Abstract: Systems, methods, and other embodiments associated with anomaly detection using tripoint arbitration are described. In one embodiment, a method includes identifying a set of clusters that correspond to a nominal sample of data points in a sample space. A point z is determined to be an anomaly with respect to the nominal sample when, for each cluster, a tripoint arbitration similarity between data points in the cluster calculated with z as arbiter is greater than a threshold.

Abstract: Systems, methods, and other embodiments associated with tripoint arbitration for data classification are described. In one embodiment, a method includes receiving a query data point for classification, an arbiter point, a first set of classified data points belonging to a first class and a second set of classified data points belonging to a second class. A class-aggregated similarity metric for the query data point and each set is determined based, at least in part, on tripoint arbitration coefficients for data point pairs that include the query data point and a classified data point in the set, as calculated using the arbiter point. The query data point is classified based on a comparison of the similarity metric determined for the first set and the similarity metric determined for the second set.

Abstract: Techniques are described for performing cluster analysis on a set of data points using tri-point arbitration. In one embodiment, a first cluster that includes a set of data points is generated within volatile and/or non-volatile storage of a computing device. A set of tri-point arbitration similarity values are computed where each similarity value in the set of similarity values corresponds to a respective data point pair and is computed based, at least in part, on a distance between the respective data point pair and a set of one or more arbiter data points. The first cluster is partitioned within volatile and/or non-volatile storage of the computing device into a set of two or more clusters. A determination is made, based at least in part on the set of similarity values in the tri-arbitration similarity matrix, whether to continue partitioning the set of data points.

Abstract: Systems, methods, and other embodiments associated with anomaly detection using tripoint arbitration are described. In one embodiment, a method includes identifying a set of clusters that correspond to a nominal sample of data points in a sample space. A point z is determined to be an anomaly with respect to the nominal sample when, for each cluster, a tripoint arbitration similarity between data points in the cluster calculated with z as arbiter is greater than a threshold.

Abstract: Techniques are described for performing cluster analysis on a set of data points using tri-point arbitration. In one embodiment, a first cluster that includes a set of data points is generated within volatile and/or non-volatile storage of a computing device. A set of tri-point arbitration similarity values are computed where each similarity value in the set of similarity values corresponds to a respective data point pair and is computed based, at least in part, on a distance between the respective data point pair and a set of one or more arbiter data points. The first cluster is partitioned within volatile and/or non-volatile storage of the computing device into a set of two or more clusters. A determination is made, based at least in part on the set of similarity values in the tri-arbitration similarity matrix, whether to continue partitioning the set of data points.

Abstract: Systems, methods, and other embodiments associated with anomaly detection using tripoint arbitration are described. In one embodiment, a method includes identifying a set of clusters that correspond to a nominal sample of data points in a sample space. A point z is determined to be an anomaly with respect to the nominal sample when, for each cluster, a tripoint arbitration similarity between data points in the cluster calculated with z as arbiter is greater than a threshold.

Abstract: Systems, methods, and other embodiments associated with adaptively determining a preventive maintenance schedule based on historical system operation are described. The prognostic parameter values are continuously partitioned into a number of operating states based on observed maintenance costs associated with the prognostic parameter values. The operating states range from absolutely healthy, one or more degrees of degradation, to fully degraded. A system cost function is used as the discriminant function. The system cost function is an expected maintenance cost when a given preventive maintenance (PM) schedule is adopted. The system cost function calculates the expected cost based on the observed cost of operation in each of the operating states and a probability of the computing system being in each of the operating states as determined by the PM schedule. The PM schedule that minimizes the cost function is determined to be the optimal PM schedule.

Abstract: Systems, methods, and other embodiments associated with tripoint arbitration for data classification are described. In one embodiment, a method includes receiving a query data point for classification, an arbiter point, a first set of classified data points belonging to a first class and a second set of classified data points belonging to a second class. A class-aggregated similarity metric for the query data point and each set is determined based, at least in part, on tripoint arbitration coefficients for data point pairs that include the query data point and a classified data point in the set, as calculated using the arbiter point. The query data point is classified based on a comparison of the similarity metric determined for the first set and the similarity metric determined for the second set.

Abstract: Systems, methods, and other embodiments associated with decision making with analytically combined split conditions are provided. In one embodiment, a method for classifying data is provided. An input data sample is received for classification as belonging to one of two possible classes. The input data sample includes a set of attribute values. The method includes evaluating the set of attribute values with a tree function that defines a decision boundary of a classification tree. The tree function classifies an input data sample as belonging to one of the two possible classes based, at least in part, on the attribute values of the input data sample. In another embodiment parameters of the tree function are derived by applying a gradient descent parameter update rule to the training data samples.

Abstract: Systems, methods, and other embodiments associated with adaptively determining a preventive maintenance schedule based on historical system operation are described. The prognostic parameter values are continuously partitioned into a number of operating states based on observed maintenance costs associated with the prognostic parameter values. The operating states range from absolutely healthy, one or more degrees of degradation, to fully degraded. A system cost function is used as the discriminant function. The system cost function is an expected maintenance cost when a given preventive maintenance (PM) schedule is adopted. The system cost function calculates the expected cost based on the observed cost of operation in each of the operating states and a probability of the computing system being in each of the operating states as determined by the PM schedule. The PM schedule that minimizes the cost function is determined to be the optimal PM schedule.

