Video Software Defined Radio lectures from WPI

At youtube, from Worcester Polytech Inst.:
ECE4305

ECE5312

Matlab R2014b: X11 forwarding and OpenGL

The new plotting engine in Matlab R2014b has caused some hangups and reduced quality plots for people using Matlab over X11 forwarding.

Consider starting Matlab this way:

matlab -nosoftwareopengl

figure
set(gcf,'renderermode','manual','renderer','painters')
plot(randn(100,1))

If you can’t start Matlab with the -nosoftwareopengl open, omit that open and try plotting with the

set(gcf….’painters’) line as shown above for each figure.

 

Matlab R2014b: installing the integrated OpenCV support

Initially it appears that to use OpenCV from Matlab R2014b, you will need to write your OpenCV calls in C++, using all the usual Mex stuff. This is not very convenient to me; it would be much more convenient to use the friendly syntax of Python. However the Python support in Matlab R2014b allows passing only 1xN arrays, so there would be reshaping involved to/from Python that would slow things down.

The mexopencv package that has been available for some time (and that works with earlier versions of Matlab) seems to be more user-friendly once installed–you use it much like any other Matlab toolbox, without you needing to code in C++/Mex yourself.

Bottom line: I’ll still be using Python/OpenCV without Matlab, or even C++/OpenCV, which looks easier than using Mex with Matlab. I am glad Mathworks has taken this step; maybe the mexopencv people will make the R2014b OpenCV support easier to use by making a bunch of .cpp functions to compile once and then call.

This process gets the packages downloaded and installed:
http://www.mathworks.com/help/vision/ug/install-data-for-computer-vision-system-toolbox.html

The directory with examples is at:

~/Documents/MATLAB/SupportPackages/R2014b/opencvinterface/toolbox/vision/supportpackages/visionopencv/example/

Here’s how the first example, the Foreground Detector works from Matlab:

cd ~/Documents/MATLAB/SupportPackages/R2014b/opencvinterface/toolbox/vision/supportpackages/visionopencv/example/ForegroundDetector

mexOpenCV backgroundSubtractorOCV.cpp

testBackgroundSubtractor

You will see a Video Player window pop up with cars driving by, with the cars detected outlined in white rectangles.

Matlab 2014b Python: can only pass 1xN vectors!

There seems to be a show-stopper for many uses of the Python interpreter functionality built into Matlab R2014b. It seems you cannot pass matrices!

We might investigate reshaping the matrix into a 1xN vector into Python, and reshape back to a matrix when done, but  I think Matlab will make copies at both reshapings.

I can pass
py.numpy.sqrt(2)

ans=1.4142
 and

py.numpy.sqrt([2,2])

ans=[ 1.41421356  1.41421356]

but I cannot pass
py.numpy.sqrt([2,2;2,2])
Error using py.numpy.sqrt
Conversion of MATLAB 'double' to Python is only supported for 1-N vectors.

That seems useless for a large majority of cases people would want to use Matlab for. Hopefully I am sorely mistaken.

the link below seems to confirm that you cannot pass normal 2D matrices to Python from Matlab R2014b:

http://www.mathworks.com/help/matlab/matlab_external/passing-data-to-python.html?searchHighlight=python%20array#buialof-51

Matplotlib: 3-D mesh wiregrid example

Some of the Matplotlib 3-D examples out there are a little out of date. Here is a minimal working example for the current version of Matplotlib 1.4


#!/usr/bin/env python
from mpl_toolkits.mplot3d import Axes3D # this line must come before the next line!
from matplotlib.pyplot import figure,show
from numpy import linspace,meshgrid,pi,sin #for testing
'''
key point: the line "from mpl_toolkits.mplot3d import Axes3D" needs to come before
the "from matplotlib.pyplot import ...." line in the FIRST file you run.
To be sure, I make the "from mpl_toolkits.mplot3d ..." line come first in my
main function file (the one I invoke from the command line or Spyder)
'''

def test():
    x,y = meshgrid(linspace(0,2*pi),linspace(0,2*pi))

    z = sin(x+0.5*y)
    ax = figure().gca(projection='3d')
    ax.plot_wireframe(x,y,z)
    show()

if __name__ == '__main__':
    test()


Matplotlib: force integer labeling of axis

When plotting output of simulations with Matplotlib, sometimes we want to label an axis as say “instantiation #” or “Sample #” or “try #” or the like.
To do this, you need to do:

import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
....
ax = plt.figure().gca()
...
ax.xaxis.set_major_locator(MaxNLocator(integer=True))

This will make the x-axis have integer-only labels. You can do the same for yaxis if you want.

Amateur Radio band plans: strong FM adjacent to weak signal modes thoughts

I was in an conversation with regard to a 902MHz enthusiast list, and we were discussing issues that may be unique to the 902MHz band due to the ARRL band plan, that was adapted to reflect reality in much of the USA with regard to easily adaptable commercial equipment.

The real life ham bands where the FM next to weak signal situation might occur (in the USA) based on the ARRL band plan include:
50MHz: The SSB/CW only and “any mode” meet at 50.3MHz. Maybe most FM operators stay about 51MHz? I have the equipment for this band but regrettably don’t listen often.
144MHz: I assume most weak signal work is at least 100kHz away from the popular 144.390MHz APRS frequency
220MHz: Again a case of ~100kHz spacing between FM and whatever weak signal work may exist
440MHz: The satellite band with what are typically very directive antennas keep multi-MHz separation between FM and weak-signal
900MHz: I would expect below 902.080MHz and above 902.120MHz to be trashed for weak signal operators according to the band plan.

The situation I felt hinges on:
a) what the relevant FCC specifications demand for “off-channel, in-band” emissions — is this just specified as say -60dB so many kHz from center channel, or with an emission mask. In either case, is the specification sufficient to protect a weak signal operator within N km of a powerful FM transmitter?
b) If the specification is not sufficient for weak signal protection from nearby (in range) FM transmitters, do the practical filter implementations used by the most frequently used equipment provide enough protection any way as a corollary to meeting the FCC specification?

Anaconda Python on Windows 10

Anaconda Python 64-bit also initially seems to startup and install fine on Windows 10 Technical Preview, so far.

Cygwin on Windows 10 Technical Preview

Yes, Cygwin 64-bit seems to install and run just fine on Windows 10 using VirtualBox.

AGI STK in Virtualbox on Linux

AGI STK (Systems Tool kit) does not currently run under WINE for version 10 of STK.
However, you can run STK from VirtualBox using Windows guest virtual machine.
Currently (with VirtualBox 4.3.16) you may find that STK crashes upon opening a scenario. Shutdown your virtual machine and try disabling 3D acceleration, enabling 2D acceleration, and setting video memory to at least 64MB. This “worked for me”