Extensible Toolkit for Signal Analysis

With advancements in both computational performance and resolution of experimental methods, the amount of data acquired and stored in databases is surpassing the analytical capabilities of human operators. To benefit from the large influx of data, methods from the emerging domain broadly defined as Big Data Analytics can be used to reduce the lag between data acquisition/generation and data analytics. Nanohmics proposes to develop a novel, extensible software toolkit for analyzing large time-dependent datasets of fluid flows using an array of user-friendly dimensionality reduction and analysis techniques. The toolkit will help streamline processing of numerical or experimental flow field data, and support standard and custom auxiliary tools that extends its capabilities to different input and output file formats, application-specific user interfaces, run-time diagnostics, and summary reports. Software backend components will be designed to be compatible with large datasets and distributed-memory computing resources, and they will be optimized for computational performance and reliability. These software components will also leverage redundant processing steps to accelerate investigations dependent on multiple related dimensionality reduction and analysis methods.