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FFT - Science topic
Explore the latest questions and answers in FFT, and find FFT experts.
Questions related to FFT
How can I apply Discrete Fourier Transform (DFT) in MATLAB for aeroelastic analysis of wind turbines? Specifically, I am looking for guidance on using DFT to analyze wind turbine structural response data (e.g., vibrations, loads) in the frequency domain. Any advice on MATLAB code, libraries, or methodologies for incorporating DFT into aeroelastic simulations would be greatly appreciated!
In a HAADF (High-Angle Annular Dark Field) image, both SAED (Selected Area Electron Diffraction) and FFT (Fast Fourier Transform) patterns can be derived, but they serve different analytical purposes and provide different types of information about the material being studied:
- SAED Patterns:SAED is a direct electron diffraction technique used in transmission electron microscopy (TEM). It provides detailed crystallographic information, including the identification of crystal phases, lattice parameters, and symmetry. SAED is specifically useful for analyzing small selected areas of the specimen, allowing for localized examination of crystal structure.
- FFT Patterns:FFT is a mathematical algorithm applied to convert a signal (in this case, image data) from the spatial domain to the frequency domain. In the context of TEM images, applying FFT to a HAADF image helps to analyze the periodicity and symmetry of the lattice structure across the entire field of view, not just selected areas. FFT patterns are particularly useful for identifying and analyzing spatial frequencies in the image, which correspond to the regular spacings within the crystalline material.
Differences:
- Scope: SAED analyzes specific selected areas within the sample, providing localized structural data, whereas FFT treats the image holistically to reveal overall structural periodicities.
- Output: SAED results in a diffraction pattern showing spots or rings which directly correspond to the crystal structure. FFT produces a transform that highlights all periodic components in the image, useful for quick identification of lattice defects, strains, or other periodic features.
In summary, SAED provides targeted, localized crystallographic information while FFT offers a broader, more comprehensive view of the periodic structures within the entire image. Both techniques complement each other in materials science for understanding the microscopic properties of materials.
hello All, i have generated 9 RX antennas Data, with 2 targets at same distance on different angles, now i got the complex rcs in CSV, i have tried the Angle FFT to Find the DOA of the Targets, but it is not working, can anyone Please help me.
i am attaching few data samples
The 599 samples are taken from 9 Antennas with tx frequency of 2 to 3Ghz,
Hi I have a very important question. i took needle hydrophone readings from Focused Ultrasound. I saved it as Csv file with voltage and time reading.
I have applied FFT to the signals to get output but I am not getting the signals as a wave rather it is as more like electrical signals.I want to compute Pulse duration, PRF and all those acoustic parameters I gave from function generator but I am not sure what step is wrong and where I made mistake. I will aprreciate guidance in this matter.
I transformed TEM images to FFT using Gatan software.
How can I figure out phases from the attached TEM FFT images?
Both of images are single phases?
And can I know the phases from just patterns without d-spacing?
I have been working with a material whose band structure needs to be calculated. I have obtained the suggested k-path from "SeeK Path", which is a tool of Materials Cloud, but it always showed the problem where it wrote: " Found rotation not compatible with FFT grid". I even tried to use SUMO but the problem persisted. What would be the possible reason behind it? Is it the k-point path that's creating the problem? How to remove it?
i was trying to get fast fourier transform of Cu(111) surface and i got a ring. I am wondering why it is ring
Hi,
I am trying to analyse the acoustic data available on Fluent using the FWH permeable and non permeable surface approaches over wind turbine blade section. My concern is how to make the best possible interpretation of available data on Fluent, since it only gives acoustic pressure which I used FFT to plot in the frequency domain.
The first observation I made when compared to other works is that the studied frequencies are low while FFT plots go up to 150k in my case, which according to my understanding is related to the used time step.
I would like to also get an understanding of Hanning, Hamming windowing and samples?
Secndly, how can the directivity be plotted using data from FLUENT?
If anyone has a practical document to share it would be much appreciated.
