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Questions related to Signal Analysis
  • asked a question related to Signal Analysis
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I am looking for adaptive short time Fourier transform implemented in MATLAB as a code. This can be useful for non stationary signal analysis.
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Thanks for your answer, adaptive STFT is a step that I need in a big Program so I can't use another program to implement it while I am using MATLAB for the rest of the code.
So, are there any other ideas?
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I have seen many ways to find similarity measure of multiple time series data in the literature. But, in my case, I have one time series data X of dim [n,1]. Now I want to get a similarity measure between data points of X. I tried autocorrelation. But I want to get only one or two numeric values that will represent the similarity measure.
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Dear Ritesh.
I also recommend simple statistics methods @Helena Seivane.
Regards
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Hey all. I have two signals as shown. the red one is how my output should look like (the ideal case) and the blue one is what I am actually getting. I am quite new to signal analysis so was asking what are good metrics I can use to find the similarity between my generated signal and the ideal signal? I have looked at cross correlation and SNR using MATLAB, but I wanted to know if there are more methods out there that can provide me with a clearer picture specially with regards to how shape similarity etc.
Thank you all again.
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Correlation and Normalised Cross correlation (NCC) coefficients
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Actually, I wish to understand the process and coding to define new wavelet transform. So that I can understand and modify some wavelet transform to get better results. There is inbuilt wavelet transform in MATLAB and we just have to choose wavelets. I wish to define new wavelet transform.
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By converting the signal from 2 dimensional signal to 1 dimensional, the transformation could be processed on vectors.
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I am working on a research point that employs estimation techniques. I am trying to apply an algorithm in my work to estimate system poles. I wrote an m-file and tried to apply this technique on a simple transfer function to estimate its roots .any suggestions about estimation techniques ?
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Ahmed Abdulsalam Thank you , Ahmed
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Is there any mobile App publicly available on App stores (both IoS/Android) which can be used to gather/collect/analyze signal strength measurements from the available WLAN access points? My aim is to utilize these RSS reading for WLAN based indoor localization systems.
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You can monitor your RSS of the accessed WIFI networks using the methods described in the site:https://www.lifewire.com/how-to-measure-your-wifi-signal-strength-818303
Best wishes
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Could any one please help me in suggesting some resources where I could find a comparison curve between signal strength after Multi Path propagation effect with respect to obstacle positions between transmitter and receiver.
After conduction some experiment I found that the effect was greater near Rx or Near Tx but lesser when the obstacle is in same distance from Rx and Tx. Why such phenomena happens?
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I suppose this depends on what kind of obstacles you are considering and how they are affecting the signals. If you consider an object that is scattering the signal, then the pathloss will be proportional to (d_1*d_2)^2 where d_1 is the distance from the transmitter to the obstacle and d_2 is the distance from the obstacle to receiver. For a given total propagation distance d_1+d_2, it follows that the pathloss is at its smallest when the scattering object is close to the transmitter or the receiver.
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By referring to some scientific resources we've found that the brain produces Some signals that relate to its activities and monitored by EEG. The question is "Can we force the brain to do some actions by injecting signals (in a direct or indirect way)?".
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Brain–computer interface can also restore communication to people who have lost the ability to move or speak:
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Dear Colleagues,
Please suggest any open source software for ECG signal analysis.
Thanks in advance
N Das
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I want to find the resonance and anti-resonance frequencies of an ultrasonic transducer by analyzing its impedance.
so I need to buy a impedance analyzer or spectrum analyzer or something like that.
but my budget is limited.
do you recommend any device for my application and limited budget? :D
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If you want to measure impedance in a low cost way, get yourself
1) Suitable signal generator
2) An appropriately sized current sense transformer
3) a two-channel oscilloscope.
Measure the voltage and current as you vary frequency. Oscilloscope will give you the phase relationship between current and voltage across transducer. You can then calculate the real and imaginary components of impedance. I leave it as an exercise how you might calibrate this setup. Cheers!
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Hi everyone,
This is just a 'out-of-curiosity' question, but why is the cerebellum used as the reference point? What is the reasoning? I was always told is it because it is 'silent' compared to the cortical regions, but obviously the cerebellum is also active. Is there any paper that explains the choice, or if a better reference region or method is available?
Thank you!
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Dear Haram,
I have been always heavily involved in this past discussions when we measured with telemetry EEG. The answer has been always that the cerebellum is obviously active but the neurons fires at very high frequencies whereas the most interesting frequencies for a "normal" EEG are much lower (0.5-100 Hz). That is why most likely the cerebellum is taken as reference and additionally if you have differential electrodes you get greater signal/noise ratios. Hope this helps.
