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I get a peak at 240 Hz in FFT plot of tool vibration. The accelerometr is placed at 45 mm from tool tip during machining. Turning of nickel steel of dia 60 mm and length 300 mm at spindle speed 256 RPM. PCLNR2020K tool holder. Panther conventional precision lathe it is.
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240 Hz is in the audible range. Do you hear any sound while your spindel is rotating? Perharps, it is friction-induced sound, because, for example, spindel is not well oiled.
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Our research team met one question on calculating EEG relative power and absolute power at this stage.
When we integrated all negative and positive amplitude/power data in five EEG bands (delta, theta, alpha, beta, gamma), a few relative power results became huge (i.e., 440%(44.44) or even over 1000%). We thought these values were abnormal results. The reason is that the integration result of five EEG bands with negative and positive power values could be 1 or 2 as the denominator, but the numerator could be very large for the integration of one specific band(i.e., delta). The relative power calculation is (sum of spectral power in the band)/(sum of spectral in all bands)
The attached image showed some negative and positive spectral power values.
Therefore, we would like to ask whether we need first to transfer negative value to absolute value to consider relative power or absolute power. Normally, the relative power should be around 0-100%.
Can experts help us? Could experts please share some references with us?
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First it is better to calculate the density of the power spectrum (PSD) of the signals and continue the calculations based on it. The value of the PSD is usually not negative. Delta values are usually higher than other frequency bands. Consider starting the Delta wave frequency range from 0.1 or 0.01 Hz and not from zero. If you have collected signals from several samples and the average PSD of the frequency bands in the different samples is very different, you must first normalize the PSD values calculated from each sample in each frequency band. To do this, first calculate the PSD of the data collected from each sample. Then calculate its standard deviation in each frequency band. Divide the mean values for each frequency band by its standard deviation. In this way the data is normalized. You can now use these values to calculate relative power.
Best regards
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We conducted a 32-channel EEG and used FFT to calculate each frequency's Power-Value. Some of them are negative and I don't know how to interprete those im comparison to positive Power-values.
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Dear expert:
May I ask one question?
For example, in alpha, if some output FFT values are negative and some values are positive, how to calculate the alpha absolute power? Should we integrate all negative and positive values? Or do we need to convert negative values into absolute values first? And then calculate the relative power value.
Thank you!
Zhepeng
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If you take FFT and then inverse FFT of a HR TEM image. What information is obtained from FFT and IFFT? The corresponding images are attached.
Answers will be highly appreciated
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Dear Nadeem,
from your images, I assume you took first the fft and then took only the absolut values of the result befor applying the ifft. If I guessed right you could check the Convolution_theorem. (The Fourier transform has some properties that are closly related to convolution. true you get something like a self correlation.)
Therefore you distribution shows you how self similar the particle is for different translations.
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Nodes used for this job:
------------------------
node02
node02
node02
node02
node02
node02
node02
node02
------------------------
Working directory is /data_hp/home/Rohith/2_AuAgCuPt/2_Convergence-test/1_Kp-test/7_kp
running on 8 total cores
distrk: each k-point on 8 cores, 1 groups
distr: one band on 1 cores, 8 groups
using from now: INCAR
vasp.5.3.5 31Mar14 (build Aug 31 2021 16:15:13) complex
POSCAR found type information on POSCAR Au Ag Cu Pt
POSCAR found : 4 types and 32 ions
-----------------------------------------------------------------------------
| |
| W W AA RRRRR N N II N N GGGG !!! |
| W W A A R R NN N II NN N G G !!! |
| W W A A R R N N N II N N N G !!! |
| W WW W AAAAAA RRRRR N N N II N N N G GGG ! |
| WW WW A A R R N NN II N NN G G |
| W W A A R R N N II N N GGGG !!! |
| |
| For optimal performance we recommend to set |
| NCORE= 4 - approx SQRT( number of cores) |
| NCORE specifies how many cores store one orbital (NPAR=cpu/NCORE). |
| This setting can greatly improve the performance of VASP for DFT. |
| The default, NPAR=number of cores might be grossly inefficient |
| on modern multi-core architectures or massively parallel machines. |
| Do your own testing !!!! |
| Unfortunately you need to use the default for GW and RPA calculations. |
| (for HF NCORE is supported but not extensively tested yet) |
| |
-----------------------------------------------------------------------------
-----------------------------------------------------------------------------
| |
| ADVICE TO THIS USER RUNNING 'VASP/VAMP' (HEAR YOUR MASTER'S VOICE ...): |
| |
| You have a (more or less) 'large supercell' and for larger cells |
| it might be more efficient to use real space projection opertators |
| So try LREAL= Auto in the INCAR file. |
| Mind: At the moment your POTCAR file does not contain real space |
| projectors, and has to be modified, BUT if you |
| want to do an extremely accurate calculation you might also keep the |
| reciprocal projection scheme (i.e. LREAL=.FALSE.) |
| |
-----------------------------------------------------------------------------
LDA part: xc-table for Pade appr. of Perdew
POSCAR, INCAR and KPOINTS ok, starting setup
FFT: planning ...
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Even with these warnings, did your calculation go all the way? As suggested, use LREAL=Auto or LREAL=.FALSE. (Sometimes that warning remains even after adding this tag).
For the apparent NCORE issue, would be great if you could share your INCAR file so I can take a look.
Best,
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Suppose we should do calculations in frequency domain, so we should use FT of a continuous-time signal that is zero outside a boundary: X(w) = F{x(t)}. We know that FFT can be used for DFT on computers, but it assumes that the signal is periodically repeated outside the boundary.
Then we obtain Y(w) after our desired frequency-domain calculations. Now we want to estimate the time-domain signal, y(t) by applying the inverse FFT: y(t) = IFFT{Y(w)}. But this inversed transform is also assuming that the signal is periodic and is defined from -inf to inf.
Is there any way (i.e. numeric calculations on computers) to obtain the best estimate of out nonperiodic time-domain signal, y(t)?
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Actually I have two interferogram ( containing Young's fringes). One interferogram is recorded without any perturbation in the path of light rays. While recording the second interferogram, I have kept a glass plate in the path of one light beam. Now I want to get information about refractive index of the glass plate by analysing the interferogram. Both Interferogram have been recorded usin a CCD camera. Can anyone help me?
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You can do this analysis with MATLAB software or any other programming software such as Lab View software.
Best regards.
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I have calculated the gear meshing frequency of planetary gearbox to be 786 Hz. However, when a FFT is performed on the data acquired for the same planetary gearbox I could see peak around 645 Hz and not at 786 Hz.
The calculated mesh frequency was done based on the speed and number of teeth. But the signals acquired during operation was under loaded condition.
Does external load change the natural frequency and meshing frequency of gear?
