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Adrian C. Keister

Recent Posts

Python at NIWeek 2018

Python made a rather large splash at NIWeek 2018. It got a number of mentions in various settings, the most notable of which was Collin Draughon's talk "Automating Measurements with Python." LabVIEW 2018's Python Node makes it very straight-forward to call a simple Python script from within LabVIEW. You set up the Python environment, invoke the Python Node, and then tear down the environment when you're done. 

Introducing TestScript:  Free Python/LabVIEW Connector

Summary: Test engineers typically add manual-control screens to LabVIEW applications. While it would be helpful to repetitively execute varying parts of those manual-control screens, LabVIEW is not optimal for dynamic scripting, or on-the-fly sequencing with flow control.  (Imagine editing the source code of Excel each time you wanted to create a macro.)  And while Python is built for scripting, it requires advanced custom coding to interface with LabVIEW. 

Would you mind if we ask a few questions?

In a previous post , I wrote about how data science can be an important part of systems integration.  In this post, I would like to outline a few more details about how that happens by exploring the following graphic:

Data Science and Systems Integration

 

Data Science. Systems Integration. For many systems integrators, the two seem to lie at an ideal hand-off point with nothing in common. The systems integrators generate the data and store it. Once that is done, the systems integrators have finished the job. The customers do what they like with the data, problem solved. I would argue, however, that there are significant opportunities for helping customers at a higher level (higher, meaning not only further away from hardware but also higher up in the customer's management hierarchy) in the realm of data science.

The Numerical Analysis of Finding the Height of a Circular Segment

 For best results in viewing this page, right-click an equation on this page, select Math Settings -> Math Renderer -> HTML-CSS.

Finding the height of a circular segment (the green area in the figure below) given the area is an interesting problem in numerical analysis.

Quaternions for Rotations in Native LabVIEW

Suppose you find yourself needing to rotate a point in three-dimensional space about an arbitrary axis. This problem comes up frequently in robotic kinematics, for example. You can use Euler angles and rotation matrices. However, this approach, while computationally efficient, has a few drawbacks. One is that it is not particularly easy to invert. Another is gimbal lock. Quaternion rotations do not suffer from either of these drawbacks. While quaternions are not quite as computationally efficient as rotation matrices, modern computer hardware makes this drawback less important for most applications.

The Hough Circle Transform in Native LabVIEW

 The Hough Circle Transform has been known for some time.[1] The Hough Circle Transform takes in data and a known radius, and outputs the center of the circle with that radius, that best fits the data. It does this by setting up an accumulator grid, all initialized to zero, in which each data point votes on where it thinks the circle center is. A more formal description follows.