# Tag Info

5

Well this goes to show that Fourier series is just approximation that gets more and more correct when you add more harmonics. Take a look at this: $\dfrac{4\sin\theta}{\pi}$ is just first harmonic. Harmonics are integer multiple of base frequency as you can see: $\sin3\theta$, $\sin5\theta$ etc. And the more of them you add to the first harmonic the more ...

4

The only signal, that really has just one frequency component, is an infinite sine signal. Limiting the signal duration in any way is bound to produce other frequency components, as time limiting can be thought of as multiplying with a rectangle window, which translates to convolution with an si-function in the frequency domain, thus introducing new ...

4

It is the objective of the receiver to make the best estimate for each symbol as to what was transmitted. This is often done by ultimately determining a decision time in each sample (through timing recovery) on the waveform after it has been processed by the receiver (equalization and matched filtering) in which to sample the waveform and make a decision as ...

3

This seems like more of a semantics problem. A signal is periodic with time $T$ if $$x(t+n\cdot T) = x(t), n \in \mathbb{Z}$$ So the signal is periodic in $0.5$ since the for $T = 0.5 \cdot n$ the argument of the cosine is an integer multiple of $2 \pi$. Since it's periodic in $0.5$ it's also periodic in all integer multiples of $0.5$, i.e $1$, $1.5$, $2$...

3

@Hustler. Hi. I suggest you use all 48000 vibration samples. As a general rule: In mathematical analysis of measured data it's preferred that you use all available data to estimate some physical quantity. If you perform 48000-point DFTs (discrete Fourier transforms) your first DFT bin frequency will be zero Hz and your DFT bin spacing will be (as jithin said)...

3

It is common in DSP practice to define some convenient center for a filter as being at time 0, even though we cannot build non-causal systems in practice. You see this most when you're designing a symmetrical filter, and you define t = 0 as the filter center, but it happens elsewhere. You do this because it makes the analysis easier, and you justify it by ...

3

Actual channels are always causal (like everything else in the physical universe). Actual (discrete-time) channels also sometimes have one tap that is considerably larger than the rest; an example impulse response would be h = [0.1, 1.5, 0.2]. Some authors prefer to define h[0] as the largest tap; in my example, we'd have h[-1] = 0.1, h[0] = 1.5, and h[1] = ...

2

The partial fraction form helps in calculating the z-transform inverse since we can get the inverse of each term in partial fraction by inspection and hence get the inverse of the whole transfer function. The factored form can directly give poles and zeros by equating the numerator and denominator to zero.

2

The same effect will happen on the time-domain sequence too, due to the fact that DFT time and frequency domains are exact duals apart from a scaling factor and reversal. Therefore, by properly zero padding (to the center of) the FFT data, the corresponding time-domain signal will be interpolated; i.e., more samples from the same signal will be obtained.

2

So, I myself have taught based on material that uses that scheme, "P/S" and "S/P" after and before the transforms. Personally, it's nonsense. What the author tries to say is: The IFFT is a mapping of sample vectors to vectors. So, you need a vector as input, not a stream of samples. What they instead say is: We use the terminology from very basic ...

2

The only reason I can think of may be to preserve the frequencies over which original signal had spectrum (before squaring). When you square a signal $x[n]$, whose spectrum $X(e^{j\omega})$ extends from $-\omega_0 \le \omega \le \omega_0$, you are multiplying by itself. So in frequency domain, the effect is to (periodically) convolve $X(e^{j\omega})$ by ...