Is there any reference to calculate the theoretical gear mesh frequency in relationship with load.
Attached FFT plot.
Thanks in advance for sharing you knowledge.
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Gear meshing frequency is a kinematic (rotation speed-related) parameter. If you have another maximum in the spectrum under loading conditions, this effect can probably be related to another source (gear coupling or bearing). Of course, if you have reduced rotation speed under load (motor power drop), you will have shifted the frequency of gear meshing.
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I need to perform Sparse FFT on 1D signal , where can i found the corresponding code in MATLAB?
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I need to decompose a signal on Matlab but I ended up having IMFs that with FFT instead of having one peak it shows several. Does anyone know how to decompose a signal with this method?
I'd be appreciated someone who could help me out
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I am using the code of TVf EMD, I got good results for the application in the power system. I am using spectral analysis too. If you have any doubts about this. Please let me know.
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Hello I am trying to reconstruct the far field pattern of a patch antenna at 28 GHz (lambda = 10.71 mm ). I am using a planar scanner to sample the near field with a probe antenna. The distance between patch and probe is 5 cm. The resolution of the scan is 1.53 x 1.53 mm². The total scanned surface is 195x195 mm. The NF patterns are shown in the NF_raw file.
The complex near field is then transformed using the 2D IFFT to compute the modal spectrum of the plane waves constructing the scanned near field. (See C. A. Balanis (17-6 a and 17-7b) for this). The modal components are shown in the IFFT file. The problem is that is observe an oscillation in the phase of those modal components that reminds me of aliasing effects in digital images (Moiré pattern).
This effects also procreate when I resample the modal spectrum in spherical coordinates, as seen in the Sampling file. The transformed phase changes therefore too fast per radian. The absolute value of the pattern looks reasonable.
Could someone explain why these effects occur and what steps I can implement to prevent them? Thank you for any helpful input.
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I scaled the phase wrong!
Here are the correct ones (I think) and the input files as well
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The background is, we are trying to calculate an index relying on high frequency band over 100Hz with only 128Hz signal. The assumption is that: Say we have a 128Hz signal, while using fft to convert it into frequency spectrum which will get information from 0-64Hz according to Nyquist. Then, if we have the original signal subtracting ifft of the 0-64Hz spectrum, will it produce some information of 64-18Hz band?
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If your signal contains information between 0..64 Hz and 64..128Hz, and you sample it as 128Hz, the sampling process will fold-over (alias) everything in the 64..128Hz band backwards into the 0..64Hz band. So for example, a 74Hz tone will be folded over to 54Hz. A 100Hz tone will be folded over to 28Hz. (Signals above 128Hz will also get aliased into the band) So to answer your question - Yes the output of your FFT will contain information from the 64..128Hz band. But it is indistinguishable from the information in the 0..64 Hz band. If you know that there is no signal between 0..64Hz then nothing has been lost - you can fully reconstruct the signal. But if you DID have something in there, then you can't separate the two signals and they are forever combined.
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i am trying to plot FFT spectra of bootstrap switch Sample and hold circuit, i have got the fundamental frequency and harmonics distortion components in the graph. i want my graph like 2nd image but i am not getting noise. can anyone please help me out where am i making mistake?
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In order to get signal with noise like the bottom figure, you have to input the same signal to the ADC containing a sinewave signal in addition to noise assuming noiseless circuit. In order to get noise at the output you have to input a noise signal in addition to the pure sine wave input.
The other solution is to introduce the noise sources in the circuit itself.
Best wishes
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We tried to identify the secondary phase by comparing the d-spacing from the lattice fringes and secondly by taking localized FFT.
a) Is FFT the localized variant of the SAED pattern?
b) How to differentiate between the two different phases and if the same phase has two different orientations in FFT?
c) How to make sure that our crystallites are well oriented along the zone axis?
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Yes you can then use DFT or Fourier Reverse Converter after FFT and subtract the primary phase from the secondary phase to achieve the primary phase. Of course, there are various other ways to obtain phase from FFT or RAW FFT.
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if anyone can suggest any document/video/paper which shows/tells how to perform 2D-FFT analysis to determine the size of magntic domains in MFM images
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I want to detect anomaly from a streaming data. Using FFT or DWT history is it possible to detect anomaly on the fly (online) . It will help a lot if anybody could suggest some related resources.
Thanks.
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why not consider using S-transform as it combines the properties of FFT and wavelet transform.
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How to combine multiple frequency response function (FRF) files into a single FRF file ? If I have multiple FRF data files from any FFT analyzer then how could it be possible to combine them into a single FRF files within a certain frequency range ??
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What is the purpose of combining the different frequency responses.
Are all the frequency spectrums cover the same frequency range?
Do you want that they appear distinguishable from each other then you can overlap function in Excel graphs. To implement overlap please follow the u-tube:https://www.youtube.com/watch?v=AjCDYXk6NkE&ab_channel=MyExcelOnline.com
Best wishes
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Hi all,
I am doing EEG preprocessing using FFT. The sampling rate is 128 Hz, epoch length is 2s, 256 data points. After I applied fft(), there are still 256 points. What is the frequency resolution and frequency range of the result?
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As your signal length is 2 seconds, the frequency resolution is 1/2s or 0.5 Hz.
The first 128 points represent frequencies from 0 to 63.5 Hz, the last 127 points represent frequencies from -63.5 to 0.5 Hz, the 129 point is +64 or -64 Hz.
You can rotate the spectrum with function ifft to order frequencies from -64 to +63.5 Hz. frequency 0 is then on point 129.
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The Hough transform is a feature extraction technique used in image analysis, computer vision, and digital image processing. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure. This voting procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing the Hough transform.
The image below shows transforming Hough from a cell hologram. How do I get image edge pixels?
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Dear Researchers,
suppose I have an wideband is signal with F_low=200MHz and F_high 400MHz. I want to decompose this wideband signals into 26 narrowband signals using 256-points FFT.
What I understood is that each FFT point will represents a frequency bin. does it mean we will end up with 256 narrowband signals? if yes, why published works said that we apply 256-points FFT to decompose this wideband into 26 narrowband signals?
I am just confused with these terms (FFT points, Frequency bins and narrowband signals).
I appreciate your clarification
Thank you for your responses
Researcher
Bakhtiar
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Each FFT point materializes precisely the corresponding narrowband contribution. The corresponding bandwidth depends precisely of the number of points of the FFT. The more points the longer signal sample required, which means that if your signal is not stationary over a long time you have to renounce to a high resolution in the frequency domain...
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BICGSTAB-FFT can be used in DDA because the special form of its interaction matrix, which makes the time complexity reduce from O(N^2) to O(NlogN). Iterative method such as bicgstab can also be used to solve the matrix in finite element method, but it seems FFT(fast fourier transform) can not be implemented in this case.