Regards
Attached image shows plot of Fast fourier transform of ECG that had applied a lowpass filter to (15 Hz, zero-phase shift FIR with 24 dB/octave roll-off). The frequency bands increase in increments of 0.03125Hz from 0 to 15 Hz. HRV (< 0.4 Hz) and HR (~1.5 Hz) peaks are clear, but I'm unsure what the harmonics are specifically or their significance. I've found suggestions they are related to subtle variations in the PQRST waveform but find very little literature detailing their significance or meaning, besides a preprint Kotriwar, Y., Kachhara, S., Harikrishnan, K. P. & Ambika, G. Higher order spectral analysis of ECG signals. arXiv (2018) doi:10.48550/arxiv.1809.08451.
Any advice or further references related to these harmonics and their meaning would be greatly appreciated. Thanks
Time frequency images and curves sorted by power or dB versus time are the main constituents of the EEG information. Therefore, video tutorials, links, manuals or paper-based suggestions as well as webinars are needed.
I simulated a copper coil in comsol, applied a pulse excitation current to the coil, and set a probe around the coil to measure the potential. I used the mef module and transient research, but it reported an error in the end. I am going to try I tried the frequency domain to time domain FFT research, but I don't know how to connect the time domain pulse signal with the frequency domain. I hope to get your help, thank you!
There are detailed model diagrams and pulse diagrams below.
Complex numbers are involved almost everywhere in modern physics, but the understanding of imaginary numbers has been controversial.
In fact there is a process of acceptance of imaginary numbers in physics. For example.
1) Weyl in establishing the Gauge field theory
After the development of quantum mechanics in 1925–26, Vladimir Fock and Fritz London independently pointed out that it was necessary to replace γ by −iħ 。“Evidently, Weyl accepted the idea that γ should be imaginary, and in 1929 he published an important paper in which he explicitly defined the concept of gauge transformation in QED and showed that under such a transformation, Maxwell’s theory in quantum mechanics is invariant.”【Yang, C. N. (2014). "The conceptual origins of Maxwell’s equations and gauge theory." Physics today 67(11): 45.】
【Wu, T. T. and C. N. Yang (1975). "Concept of nonintegrable phase factors and global formulation of gauge fields." Physical Review D 12(12): 3845.】
2) Schrödinger when he established the quantum wave equation
In fact, Schrödinger rejected the concept of imaginary numbers earlier.
【Yang, C. N. (1987). Square root of minus one, complex phases and Erwin Schrödinger.】
【Kwong, C. P. (2009). "The mystery of square root of minus one in quantum mechanics, and its demystification." arXiv preprint arXiv:0912.3996.】
【Karam, R. (2020). "Schrödinger's original struggles with a complex wave function." American Journal of Physics 88(6): 433-438.】
The imaginary number here is also related to the introduction of the energy and momentum operators in quantum mechanics：
Recently @Ed Gerck published an article dedicated to complex numbers:
Our question is, is there a consistent understanding of the concept of imaginary numbers (complex numbers) in current physics? Do we need to discuss imaginary numbers and complex numbers ( dual numbers) in two separate concepts.
_______________________________________________________________________
2023-06-19 补充
On the question of complex numbers in physics, add some relevant literatures collected in recent days.
1) Jordan, T. F. (1975). "Why− i∇ is the momentum." American Journal of Physics 43(12): 1089-1093.
2）Chen, R. L. (1989). "Derivation of the real form of Schrödinger's equation for a nonconservative system and the unique relation between Re (ψ) and Im (ψ)." Journal of mathematical physics 30(1): 83-86.
3) Baylis, W. E., J. Huschilt and J. Wei (1992). "Why i?" American Journal of Physics 60(9): 788-797.
4）Baylis, W. and J. Keselica (2012). "The complex algebra of physical space: a framework for relativity." Advances in Applied Clifford Algebras 22(3): 537-561.
5）Faulkner, S. (2015). "A short note on why the imaginary unit is inherent in physics"; Researchgate
6）Faulkner, S. (2016). "How the imaginary unit is inherent in quantum indeterminacy"; Researchgate
7）Tanguay, P. (2018). "Quantum wave function realism, time, and the imaginary unit i"; Researchgate
8）Huang, C. H., Y.; Song, J. (2020). "General Quantum Theory No Axiom Presumption: I ----Quantum Mechanics and Solutions to Crisises of Origins of Both Wave-Particle Duality and the First Quantization." Preprints.org.
