I am a labview new starter.
Here is my problem,I am working on a data processing LabVIEW program,now I use Threshold Detector VI to get the signals which have crossing the threshold,and I have the indices of these signals.but the indices are related to the amplitude of the signals,what I want is the time crossings of these signals.
What can I do?
Thank you very much!
You can use Get Waveform Time Array to get an array of timestamps, then use your threshold indices to select from the timestamp array.
Related
I have a signal 3M samples long. I want to subsample it. I know that to avoid aliasing I need to filter off frequencies higher than Nyquist frequency. I know that I can do that by convolution with certain filters (e.g. Butterworth), but I know that way some of the high frequencies is preserved.
I wonder whether I can just zero the unwanted frequencies in the frequency domain and use inverse FFT to go back to time domain. Is such approach numerically correct? I know that certain libraries speed-up convolution with use of FFT.
It's not really correct. Zeroing out the top frequencies in the FFT will only zero out frequencies with wavelengths that divide the FFT length. If you were to frequency-shift your signal by half a bin and do another FFT, you'd find that there is some leakage and the upper frequencies are not all zero.
The result will be pretty close, but taking an FFT of the whole signal is a very expensive way to get just pretty close.
You should just use a normal filter. As long as you leave a reasonable amount of room between the filter cut-off frequency and the Nyquist frequency, it's easy to ensure that any aliasing error will be much smaller than quantization error and other noise.
I have a simple flowgraph for QPSK transmitter with USRP.
After execution, there is lage sidelobes, that pulsate.
During the periods of large sidelobes, there is a drop in amplutude of main lobe.
There is no such pulsations if I make similar transmitter with Matlab.
I suscpect discontinues in sorce.
Comments and advice are appreciated.
Your pool of random data is far too short; you'll see data periodicity in spectrum very quickly; it might be that this is exactly what happens. So, try with num_samples 2**20 instead.
You can observe your transmit spectrum yourself before even transmitting it: use the Qt GUI frequency sink or waterfall sink with an FFT length that corresponds to the FFT length you use in gqrx.
Your sample rate is at the least end of all possible sampling rates. Here, the roll-off of the interpolation filters inside the USRP will definitely show. Don't do that to yourself. Use sps = 16, samp_rate = 1e6 instead.
Make sure you're not getting any underruns in your tranmitter, nor overruns in your receiver. If that happens at these incredibly low sampling rates, something is wrong with your computer setup
Changes make no difference. The following is # 2**20 number of samples, 1 MHz sample rate and 20 samples per symbol. There is no underrun.
# 5 Mhz sample rate I start receiving underrun.
I found the problem and a solution.
The problem is that the level of the signal after modulator is too strong for the USRP input. After modulator the abs value of the signal reach 9. I don't know the maximum level of the signal that USRP expects. I presume something like 1 peak to peak
The solution is to restrict the level by multiplication with a constant. With constant=0.5, there is still distortions. Value of 0.2 is ok.
Here is the new flowgraph:
I want to create some simple heart rate monitor in LabVIEW.
I have sensor which gives me heart workflow (upper graph): Waveform
On second graph (lower graph) is amount of hills (0 - valley, 1 - hill) and that hills are heart beats (that is voltage waveform). From this I want to get amount of those hills, then multiply this number by 6 and I'll get heart rate per minute.
Measuring card I use: NI USB-6009.
Any idea how to do that?
I can sent a VI file if anyone will be able to help me.
You could use Threshold Peak Detector VI
This VI does not identify the locations or the amplitudes of peaks
with great accuracy, but the VI does give an idea of where and how
often a signal crosses above a certain threshold value.
You could also use Waveform Peak Detection VI
The Waveform Peak Detection VI operates like the array-based Peak
Detector VI. The difference is that this VI's input is a waveform data
type, and the VI has error cluster input and output terminals.
Locations displays the output array of the peaks or valleys, which is
still in terms of the indices of the input waveform. For example, if
one element of Locations is 100, that means that there is a peak or
valley located at index 100 in the data array of the input waveform.
Figure 6 shows you a method for determining the times at which peaks
or valleys occur.
NI have a great tutorial that should answer all your questions, it can be found here:
I had some fun recreating some of your exercise here. I simulated a squarewave. In my sample of the square wave, I know how many samples I have and the sampling frequency. As a result, I calculate how much time my data sample represents. I then count the number of positive edges in the sample. I do some division to calculate beats/second and multiplication for beats/minute. The sampling frequency, Fs, and number of samples, N or #s are required to calculate your beats per minute metric. Their uses are shown below.
The contrived VI
Does that lead you to a solution for your application?
I'm reading data from the microphone and want to perform some analysis on it. I'm attempting to generate a spectrum analyser something like this:
What I have at the moment is this:
My understanding is that I need to perform a Fourier analysis - a Fast Fourier Transform ? - to extract the component frequencies and their amplitudes.
Can someone confirm my understanding is correct and exactly what type of Fourier transform I need to apply?
At the moment, I'm getting frames containing 4k samples from the mic (using NAudio). The buffer I've got is 16bits/sample (Signed Short). For reference, the above plot shows approx half a frame
I'm coding in VB so any .Net libraries/examples (preferably on NuGet) would be of most use. I believe implementations vary considerably so the less I have to massage my data, the better.
The top plot is that of a spectrograph, where each vertical time line is colored based on the magnitudes of the result from an FFT (likely windowed) of a slice in time (possibly overlapped) of the input waveform. The number of vertical points to plot (the frequency resolution) is related to the length of the FFT. Almost any FFT will do. If you use the most common complex-to-complex FFT, just set the imaginary portion of each complex input sample to zero, copy a slice in time of samples of your input waveform to the "real" part, FFT, and take the magnitude or log magnitude of each complex result bin, then map these values to colors per your preference.
I am working on a program that needs to fit numerous cosine waves in order to determine one of the parameters for the function. The equation that I am using is y = y_0 + Acos((4*pi*L)/x + pi) where L is the value that I am trying to obtain from the best fit line.
I know that it is possible to do this correctly by hand for each set of data, but what is the best way to automate this process? I am currently reading in the data from text files, and running a loop with the initial paramiters changing until I have an array of paramater values that have an amplitude similar to the data, then I check the percent difference between points on the center peak and two end peaks to try to pick the best one. It in consistently picking lower values than what I get when fitting by hand (almost exactly one phase off). So is there a way to improve this method, or another method that works better?
Edit: My LabVIEW version has a cos fitting VI which is what I am using, the problem is when I try to automate the fitting by changing the initial parameters using a loop, I cant figure out how to get the program to pick the same best fit line as a human would pick.
Why not just use a Fast Fourier Transform? This should be way faster than fitting a cosine. In the result vector of complex numbers look for the largest peak of in the totals. You're given frequency (position in the FFT result vector), amplitude and phase.
You can evaluate the goodness of the fit by computing the difference between fitting curve and your data. A VI does this in the "Advanced curve fitting" palette. Then all you have to do is pick up the best fit.