Does this mean, suppose with the same number of elements (or matrix size), finite element method will principally be more time consuming than DDA simply because the more time consuming matrix vector multiplication in bicgstab (suppose bicgstab runs the same number of iterations).
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I try to transmit LFM signal using SDR platform (usrp-2932) and receive The Signal using another usrp of The same version , The TX and RX ports are connected through RF Cable With certain length .
MY question is : it Must appear single bin at certain frequency after applying FFT, but The received pin is very fast , what is The factors that affect this phenomena?
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Hello Mohamed, I'm not so sure I understand your question but you seem to be asking why the receiver response is at a single frequency and what are the factors that may cause the target response to be smeared over neighbouring frequency cells (please correct me if I am wrong). I shall answer my interpretation of the question in the hope this is what you want to know about.
After the FFT processing of the baseband or IF signal of an LFM radar the frequency bins correspond to range. A single stationary target, ideally a point scatterer, will yield a single frequency in the baseband or IF of a LFM radar. It may be possible that this response sits in just one frequency cell (i.e. one range cell) but it could be straddling two neighbouring cells, depending on the precise range of the target. An extended target could give a response extended over several neighbouring range cells, i.e. frequency cells, and will definitely do so if its down range extent exceeds the range resolution of the radar. Furthermore, if the frequency modulation is not truely linear, then the response of a point scatterer will become smeared over several neighbouring cells. The extent of the smearing depends of the degree of non-linearity in the FM and tends to worsen with increasing range. I hope this helps.
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In courses about DSP that I did at university we only covered theoretical material, I am looking for a good book that covers practical implementation of DSP in MATLAB like designing filters and DFT or FFT.
also looking for good books on signal processing with MATLAB in general.
Thanks.
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you can use DSP tool in Matlab.
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Dear all, I have some real data (about 32 equidistant points), and I fitted it a Fourier transform function using the FFT method. Indeed I get 32 complex Fourier coefficients, which correspond to the obtained 16 positive frequencies. I want to apply a low pass filter to smooth the obtained fitted function. Actually I take the fifth frequency as a low pass threshold (so I take only the first five frequencies which correspond to 30% of the total frequencies). I have chosen this threshold, basing on a visual interpretation of the fitted curve. Can anyone suggest a more robust or efficient method to choose the threshold frequency for low pass filter?
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I suggest filter design Matlab tool
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Good day all,
the pictures is one of the background noise I captured in anechoic box, in FFT and unit is dB.
Can any body provide me a suggestion on why there is a peak at 12 kHz, 13.5kHz, 15kHz, and 18kHz?
I believe that it is not originated from any structural issues, justification is that I tried excite my chamber with diffuse pink noise, but dB value at these peak stay the same.
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You can measure a noise with such sampling rate up to app.25Hz only - see Aliasing phenomena. The sensor using for such measurement must have appropriate range of frequencies operation as well.
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The complex signal may be real-imaginary or magnitude-angle form.
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Akramul Haque , which version of MATLAB are you using? In MATLAB 2021 version there is block called 'Waveform Generator' which require signal magnitude, frequency in Hz and phase in radians. These parameters can be entered directly or can be passed through workspace. Make sure to select appropriate sample time for waveform generator.
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I have torques and angular positions data (p) to model a second-order linear model T=Is2p+Bsp+kp(s=j*2*pi*f). So first I converted my data( torque, angular position ) from the time domain into the frequency domain. next, frequency domain derivative is done from angular positions to obtain velocity and acceleration data. finally, a least square command lsqminnorm(MATLAB) used to predict its coefficients, I expect to have a linear relation but the results showed very low R2 (<30%), and my coefficient not positive always!
filtering data :
angular displacements: moving average
torques: low pass Butterworth cutoff frequency(4 HZ) sampling (130 Hz )
velocities and accelerations: only pass frequency between [-5 5] to decrease noise
Could anyone help me out with this?
what Can I do to get a better estimation?
here is part of my codes
%%
angle_Data_p = movmean(angle_Data,5);
%% derivative
N=2^nextpow2(length(angle_Data_p ));
df = 1/(N*dt); %Fs/K
Nyq = 1/(2*dt); %Fs/2
A = fft(angle_Data_p );
A = fftshift(A);
f=-Nyq : df : Nyq-df;
A(f>5)=0+0i;
A(f<-5)=0+0i;
iomega_array = 1i*2*pi*(-Nyq : df : Nyq-df); %-FS/2:Fs/N:FS/2
iomega_exp =1 % 1 for velocity and 2 for acceleration
for j = 1 : N
if iomega_array(j) ~= 0
A(j) = A(j) * (iomega_array(j) ^ iomega_exp); % *iw or *-w2
else
A(j) = complex(0.0,0.0);
end
end
A = ifftshift(A);
velocity_freq_p=A; %% including both part (real + imaginary ) in least square
Velocity_time=real( ifft(A));
%%
[b2,a2] = butter(4,fc/(Fs/2));
torque=filter(b2,a2,S(5).data.torque);
T = fft(torque);
T = fftshift(T);
f=-Nyq : df : Nyq-df;
A(f>7)=0+0i;
A(f<-7)=0+0i;
torque_freq=ifftshift(T);
% same procedure for fft of angular frequency data --> angle_freqData_p
phi_P=[accele_freq_p(1:end) velocity_freq_p(1:end) angle_freqData_p(1:end)];
TorqueP_freqData=(torque_freq(1:end));
Theta = lsqminnorm((phi_P),(TorqueP_freqData))
stimatedT2=phi_P*Theta ;
Rsq2_S = 1 - sum((TorqueP_freqData - stimatedT2).^2)/sum((TorqueP_freqData - mean(TorqueP_freqData)).^2)
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Hello every one
I Recently started working on POD Galerkin's method for Approximating the PDE's (N-S and Energy Equation), i stuck at the calculating Laplacian operator on POD modes (These are orthonormal basis ) , earlier I used FFT but its not working because i have non periodic data, So is their any another way to compute this .
In the image T0 will be the ensembled average of Temperature and Phi will be the POD modes ...
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Thanks alot Nazanin Fallahi ....I will look into it ..
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I want to decompose some sensor data using wavelet. The FFT shows that my data does not contain higher frequency component(It has an influential DC component). Therefore which mother wavelet would be appropriate for decomposition?
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There are many wavelets, and to choose the correct wavelet, you need to consider the application you are going to use it for. I hope this discussion will help you :).
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Dears.
I have a system where the input was a constant value, I plotted the output in function of time.
Later I used FFT to get amplitude per frequency.
The total sampling time was 1.3 second.