9）Karam, R. (2020). "Why are complex numbers needed in quantum mechanics? Some answers for the introductory level." American Journal of Physics 88(1): 39-45.
Hello everyone,
Currently, I am curios about Fast Fourier Transform (FFT). There are several general formula of this number.
Following an MIT video of Professor Gilbert Strang, he mentioned that the general formula is 1/2*N*log2(N) with N is the number of samples. However, when I tried to calculate the number of multiplication with N = 256 samples, those amount of operation are bigger than those from formula. So, I am wondering that what the minimum number of samples are to satisfy the aforementioned formula.
Thanks for your consideration.
This question discusses the YES answer. We don't need the √-1.
The complex numbers, using rational numbers (i.e., the Gauss set G) or mathematical real-numbers (the set R), are artificial. Can they be avoided?
Math cannot be in ones head, as explains [1].
To realize the YES answer, one must advance over current knowledge, and may sound strange. But, every path in a complex space must begin and end in a rational number -- anything that can be measured, or produced, must be a rational number. Complex numbers are not needed, physically, as a number. But, in algebra, they are useful.
The YES answer can improve the efficiency in using numbers in calculations, although it is less advantageous in algebra calculations, like in the well-known Gauss identity.
For example, in the FFT [2], there is no need to compute complex functions, or trigonometric functions.
This may lead to further improvement in computation time over the FFT, already providing orders of magnitude improvement in computation time over FT with mathematical real-numbers. Both the FT and the FFT are revealed to be equivalent -- see [2].
I detail this in [3] for comments. Maybe one can build a faster FFT (or, FFFT)?
The answer may also consider further advances into quantum computing?
[2]
Preprint FT = FFT
[2]
Preprint The quantum set Q*
Can we create a faster FFT? A faster Fast Fourier Transform?
Our answer is YES. This question is a call for collaboration with that goal, when we can already use the first results. It is not an utopian goal.
Today, the FFT is already tremendously important, from digital communication, to DVDs, worldwide atomic weapons test ban, and much more.
Fast, inexpensive digital devices are readily available from Intel Corporation, AMD and others today.
These devices are also using quantum mechanics and multiple states of spin -- as mass-produced quantum computers working with repeatability, precision, understanding, and flawlessly. A faster FFT will make them even more useful.
Can different, cheap software make a difference, even with current hardware? The article at https://lnkd.in/g2H6_5n2
and the preprints
show that much faster digital calculation of the Fourier Transform (faster than the FFT) using digital computers is possible today, using absolutely exact rational numbers, and just different software. It is not an utopian goal.
Quantum evolution seems to be facing Noah's ark moment, the moment of true need.
We need quantum computing now, and a faster FFT can also bring it. For calculating long prime numbers, all that is needed is to use the well-known Shor's algorithm in a faster FFT setting, to find periodicity.
Other algorithms can follow, to solve faster and more efficiently other difficult problems, such as in gaming and error correction. Science can also benefit, such as in protein folding, new medicines, new sources of energy, and propulsion means.
How can you help?
When I do Time-Frequency analysis using choi William's distribution for a Sine wave (using real data) I should get a single line indicating only one frequency should present over the entire time duration. But I see two lines which is mirror image of the above. Even for FMCW i see the same way.
But if I perform the same for Complex data, I see only one line. Both are having same no of samples (1024) while performing CWD.
Our answer is YES. A new question (at https://www.researchgate.net/post/If_RQ_what_are_the_consequences/1) has been answered affirmatively, confirming the YES answer in this question, with wider evidence in +12 areas.
This question continued the same question from 3 years ago, with the same name, considering new published evidence and results. The previous text of the question maybe useful and is available here:
We now can provably include DDF [1] -- the differentiation of discontinuous functions. This is not shaky, but advances knowledge. The quantum principle of Niels Bohr in physics, "all states at once", meets mathematics and quantum computing.
