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1. 发明申请

US20090259438A1 RELATIVE NOISE 有权
标题翻译：相对噪音
公开(公告)号：US20090259438A1
公开(公告)日：2009-10-15
申请号：US12102537
申请日：2008-04-14
申请人： Ronald Bonner , Gordana Ivosev , Min Yang
发明人： Ronald Bonner , Gordana Ivosev , Min Yang
IPC分类号： H04B15/00 , G06F15/00
CPC分类号： G06K9/0051 , G01N21/274
摘要： Relative noise is a single scalar value that is used to predict the maximum value of the expected noise at any point and is calculated from the measured signal and a mathematical noise model. The mathematical noise model is selected or estimated from an observation that includes statistical and/or numerical modeling based on a population of measurement points. An absolute noise for a plurality of points of the measured signal is estimated. An array of values is calculated by dividing each of a plurality of points of the absolute noise by a corresponding expected noise value calculated from the mathematical noise model. The relative noise is calculated by taking a standard deviation of a plurality of points of the array. The relative noise can be used to calculate scaled background signal noise, filter regions, denoise data, detect false positives from features, calculate S/N, and determine a stop condition for acquiring data.
摘要翻译：相对噪声是单个标量值，用于预测任何点处预期噪声的最大值，并根据测量信号和数学噪声模型计算。从包括基于测量点总数的统计学和/或数值模拟的观察中选择或估计数学噪声模型。估计测量信号的多个点的绝对噪声。通过将绝对噪声的多个点中的每一个除以由数学噪声模型计算的对应的预期噪声值来计算值的数组。通过取数组的多个点的标准偏差来计算相对噪声。相对噪声可用于计算缩放背景信号噪声，滤波器区域，去噪数据，检测特征中的误报，计算S / N，并确定采集数据的停止条件。

2. 发明授权

US07865322B2 Relative noise 有权
标题翻译：相对噪音
公开(公告)号：US07865322B2
公开(公告)日：2011-01-04
申请号：US12102537
申请日：2008-04-14
申请人： Gordana Ivosev , Ronald Bonner , Min Yang
发明人： Gordana Ivosev , Ronald Bonner , Min Yang
IPC分类号： G06F15/00 , H04B15/00
CPC分类号： G06K9/0051 , G01N21/274
摘要： Relative noise is a single scalar value that is used to predict the maximum value of the expected noise at any point and is calculated from the measured signal and a mathematical noise model. The mathematical noise model is selected or estimated from an observation that includes statistical and/or numerical modeling based on a population of measurement points. An absolute noise for a plurality of points of the measured signal is estimated. An array of values is calculated by dividing each of a plurality of points of the absolute noise by a corresponding expected noise value calculated from the mathematical noise model. The relative noise is calculated by taking a standard deviation of a plurality of points of the array. The relative noise can be used to calculate scaled background signal noise, filter regions, denoise data, detect false positives from features, calculate S/N, and determine a stop condition for acquiring data.
摘要翻译：相对噪声是单个标量值，用于预测任何点处预期噪声的最大值，并根据测量信号和数学噪声模型计算。从包括基于测量点总数的统计学和/或数值模拟的观察中选择或估计数学噪声模型。估计测量信号的多个点的绝对噪声。通过将绝对噪声的多个点中的每一个除以由数学噪声模型计算的对应的预期噪声值来计算值的数组。通过取数组的多个点的标准偏差来计算相对噪声。相对噪声可用于计算缩放背景信号噪声，滤波器区域，去噪数据，检测特征中的误报，计算S / N，并确定采集数据的停止条件。

