New analysis functions ensure more productive analysis processes: For example, users can now adjust measurement data to curves (“curve fitting”). The approximation is based on the Gauss-Newton algorithm with a step-width control, based on Levenberg-Marquardt.
Thanks to the new interval functions in imc FAMOS 7.2, periodic processes can be precisely fractionized into their individual intervals, even with variable period lengths. Thus, characteristic values can be determined for the individual intervals, e.g., the timing of an internal-combustion engine.
In addition to the new analysis functions, the new version is characterized above all by being more user-friendly. For example, the function list has been re-grouped and a search function has been added to search for functions and topics.
“Frequently, users are looking for the right function for a certain task. The new search feature will help them in that it lists all the functions that are suitable for the search term. This ensures the best possible overview and saves time”, says Oliver Abendschön, Product Manager for imc FAMOS at imc Test & Measurement GmbH.
Another special feature of the new imc FAMOS 7.2 version is the direct querying of internet resources. For example, geographical data from online mapping services, Standards from internet pages or user data from a name directory can be queried and used in calculations and reports.
A free, 30-day trial version of imc FAMOS 7.2 is available here:
http://www.imc-berlin.com/download-center/product-downloads/imc-famos/software
From Big Data to Smart Data with imc FAMOS 7.2
imc Meßsysteme GmbH is releasing version 7.2 of the imc FAMOS signal analysis software. In addition to new functions for matrix calculations, interval determination and nonlinear curve adaptation, an optional database package is available to users. It allows the connection of existing databases as data source and data sink. imc FAMOS extends the possibilities to import large volumes of data from existing databases and to analyze them automatically. This makes it even more comfortable for users to go from “Big Data” to “Smart Data”.