Without infinitesimals or epsilon-deltas, DDF is possible, allowing quantum computing [1] between discrete states, and a faster FFT [2]. The Problem of Closure was made clear in [1].
Although Weyl training was on these mythical aspects, the infinitesimal transformation and Lie algebra [4], he saw an application of groups in the many-electron atom, which must have a finite number of equations. The discrete Weyl-Heisenberg group comes from these discrete observations, and do not use infinitesimal transformations at all, with finite dimensional representations. Similarly, this is the same as someone trained in infinitesimal calculus, traditional, starts to use rational numbers in calculus, with DDF [1]. The similar previous training applies in both fields, from a "continuous" field to a discrete, quantum field. In that sense, R~Q*; the results are the same formulas -- but now, absolutely accurate.
New results have been made public [1-3], confirming the advantages of the YES answer, since this question was first asked 3 years ago. All computation is revealed to be exact in modular arithmetic, there is NO concept of approximation, no "environmental noise" when using it.
As a consequence of the facts in [1], no one can formalize the field of non-standard analysis in the use of infinitesimals in a consistent and complete way, or Cauchy epsilon-deltas, against [1], although these may have been claimed and chalk spilled.
Some branches of mathematics will have to change. New results are promised in quantum mechanics and quantum computing.
This question is closed, affirming the YES answer.
REFERENCES
[2]
Preprint FT = FFT
[3]
Preprint The quantum set Q*
Hello,
I am trying to implement a frequency-PCA in Matlab/fieldtrip (or Python alternatively).
The idea of this approach is to identify the major frequency components in the EEG data in a data-driven way. Each component is characterized by a dominant frequency and a topography (electrodes that contribute most to it) (like in Barry & De Blasio 2017 for example).
If I understood correctly the steps are:
1. Compute an FFT for each subject and recording
2. Apply PCA on the obtained power spectra across all the subjects and recordings
3. Apply promax or varimax rotation on the obtained coefficients.
I have tried these steps in Matlab, pseudocode below:
powerspectra = [];
for subject in subjects:
for recording in recordings:
cfg = [];
cfg.output = 'pow';
cfg.channel = 'all';
cfg.method = 'mtmfft';
cfg.taper = 'hann';
cfg.keeptrials = 'no';
cfg.foilim = [1 40];
FFT_EEG = ft_freqanalysis(cfg, data_eeg);
powerspectra = [powerspectra; FFT_EEG.powspctrm];
end
end
[coeff,score,latent] = pca(powerspectra);
% Perform Promax rotation on the principal components
rot_matrix = promax(coeff,3);
% apply the rotation to the principal components
rot_coeff = coeff*rot_matrix';
However, the resulting components do not look meaningful (except maybe the first component, see figures attached).
There must be something I'm missing in my implementation? I would greatly appreciate any hint or lead. Thank you
Hello everyone,
I’m doing some experimental measurements of ultrasound in a water tank. The goal is to get the pressure distributions in the water and on the surface, where the ultrasonic transducer is attached. All measurements are done with a Brüel & Kjaer 8103 hydrophone. I have an established workflow but I’m unsure whether all processing steps are correct. Most of the equations I found in the B&K technical documentation that came with the hydrophone (unfortunately very outdated, from 1992, even though the hydrophone was purchased recently). Do you think the steps below are correct?
Firstly, the hydrophone is connected to an amplifier and oscilloscope. The data is obtained as a time wave with 10000 data points, in mV. Sampling frequency is around 10 MHz (the frequency range that I’m interested in is between 20-60 kHz). Then, the correction of the amplifier is applied, by calculating the gain. This is done by dividing the output sensitivity (mV/Pa) by the transducer sensitivity (mV/Pa), resulting in values between 4-40 depending on the amplifier settings. Then the raw amplitude values are divided by 10^(gain/20).
I then carry a Fast Fourier transform on a window correction using the Hanning window. I do the FFT in R, using the fft() function. To get the amplitude I use 4* absolute values after the FFT divide by the total number of data points. After having frequency resolved data (which I ensured is correct by running it with a signal of known frequency) I correct for the sensitivity of the hydrophone, obtained by the manufacturer. I do this by calculating a transfer factor:
10e8*10^(sensitivity/20). The values in Pa are obtained by dividing the amplitude (after FFT) by the aforementioned transfer factor.
