Re: Strategies to save/display low sample-rate data

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Aha! THANKS!

Kevin


> On Apr 10, 2024, at 11:45 AM, Daniel Estévez <daniel@destevez.net> wrote:
>
> Hi Kevin,
>
> milli-Hz, not Mega-Hz. 0.078125 Hz = 78.125 mHz.
>
> On 10/04/2024 20:43, Kevin McQuiggin wrote:
>> Hi Daniel:
>> I'm confused re the math here, or maybe the concept! Please forgive what may be a dumb question.
>> Where does 78 MHz for frequency resolution come from? 80 SPS using analytic sampling (IQ) means a bandwidth of 80 Hz. 1024 bins in the FFT with an 80 Hz bandwidth gives 80/1024 or 0.078125 Hz per bin.
>> I see the "78" in there but how does this get interpreted as 78 MHz? I might have missed something earlier in the thread.
>> 73,
>> Kevin VE7ZD
>>> On Apr 10, 2024, at 11:28 AM, Daniel Estévez <daniel@destevez.net> wrote:
>>>
>>> On 10/04/2024 19:44, John Ackermann N8UR wrote:
>>>> On 4/10/24 11:29, Fons Adriaensen wrote:
>>>>> Both the decimation and 80 size 1024 FFTs per second should be peanuts
>>>>> for any modern PC...
>>>>>
>>>>> And of course you don't need to do the FFT again for every sample,
>>>>> it just generates a lot of redundant data.
>>>> I understood that if you have a 1024 bin waterfall, it takes that many samples to fill it and output a vector. With a sample rate of 80, that means about 12.8 seconds to show one line of the waterfall. Or do I have that wrong?
>>>> (I used 80 samples/sec for simplicity. The actual rate after decimating from a 1.536 ms/s stream is 93.75.)
>>>
>>> Hi John,
>>>
>>> Yes, that is correct. Ultimately you're hitting the uncertainty principle for the Fourier transform. A 1024-point FFT at 80 samples/s has a frequency resolution of 78 mHz. You need to process at least 1 / 78 mHz = 12.8 seconds of signal to achieve that resolution.
>>>
>>> Best,
>>> Daniel.
>>>
>