New Matlab programming contest: Sensor – Rules
Compressive Sensing is the 21st MATLAB Online Programming Contest.
The Problem
A typical digital camera contains a sensor with many millions of individual pixels. After the picture is taken, pixel data is pulled off the chip and placed in memory. Once in memory, the image data is compressed using something like the JPEG algorithm, at which point the raw pixel data is thrown away. On reflection, this seems to be sub-optimal. We went to a lot of trouble (costing us valuable time, energy, and memory) to get all that data off the chip, only to immediately toss most of it in the trash. Why not just grab fewer pixels in the first place? That’s the general idea behind compressive sensing, and it’s the basis for this contest. [See reference here] For this contest, you are to reconstruct the actual pixel values of an “uncompressed” sensor image. You will do this by requesting “compressed” sensor values, each of which is the sum of pixel values within the uncompressed image. To query the values, supply a mask for the region you’re interested in, and in return you will receive the sum of the pixel data corresponding to that mask. There is a limit on how many compressed sensor values you can request in order to attempt to reconstruct the uncompressed pixel values. Here’s what it looks like in practice. Imagine starting with the matrix A shown below.
A = [ 0 0 0 0 0 ] [ 0 1 0 0 3 ] [ 0 1 7 0 0 ] [ 0 8 2 2 0 ] [ 0 0 0 0 0 ]
Now suppose you had a query mask that looks like this:
mask = [ 0 0 0 0 0 ] [ 0 1 1 0 0 ] [ 0 1 1 0 0 ] [ 0 0 0 0 0 ] [ 0 0 0 0 0 ]
Here is a picture of the matrix with a mask superimposed.
Note that the sum of the pixels in the masked region is 9.
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More on http://www.mathworks.com/matlabcentral/contest/contests/2/rules
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Problem with cross-validation using Matlab Neural Net Toolbox
From Jorge
Dear all,
I am using Matlab Neural Net Toolbox, to forecast time series. I am using NEWELM (Elman network) and submitting a single input (time serie) to the network.
The point is that I can not manage to make cross-validation. All the process has been done with a single training set, despite I have left the default function “dividerand” on. In other words, I have submitted a 50 points time serie, and all the 50 points have been used by Matlab to train the net. No validation interval was taken (despite valRatio is set 20%).
I guess the problem is related to the way I am submiting the input sequence:
P = [p1 p2 p3...p50];
Pseq = con2seq(P);
net = newelm(P,T,S);
net.trainFcn= ‘traingdx’
[net,TR] = train(net,Pseq);
=> for some unknown reason the split in training,validation and test set is not working.
Any help, please?
For some unknown reason
Popularity: 1% [?]
Distributed detection system
Dear all; I’m working now on distributed detection system, and trying to generate the figures in ” STC for distributed detection in WSN”, please if any one can help me to regenerate these figures. maaljarrah08@eng.just.edu.jo
Popularity: 1% [?]
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