Meeting Tuesday, February 28: Modeling and Averaging Data

Meeting Logistics


  • Location: Mopac C, 1S13 @ NI headquarters 11500 North MoPac Expressway, Austin TX
  • Date: Tuesday, February 28, 2017
  • Time: 6:00 PM Central Time
  • Duration: ~2 hours
  • Format: Presentations
  • Food: An accurate headcount helps here so please register if you are attending in-person. If you are not an NI employee, please register here. NI employees should use internal invitation; if you are not already on the list, please contact If you are attending remotely, there is no need to register, just join us using the Remote Attendance coordinates below.


Meeting Topics


Averaging data in FPGA

Presenter: Aaron Gelfand


Averaging data can take many forms, for a project, I was requested to implement a time weighted average to smooth data spikes. The time weighting formula could be adjusted to a user specified value (# of averages), and also had to account for situations in which older data did not yet exist. FPGA code requires data to be fixed in length. Although not successfully implemented in FPGA during the project (had to move it up to the RT layer), this presentation will show how I was eventually able to implement the code in the FPGA, and some of the changes made to make it scaleable for a large number of channels while using minimal FPGA resources.


Data Modeling using Objects

Presenter: Damien Gray


The various minimization and fitting VIs included in LabVIEW were written a long time ago (late 90s?). Some include options to dynamically call a function, others do not. Most have a fixed set of starting conditions that do not give you the flexibility you need when modeling things like third order surfaces or highly nonlinear functions. A bit of copy / paste into an object-based model solves most of these problems, and allows you the flexibility needed to successfully model something complex. I have completed basic functionality (Nelder-Mead and Conjugate Gradient minimizers with arbitrary functions) and currently use these methods for modeling complex physical systems. There will be examples!




Remote Attendance

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