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That is more or less what happens in a (monochrome) camera only in 2-d (or 3-d if you like). I think you can think of it as a continuous time convolution that happens to be sampled at some multiple >= 1 times the kernel width. Ie a sinc filter in the freq domain. As long as the main lobe is within your passband you should be able to flatten using an ...


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If you have a sample of a piano, say a 10000 samples of a 440 Hz A4 sampled at 48kHz and you want to play an octave above that, 880 Hz A5 using a 48kHz D/A converter you would have to «compress» the original waveform to 5000 samples. A means to do that is downsampling such that you keep and shift the low frequency content, but remove the high frequency ...


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If your motivation was to increase the sampling rate, then yes you are correct, once you filter you will have completed interpolation and have a higher rate copy of your signal. The zero insert create replicas (images) in the spectrum. When you filter out these images properly, you will then have a higher sampled (interpolated) waveform. The comment in the ...


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So, apparently this is the way the web audio works, it cannot handle smooth transitions when playing back sound, which results in such clicks or crackling. This, this and this links helped me come to a conclusion that a custom ADSR envelope is needed. When I applied such envelope to gradually but very fast increase the amplitude of the sound at the beginning ...


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Take your sine wave at $f = 5\mathrm{kHz}$; $x(t) = \sin 2 \pi f\, t$. If you sample it at $F_s$ then you get a result $y[k] = \sin \frac{2 \pi f}{F_s} k$*. Because of the trigonometric identity $\sin \theta = \sin (2\pi + \theta)$, you can't tell the difference between sine waves at $f$ and $f \pm nF_s$. Because of the trigonometric identity $\sin \theta ...


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You're right, in the real world, 2x is not enough to capture a sound at a given frequency accurately. In your chart, only the first one would sound like a sine wave. As you approach the Nyquist frequency, you'll create a siren like sound and when you reach the exact frequency you'll record a pulse-wave approximation of a sine wave at an amplitude that will ...


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There is aliasing at 3 kHz and 4.5 kHz. The 2 kHz does not, but since 2 kHz and 3 kHz are equally far apart from the 2.5 kHz Nyquist frequency, they look similar. Same with why 0.5 kHz and 4.5 kHz look similar.


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From the Nyquist sampling theorem, the minimum required sampling rate to avoid aliasing is $2f_m$, where $f_m$ is the maximum frequency of the message signal. Can you please add some plots for the question description to view the results.


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Zigbee is primarily designed to be cheap, not fast or efficient. Maximum data rate is 250kb/s so 2 MHz sample rate is more than enough to capture any data signal. The "2MHz bandwidth" number mainly refers to the RF channel layout. Zigbee channels are 2 MHz wide and spaced 5 MHz apart. This primarily determines what RF filtering needs to happen to ...


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To visualize the frequencies of discrete time signals beyond the sampling rate, simply insert $M-1$ zeros in between each sample and scale the signal by $M$. This will extend the frequency axis by $M$ where $M$ is any positive integer. What you will see is the periodicity in the frequency domain as given for discrete time signals.


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My understanding is that, while the analog antialising filter on the USRP can be set to larger than the bandwidth required for my sampling rate, the digital filter is determined by the sampling rate and cannot be changed. no. None of the USRPs allow that: all USRPs / RF daughterboards (that you can still use) don't have adjustable frontend filters, as the ...


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