Abstract: A sensor board includes at least one sensor in communication with a controller having a single board address to facilitate sensor polling by standard, product-independent modules of a circuit board for a computer server or network appliance. Miniature low-cost sensor boards can be placed in multiple locations on circuit boards without the necessity of being specified and integrated into the circuit board during the design phase to reduce or eliminate associated design risks or delays.

Abstract: Systems, methods, and other embodiments associated with decision making with analytically combined split conditions are provided. In one embodiment, a method for classifying data is provided. An input data sample is received for classification as belonging to one of two possible classes. The input data sample includes a set of attribute values. The method includes evaluating the set of attribute values with a tree function that defines a decision boundary of a classification tree. The tree function classifies an input data sample as belonging to one of the two possible classes based, at least in part, on the attribute values of the input data sample. In another embodiment parameters of the tree function are derived by applying a gradient descent parameter update rule to the training data samples.

Abstract: A method and system for identifying voids in solder balls in a ball-grid array (BGA) using an image of the BGA include localizing an image of a solder ball on the BGA image, the solder ball image having a radius and having multiple points each having an image intensity, and producing a void-free model image of the solder ball based on the radius of the solder ball image, the void-free model image having multiple points each having an image intensity. The method and system also include computing a difference between the image intensities of the points of the solder ball image and the image intensities of the points of the void-free model image to produce a residual image, and identifying a void using the residual image.

Abstract: Computer implemented system and method for publishing evaluated information comprising collecting one or more sets of data from one or more users, generating one or more sets of augmented collected data by augmenting the one or more sets of data collected from the one or more users, wherein the augmenting the one or more sets of data includes implementing a typesetting function configured to associate identifier information with the collected one or more sets of data, detecting a request from the one or more users, providing at least one additional user at least a portion of the augmented collected data, wherein at least a portion of the provided augmented collected data includes identifier data associated with the one or more users, and assigning a recognized number to the augmented collected data.

Abstract: A sensor board includes at least one sensor in communication with a controller having a single board address to facilitate sensor polling by standard, product-independent modules of a circuit board for a computer server or network appliance. Miniature low-cost sensor boards can be placed in multiple locations on circuit boards without the necessity of being specified and integrated into the circuit board during the design phase to reduce or eliminate associated design risks or delays.

Abstract: A method and system for identifying voids in solder balls in a ball-grid array (BGA) using an image of the BGA include localizing an image of a solder ball on the BGA image, the solder ball image having a radius and having multiple points each having an image intensity, and producing a void-free model image of the solder ball based on the radius of the solder ball image, the void-free model image having multiple points each having an image intensity. The method and system also include computing a difference between the image intensities of the points of the solder ball image and the image intensities of the points of the void-free model image to produce a residual image, and identifying a void using the residual image.

Abstract: One embodiment of the present invention provides a system that cancels fan noise in a computer system. During operation, the system obtains a fan noise signal using a microphone. Next, the system generates a spectral pattern based on the obtained fan noise signal. The system then uses the spectral pattern to identify a corresponding cancellation spectrum in an anti-spectra library. Next, the system generates a noise-canceling signal using the cancellation spectrum. Note that the amount of computation required to cancel fan noise is reduced because generating the noise-canceling signal using the anti-spectra library requires less computation than generating the noise-canceling signal using dynamic noise-cancellation techniques.

Abstract: One embodiment of the present invention provides a system that identifies a failure mechanism for a component. The system first constructs a library of failure signatures from a set of components, wherein each failure signature in the library characterizes a known failure mechanism associated with a component in the set of components. Next, the system receives a telemetry signal which is measured from a component under surveillance. The system then determines if there is an anomaly in the telemetry signal. If so, the system performs pattern-matching operations between the telemetry signal and failure signatures in the library. Next, if a match is found, the system identifies the failure mechanism for the component under surveillance based on the matching failure signature.

Abstract: A system that reduces the size of a design data set. During this design data set reduction operation, the system computes a decision boundary which separates a first group of data patterns in a training data set from a second group of data patterns in the training data set. For each data pattern in the training data set, the system determines if removing the data pattern from the training data set substantially affects the resulting decision boundary. If so, the system marks the data pattern as a key pattern. The system then removes all data patterns that are not marked as key patterns to produce a reduced training data set which represents the decision boundary.

Abstract: One embodiment of the present invention provides a system that constructs a classifier that distinguishes between different classes of data points. During operation, the system first receives a data set, which includes class-one data points and class-two data points. For each class-one data point in the data set, the system uses a separating primitive to produce a set of point-to-point separating boundaries, wherein each point-to-point separating boundary separates the class-one data point from a different class-two data point. Next, the system combines separating boundaries in the set of separating boundaries to produce a point-to-class separating boundary that separates the class-one data point from all of the class-two data points in the data set.