Abstract: A computing device includes a processor and memory storing instructions that are executable to determine a median of a first mixture distribution. The instructions are also executable to determine a parent mean, a parent standard deviation, and boundaries for each of multiple segments in the first mixture distribution. The instructions are also executable to determine a segment mean and a segment second moment for each segment based on the parent mean, the parent standard deviation, and the boundaries for the respective segment. The instructions are also executable to determine a scaled probability for each segment. The instructions are also executable to determine a mixture mean and a mixture standard deviation for the first mixture distribution based on the segment mean, the segment second moment, and the scaled probability for each segment in the first mixture distribution.

Abstract: A semiconductor device comprises a plurality of transistor mismatch circuits formed on a semiconductor wafer; and a characterization circuit formed on the semiconductor wafer. The characterization circuit is coupled to receive input provided by the absolute value circuits simultaneously which themselves receive inputs from the mismatch circuits simultaneously and is configured to output a standard deviation of mismatch between transistors in the mismatch circuits.

Abstract: A system comprises a workload evaluator that is operable to receive a representative workload that is representative of competing demands for capacity of at least one shared computing resource. The workload evaluator evaluates the representative workload and computes a metric representing a degree of burstiness of demands present in the representative workload. The metric representing degree of burstiness of the representative workload may be used for estimating an upper bound on quality of service provided by a workload manager to the representative workload. The metric may also be used for evaluating at least one scheduler parameter setting of the workload manager to aid in determining an optimal parameter setting based at least in part on the estimated impact of the representative workload on QoS provided by the workload manager.

Abstract: A method for multivariate analysis using a mathematical model for generating model data and actual data for more than one variable includes for each variable, determining a difference between the model data and the actual data. The model data is substantially representative of more than one variable. The method also includes for each variable, determining a fractional impact on performance. The method further includes for each variable, determining a weighted deviation based on the determined difference and the determined fractional impact. The method also includes transmitting the weighted deviation to an output device.

Abstract: Provided herein is a method of determining the similarity between a first multivariate data set and a second multivariate data set. The method may be applied to rapidly assess the similarity between fluorescence spectroscopy multivariate data sets in quality control analysis. The method includes representing the data of a first and a second multivariate data set in matrix form to yield a multivariate data matrix and calculating the magnitude of an additive and subtractive combination of each multivariate data matrix. The concept of a penalty parameter is introduced to set a detectable limit of variance between the first multivariate data set and the second multivariate data set and the penalty parameter is used in combination with the magnitude of an additive and subtractive combination of each multivariate data matrix to determine a similarity value.

Abstract: A method for multivariate analysis using a mathematical model for generating model data and actual data for more than one variable includes for each variable, determining a difference between the model data and the actual data. The model data is substantially representative of more than one variable. The method also includes for each variable, determining a fractional impact on performance. The method further includes for each variable, determining a weighted deviation based on the determined difference and the determined fractional impact. The method also includes transmitting the weighted deviation to an output device.

Abstract: Embodiments include methods, apparatus, and systems for analyzing data in an infrastructure. One embodiment includes a method that senses environmental data at equipment racks in an infrastructure, identifies patterns in the environmental data, and uses the patterns to modify the infrastructure to improve thermal management in the infrastructure.

Abstract: A computer based proactive process control technique used to predict the capability of a manufacturing process. A solid model of statistical process control is created by a computer program to simulate the Bell Curve of the data or the data with in +/?3 standard deviation. With product knowledge and process knowledge, it is possible to setup and control the manufacturing process to yield a desired level. The computer program operates within a communication media network. Suppliers and manufactures through their main servers are connected to floor computers, which are data input and output computers. All the servers are connected to the main server of a prime contractor. Manufacturing data from the supplier's field computers goes to their respective servers, and that data in turn goes to the main server of the prime contractor. From the prime contractor server, the data can be retrieved through computers that are data reviewing stations.

Abstract: A method for extracting a signal of interest from a signal generated from at least one subject, comprises the steps of (A) obtaining a time-series signal generated from the subject as a data group consisting of a plurality of pieces of data, (B) obtaining a plurality of extracted data groups from the data group, wherein each extracted data group comprises a predetermined number of pieces of data selected from the data group, (C) calculating standard deviations of the plurality of extracted data groups to obtain a standard deviation group, and (D) referencing each standard deviation included in the standard deviation group and selecting the signal of interest.