# Tag Info

6

No, the data in the USB cable is digital. Namely there is either an error in the data or the data is the same at any place along the cable.

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On a basic level, in my opinion, a DSP chip must, as a minimum requirement, be able to optimally perform the convolution summation used to compute the output of an FIR filter. $$y[n] = \sum\limits_{k=0}^{K-1} h[k] \, x[n-k]$$ $y[n]$ = FIR filter output $x[n]$ = FIR filter input $h[k]$ = impulse response of FIR filter (also FIR coefficients) To do this in \$...

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You can use adaptive filters for this task. Feed the hardware with uniform white noise and collect the output. Then feed the adaptive filter with exactly the same noise sequence and use the output of the hardware you recorded as the desired response of the adaptive filter. The filter can then be trained with standard algorithm such as LMS. Of course, you ...

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Your calculations are correct, although you should do some kind of sliding average over a few hundred samples or so. The problem seems to be that the sound level meter employs an A-weighting, while your code does not. If there is low frequency noise in your measurement environment, the readings at "silence" can easily differ by 40-50dB. Try measuring a test ...

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First of all - don't use 80kHz (I don't know why you came up with this number as a limit for MATLAB, whereas it is 1e6 in fact) but something that is more reasonable and standardized, i.e. 96kHz (or 88.2kHz if you wish...). Another thing is that your sound card might not be able to serve such high sampling frequency. You should check that in playback options ...

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I have made a benchmark on a Freescale K70 (Arm Cortex M4 120MHz) at https://community.freescale.com/thread/327833 Poul-Erik.

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Usually, even before someone builds a processor, they write a specification of the instruction set, and then they build a simulator for that. The same applies to TI's C67xx series: https://processors.wiki.ti.com/index.php/List_of_Simulator The wiki article strangely says that TI is moving away from simulation in Code Composer Studio 6, which would honestly ...

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Good approach to first do a rough calculation of the bandwidths you need. Couple of remarks on that: You forgot a factor of 3, that camera has three "color pixels" per image pixel Your use case screams "I should be using a commercial off-the-shelf USB camera"; don't engineer something very complex if it doesn't have a value proposition ...

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My first guess would be: Use a swept sinus signal and signal analyzer.

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...a neural network that can decide wether a pattern produced by the movement of a hand near capacitive sensors is as expected, or random. The neural network is supposed to learn himself how the different channels react, in wich order, so i don't have to tell anything to the programm concerning the physical distance between two electrodes or whatever. At ...

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The conclusion of my discussion with hops and Marcus was that getting a DSP to operate with an ADC and DAC at the latency required for my application (<=250ns for 4MHz) would be difficult and require writing some custom interface. We concluded that the best option is to continue to work with the FPGA and improve my VHDL implementation using pipelining, ...

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In your question, you state that your ultimate goal is to compute the discrete Fourier transform (DFT) from nonuniformly-sampled input data. It is not necessary to resample your data at all. In fact, there are several fast algorithms, known variously as the unequally spaced fast Fourier transform (USFFT) and the nonuniform fast Fourier transform (NUFFT) ...

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You are essentially referring to nonuniform signal sampling in which data samples are not acquired at exact periods but have same random jitter deviations from their exact uniform timing positions. Now under some suitable conditions (such as the jitter length being less than a period), there are very effective algorithms which can perfectly convert the ...

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I've used one of the miniDSP kits (actually three USB Streamers) in a design project, and it did perform well, but we never did any of the actual DSP onboard and instead relied on computer software. Also, they tend to keep their onboard firmware locked down pretty tight at least for the model we used. Your options mostly go like this and people are free to ...

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I think you might want to have a look at the stuff from XMOS. They have nice solutions (hardware, software, IDE) for developing audio applications on real(time) hardware. And besides that also nice development boards.

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Short answer: If your sampling rate is higher than your throughput, you must use some kind of polyphase decomposition of your filters to work. So if you can't do that, you can't use a 100 MHz-clockable design to filter something with a sample rate of 1000 MS/s. It's as simple as that. However, your example is exactly the standard example of a filter that ...

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This is a little bit of a tricky question because it is homework and a direct answer would simply equate to doing that homework which is not what this website is about. Apart, from the purely technical bit of the right decisions, which you should do entirely on your own, I am interpreting this question as "There are so many options out there! Where do I ...

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More details on the matched filter approach: Matched filter is the same thing as cross-correlation. Input A is a template of what you expect the click to sound like, and input B is the live stream of audio. You cross-correlate them, which will produce low-level noise unless there's a match and then it will produce a large spike. Most efficient way to do ...

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Probably the easiest (and most accurate) way to do this is the matched filter approach suggested above. Basically, you can record each click independently and use those as matched filters against the microphone signal. If the clicks are always the same, click A will ring up very strong on the A matched filter. Same with B and C. To implement the ...

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