I know that for the power spectrum plot it give the frequency range where the strong variation occur.
but how to interpret it in my case since when you look at the frequency it is clearly a very slow one (10^-5)
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Willy Vargas I still couldn't get it well, the FFT was performed on the output signal, the input was a constant.
could you please make it clearer?!!
Thank you in advance
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Dear I would like to plot the FFT while I am having only a table of two vectors times and amplitude!
is that possible? could someone guide me!
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Ramadan Kareem!
What i introduce here is a proposal.
If you have a table with two vectors, then one vector , the amplitude vector can be used to represent the amplitudes of the fft components. The time can be used to represent the frequency of the fft components. If you have N samples with a sampling frequency fs, then the frequencies will be kfs/N , where k is the index of the frequencies of the fft solution having the value from k=1 to N. So, the time can be scaled from 1 to N.
Best wishes
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Hello,
Here, I read:
"Considering the convenience of the actual processing, in the c code of webrtc, the frequency-domain power spectrum after the FFT transformation is divided into 32 subbands, so that the value of each specific subband Xw(p, q) can be 1 bit. It means that a total of 32 bits are needed, which can be represented by only one 32-bit data type."
Now, so far my understanding is that:
When you take FFT (e.g real fft (rfft) that will result in Fs/2) the number of frequency bins will be dependent upon NFFT size. E.g If I set the NFFT size to 512, the total number of frequency bins will be 257 with a frequency step size = 31.25 Hz at sampling rate = 16000 and obviously will vary with respect to the sampling rate.
Are these numbers of bins formed equal to subbands? Or subbands are different things? And if they are different things, is there any link to study them in easy way?
Regards,
Khubaib Ahmad
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how do you calculate the number of bands?
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Hi all,
I have done a CFD for a cylinder and obtained a transient pressure data at a point using DES. I have put time step size 1e-04 seconds. I got a frequency (ater FFT of the data) of 540 Hz. Now when I performed the same in experiment with 5000 Hz sample frequency, I obtained various frequencies at 100, 200, 500 and 900 Hz. while the 500 Hz is nearer to my CFD it should be the largest mode since analytically also the same frequency is obtained using a/2L formula. Please help !
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Dr. Rameez Badhurshah , thanks a lot!
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i am recording the vibrations from accelerometer while vehicles are passing from the bridge. i recorded the vibrations produced from vehicles. now the problem is how can i address the effect of vehicle mass on the fundamental frequency. how can i find the solution of this problem. the frequency is increasing when vehicle is present. but its agaist the physics because when mass increase the frequency decrease because they are inversely propotional to each other. the frequency is decreasing when vehicle is not present at the bridge. so this is the actual problem i am facing right now for my research. is it possible to find natural frequency while vehicle is present on the bridge or is there any way to find natural frequency. i read your suggestion about find the frequency when vehicel leave the bridge it sounds valid but is there any other method to address this problem
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We are not answering to be explored. The community wants to learn from questions and (!) answers. The quality - not the number - of answers and discussions matters. Your strategy hinders the exchange between the discussing scientists.
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Good afternoon, I would like to ask you the method to get acceleration PSD profile from time-acceleration data which was obtained through vibration test.
First of all, I though that the time-acceleration data should be changed to frequency-acceleration data. Therefore, I used FFT.
And then, I though that transformed data to frequency-acceleration data should be squared and divided by their own frequency. Because as seeing Acceleration graph, the parameter of X-axis is frequency(Hz) and that of Y-axis is (g^2/Hz).
So, I wrote matlab code like below;
(Here, THM is the time-acceleration data)
time1=THM(:,1);
t_leng1=length(time1);
dt1=time1(2)-time1(1);
Freq1=(0:t_leng1-1)/dt1/t_leng1;
x=THM(:,2);
xft=fft(x);
xft=xft(1:t_leng1/2+1);
psdx = (abs(xft).^2/Freq1');
psdx = 2*psdx(2:end-1);
figure(1)
plot(time1, x);
hold
xlabel('Time(sec)');
ylabel('Acclearation(g)');
title('Time-domain Accelaration of X axis');
figure(2)
plot(Freq1(2:t_leng1),abs(psdx(2:end-1)))
hold
xlim([2 200])
xlabel('Frequency (Hz)');
ylabel('Accelaration (g)');
title('Frequency-domain Accelaration of X axis');
Freqq=Freq1(2:t_leng1)';
Xresopons=abs(xft(2:t_leng1))/t_leng1*2;
However, when running this code, the errors appear like below;
Error using plot
Vectors must be the same length.
Error in FFT_PSd (line 59)
plot(Freq1(2:t_leng1),abs(psdx(2:end-1)))
Here are my questions.
1. Is the correct method to obtain the acceleration PSD graph?
2. if it is correct, how can I solve this matlab error.
3. If it is incorrect, please let me know the method.
Thank you.
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You have to simulate your model to get the acceleration as a function of time.
Once you get the acceleration as a function of time you proceed as described in this forum to get psd using fft.
Best wishes
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I have many samples of a arrays representing signals in the time domain that I want to classify using artificial neural networks. Do I need to use feature extraction methods (such as the FFT or WT) before applying them to the ANN ? or do I apply the signal directly as it is (600 dim array) to the NN.
Thanks in advance.
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You are performing a sequence classification in which a sequence is in the time domain.
Applying FFT to the input sequence can speed up the learning process. Moreover, it can introduce new features to be learned, but keep in mind to use only the real part of the transformed sequence.
However, you will lose the time-dependent features, since the FFT transforms the input from the time domain to the frequency domain.
Instead, you can apply (short-time FT) to the input sequence, it somehow keeps time-dependent features and exploits the features from the frequency domain.
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I have developed matlab code of OFDM index modulation based on look up table but not getting the desired curve...i want curve as shown in link given below on page 5544....somebody plz help me with the coding part...there are many mistakes in my code..plz help me to rectify the code...
clear all;
close all;
clc;
nsym=10^2;
%nbitspersym=12
bits=(1:12)
n=4
g=2;
%g1=bits(1:6) % division of 16 FFT size into 8 FFT subcarriers.
%g2=bits(7:12) % division of 16 FFT size into 8 FFT subcarriers
ipbits = randi([0,1], 1, 600) % generation of random bits
M=4;
Z=reshape(ipbits,100,6)
k=log2(M)
%n=nFFT/g;
EbN0=1:10
for i1=1:10
EbN0dB=i1
BER=[];
for i=1:100
p1=Z(i,:)
% p1=ipbits(1:6) % it will take first 8 bits
%p1=[1 1 0 1 1 0];
%p2 = ipbits(7:12) ;
s1 = p1(1:2)
s2= p1(3:6)
% s3=p1(5:6);
% s4= p1(7:8);
% s33=s2
s22=reshape(s2,2,2).'
s222=bi2de(s22, 'left-msb')
ipMod = (1/sqrt(2))*qammod(s222 , M)
F=zeros(4,1);
if s1==[0 0]
F(1,1)=ipMod(1)
F(2,1)=ipMod(2)
F(3,1)=0
F(4,1)=0
elseif s1==[0 1]
F(1,1)=0
F(2,1)=ipMod(1)
F(3,1)=ipMod(2)
F(4,1)=0
elseif s1==[1 0]
F(1,1)=0
F(2,1)=ipMod(1)
F(3,1)=0
F(4,1)=ipMod(2)
elseif s1==[1 1]
F(1,1)=ipMod(1)
F(2,1)=0
F(3,1)=0
F(4,1)=ipMod(2)
end
F.'
