conversion of 1d signal to 2d signal for CNN operation I have a 1D signal data sampled at 25kHz and I want to modify in to 2D signal so as to modify it as the input to ConvolutionalNets as they work only on 2 dimensional data.
What are possible ways to convert the signal from 1D to 2D for such calculations. I have seen some papers but I couldnt find any relevant understanding how the conversion of signal is modified as to apply to CNN operations. 
For example if I have 25 data samples in the input and if i rearrange in to a 2d signal as 5X5 sized(inbuilt function in matlab: "reshape").If I am applying 3x3 filter for convolution operation, smallest filter and the output of that 5x5 size input will be 3x3 size.
Can I apply such input to any convnets? Is that a valid type of conversion to apply for CNN ? In such kind of inputs I understand there will be relation in row wise but not column wise.
Any explanation or suggestion would be helpful. 

Updated for the comment 

I want to convert 1d signal in to 2d image signal. In an image a pixel will have relation with the adjacent 8 pixels. If I am rearranging a 1D signal to 2D signal as quoted in the example , the row 1 will have 5 data samples and next row starts with 6th sample and so on. If I take a sample in row 2, 2nd sample, I have relation first and third sample in the same row but nothing with samples from row 1 and row 3. So If Im converting such way is that appropriate ? In the paper mentioned ( Fig 1) I wanted to know how the 1D signal is converted and applied to CNN link
There are a couple things with that question. 

1) Why do you need 2D convolutions exactly? You could think of a 1D signal as an $Nx1$ 2d signal if you like ;) Then you can design your convolutional filters to be $7x1$, $5x1$, $3x1$ etc. The pooling you only apply in the x-direction. And there you go. It's a 2D convolutional neural network, which works on 1D signal. Just kidding, it just a CNN.

2) Simple fully-connected deep learning neural networks are better for handling 1D data, as far as I know. Of course it's best if your data is evenly sampled. CNN comes handy when the fully connected structure explodes the computation. In your case, that should not be a big issue.

If your 1d data vector is too large, just try subsampling, instead of a convolutional architecture.

3) Converting a 1d data to 2d is probably valid only if you know in advance that this 1d manifold carries non-uniform neighborhood information, which could be represented with a 2D matrix with nearby connections. The paper probably inserts a layer to the network which re-arranges the indices into a 32x32 matrix. The weights or the indices where to put each value maybe learnt by the network itself. You would need to write the backprop for this though.