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I want to identify a peptide signal for a gene. All the tools like signalP is not showing anything and other tools are not showing sequence. Could someone suggest some tools?
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Did you tried the latest SignalP 6.0?
Best regards
PS. If still difficult, did you consider to use the AA sequence instead of gene sequence (here are programs out there that will do the job)?
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Hello everyone,
for my thesis I want to extract some voice features from audio data recorded during psychotherapy sessions. For this I am using the openSMILE toolkit. For the fundamental frequency and jitter I already get good results, but the extraction of center frequencies and bandwidths of the formants 1-3 is puzzling me. For some reason there appears to be just one formant (the first one) with a frequency range up to 6kHz. Formants 2 and 3 are getting values of 0. I expected the formants to be within a range of 500 to 2000 Hz.
I tried to fix the problem myself but could not find the issue here. Does anybody have experience with openSMILE, especially formant extraction, and could help me out?
For testing purposes I am using various audio files recorded by myself or extracted from youtube. My config file looks like this:
///////////////////////////////////////////////////////////////////////////
// openSMILE configuration template file generated by SMILExtract binary //
///////////////////////////////////////////////////////////////////////////
[componentInstances:cComponentManager]
instance[dataMemory].type = cDataMemory
instance[waveSource].type = cWaveSource
instance[framer].type = cFramer
instance[vectorPreemphasis].type = cVectorPreemphasis
instance[windower].type = cWindower
instance[transformFFT].type = cTransformFFT
instance[fFTmagphase].type = cFFTmagphase
instance[melspec].type = cMelspec
instance[mfcc].type = cMfcc
instance[acf].type = cAcf
instance[cepstrum].type = cAcf
instance[pitchAcf].type = cPitchACF
instance[lpc].type = cLpc
instance[formantLpc].type = cFormantLpc
instance[formantSmoother].type = cFormantSmoother
instance[pitchJitter].type = cPitchJitter
instance[lld].type = cContourSmoother
instance[deltaRegression1].type = cDeltaRegression
instance[deltaRegression2].type = cDeltaRegression
instance[functionals].type = cFunctionals
instance[arffSink].type = cArffSink
printLevelStats = 1
nThreads = 1
[waveSource:cWaveSource]
writer.dmLevel = wave
basePeriod = -1
filename = \cm[inputfile(I):name of input file]
monoMixdown = 1
[framer:cFramer]
reader.dmLevel = wave
writer.dmLevel = frames
copyInputName = 1
frameMode = fixed
frameSize = 0.0250
frameStep = 0.010
frameCenterSpecial = center
noPostEOIprocessing = 1
buffersize = 1000
[vectorPreemphasis:cVectorPreemphasis]
reader.dmLevel = frames
writer.dmLevel = framespe
k = 0.97
de = 0
[windower:cWindower]
reader.dmLevel=framespe
writer.dmLevel=winframe
copyInputName = 1
processArrayFields = 1
winFunc = ham
gain = 1.0
offset = 0
[transformFFT:cTransformFFT]
reader.dmLevel = winframe
writer.dmLevel = fftc
copyInputName = 1
processArrayFields = 1
inverse = 0
zeroPadSymmetric = 0
[fFTmagphase:cFFTmagphase]
reader.dmLevel = fftc
writer.dmLevel = fftmag
copyInputName = 1
processArrayFields = 1
inverse = 0
magnitude = 1
phase = 0
[melspec:cMelspec]
reader.dmLevel = fftmag
writer.dmLevel = mspec
nameAppend = melspec
copyInputName = 1
processArrayFields = 1
htkcompatible = 1
usePower = 0
nBands = 26
lofreq = 0
hifreq = 8000
usePower = 0
inverse = 0
specScale = mel
[mfcc:cMfcc]
reader.dmLevel=mspec
writer.dmLevel=mfcc1
copyInputName = 0
processArrayFields = 1
firstMfcc = 0
lastMfcc = 12