%=ifft(Sq1,N)*sqrt(N)%
xt=ifft(F).'
%xt=xt.'
xt=[xt(4) xt]
%xt=(nFFT/sqrt(nDSC))*ifft(fftshift(xF.')).';
%x_with_cp = [x(4) x]; % cp addition
% adding noise
nt = (randn + j*randn)./sqrt(2)
yt = xt +( (10.^(-EbN0dB/10)).*nt)
yyt=yt(2:5)
yyt1=fft(yyt)
[temp, idx] = sort( yyt1, 'descend' )
n = temp(1:2)
idx = idx(1:2)
%zzt=(abs(yyt1))
zzt1=(abs(yyt1).^2)
[max1, ind1] = max(zzt1)
zzt1(ind1) = -Inf;
[max2, ind2] = max(zzt1)
zzt1(ind2) = -Inf;
rec=[(ind1) (ind2)]
a1=1;
while(a1==1)
if rec== [1 2]|rec== [2 1]
bitts=[0 0]
a1=0;
elseif rec== [2 3]|rec== [3 2]
bitts=[0 1]
a1=0;
elseif rec== [2 4]| rec== [4 2]
bitts =[1 0]
a1=0;
elseif rec==[4 1]|rec==[1 4]
bitts=[1 1]
a1=0;
elseif rec==[3 1]|rec==[1 3]
BER12=0
break
% bitts=[1 1]
elseif rec==[4 3]|rec==[3 4]
BER12=0
break
% bitts=[0 0]
end
% finding the minimum distance between received vector and original symbols
% original symbols
orig=[0 0]
conv=bi2de(orig, 'left-msb')
ipMod = (1/sqrt(2))*qammod(conv , M)
orig1=[0 1]
conv1=bi2de(orig1, 'left-msb')
ipMod1 = (1/sqrt(2))*qammod(conv1 , M)
orig2=[1 0]
conv2=bi2de(orig2, 'left-msb')
ipMod2 = (1/sqrt(2))*qammod(conv2 , M)
orig3=[1 1]
conv3=bi2de(orig3, 'left-msb')
ipMod3 = (1/sqrt(2))*qammod(conv3 , M)
xxt=[ipMod ipMod1 ipMod2 ipMod3]
D1 =norm(n(1) - xxt(1))
D2 = norm(n(1) - xxt(2))
D3 = norm(n(1) - xxt(3))
D4 = norm(n(1) - xxt(4))
D=[D1 D2 D3 D4]
D5=min(D)
result = find(D==min(D))
DD1 = norm(n(2) - xxt(1))
DD2 = norm(n(2) - xxt(2))
DD3 = norm(n(2) - xxt(3))
DD4 = norm(n(2) - xxt(4))
DD=[DD1 DD2 DD3 DD4]
DD5=min(DD)
result1 = find(DD==min(DD))
G=[DD5 D5]
[tempp, idxx] = sort( G, 'descend' )
nn = tempp(1:2)
idxx = idxx(1:2)
idx1=sort(idxx) % sort according to tempp;
% idx1=[result result1]
%
% reeesult=[result result1 result2 result3]
% [temp1, idx1] = sort( F, 'ascend' )
% n = temp1(1:2)
% idx1 = idx1(1:2)
% [min1, ind1] = min(F)
% F(ind1) = Inf;
% [min2, ind2] = min(F)
% F(ind2) = Inf;
% rec1=[(ind1) (ind2)]
if idx1== [1 2]
biitts=[0 0 0 1]
elseif idx1==[2 1]
message('i m here')
biitts=[ 0 1 0 0]
elseif idx1== [2 3]
message('i m here')
biitts=[0 1 1 0 ]
elseif idx1==[3 2]
message('i m here')
biitts=[ 1 0 0 1 ]
elseif idx1== [2 4]
message('i m here')
biitts =[0 1 1 1]
elseif idx1==[4 2]
message('i m here')
biitts =[1 1 0 1]
elseif idx1==[4 1]
message('i m here')
biitts=[1 1 0 0]
elseif idx1==[1 4]
message('i m here')
biitts=[ 0 0 1 1]
elseif idx1==[3 1]
message('i m here')
biitts=[1 0 0 0]
elseif idx1==[1 3]
message('i m here')
biitts=[0 0 1 0]
elseif G1==[1 1]
biitts=[0 0 0 0]
elseif G1==[2 2]
biitts=[0 1 0 1]
elseif G1==[3 3]
biitts=[1 0 1 0]
elseif G1==[4 4]
biitts=[1 1 1 1]
end
S=[bitts biitts]
% A2 = sort(F(:))
% out=A2(1)
% out1 = A2(2)
% result33 = find(DDDD==min(DDDD))
BER12=sum(abs(p1-S))/6
end
BER=[BER BER12];
end
T_BER(i1)=sum(BER)/100
end
semilogy(EbN0,smooth(abs(T_BER)),'*-');
xlabel('SNR');
ylabel('EbN0dB');
legend('BER curve for OFDM index modulation');
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Hi, Samriti , I'm new in the field of NOMA aided spatial modulation, can you share your code with me. I would appreciate it a lot.
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Hello, I need to find the amplitude of the FFT of a real signal in Matlab. I would like to get the same amplitude in the frequency domain (with fft) and in the time domain.
I've read about some methods:
1) Division by N: amplitude = abs(fft (signal)/N), where "N" is the signal length;
2) Multiplication by 2: amplitude = 2*abs(fft(signal)/N;
3) Division by N/2: amplitude: abs(fft (signal)./N/2);
4) Others follow Parseval's theorem: amplitude = abs(fft(signal)./factor), being "factor" equal 1/fs (fs - sampling frequency).
Can anybody help me?
Thank you
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I know that my answer to your question is very late. As your question is a basic question I would like to give my opinion.
- The peak value of a sinusoidal wave can be found in time domain by discretizing the time domain signal and build a time sequence of amplitudes. By comparing the values we can locate the highest amplitude which can be considered as corresponding to the peak value. The value located will be most accurate when one one samples at the time of the peaks and also when increases the sampling frequency.