3. 发明授权

US08530828B2 Systems and methods for reducing noise from mass spectra 有权
标题翻译：用于降低质谱噪声的系统和方法
公开(公告)号：US08530828B2
公开(公告)日：2013-09-10
申请号：US13437837
申请日：2012-04-02
申请人： Gordana Ivosev , Ronald Bonner
发明人： Gordana Ivosev , Ronald Bonner
IPC分类号： H01J49/26 , B01D59/44 , H04B15/00 , G06F17/00
CPC分类号： H01J49/26 , H01J49/0036
摘要： A plurality of scans of a sample are performed, producing a plurality of mass spectra. Neighboring mass spectra of the plurality of mass spectra are combined into a collection of mass spectra based on sample location, time, or mass. A background noise estimate is calculated for the collection of mass spectra. The collection of mass spectra is filtered using the background noise estimate, producing a filtered collection of one or more mass spectra. Quantitative or qualitative analysis is performed using the filtered collection of one or more mass spectra. The background noise estimate is calculated by dividing the collection of mass spectra into two or more windows, for example. For each window of the two or more windows, all spectra within each window are combined, producing a combined spectrum for each of the two or more windows. For each combined spectrum, a background noise is estimated.
摘要翻译：执行样本的多次扫描，产生多个质谱。基于样品位置，时间或质量将多个质谱的相邻质谱合并成质谱图。计算质谱收集的背景噪声估计。使用背景噪声估计来过滤质谱的收集，产生一个或多个质谱的过滤集合。使用一个或多个质谱的过滤集合进行定量或定性分析。例如，通过将质谱图的集合除以两个或多个窗口来计算背景噪声估计。对于两个或多个窗口的每个窗口，组合每个窗口内的所有光谱，为两个或多个窗口中的每个窗口产生组合光谱。对于每个组合光谱，估计背景噪声。

4. 发明授权

US08073639B2 Method for identifying a convolved peak 有权
标题翻译：识别卷积峰的方法
公开(公告)号：US08073639B2
公开(公告)日：2011-12-06
申请号：US12200636
申请日：2008-08-28
申请人： Gordana Ivosev , Ronald Bonner
发明人： Gordana Ivosev , Ronald Bonner
IPC分类号： G06F17/00 , G06F17/40
CPC分类号： G06K9/00543 , G01N30/72 , G01N30/8682 , G01N2030/862 , H01J49/0036
摘要： A method for identifying a convolved peak is described. A plurality of spectra is obtained. A multivariate analysis technique is used to assign data points from the plurality of spectra to a plurality of groups. A peak is selected from the plurality of spectra. If the peak includes data points assigned to two or more groups of the plurality of groups, the peak is identified as a convolved peak. Principal component analysis is one multivariate analysis technique that is used to assign data points. A number of principal components are selected. A subset principal component space is created. A data point in the subset principal component space is selected. A vector is extended from the origin of the subset principal component space to the data point. One or more data points within a spatial angle around the vector are assigned to a group.
摘要翻译：描述了用于识别卷积峰的方法。获得多个光谱。多变量分析技术用于将数据点从多个频谱分配到多个组。从多个光谱中选出峰。如果峰值包括分配给多个组中的两个或更多个组的数据点，则将该峰识别为卷积峰。主成分分析是一种用于分配数据点的多变量分析技术。选择了多个主要组件。创建子集主体组件空间。选择子集主体组件空间中的数据点。向量从子集主体组件空间的起点扩展到数据点。在矢量周围的空间角度内的一个或多个数据点被分配给一个组。

5. 发明授权

US08180581B2 Systems and methods for identifying correlated variables in large amounts of data 有权
标题翻译：用于在大量数据中识别相关变量的系统和方法
公开(公告)号：US08180581B2
公开(公告)日：2012-05-15
申请号：US12474418
申请日：2009-05-29
申请人： Gordana Ivosev , Ronald Bonner
发明人： Gordana Ivosev , Ronald Bonner
IPC分类号： G06F17/00 , G06F17/40
CPC分类号： G06K9/6247 , H01J49/0036
摘要： Groups of correlated representations of variables are identified from a large amount of spectrometry data. A plurality of samples is analyzed and a plurality of measured variables is obtained from a spectrometer. A processor executes a number of steps. The plurality of measured variables is divided into a plurality of measured variable subsets. Principal component analysis followed by variable grouping (PCVG) is performed on each measured variable subset, producing one or more group representations for each measured variable subset and a plurality of group representations for the plurality of measured variable subsets. While the total number of the plurality of group representations is greater than a maximum number, the plurality of group representations is divided into a plurality of representative subsets and PCVG is performed on each subset. PCVG is performed on the remaining the plurality of group representations, producing a plurality of groups of correlated representations of variables.
摘要翻译：从大量的光谱数据中识别变量的相关表示组。分析多个样本，并从光谱仪获得多个测量变量。处理器执行多个步骤。多个测量变量被分成多个测量的可变子集。对每个测量的可变子集执行随后的可变分组（PCVG）的主成分分析，为每个测量的可变子集产生一个或多个组表示，并为多个测量的可变子集产生多个组表示。虽然多个组表示的总数大于最大数，但是多个组表示被划分为多个代表子集，并且对每个子集执行PCVG。对剩余的多个组表示执行PCVG，产生多组变量的相关表示。

