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0
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1answer
32 views

The error between the theoretical value and the simulation value of imaginary part of the FFT results

As we know, a complex analytic signal can be obtained through Fourier transformation of a real value function, and the imaginary part can be reconstructed by Hilbert transform of real part. But I have ...
0
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0answers
40 views

Instantaneous power estimation by discrete hilbert transform - how far does it smooth?

In my research area, instantaneous power in a specific frequency band is commonly estimated by the following procedure: Apply a bandpass filter on the raw signal (e.g. 80-90Hz bandpass). Estimate ...
1
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0answers
41 views

How to get the quaternionic analytic signal of a image?

The 2D discrete image signal's quaternionic fourier transform can be described as F(u,v), and its 2D analytic signal often be described as: $$ F_q(u,v) = (1 + \mbox{sgn}(u))(1 + \mbox{sgn}(v)) F(u,v) ...
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2answers
115 views

number of possible component in sinusoidal model

Suppose that we have the following model $y(t) = A_1\sin(\omega_1 t+\phi_1) + A_2\sin(\omega_2 t+\phi_2) + ... + A_p\sin(\omega_p t+\phi_p) + z(t)$ My question is not related to how to determine the ...
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0answers
61 views

FFT of complex envelope

I have I and Q samples of a time domain signal, sampled at a rate T, both of length L. I create the complex envelope, C = sampleI+j*sampleQ. Then take the FFT of ...
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2answers
89 views

How to create a band limited signal of sine waves that is nearly uniform distributed

I would like to create a signal that is nearly uniform distributet (as depicted in Figure 1). It must consist of some single sine waves (e. g. 400 Hz, 430 Hz, 500 Hz) or a random band limited signal ...
2
votes
2answers
94 views

Can signal processing be applied successfully to price data from financial markets?

Price/volume data from financial markets are like signals. Can these information be applied to signal processing to generate profits successfully?
1
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2answers
190 views

understanding analytic signals

I know that a function $f_a\in \text{L}^2(\mathrm{R})$ is an analytic signal if its Fourier transform is 0 for negative frequencies, i.e. $\hat{f_a}(\omega)=0$ if $\omega<0$. We can characterize ...
2
votes
1answer
93 views

Revert anayltic signal(length(N/2)) to real signal (length(N))

As a beginner in signal processing, i'll try to explain my problem most thoroughly. I'll firstly explain the idea of the transformation the real valued data has undergone. This includes the ideas and ...
2
votes
2answers
227 views

Prevent negative image with BPSK modulation in a complex signal

In a baseband complex signal, I need to generate a BSPK modulated signal at a given frequency. If I generate a CW at this frequency, all is good. However, when I then BPSK modulate the CW, I get a ...
0
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1answer
2k views

Envelope of time domain signal // Hilbert transform

to get the envelope of a time domain signal, s(t), I use the absolute of its analytical time domain signal. But there occur the problem that sometimes the envelope is not as desired a true envelope ...
2
votes
1answer
156 views

Spectrum and Spectrogram explanation

I'm reading this material but I don't understand this example: "An example of a 94% overlap transform processing could be demonstrated by a 279 sample frame with 262 samples overlapped from each ...
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0answers
444 views

calculate root mean square, ratio of power, constant false alarm, … of a signal

I am new to Digital Signal Processing, and am reading a paper. I don't know how they can extract some features from signal, like root mean square (RMS), Constant False Alarm Rate - CFAR, Mean Value ...
5
votes
1answer
295 views

Hilbert transform: analytic signal

I want to observe how the signal's phase changes over time and I know Hilbert transform can be used here to get the analytic signal, from which I can extract the amplitude and phase. I know that to ...
3
votes
1answer
106 views

signals comparison metrics

Am new to signal processing and was wondering when given two signals, what are the widely used statistical analysis methods to understand the relationship between them?
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3answers
316 views

How should I pre-process a real valued signal in order to use Kay's estimator?

I have 100,000 samples of a signal $x[n]$ that was sampled at 20kHz. The data is vibration data from a rotating machine, and contains a significant spectral component related to the speed of the ...