Final values are around 80 KPa. Unfortunately I have no exact expectation of the range of the pressure I would expect with ultrasound transducer powers that I use. Any suggestions/recommendations are very welcomed. There are many questions asked that I have come across on how to transform the raw voltage signal in pressure but this only leads to more confusion.
Transfer factor formula obtained from here: https://www.translatorscafe.com/unit-converter/de-DE/microphone-sensitivity/
Thanks a lot
Some complex compounds, e.g. microplastic, giant PAHs, their mass spectrum peaks are as follows. The mass differences have obivious periodicity.B. Apicella used FFT and autocorrelation function to simplify mass spectrum. It's a powerful tool but i can't understand how to convert time space to mass space. Do anyone have a similar experience? How to simplify mass spectrum by FFT ? I need some practical advice.
Ref:
"Fast Fourier Transform and autocorrelation function for the analysis of complex mass spectra"
Dear All,
How can I make Matlab/Simulink exclude inter-harmonics when calculating THD value??, because its FFT tool in powergui block includes inter-harmonics in calculations.
I am trying to calculate the bispectrum of EEG epochs using bispecd.m of HOSA toolbox in MATLAB. However, i am confused by the parameters of the function, such as nfft, wind, nsamp, overlap. What are they supposed to mean and how are they related to my data? I have 256 points epochs sampled in 128Hz. Lot of thanks!
Link to MATLAB HOSA toolbolx: https://ww2.mathworks.cn/matlabcentral/fileexchange/3013-hosa-higher-order-spectral-analysis-toolbox
I have acquired the rat EEG data using three electrodes. I want to do the FFT analysis of the eeg bands. Is it possible to effectively do using eelgab? because I am trying it for such a long time and every time I am getting negative values in power spectral analysis. Can anybody help me that I am going in the right direction or not?
Brain Tumor Imaging Protocol will reduce variability and increase accuracy in determining progression and response of investigational therapies.
In the pictures below, with the FFT and DFT methods and the PCA phase recovery, which is common in optical microscopes, I obtained the magnetic resonance imaging (MRI) phase of the human brain tumor and the phase obtained.
Can the process be performed on MRI without prescribing Jumpstarting Drugs (JBTDDC)?
I'm searching for new collaborations. I'm focused mostly on numerics on obtaining solitons bifurcating from band edges and localized modes. I'm specialized in shooting methods with more than one parameter, NR optimized for CUDA obtaining solutions, parametric curve step to obtain branches that cannot be described by a function. Optimization of split-step FFT dynamics with coupled Nonlinear partial differential equations, Ex: SHG was the harder one. I worked previously with V. Konotop, F. Abdullaev and B. Malomed. I was first author on all papers. They provided the equations and researched on obtaining solutions.
Hi, I have already completed recording data of visual stimuli using emotive epoc x 14 channels. Now I want to filter all those signals to alpha and lower beta waves as well as I want to do some operations such as DFT, FFT, PSD operation, and also some statistical analysis such as maximum value, minimum value, standard deviation, mean, etc.
My goal is to create a dataset using above-mentioned data and use them for machine learning purpose. So, I would be grateful if you mention the software for the best user-friendly experience for the operation mentioned above.
Thank you.
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.
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?
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.
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
Nodes used for this job:
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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
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| |
| 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) |
| |
-----------------------------------------------------------------------------
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| |
| 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.) |
| |
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LDA part: xc-table for Pade appr. of Perdew
POSCAR, INCAR and KPOINTS ok, starting setup
FFT: planning ...
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)?
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.
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
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.
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?
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?
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?
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
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.
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 ??
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?
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?
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
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).
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?
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.
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?
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.
The complex signal may be real-imaginary or magnitude-angle form.
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.
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)
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 ...
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?
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)
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!
Hello,
I am studying the Webrtc AEC3 algorithm from "https://topic.alibabacloud.com/a/an-introduction-to-webrtcs-echo-cancellation-aec-aecm-algorithm_8_8_10267058.html".
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
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 !
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
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.
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