two-dimensional prominent point processing algorithm; (5) a one-dimensional phase estimate of higher order two dimensional phase errors; and, (6) a fast SHARP parametric autofocus algorithm.

Abstract: Biopolymeric array scanners that are capable of automatically selecting a dye specific scale factor to employ for a plurality of different dyes, as wells as methods for making and using the same, are provided. In many embodiments, the actual dye specific scale factor automatically selected by the scanner is one that is equal to a preset “master” scale factor, so that the scanner reads any supported dye using the same constant scale factor. The dye specific scale factor selection is typically made by reference to a collection of nominal scale factors for each member of the plurality of dyes. In using the subject scanners, a user simply inputs the one or more dyes being used in a given array assay, and the scanner automatically reads the array using an automatically chosen dye specific scale factor for the selected dyes. Also provided are methods of obtaining collections of nominal scale factors and computer readable mediums comprising the same.

Abstract: Biopolymeric array scanners that are capable of automatically selecting a dye specific scale factor to employ for a plurality of different dyes, as wells as methods for making and using the same, are provided. In many embodiments, the actual dye specific scale factor automatically selected by the scanner is one that is equal to a preset “master” scale factor, so that the scanner reads any supported dye using the same constant scale factor. The dye specific scale factor selection is typically made by reference to a collection of nominal scale factors for each member of the plurality of dyes. In using the subject scanners, a user simply inputs the one or more dyes being used in a given array assay, and the scanner automatically reads the array using an automatically chosen dye specific scale factor for the selected dyes. Also provided are methods of obtaining collections of nominal scale factors and computer readable mediums comprising the same.

Abstract: A method and system for extracting data signals from a scanned image resulting from optical, radiometric, or other types of analysis of a molecular array. The positions of corner features are first located. Then, an initial feature coordinate grid is determined from the positions of the corner features. A refined feature coordinate grid is then calculated based on the positions of strong features, and is used to identify the positions of weak features and the positions of the local background regions surrounding all features. Finally, signal intensity values are extracted from the features and their respective local background regions in the scanned image, and background-subtracted signal intensity values, background-subtracted and normalized signal intensity ratios, and variability information and confidence intervals are determined based on the extracted values.

Abstract: A method and system for employing pixel-based, signal-intensity data contained within areas of a scanned image of a molecular array corresponding to features and feature backgrounds in order to determine whether or not the features or feature backgrounds have non-uniform signal intensities and are thus outlier features and outlier feature backgrounds. A calculated, estimated variance for the signal intensities within a feature or feature background is compared to a maximum allowable variance calculated for the feature or feature background based on a signal intensity variance model. When the experimental variance is less than or equal to the maximum allowable variance, the feature or feature background is considered to have acceptable signal-intensity uniformity. Otherwise, the feature or feature background is flagged as an outlier feature or outlier feature background.

Abstract: Biopolymeric array scanners that are capable of automatically selecting a dye specific scale factor to employ for a plurality of different dyes, as wells as methods for making and using the same, are provided. In many embodiments, the actual dye specific scale factor automatically selected by the scanner is one that is equal to a preset “master” scale factor, so that the scanner reads any supported dye using the same constant scale factor. The dye, specific scale factor selection is typically made by reference to a collection of nominal scale factors for each member of the plurality of dyes. In using the subject scanners, a user simply inputs the one or more dyes being used in a given array assay, and the scanner automatically reads the array using an automatically chosen dye specific scale factor for the selected dyes. Also provided are methods of obtaining collections of nominal scale factors and computer readable mediums comprising the same.

Abstract: A method and system for estimating a global background-signal correction for each channel of a feature-based data set, measured by a molecular array scanner, that contributes a feature-intensity data subset to the feature-based data set. The method and system of one embodiment of the present invention selects a set of features for which the measured feature intensities in two or more channels are relatively low and for which the ratio of measured feature intensities follow a central trend in a distribution of feature-intensity ratios for all features within the data set. An ideal feature is computed from the selected set of low-intensity features, from which separate global, residual background-signal corrections for each channel can be calculated and applied to that channel's feature-intensity data subset within the feature-based data set.

Abstract: A method and system for normalizing two or more molecular array data sets. Input molecular array data sets are separately globally normalized by, for example, dividing the feature-signal magnitudes of each data set by the geometric mean of the feature-signal magnitudes of the data set. The globally normalized feature signal magnitudes within each data set are ranked in ascending order. A numeric function is created that relates feature-signal magnitudes of the data sets. Only a subset of the features, obtained by selecting features that are similarly ranked in the separate feature-signal-magnitude rankings for the data sets, is used to construct the numeric function. The numeric function is smoothed by one of many possible different smoothing procedures.

Abstract: A method and system for normalizing two or more molecular array data sets. Input molecular array data sets are separately globally normalized by, for example, dividing the feature-signal magnitudes of each data set by the geometric mean of the feature-signal magnitudes of the data set. The globally normalized feature signal magnitudes within each data set are ranked in ascending order. A numeric function is created that relates feature-signal magnitudes of the data sets. Only a subset of the features, obtained by selecting features that are similarly ranked in the separate feature-signal-magnitude rankings for the data sets, is used to construct the numeric function. The numeric function is smoothed by one of many possible different smoothing procedures.

Abstract: A method and system for employing pixel-based, signal-intensity data contained within areas of a scanned image of a molecular array corresponding to features and feature backgrounds in order to determine whether or not the features or feature backgrounds have non-uniform signal intensities and are thus outlier features and outlier feature backgrounds. A calculated, estimated variance for the signal intensities within a feature or feature background is compared to a maximum allowable variance calculated for the feature or feature background based on a signal intensity variance model. When the experimental variance is less than or equal to the maximum allowable variance, the feature or feature background is considered to have acceptable signal-intensity uniformity. Otherwise, the feature or feature background is flagged as an outlier feature or outlier feature background.

Abstract: A method and system for extracting data signals from a scanned image resulting from optical, radiometric, or other types of analysis of a molecular array. The positions of corner features are first located. Then, an initial feature coordinate grid is determined from the positions of the corner features. A refined feature coordinate grid is then calculated based on the positions of strong features, and is used to identify the positions of weak features and the positions of the local background regions surrounding all features. Finally, signal intensity values are extracted from the features and their respective local background regions in the scanned image, and background-subtracted signal intensity values, background-subtracted and normalized signal intensity ratios, and variability information and confidence intervals are determined based on the extracted values.