10-01-2015 04:48 AM
Hi,
I'm trying to develop a sensitivity map of ultrasonic beam projection. The beam is assumed to be a straight line in 64 x 64 pixels. The background values are 0 and the beam line values are 1. My goal here is to make a mountain-like shape of the given projection. I'm thinking the best way is to apply Gaussian filter. Can anyone guide me how I can achieve this? I attached an example of 64 x64 beam sensitivity map.
Tq
Solved! Go to Solution.
10-01-2015 11:35 AM
I'm sorry, I don't understand the question and the text file of 0's and 1's in some inexplicable pattern means nothing to me.
Bob Schor
10-09-2015 11:15 AM
Hi Bob,
Thank you for the reply and sorry for the late reply. You definitely right! I Actually I send the wrong projection beam file. I attached the correct projection beam file. I checked with other website and found exactly the same as what I trying to achieve. Please check at this website
However the program is in matlab code which I not familiar with. At this time I still trying to create the VI but still disappointed me.
Tq
10-09-2015 12:55 PM
Aha, now I think I understand your text file. I'd double-clicked it, which opened it in NotePad, where it looked like a whole bunch of zeros with strange formatting (I couldn't understand why every third line had blanks at the end). I finally reallized it was an enormously wide (504-character) text file, and really represented a 63-by-64 array of numbers. [Next time, tell us that first!].
Now I think I "get it", and I think I understand what you are trying to do. This doesn't seem to be particularly difficult to me, and there might even be some built-in LabVIEW functions that will speed this up, but aren't you just convolving a small 2D Gaussian with a larger 2D dataset? Of course, you might have to worry about "edge effects" and it might be computationally slow (if you "do it yourself"), but I'm not sure I see the problem, unless, of course, you don't know LabVIEW and "just want an answer" (which is not the purpose of the Forum, to my way of thinking).
Bob Schor
10-09-2015 01:21 PM
Or you could just fit it to a 2D gaussian. Search the forum for some examples.
10-15-2015 12:27 PM
Hi,
I solved the problem. Thanks for the support.
10-15-2015 01:20 PM
Dear Ledang,
Please post the Solution! When viewers of the Forum see that a problem has a Solution, and they have a similar problem, they want to "see the solution"!
Bob Schor