6. 发明申请

US20090254314A1 SYSTEMS AND METHODS FOR IDENTIFYING CORRELATED VARIABLES IN LARGE AMOUNTS OF DATA 有权
标题翻译：用于在大量数据中识别相关变量的系统和方法
公开(公告)号：US20090254314A1
公开(公告)日：2009-10-08
申请号：US12474418
申请日：2009-05-29
申请人： GORDANA IVOSEV , Ronald Bonner
发明人： GORDANA IVOSEV , Ronald Bonner
IPC分类号： H01J49/26 , G06F15/00
CPC分类号： G06K9/6247 , H01J49/0036
摘要： Groups of correlated representations of variables are identified from a large amount of spectrometry data. A plurality of samples is analyzed and a plurality of measured variables is obtained from a spectrometer. A processor executes a number of steps. The plurality of measured variables is divided into a plurality of measured variable subsets. Principal component analysis followed by variable grouping (PCVG) is performed on each measured variable subset, producing one or more group representations for each measured variable subset and a plurality of group representations for the plurality of measured variable subsets. While the total number of the plurality of group representations is greater than a maximum number, the plurality of group representations is divided into a plurality of representative subsets and PCVG is performed on each subset. PCVG is performed on the remaining the plurality of group representations, producing a plurality of groups of correlated representations of variables.
摘要翻译：从大量的光谱数据中识别变量的相关表示组。分析多个样本，并从光谱仪获得多个测量变量。处理器执行多个步骤。多个测量变量被分成多个测量的可变子集。对每个测量的可变子集执行随后的可变分组（PCVG）的主成分分析，为每个测量的可变子集产生一个或多个组表示，并为多个测量的可变子集产生多个组表示。虽然多个组表示的总数大于最大数，但是多个组表示被划分为多个代表子集，并且对每个子集执行PCVG。对剩余的多个组表示执行PCVG，产生多组变量的相关表示。

7. 发明授权

US07587285B2 Method for identifying correlated variables 有权
标题翻译：识别相关变量的方法
公开(公告)号：US07587285B2
公开(公告)日：2009-09-08
申请号：US11848717
申请日：2007-08-31
申请人： Gordana Ivosev , Ronald Bonner
发明人： Gordana Ivosev , Ronald Bonner
IPC分类号： G06F17/00 , G06F17/16
CPC分类号： G06K9/6232 , H01J49/0031
摘要： According to various embodiments, variables are grouped in an unsupervised manner after principal component analysis of a plurality of variables from a plurality of samples. A number of principal components are selected. A subset principal component space is created for those components. A starting variable is selected. A spatial angle is defined around a vector extending from the origin to the starting variable. A set of one or more variables is selected within the spatial angle. The set is assigned to a group. The set is removed from further analysis. The process is repeated starting with the selection of a new starting variable until all groups are found.
摘要翻译：根据各种实施例，在来自多个样本的多个变量的主成分分析之后，变量以无监督的方式分组。选择了多个主要组件。为这些组件创建子集主体组件空间。选择起始变量。围绕从原点延伸到起始变量的向量定义空间角度。在空间角度内选择一组或多个变量。该集合被分配给一个组。该集合从进一步分析中删除。重复该过程，首先选择一个新的起始变量，直到找到所有组。

