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Computer program generates a magnetic output tape containing time and averaged data values of a specified number of major frames over a specified time interval. A decommutation system is used to acquire the raw data, which is then reformatted and averaged.
In our final report for DE-C0022186, we describe the work we did on this grant towards the goals we proposed. Our first goal was characterizing fundamental limits for sketching of discrete high-dimensional matrices with low-dimensional structures. Our second main goal was designing algorithms for data reconstruction from sketches. We focus on approaches that are either specifically designed for non-real-valued data (binary, finite field) or that will translate more readily to that setting.
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Embodiments are generally directed to a low-overhead mechanism to detect address faults in ECC-protected memories. An embodiment of an apparatus includes a memory array; an error correction code (ECC) encoder for the memory array to encode ECC values based on a data value and a respective address value for the data value; and an ECC decoder for the memory array to decode ECC values that are based on data values and respective addresses for the data values; wherein the apparatus is to detect and correct an error in an address value based on an ECC value, address value, and data value stored in the memory.
Value of information (VOI) provides the ability to identify and prioritize useful information gathering for a geothermal prospect, either hydrothermal or for enhanced geothermal systems. Useful information provides a value greater than the cost of the information; wasteful information costs more than the expected value of the information. In this project we applied and refined VOI methodologies on selected geothermal prospects.
A computer-implemented method for compressing video data comprises receiving a sequence of video data values, each video data value being a digital value from a successive one of a plurality of pixels that form a video sensor, the sequence of video data values resulting from successive frames of video captured by the video sensor; extracting the video data values for each pixel in turn to create a plurality of pixel data streams, each pixel data stream including the video data value for each frame of captured video for the pixel; and applying data compression to each pixel data stream to create compressed data for each pixel data stream.
A computer-implemented method for compressing video data comprises receiving a sequence of video data values, each video data value being a digital value from a successive one of a plurality of pixels that form a video sensor, the sequence of video data values resulting from successive frames of video captured by the video sensor; extracting the video data values for each pixel in turn to create a plurality of pixel data streams, each pixel data stream including the video data value for each frame of captured video for the pixel; and applying data compression to each pixel data stream to create compressed data for each pixel data stream.
Described herein are various technologies pertaining to detecting tampering of a seal based upon quantum optical communication via a communications channel comprising the seal. A plurality of pulses of light encoded with random data are transmitted on the communications channel, whereupon they are received and their data values measured. The measured data values of the pulses are then compared to the known transmitted data to determine a correlation statistic between the transmitted and received data values. Tampering with the seal can be detected based upon identifying that the correlation statistic has dropped below a threshold non-tampered level of correlation between transmitted and received values.
An SEU protection circuit comprises first and second storage means for receiving primary and redundant versions, respectively, of an n-bit wide data value that is to be corrected in case of an SEU occurrence; the correction circuit requires that the data value be a 1-hot encoded value. A parity engine performs a parity operation on the n bits of the primary data value. A multiplexer receives the primary and redundant data values and the parity engine output at respective inputs, and is arranged to pass the primary data value to an output when the parity engine output indicates `odd` parity, and to pass the redundant data value to the output when the parity engine output indicates `even` parity. The primary and redundant data values are suitably state variables, and the parity engine is preferably an n-bit wide XOR or XNOR gate.
Systems, apparatuses, and methods for reducing inter-node bandwidth are contemplated. A computer system includes requesting nodes sending transactions to target nodes. A requesting node sends a packet that includes a register identifier (ID) in place of a data value in the packet. The register ID indicates a register in the target node storing the data value. The register ID uses fewer bits in the packet than the data value. The data value may be a memory address referencing a memory location in the target node. The received packet may also include an opcode indicating an operation to perform on the targeted data value.
An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.
An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in addition to serving the Earth Science community as one of the major Distributed Active Archives Centers (DAACs), provides much more than just data. Among the value-added services available to general users are subsetting data spatially and/or by parameter, online analysis (to avoid downloading unnecessarily all the data), and assistance in obtaining data from other centers. Services available to data producers and high-volume users include consulting on building new products with standard formats and metadata and construction of data management systems. A particularly useful service is data processing at the DISC (i.e., close to the input data) with the users algorithm. This can take a number of different forms: as a configuration-managed algorithm within the main processing stream; as a stand-alone program next to the on-line data storage; as build-it-yourself code within the Near-Archive Data Mining (NADM) system; or as an on-the-fly analysis with simple algorithms embedded into the web-based tools. Partnerships between the GES DISC and scientists, both producers and users, allow the scientists to concentrate on science, while the GES DISC handles the data management, e.g., formats, integration, and data processing. The existing data management infrastructure at the GES DISC supports a wide spectrum of options: from simple data support to sophisticated on-line analysis tools, producing economies of scale and rapid time-to-deploy. At the same time, such partnerships allow the GES DISC to serve the user community more efficiently and to better prioritize on-line holdings. Several examples of successful partnerships are described in the presentation.
Three areas of improvement in COSMIC/NASTRAN, 1989 release, were incorporated recently that make the analysis program run faster on large problems. Actual log files and actual timings on a few test samples that were run on IBM, CDC, VAX, and CRAY computers were compiled. The speed improvement is proportional to the problem size and number of continuation cards. Vectorizing certain operations in BANDIT, makes BANDIT run twice as fast in some large problems using structural elements with many node points. BANDIT is a built-in NASTRAN processor that optimizes the structural matrix bandwidth. The VAX matrix packing routine BLDPK was modified so that it is now packing a column of a matrix 3 to 9 times faster. The denser and bigger the matrix, the greater is the speed improvement. This improvement makes a host of routines and modules that involve matrix operation run significantly faster, and saves disc space for dense matrices. A UNIX version, converted from 1988 COSMIC/NASTRAN, was tested successfully on a Silicon Graphics computer using the UNIX V Operating System, with Berkeley 4.3 Extensions. The Utility Modules INPUTT5 and OUTPUT5 were expanded to handle table data, as well as matrices. Both INPUTT5 and OUTPUT5 are general input/output modules that read and write FORTRAN files with or without format. More user informative messages are echoed from PARAMR, PARAMD, and SCALAR modules to ensure proper data values and data types being handled. Two new Utility Modules, GINOFILE and DATABASE, were written for the 1989 release. Seven rigid elements are added to COSMIC/NASTRAN. They are: CRROD, CRBAR, CRTRPLT, CRBE1, CRBE2, CRBE3, and CRSPLINE.
A system and method for monitoring an apparatus or process asset including creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto; acquiring a set of observed signal data values from the asset; determining an operating mode of the asset for the set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a set of estimated signal data values from the selected process submodel for the determined operating mode; and determining asset status as a function of the calculated set of estimated signal data values for providing asset surveillance and/or control.