So there are two requirements to increase the accuracy:
- Aligning the sampling with peaks
- Increasing the sampling frequency
Let is assume that we transformed this signal to frequency domain.
It is so that the DFT is just the sampling of the frequency spectrum of the signal.
So one can locate a signal in the discrete frequency domain with the resolution of
fs/2N. One always hits a signal at the intended frequency +- fs/2N.
As for the Fourier transform of single sine wave with a time window N/fs, one gets sinx/x Fourier transform. with the sine wave at the center of this curve.
The discrete Fourier transform will locate the point of maximum amplitude with the resolution of fs/2N as i hinted previously.
So, there will be an error in locating the peak value either in the time domain and in frequency domain because of the discritization.
Best wishes
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Dear Community,
I am measuring heart beats through a PPG signal in realtime. So far I processed the raw PPG with a bandpass filter in the range [0.4, 4] Hz. Then, a peak detection algorithm was applied. However, it is sensitive to different noise sources, leading to errors in the heart beat detection.
I though about another simple and robust method for heart beat detection, e.g. through correlation. The FFT is too computationally expensive for the ESP32 system, where we implement the algorithm.
Which robust algorithm do you recommend for heart beat detection?
Thanks, blessings!
Fernando
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Thank
Ijaz Durrani
for your kind help.
Warm regards,
Fernando
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During parameter study for a project (signal processing applied in mechanical vibration), when I decrease the frequency to a very low value (less than 200 mHz, for a interested range of frequency of a few hundred Herz), the frequency response I have gets "squishy" (instead of a single line going up or down, I have a waveform going up or down).
Checking on some forums online yield me the connection to "dirac delta function", but I do not understand it fully. So I'd like to ask if there is any connection between dirac delta function and frequency resolution, with regards to FFT in vibration analysis. Thanks for your help.
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Roberto Ferretti Don't worry about that. I have a... strange explanation for it. Both in terms of "why the grey line is heavily smeared" and "why do we see peaks at 50 150 Hz and so on".
The first is a combination of very low frequency resolution plus window function. The second... noise. Probably noise. Or some equivalent "freak accident". More recently, I have repeated the experiment under the same conditions, the change is only "hitting different point" - but considering the graph above is the FFT of input force signal, there should be no change. Yet, the recent experiments show practically no such peaks at all.
Another answer (and a more technical one) is the influence of powerline (AC 50 Hz) and some internal harmonic of the measuring system under certain conditions...
Yeah, it is still quite fuzzy.
But still, thanks for your help.
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How should I exactly process the vertical acceleration signal (with respect to time) to be able to adapt it on the ISO2631 curves (as attached). As far as I know, First I should find rms of the acceleration, then FFT and after that 1/3 octave filter. is there any matlab code for this process? or any pdf that help with using iso2631 figure?
another question is, what if the peak point of the fft curve of the acceleration signal does not meet one of the ISO2631 curves (as shown in the attached picture)? Is there any interpolation method to find the exact amount of time that the human body can endure the vibration in this case?
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Some years ago I found this matlab script to calculate the ISO2631 parameters.
Is not my work and I´m not sure if it is correct, but I think it could help you to figure out how to apply the ISO2631.
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After taking the frequency spectrum of an impulse force (from a shaker, sampled from a vibration analysis test), there are noticeable peaks at 50 and 150 Hz (presumably due to some electrical/electronic artefacts or some mismatch impedance between the shaker's coil and its structure). This specific experiment aims to study the effect of frequency resolution on the spectrum.
When the frequency resolution is smaller (same bandwidth, or F_max - F_min, is used, and more spectral/FFT lines are used), these sudden peaks start to appear. It should be noted that the original input signal is the same. The question is why this happens?
Thanks for your help
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The graph shows the conversion of a "knocking force" (man-made impulse) from time to the frequency domain. Thus, the peaks should not be there in the first place. Time duration of the signal or the measurement time (defined by the software) is 1/delta f (inverse of the frequency resolution).
About the different heights, it can be contributed to the different coefficients in Fourier transform (this is made by having different frequency resolution)
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I have Frequency Weighted RMS Acceleration Data for ISO-2631-1 ride comfort analysis in time domain.
I want to compare it with ISO-2631-1 Time Exposure Limit plot by superimposing the available FWRMS acceleration above mentioned (As shown in attached Figure). So can I directly use FFT to above data to obtain it Frequency domain from Time domain and superimpose on Time Exposure Limit graph available in frequency domain?
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Dear Ijaz sir, thanks a lot.,actually I have frequency weighted acceleration data in time domain, and my question is simply using FFT on this, will the data become frequency weighted acceleration on frequency domain? or I have to directly convert acceleration data from time domain to frequency weighted?
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Dear All,
Hope you are fine and doing well in this pandemic.
I am working on a FORTRAN program, where I need to sum the Gaussian functions over time. I have observed that my computing time is scaling linearly with the number of iterations. I found FFT is useful for such tasks while reading about techniques to reduce the iterations. So, my question is if FFT can efficient for my task, how can I implement it?.
Pseudocode or suggestions are appreciated.
Cheers!
Anji
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did you read "Five-step FFT algorithm with reduced computational complexity", please see the follwoing link, may be it will help you.
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I did the FFT of a periodic current profile from the axis of a machine tool. The FFT amplitude profile is also periodic, i.e. the profile has a peak at 7, 12, 17, 22, 27 Hz, i.e. every 5 Hz I do have a new peak. I really do not understand if this behaviour is a consequence of some physical phenomenon or it is some kind of numerical error. Anyone can help?
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Assuming that the axis is rotating, do you know the rotation speed? If so, what is the associated frequency?
One possible explanation would be that the frequency based on rotation speed is not 7 Hz but 17 Hz, for example, and that the amplitude of the 17 Hz signal is modulated by another 5 Hz signal plus harmonics, e. g. by transversal vibration of the axis, resulting in "sidebands".
It would be interesting to see the signal both in time domain and in frequency domain.
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I am working on a project related to sEMG classification. In order to process the signal, I am trying to filter the signal, especially power interference. The signal is acquired from Delsys Bagnoli 16, and the sample rate was 4000. I collected 6 signals simultaneously, and some signals showed this abnormal behaviour in fft (see the figure, zoomed to 0-700Hz). Therefore, I tried notch filters to remove the spikes, but it seems like it has other spikes around 50Hz. For example, see the next figure. I need to know the reason for this behaviour. Is it due to fft calculation? (see the last figure, Matlab code. it was recommended by the Matlab (https://www.youtube.com/watch?v=VFt3UVw7VrE , at 5:17)) or due to a filtering problem at the amplifier?
