Starting from a signal, how to determine correct Samples/Symbol value?
Here a link to the file i'm using:
gfile271
And there is my flowgraph:
By observation!
First, make sure you've correctly frequency-synchronized, i.e. your signal is centric to f=0. While you're doing that, filtering the signal to contain mostly payload signal and least feasible noise is probably a good idea.
Then, find the periodicities in your signal: That should be your symbol rate. Use a resampler (MMSE resampler, for example, was called "fractional resampler" until recently) to make that symbol rate fit into an integer amount of samples - tada, that's your samples per symbol now!
Related
Using pyiron, I want to calculate the mean square displacement of the ions in my system. How do I see the total displacement (i.e. not folded back by periodic boundary conditions) without dumping very frequently and checking when an atom passes over the boundary and gets wrapped?
Try to compare job['output/generic/unwrapped_positions'][-1] and job.structure.positions+job.output.total_displacements[-1]. If they deliver the same values, it's definitely fine both ways. If not, you can post the relevant lines in your notebook here.
I'd like to add a few comments to Jan's answer:
While job['output/generic/unwrapped_positions'] returns the unwrapped positions parsed from the output files, job.output.total_displacements returns the displacement of atoms calculated from each pair of consecutive snapshots. So if an atom moves more than half the box length in any direction, job.output.total_displacements will give wrong coordinates. Therefore, job['output/generic/unwrapped_positions'] is generally more trustworthy, but it is not available in all the codes (since some codes simply do not provide an output for unwrapped positions).
Moreover, if an interactive job is used, it is possible that job.structure.positions does not return the initial positions, i.e. job.structure.positions+job.output.total_displacements won't be initial positions + displacements.
So, in short, my answer to your question would be rather "Use job['output/generic/unwrapped_positions'] and if it's not available, use job.structure.positions+job.output.total_displacements but be aware of potential problems you might be running into."
I'm trying to check the FCS of an ethernet frame thanks to tools on different website.
I first used this website:
http://depa.usst.edu.cn/chenjq/www2/software/crc/CRC_Javascript/CRCcalculation.htm and find the next FCS : 0xD4C3C62F (the frame below)
Then, I tried this one : http://www.scadacore.com/field-applications/programming-calculators/online-checksum-calculator/ and I found the correct CRC : 0x7AD56BB3 but nothing of the different kind of CRC-32 (normal, reversed...) correspond to the CRC find on the first website.
Is there any link between algorithms?
Thank you!
Here is the hexadecimal frame (no start of frame) :
000AE6F005A3001234567890080045000030B3FE0000801172BA0A0000030A00000204000400001C894D000102030405060708090A0B0C0D0E0F10111213
Beware of online CRC calculators.
The Ethernet CRC of your string is actually 0xb36bd57a. It is stored in reverse order in the stream, which is why you wrote it incorrectly as 0x7AD56BB3.
There are many CRC definitions, including many 32-bit CRC definitions. See the RevEng catalog for examples. The one you want happens to be called "CRC-32", with this definition.
The "CCITT-32" (a name I have not seen before) being calculated in your first link is some other definition. It does not even appear in the RevEng catalog.
A more descriptive and clear update to #Mark Adler's answer (I am new here so I can't edit or comment)
The CRC you are searching for is called CRC-32/ISO-HDLC.
You can check the following online calculator and check the one named "CRC-32":
https://crccalc.com/
Each CRC32 algorithm has its own parameters for its generation, like polynomial, init,...etc
The IEEE802.3 standard defined the CRC32 algorithm parameters for the FCS field to have the polynomial(0x04c11db7), init/xorIn(0xffffffff), xorout(0xffffffff)
I'm looking at the stratum protocol and I'm having a problem with the nbits value of the mining.notify method. I have trouble calculating it, I assume it's the currency difficulty.
I pull a notify from a dogecoin pool and it returned 1b3cc366 and at the time the difficulty was 1078.52975077.
I'm assuming here that 1b3cc366 should give me 1078.52975077 when converted. But I can't seem to do the conversion right.
I've looked here, here and also tried the .NET function BitConverter.Int64BitsToDouble.
Can someone help me understand what the nbits value signify?
You are right, nbits is current network difficulty.
Difficulty encoding is throughly described here.
Hexadecimal representation like 0x1b3cc366 consists of two parts:
0x1b -- number of bytes in a target
0x3cc366 -- target prefix
This means that valid hash should be less than 0x3cc366000000000000000000000000000000000000000000000000 (it is exactly 0x1b = 27 bytes long).
