Since you are seeing your filter coefficients and much larger than your signal, this is indicative that you have a very large "impulse" at the start of your signal. The coefficients of your filter is the impulse response for the filter, so that is exactly what you are seeing: the response to an impulse.
Review the start of your time domain data for a very ...
Below is an attempt to do what you're asking in Python.
First, the dashed item:
Then the sensor. It's uniform,so just comes out as black.
Then the output of the sensor (convolve the thing to be measured with the sensor).
Finally, the output of the deconvolution.
Note that the output is not precisely the same as the input, but it's pretty close.
View your frequency response after your low pass filter on a dB scale to better show the limitations of your filter.
Use a multiband filter with the least squares algorithm for an optimized rejection filter for zero-fill interpolation. This will concentrate the rejection to be specifically where the images are that need to be removed.
Given your original ...
The result of your plot is what you would see when you have timing offset error but no frequency error. Given that it is over-sampled you can confirm this by manually adjusting your decision point a few samples either way. Even better plot out an eye diagram such as what I show below with all your samples for 16 QAM and it should be clear from that if this ...
Yes you are right.
Thresholding alone cannot perform data compression. By the way, I assume you meant thresholding of transform (DCT) coefficients.
Quantization helps you reduce the number of states the variables can take; hence reduce the number of bits necessary to encode the codebook (totality of codewords).
Thresholding, is applied after (or ...