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A possibility: in many countries, the power supplied from an outlet is alternating current at 50Hz or 60Hz. Therefore, many electrical devices (eg, fluorescent lamps) generate noise at that frequency.
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Does anyone know any book that contains general descriptions and basic mathematical formulation of frequency analysis methods like PSD, FFT or Allan variance?
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There is a good book which contains a part on signal processing including the the signal transforms. This book is Digital communications by Glover and Grant.
Best wishes
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Hi,
I am keen to know that If I have two signals say :
Farend_signal (y[n]) and Nearend_signal (x[n])
and x[n] also contains an echo of y[n] with some delay.
Is there any technique to align these two signals temporally is Spectrogram? For example, any correlation technique to find the temporal displacement between signals after making a spectrogram(STFT) of two signals?
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If the spectrograms of the same signal are compared, then the following can be done: 1) integrate both spectrograms over frequency - as a result, we get two functions of time; 2) calculate the cross-correlation function of these two functions of time - the shift of the maximum of this cross-correlation function along the time axis is the value required to align the spectrograms.
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I have tried to measure the third harmonic of discharge current performing FFT of the discharge current signal. I found out that the amplitude of the third harmonic is not prominent enough to detect.
Can anyone suggest me some methods to measure the same distinctly?
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A first thing you can try is to insert a high pass filter into your measurement circuit in order to get rid of any low frequency disturbance of your signal.
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Currently, I have a series of human motion data recorded by accelerometer sensor at a sampling rate 100HZ. Based on literature review, many papers extract the feature called “First 5 FFT coefficients” from the sliding windows. They claimed that the first 5 of the fast-Fourier transform coefficients are taken since they capture the main frequency components, and the use of additional coefficients did not improve the accuracies [1][2].
I googled a lot and it seems Numpy.fft module could solve my problem. However, I am not sure this module is appropriate for my purpose. From the document, the numpy.fft.fft() function accepts either a real or complex array as an input argument, and returns a complex array of the same size that contains the Fourier coefficients. The real part contains the coefficients for the cosine terms and the imaginary part contains the negative of the coefficients for the sine terms [3].
I am not sure about the relationship between First 5 FFT(fast-Fourier transform) coefficients and output from fft module in Numpy. Sorry for my poor signal processing background. Any hints or suggestions will be much appreciated.
Reference
[1] Incel, Ozlem Durmaz. "Analysis of movement, orientation and rotation-based sensing for phone placement recognition." Sensors 15.10 (2015): 25474-25506.
[2] Lee, Jin, and Jungsun Kim. "Energy-efficient real-time human activity recognition on smart mobile devices." Mobile Information Systems 2016 (2016).
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Thank you for all the valuable information or suggestion. I just got the confirmation from the authors listed in the reference. The real part of first five coefficients was used in their paper based on scipy module.
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This project is related to transient states.
I want to study the behaviour of a circuit (like a current source) by suddenly increasing the resistance from one value (5 ohms) to another (10 ohms).
At first, I used Brick resistance, which I could not achieve to my goal.
Now I use Carbon resistance which has not worked.
Which resistance are used for high frequencies (100ns; 10 Mhz)?
If I use carbon resistors, is there a proposed circuit that I can increase their frequency?
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Dear Asadi,
Now I think I got what do you want to achieve.
You want to measure the TC of the solar panel or any PV array.
You defined the TC = FFT(I)/FFT(V)
So, you want to get the admittance spectroscopy of the twp port network under certain load and illumination condition.
It is clear to me that you do not yet developed such admittance spectroscopy y(f)
So, as I was a pioneer for the admittance spectroscopy of the solar cells i want to stress some facts:
y(f) depends on the operating point of the array. As the operating voltage increases the admittance will be smaller and vice verse. So, the admittance curve is not the same at all operating conditions of the solar panel.
As the panel is nonlinear such that I versus V is not linear, then one has to speak about small signal admittance.
Then one can conclude in order to use this method in diagnosing the panel, one has to keep constant the operating conditions of the panel such as the load and or the terminal voltage and the illumination intensity.
Now we come to the important question how we measure y(f)?
We apply a small signal ac voltage on the panel and measure the the drawn AC current in magnitude and phase.
The ac voltage source is coupled to the loaded and illuminated solar panel by a coupling capacitor. The ac current is sensed by either a sense resistance or by a hall probe. The use of sensing resistance is the most common method.
I made such measurements for the small signal characterization of large area solar cells. I developed a method to measure the ac admittance.
Really you can use them again for diagnosis of solar panels.
I would like that you follow the paper in the link:
You can use the measuring method brought in the paper or use a more advanced method for admittance measurement by following the paper:
Best wishes
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I have a question based on application of the FFT. It has been 30 years since I worked with FFT’s so, please explain as if explaining to a neophyte as there are many cobwebs. I will explain the image as basically as I can since I do not know if anyone will have seen this type of medical image before.
The image below shows the Doppler spectrum produced by an FFT. The FFT bins correspond to velocity through the Doppler equation (note the scale to the right of the Doppler spectrum). There is now a “raging” debate in the medical field about the lighter envelope portion of the spectrum (red dotted line) that basically duplicates the darker envelope (modal velocity traced in light green) below the baseline. Some are arguing that this higher velocity (red envelope) is artifactual based on the FFT processing and some are arguing that this is the result of a hemodynamic situation which results in an increased velocity.
I want to discuss one more general point about Doppler FFT processing before I can really described the situation and pose the question. The Doppler signal is separated into an I and Q channel so as to detect flow direction (represented as flow signals above or below the baseline). I am well aware of the “mirroring” that can occur in the Doppler spectrum when some of the signal “crosstalks” between the I and Q channel. If there is not perfect separation between the two channels, we see the result as replication of the real signal “mirrored” across the baseline.
I will briefly describe the situation. This picture represents the imaging of a prosthetic mitral valve (top image with color) and a continuous wave Doppler, spectrum below. Continuous wave implies that the flow velocity is detected along the entire Doppler line (dotted white line down the center of reference image above spectrum). In this case, the valve has been replaced with a mechanical prosthetic valve with metal discs. A well understood artifact that occurs sometimes in ultrasound (and frequently with specular reflectors like metal which acts like a mirror to sound waves) is a reverberation artifact. In essence, the sound, instead of making a single path down and back from each part of the image, makes two or more paths (reverberates) between the strong reflectors. The result is that the specular reflectors (and of course every structure between the specular reflectors, is duplicated a second time (and possibly more times) below the location of the actual structure within the ultrasound image. The fact that this reverberation artifact is happening in this image is confirmed with other views I am not including.