Floating point representation of difficulty shows how much current target is harder than the one used in the genesis block.
Satoshi decided to use 0x1d00ffff as a difficulty for the genesis block, so the target was
0x00ffff0000000000000000000000000000000000000000000000000000.
And 1078.52975077 is how much current target is greater than the initial one:
$ echo 'ibase=16;FFFF0000000000000000000000000000000000000000000000000000 / 3CC366000000000000000000000000000000000000000000000000' | bc -l
1078.52975077482646448605
I would like to know if anyone knows how to perform a cross-correlation between two audio signals on iOS.
I would like to align the FFT windows that I get at the receiver (I am receiving the signal from the mic) with the ones at the transmitter (which is playing the audio track), i.e. make sure that the first sample of each window (besides a "sync" period) at the transmitter will also be the first window at the receiver.
I injected in every chunk of the transmitted audio a known waveform (in the frequency domain). I want estimate the delay through cross-correlation between the known waveform and the received signal (over several consecutive chunks), but I don't know how to do it.
It looks like there is the method vDSP_convD to do it, but I have no idea how to use it and whether I first have to perform the real FFT of the samples (probably yes, because I have to pass double[]).
void vDSP_convD (
const double __vDSP_signal[],
vDSP_Stride __vDSP_signalStride,
const double __vDSP_filter[],
vDSP_Stride __vDSP_strideFilter,
double __vDSP_result[],
vDSP_Stride __vDSP_strideResult,
vDSP_Length __vDSP_lenResult,
vDSP_Length __vDSP_lenFilter
)
The vDSP_convD() function calculates the convolution of the two input vectors to produce a result vector. It’s unlikely that you want to convolve in the frequency domain, since you are looking for a time-domain result — though you might, if you have FFTs already for some other reason, choose to multiply them together rather than convolving the time-domain sequences (but in that case, to get your result, you will need to perform an inverse DFT to get back to the time domain again).
Assuming, of course, I understand you correctly.
Then once you have the result from vDSP_convD(), you would want to look for the highest value, which will tell you where the signals are most strongly correlated. You might also need to cope with the case where the input signal does not contain sufficient of your reference signal, and in that case you may wish to (for example) ignore values in the result vector below a certain level.
Cross-correlation is the solution, yes. But there are many obstacles you need to handle. If you get samples from the audio files, they contain padding which cross-correlation function does not like. It is also very inefficient to perform correlation with all those samples - it takes a huge amount of time. I have made a sample code which demonstrates time shift of two audio files. If you are interested in the sample, look at my Github Project.
The only effect AudioUnit on iOS is the "iTunes EQ", which only lets you use EQ pre-sets. I would like to use a customized eq in my audio graph
I came across this question on the subject and saw an answer suggesting using this DSP code in the render callback. This looks promising and people seem to be using this effectively on various platforms. However, my implementation has a ton of noise even with a flat eq.
Here's my 20 line integration into the "MixerHostAudio" class of Apple's "MixerHost" example application (all in one commit):
https://github.com/tassock/mixerhost/commit/4b8b87028bfffe352ed67609f747858059a3e89b
Any ideas on how I could get this working? Any other strategies for integrating an EQ?
Edit: Here's an example of the distortion I'm experiencing (with the eq flat):
http://www.youtube.com/watch?v=W_6JaNUvUjA
In the code in EQ3Band.c, the filter coefficients are used without being initialized. The init_3band_state method initialize just the gains and frequencies, but the coefficients themselves - es->f1p0 etc. are not initialized, and therefore contain some garbage values. That might be the reason for the bad output.
This code seems wrong in more then one way.
A digital filter is normally represented by the filter coefficients, which are constant, the filter inner state history (since in most cases the output depends on history) and the filter topology, which is the arithmetic used to calculate the output given the input and the filter (coeffs + state history). In most cases, and of course when filtering audio data, you expect to get 0's at the output if you feed 0's to the input.
The problems in the code you linked to:
The filter coefficients are changed in each call to the processing method:
es->f1p0 += (es->lf * (sample - es->f1p0)) + vsa;
The input sample is usually multiplied by the filter coefficients, not added to them. It doesn't make any physical sense - the sample and the filter coeffs don't even have the same physical units.
If you feed in 0's, you do not get 0's at the output, just some values which do not make any sense.
I suggest you look for another code - the other option is debugging it, and it would be harder.
In addition, you'd benefit from reading about digital filters:
http://en.wikipedia.org/wiki/Digital_filter
https://ccrma.stanford.edu/~jos/filters/