I have two User one of them sending over freq (0-B] and the other transmit over (B-2B]. Both of them are using OFDM and each one has Nc subcarriers. I wanted to simulate the received signal using Matlab.
One way to implement, I beleive is that I say user one have 2*Nc subcarriers and only use the first Nc subcarriers and the second user also has 2Nc subcarrier and only ise subcarriers [Nc+1-2Nc). in this case I should use FFT/IFFT size of 2Nc:
Received signal would be Y = ifft([d_1 0*d2],2Nc)+ifft([0*d_1 d_2],2Nc) where d_1 and d_2 are the data of the first and the second users. and at the receiver I can compute d=fft(Y, 2Nc) and the first half would be for user1 and second half for user2.
But I want to use FFT size of Nc, i.e., compute ifft(d_1,Nc) and ifft(d_2,Nc).
If I add these two iffts directly, I will get incorrect results as the IFFT results does not show that the two data occupying different subcarriers.
My question is, in this case, how I should change (probably up-convert) the ifft result of the second UE?
Furthermore, after constructing the received signal, what operation do I need to get back the transmitted data? Should I again use fft(y,2Nc) ?
Your help is highly appreciated.
Regards,
Related
I have some data which is time-stamped by a NMEA GPS string that I decode in order to obtain the single data point Year, Month, Day, etcetera.
The problem is is that in few occasions the GPS (probably due to some signal loss) goes boinks and it spits out very very wrong stuff. This generates spikes in the time-stamp data as you can see from the attached picture which plots the vector of Days as outputted by the GPS.
As you can see, the GPS data are generally well behaved, and the days go between 1 and 30/31 each month before falling back to 1 at the next month. In certain moments though, the GPS spits out a random day.
I tried all the standard MATLAB functions for despiking (such as medfilt1 and findpeaks), but either they are not suited to the task, either I do not know how to set them up properly.
My other idea was to loop over differences between adjacent elements, but the vector is so big that the computer cannot really handle it.
Is there any vectorized way to go down such a road and detect those spikes?
Thanks so much!
you need to filter your data using a simple low pass to get rid of the outliers:
windowSize = 5;
b = (1/windowSize)*ones(1,windowSize);
a = 1;
FILTERED_DATA = filter(b,a,YOUR_DATA);
just play a bit with the windowSize until you get the smoothness you want.
I Have a Sensor (Gyro) that connected to my python program (with socket UDP) and send data to python console in real-time but with 200 Hz frequency.
I want to change this frequency of coming data to my console but could not find a good way to do it.
I was thinking about doing it with filters like Mean an waiting for idea?
If you want to have regular updates, use a windowing mechanism. Take the last n values and store the average. Then, discard the next two values and take the last n values again. This example would yield values with a frequency of 200 Hz/2.
If you only want to see events when changes have occured, store the last value, compare the current value with the last one and emit an event if it has changed, updating the stored value. As you're dealing with sensors (and thus, a little fuzziness), you probably want to implement a hysteresis.
You can even raise the frequency by creating extra values in between the received ones through interpolation. For a steady frequency, you would have to take care about your timing though.
I'll briefly explain my purpose in case another solution would be much easier.
I have a server and a system. The system sends a "live-stat" packet twice a second and logs the time it was sent (epoch time). The server receives the packet and logs the receiving time.
I would like to know which of the packets arrived and the latency of their arrival.
Currently I'm inserting this data to an unorganized matrix and i would like to compare the two matrices (server and system) to calculate the latency.
Is there any matlab command that could help me?
Another point is that I don't know how to get a numerical value for the latency, is there any formula or tool that i could use? (I''m currently going to show the deviation of the latency on a graph).
I'm talking about 500,000 packets per system and i'm testing this on ~50 systems so i'm looking for the most efficient calculation.
I am new with signal processing, i have following signals which i've got after some pre-processing on original signals.
You can see some of them has some similarities with others and some doesn't. but the problem is They have various range(in this example from 1000 to 3000).
Question
How can i analysis their properties scale-free(what i mean from properties is statistical properties of signals or whatever)??
Note that i don't want to cross-comparing the signals, i just want independent signals signatures which i can run some process on them sometime later.
Anything would help.
If you want to make a filter that separates signals that follow this pattern from signals that don't, well, there's tons of things you could do!
Just think practically. As a first shot at it, you could do something like this (in this order):
Check if the signals are all-positive
Check if the first element is close in value to the last element
Check if the maximum lies "in the middle" somewhere
Check if the first value is small, then the signal grows, then shrinks again
Check if the growth rates are gradual. You could for example analyze their derivatives (after smoothing):
a. derivative should be all-positive for a while, then all-negative.
b. derivative should be smooth (no jumps greater than some tolerance)
Without additional knowledge about the signal's nature/origin, it's going to be hard to come up with more meaningful metrics than these...
I am trying to make a fatigue analysis from a load series and I woudl like to extract the equivalent moment for
number cycles=1000000
years=25
I have a time series for one hour that looks like:
Then I have read that Rainflow Analysis is a very good tool to extract the cycles from a time history. Therefore I apply:
%Rainflow moment
dt=time(2)-time(1);
[timeSeriesSig, extt] = sig2ext(timeSeries, dt);
rf = rainflow(timeSeriesSig,extt);
I read that
OUTPUT
rf - rainflow cycles: matrix 3xn or 5xn dependend on input,
rf(1,:) Cycles amplitude,
rf(2,:) Cycles mean value,
rf(3,:) Number of cycles (0.5 or 1.0),
rf(4,:) Begining time (when input includes dt or extt data),
rf(5,:) Cycle period (when input includes dt or extt data),
If I am interested in the number of cycles what does the term rf(3,:) mean? It is only containing 0.5 and 1 in the vector. I want to obtain an histogram with the number of cycles per bin amplitude. Thanks
If you are using code from the File Exchange or another source, it helps to link to where you obtained it.
Here, rf(3,:) is showing you whether the amplitudes and other outputs refer to half or full cycles. The rainflow algorithm finds half-cycles first, and then matches tensile and compressive half-cycles of equal amplitude to find full cycles. There are normally some residual half-cycles.
If you examine the rfhist function contained within that same File Exchange submission, you will see how they handle full and half amplitudes. Basically, two half-cycles at any given load amplitude will be counted as one full cycle when creating the histogram.