2

The trigonometric functions are essentially exponential. Thus, a doubling of the argument corresponds to a squaring of the function (in a sense). In this case, it can be seen by applying the angle addition formula: \begin{aligned} \cos( 2\theta ) &= \cos( \theta + \theta ) \\ &= \cos(\theta)\cos(\theta) - \sin(\theta)\sin(\theta) \\ &= \cos^... 2 If it helps any, generate a unit amplitude sinewave at 1 Hz and its square: Then the sinewave and its square look like this: You can see the DC component: the averaged value of the squared sinewave (averaged over an integer number of periods) is 1/2. And the red sinewave frequency is exactly doubled, so the period is halved. The DC and doubled frequency ... 2 Simple: Euler's formula: e^{i\theta} = \cos(\theta) + i \sin(\theta) $$and$$ e^{-i\theta} = \cos(\theta) - i \sin(\theta) $$Add them together:$$ e^{i\theta} + e^{-i\theta} = 2 \cos(\theta) $$You should see that in your equation. From there it is usually stated:$$ \cos(\theta) = \frac{ e^{i\theta} + e^{-i\theta}}{ 2 } $$See my article The ... 2 The short answer is do not null out the "mirror frequencies" that are located above f_s/2 that match the frequencies you want to keep. If your FFT was generated from a real signal, then when you do the IFFT you will get the real signal back as long as you did not zero out those upper frequencies (as you did). The DFT (which the FFT computes) returns ... 2 If your signal is an analytical signal, then envelope(real(X)) would simply be your answer. This would be identical to using the Hilbert transform to extract the Quadrature part of the signal from the Inphase part. If your signal is not analytical, your envelope would simply be:$$G = \sqrt{I^2 + Q^2} $$2 See section 2.4.3 of this reference https://web.stanford.edu/~dntse/Chapters_PDF/Fundamentals_Wireless_Communication_chapter2.pdf If the doppler spectrum has to be gaussian, the auto-correlation of tap gains should be gaussian (which is correctly mentioned in other answer but I somehow felt more details were not captured). For a coherence time T_c, ... 1 If you want a strictly real result from an IFFT, then you have to force the input to be conjugate symmetric. That way, all the imaginary components will cancel out to (almost) zero (except for rounding “errors” or microscopic numerical noise). 1 For a real signal, the FFT is symmetric complex numbers in general. That is X[((-k))_N] = X[((k))_N]. When you did this c(round(n*1000/fs):end)=0 you have disturbed the symmetry and hence, the ifft of this new signal will no longer be real. See a simple example >> x=[1 3 5 6]; >> y=fft(x); >> y(1:2)=0; >> ifft(y) ans = -1.... 1 It is the error with respect to size of jump. It has nothing to do with error at discontinuity. As per the explanation given, suppose the perfect square is having values 0 and 1. Just after the discontinuity of a rise time, the perfect square would take value 1, while the reconstructed wave would take the value 1.09 at the peak of overshoot. Here the ... 1 Myth: DTFT is Sinc-interpolated DFT. Problem with the above statement: Sinc is not 2\pi-Periodic function, but all DTFTs are. Correct Answer: Theoretical, Continuous-\omega 2\pi-Periodic DTFT can be obtained by continuous Lagrangian-interpolation of the DFT Samples. So that the values at \omega = 2\pi k/N will be the DFT Samples X[k] for k=0,1,... 1 The second term can be expanded to$$\begin{align} 2|a_k|\left(\frac{ e^{i(2\pi f_k t+\angle a_k)} + e^{-i(2\pi f_k t+\angle a_k)}}{2}\right) &= |a_k|e^{i\angle a_k}e^{i(2\pi f_k t)}+|a_k|e^{-i\angle a_k}e^{-i(2\pi f_k t)} \\ &= a_k e^{i(2\pi f_k t)}+a_k^*e^{-i(2\pi f_k t)} \\ \end{align} Since $a_k=|a_k|e^{i\angle a_k}$ and \$a_k^*=|a_k|e^{-i\...

1

You can use various methods to interpolate the channel - Linear, Polynomial, Sinc Interpolation etc. But what you need to keep in mind is synchronization. You have to make sure that frequency and timing offsets are eliminated or accounted for. Otherwise you will see an error floor in the Channel Estimation Error. Means your Mean Square Error (MSE) for ...

1

I think the accepted answer is not quite strong enough to state the real problem: If you don't up-sample you run the risk of significant aliasing. Let's look at a simple example: sample rate of 25.6 kHz and a strong signal component at 11.5kHz. If you just square it you end up with a component at DC (which is representative of the average energy) and ...

1

Yes, the Doppler effect applies to any electromagnetic wave. No matter whether that is an actual Radar pulse, or a microwave oven falling from an office tower, or a passenger aircraft's transponder, or an LPWAN device. But, this possibility is especially limited for LPWAN devices, as I'll explain below. Low Doppler shift due to low carrier frequency The ...

1

Yes I would suggest to use resample_poly in scipy. When doing upsampling, you would get artefacts outside 12.8kHz, which you would remove via Low Pass Filtering. This is what is done by scipy.signal.resample_poly. You can enter the upsampling factor value as 36k/25.8k = 1.39534, and downsampling factor = 1. In the above method while doing low pass filtering,...

1

This is inspired by the excellent answer by @Richard Lyons (which I upvoted) and the comment by @DSP Novice. It basically combines what they said. If the width of the 5000-amplitude pulse is always the same, then simply do as Richard Lyons suggested: it works fine. If the width varies, then the following scheme could be used: I imported your raw data and ...

1

Is the width of your 5000-amplitude pulse always the same? If so, then just discard your signal samples that occur after seven seconds. If the width of your 5000-amplitude pulse varies then try lowpass filtering (experimenting with different lowpass filter bandwidths) your signal to see if the filtered signal contains the information you desire. If the width ...

1

For 3840 points, the frequency resolution between each bin is 48k/3840 = 12.5Hz. Hence you should see 2 peaks at bin 1 (corresponding to 12.5Hz) and bin 3838 (corresponding to -12.5Hz since this is a real signal). If you use 48000 points, resolution of each bin is 1Hz. Now, 48000 points cannot be represented as integer multiple of 3840 points (which ...

1

Here is it ... install pyaudo to play the generated sine signal, install numpy to help you with arrays and math, install matplotlib to plot ... I wrote this code quickly just to show how to do... some steps are commented in the code, this will play one generated signal in the choose frequency, concatenate all vectors signals and play using pyaudio at the ...

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