8. 发明申请

US20130087701A1 SYSTEMS AND METHODS FOR REDUCING NOISE FROM MASS SPECTRA 有权
标题翻译：用于减少大量光谱噪声的系统和方法
公开(公告)号：US20130087701A1
公开(公告)日：2013-04-11
申请号：US13437837
申请日：2012-04-02
申请人： Gordana Ivosev , Ronald Bonner
发明人： Gordana Ivosev , Ronald Bonner
IPC分类号： H01J49/26
CPC分类号： H01J49/26 , H01J49/0036
摘要： A plurality of scans of a sample are performed, producing a plurality of mass spectra. Neighboring mass spectra of the plurality of mass spectra are combined into a collection of mass spectra based on sample location, time, or mass. A background noise estimate is calculated for the collection of mass spectra. The collection of mass spectra is filtered using the background noise estimate, producing a filtered collection of one or more mass spectra. Quantitative or qualitative analysis is performed using the filtered collection of one or more mass spectra. The background noise estimate is calculated by dividing the collection of mass spectra into two or more windows, for example. For each window of the two or more windows, all spectra within each window are combined, producing a combined spectrum for each of the two or more windows. For each combined spectrum, a background noise is estimated.
摘要翻译：执行样本的多次扫描，产生多个质谱。基于样品位置，时间或质量，将多个质谱的相邻质谱组合成质谱的集合。计算质谱收集的背景噪声估计。使用背景噪声估计来过滤质谱的收集，产生一个或多个质谱的过滤集合。使用一个或多个质谱的过滤集合进行定量或定性分析。例如，通过将质谱图的集合除以两个或多个窗口来计算背景噪声估计。对于两个或多个窗口的每个窗口，组合每个窗口内的所有光谱，为两个或多个窗口中的每个窗口产生组合光谱。对于每个组合光谱，估计背景噪声。

9. 发明申请

US20090063592A1 METHODS FOR DATA PROCESSING 有权
标题翻译：数据处理方法
公开(公告)号：US20090063592A1
公开(公告)日：2009-03-05
申请号：US11848717
申请日：2007-08-31
申请人： GORDANA IVOSEV , Ronald Bonner
发明人： GORDANA IVOSEV , Ronald Bonner
IPC分类号： G06F17/30
CPC分类号： G06K9/6232 , H01J49/0031
摘要： According to various embodiments, variables are grouped in an unsupervised manner after principal component analysis of a plurality of variables from a plurality of samples. A number of principal components are selected. A subset principal component space is created for those components. A starting variable is selected. A spatial angle is defined around a vector extending from the origin to the starting variable. A set of one or more variables is selected within the spatial angle. The set is assigned to a group. The set is removed from further analysis. The process is repeated starting with the selection of a new starting variable until all groups are found.
摘要翻译：根据各种实施例，在来自多个样本的多个变量的主成分分析之后，变量以无监督的方式分组。选择了多个主要组件。为这些组件创建子集主体组件空间。选择起始变量。围绕从原点延伸到起始变量的向量定义空间角度。在空间角度内选择一组或多个变量。该集合被分配给一个组。该集合从进一步分析中删除。重复该过程，首先选择一个新的起始变量，直到找到所有组。

10. 发明申请

US20090063102A1 METHOD FOR IDENTIFYING A CONVOLVED PEAK 有权
标题翻译：识别转化峰的方法
公开(公告)号：US20090063102A1
公开(公告)日：2009-03-05
申请号：US12200636
申请日：2008-08-28
申请人： Gordana Ivosev , Ronald Bonner
发明人： Gordana Ivosev , Ronald Bonner
IPC分类号： G06F19/00
CPC分类号： G06K9/00543 , G01N30/72 , G01N30/8682 , G01N2030/862 , H01J49/0036
摘要： A method for identifying a convolved peak is described. A plurality of spectra is obtained. A multivariate analysis technique is used to assign data points from the plurality of spectra to a plurality of groups. A peak is selected from the plurality of spectra. If the peak includes data points assigned to two or more groups of the plurality of groups, the peak is identified as a convolved peak. Principal component analysis is one multivariate analysis technique that is used to assign data points. A number of principal components are selected. A subset principal component space is created. A data point in the subset principal component space is selected. A vector is extended from the origin of the subset principal component space to the data point. One or more data points within a spatial angle around the vector are assigned to a group.
摘要翻译：描述了用于识别卷积峰的方法。获得多个光谱。多变量分析技术用于将数据点从多个频谱分配到多个组。从多个光谱中选出峰。如果峰值包括分配给多个组中的两个或更多个组的数据点，则将该峰识别为卷积峰。主成分分析是一种用于分配数据点的多变量分析技术。选择了多个主要组件。创建子集主体组件空间。选择子集主体组件空间中的数据点。向量从子集主体组件空间的起点扩展到数据点。在矢量周围的空间角度内的一个或多个数据点被分配给一个组。

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