So some people are theorizing that just as the image is being replicated, the Doppler shift is being replicated (this I completely believe since the sound beams used to detect Doppler shifts behave in the same manner as the sound beams used to create the 2D image). However, the velocity presented is related to the Doppler frequency shift, not if the shift is detected twice, so detecting the same Doppler shift again does not explain the increase in velocity shown. In other words, detecting the same frequency twice should result in energy in the same frequency bins, not higher order bins corresponding to higher frequencies( velocities). The question is whether it is possible that since the “same” shift is being replicated and detected twice instead of once can result in an explainable result in the FFT the produces signal in higher order FFT bins (the signal in the fainter envelope traced in red). In other words, is there a mathematical explanation (like there is for the “mirroring” artifact for this “double envelope” artifact?
For completeness: others are theorizing that this is not related to the FFT processing at all, but is the result of the vena contracta and complex flow acceleration that results from impingement of this flow on the septal wall. I will not go into more detail on this theory as this relates to fluid dynamics and I know this is a forum for answering questions about Fourier Transforms.
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Considering your information about the mirror/reverberation effects, you may have to understand where the doppler effect in this environment comes from:
A (sonic) wave propagating WITH some flow is traveling faster (at the added velocity of sound and flow), a wave propagating AGAINST some flow slower. While it might be hard to understand, the Doppler shift of signal traveling back and forth the very same path of flow is non-zero (due to differing traveling times).
Consider a signal that's reflected - traveling the (about) same path twice: it's Doppler shift will be about twice as large (compared to a single travel), as it is exposed to the flow twice.
My best guess is that your mysterious signals stem from exactly this effect - about doubling the Doppler shift for a signal reflected twice as compared to the "standard" signal.
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I have a prototype tested results of a 4-bit ADC for which the FFT spectrum is attached.  2.5V p-p input was used with an input frequency of 100 MHz sampled at 800 MS/s
2.5V p-p input was used with frequency of 100 MHz sampled at 800 MS/s
I need to calculate the dynamic range of this converter. How can I do that?
Thanks in advance
Anush
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Hi!!
Usually the dynamic range (DR) of a distortion-free ADC can be calculated from its nominal resolution (N) as DR=6.02N (approximately). However, looks like your ADC has quite a lot of distortion.... in that case you should consider the spurious-free dynamic range, which is the amplitude of a sinusoid input (measured at the output) minus the amplitude of the highest distortion peak (also measured at the output).
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I am new to fpga. Can anyone help how to use inbuilt fft module of fpga
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Aparna Sathya Murthy : Thinking to use Altera. Can u share any tutorial on how to configure intel fft ip core for implementing fft and ifft.
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what is the reason for the difference in complexity
any answers or suggestions from the scholars will be highly helpful.
Thanks
Brij Mohan Kumar
M-Tech (Communication systems)
IIT, Patna
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You can find some important from this.
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I am reproducing the results reported by the author in a research paper. In which he takes the STFT of EEG signals. The input data has a shape of (1 X500) and I used Scipy library built-in function to calculate STFT and the output has shape (257 x32) I need to extract the data between two frequency bands (6-13 Hz) and (17-30 Hz). The author reports the extracted bands to be of the size of (16 X 32) and (23 X 32) but in none of the setting, I am getting this frequency resolution. I tried contacting the author but no response so far. I hope some of you could direct me in the right direction. Thank you very much.
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I have exactly the same question and, unfortunately, I was not able to catch the idea from the discussion above.
Please, I need to know how to identify the *start* and *end* frequency indices to extract for the 6-13 Hz band for example.
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I need this one to write about cepstral analysis. It doesn't seem to be available anywhere.
Full reference: Bogert, B.P., Healy, M.J. and Tukey, J.W., 1963, June. The quefrency alanysis of time series for echoes: Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking. In Proceedings of the symposium on time series analysis (Vol. 15, pp. 209-243). chapter.
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Dear All Fellows,
I hope my question is clear, I need to know which plane of my materials showing me interplanar (d-spacing), which I have calculated through FFT image. I need a litrature link, where I can check the materials planes (001, 100, 002, etc.) by puting my known d-spacing (nm).
Regards
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Following this good Discussion
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Dear friends
I am designing a underwater optical acoustic sensor Using COMSOL Multiphysics software. I would like to analyze the received signal signal by sensor in both the time domain and frequency domain. I have converted the time domain to frequency domain using FFT node which is available in the COMSOL itself but the amplitude is different. Is it correct or wrong?. Amplitude should be same or not if not same then why?
For understanding the question I have attached the figures below please find it.
Thanks & regards
Nilakanta
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For calculate amplitude of complex variables in comsol try the abs(variable) function.
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It's how to to convert spectral radiance from W/cm^2/sr/nm to W/cm^2/sr/cm-1. Fisrt one is the radiance represented by wavelength and second one is represented by wavenumber.
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these two units do not have a linear scaling relationship. rather, this depends on wavelength. wavenumber ν [cm-1] relates to wavelength λ [nm] as v = 10^7/λ. when integrating the same radiances (in their appropriate units) over the same finite spectral interval (in either wavenumber or wavelength), the answer has to be the same. from the differential dv/dλ = -10^7/λ^2, it follows that dv = -10^7/λ^2 dλ, and therefore [W/cm^2/sr/cm^-1] = [W/cm^2/sr/nm] * 10^7 / λ^2.
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Consider solving the nonlinear Schrödinger equation (NLSE) with 2D transverse dimensions. Or more simply, consider the beam propagation method (BPM).
We know that the original formulations of Feit&Fleck relied on the FFT to compute the derivatives.
In the 90s, many works about finite differences (FD) were proposed. The advantage is that the grid is more flexible and the boundary conditions are better understood and controllable (PML, TBC...). Prof. Fibich in his book suggests FD is a better approach to tackle collapses.
With the advent of GPU, FFTs with lots of points seem to be almost costless. Do you think it is not worth anymore to study finite differences? What about non-paraxial propagators?
Thank you for your suggestions.
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The accuracy of the numerical results are mainly restricted to accuracy of the models, although sometimes the numerical stablilization might due to the detalied numerical methods. GPU might be treated as one possible way to decrease the CPU times in numerical simulations. Further, for a detaield numerical simulation problem, BC might be very important.
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Generally, FFT is used to obtain the spectrum in the frequency domain. What are the informations we can get from the FFT analysis of a spectrum?
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Aparna Sathya Murthy thank you so much, madam.
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I need to classify an EEG signal (most likely using an artificila neural network) but for the inputs I would like to know the differences between using the result of the FFT or using the signal with different band-filteres for example butterworth.
The advantages of the FFT is that is much smaller (a 1 second window it will have ~60 inputs) and that the signal is still there. On the other hand using band-filters I would have as input all the signal (a 1 second window @ 256 Hz it will have 256 times the number of bands that I want to obtain). Also the signal gets distorted.
So my final question is, for classification purpouse, does the FFT contains the same information that the signal filtered?