cepLifter = 22.0
htkcompatible = 1
[acf:cAcf]
reader.dmLevel=fftmag
writer.dmLevel=acf
nameAppend = acf
copyInputName = 1
processArrayFields = 1
usePower = 1
cepstrum = 0
acfCepsNormOutput = 0
[cepstrum:cAcf]
reader.dmLevel=fftmag
writer.dmLevel=cepstrum
nameAppend = acf
copyInputName = 1
processArrayFields = 1
usePower = 1
cepstrum = 1
acfCepsNormOutput = 0
oldCompatCepstrum = 1
absCepstrum = 1
[pitchAcf:cPitchACF]
reader.dmLevel=acf;cepstrum
writer.dmLevel=pitchACF
copyInputName = 1
processArrayFields = 0
maxPitch = 500
voiceProb = 0
voiceQual = 0
HNRdB = 0
F0 = 1
F0raw = 0
F0env = 1
voicingCutoff = 0.550000
[lpc:cLpc]
reader.dmLevel = fftc
writer.dmLevel = lpc1
method = acf
p = 8
saveLPCoeff = 1
lpGain = 0
saveRefCoeff = 0
residual = 0
forwardFilter = 0
lpSpectrum = 0
[formantLpc:cFormantLpc]
reader.dmLevel = lpc1
writer.dmLevel = formants
copyInputName = 1
nFormants = 3
saveFormants = 1
saveIntensity = 0
saveNumberOfValidFormants = 1
saveBandwidths = 1
minF = 400
maxF = 6000
[formantSmoother:cFormantSmoother]
reader.dmLevel = formants;pitchACF
writer.dmLevel = forsmoo
copyInputName = 1
medianFilter0 = 0
postSmoothing = 0
postSmoothingMethod = simple
F0field = F0
formantBandwidthField = formantBand
formantFreqField = formantFreq
formantFrameIntensField = formantFrameIntens
intensity = 0
nFormants = 3
formants = 1
bandwidths = 1
saveEnvs = 0
no0f0 = 0
[pitchJitter:cPitchJitter]
reader.dmLevel = wave
writer.dmLevel = jitter
writer.levelconf.nT = 1000
copyInputName = 1
F0reader.dmLevel = pitchACF
F0field = F0
searchRangeRel = 0.250000
jitterLocal = 1
jitterDDP = 1
jitterLocalEnv = 0
jitterDDPEnv = 0
shimmerLocal = 0
shimmerLocalEnv = 0
onlyVoiced = 0
inputMaxDelaySec = 2.0
[lld:cContourSmoother]
reader.dmLevel=mfcc1;pitchACF;forsmoo;jitter
writer.dmLevel=lld1
writer.levelconf.nT=10
writer.levelconf.isRb=0
writer.levelconf.growDyn=1
nameAppend = sma
copyInputName = 1
noPostEOIprocessing = 0
smaWin = 3
[deltaRegression1:cDeltaRegression]
reader.dmLevel=lld1
writer.dmLevel=lld_de
writer.levelconf.isRb=0
writer.levelconf.growDyn=1
nameAppend = de
copyInputName = 1
noPostEOIprocessing = 0
deltawin=2
blocksize=1
[deltaRegression2:cDeltaRegression]
reader.dmLevel=lld_de
writer.dmLevel=lld_dede
writer.levelconf.isRb=0
writer.levelconf.growDyn=1
nameAppend = de
copyInputName = 1
noPostEOIprocessing = 0
deltawin=2
blocksize=1
[functionals:cFunctionals]
reader.dmLevel = lld1;lld_de;lld_dede
writer.dmLevel = statist
copyInputName = 1
frameMode = full
// frameListFile =
// frameList =
frameSize = 0
frameStep = 0
frameCenterSpecial = left
noPostEOIprocessing = 0
functionalsEnabled=Extremes;Moments;Means
Extremes.max = 1
Extremes.min = 1
Extremes.range = 1
Extremes.maxpos = 0
Extremes.minpos = 0
Extremes.amean = 0
Extremes.maxameandist = 0
Extremes.minameandist = 0
Extremes.norm = frame
Moments.doRatioLimit = 0
Moments.variance = 1
Moments.stddev = 1
Moments.skewness = 0
Moments.kurtosis = 0
Moments.amean = 0
Means.amean = 1
Means.absmean = 1
Means.qmean = 0
Means.nzamean = 1
Means.nzabsmean = 1
Means.nzqmean = 0
Means.nzgmean = 0
Means.nnz = 0
[arffSink:cArffSink]
reader.dmLevel = statist
filename = \cm[outputfile(O):name of output file]
append = 0
relation = smile
instanceName = \cm[inputfile]
number = 0
timestamp = 0
frameIndex = 1
frameTime = 1
frameTimeAdd = 0
frameLength = 0
// class[] =
printDefaultClassDummyAttribute = 0
// target[] =
// ################### END OF openSMILE CONFIG FILE ######################
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Hi,
Please pay attention to these parameters:
...
nFormants = 3
formants = 1
bandwidths = 1
...
Change the 1's with 3's
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I have come up with a mixtures of gaussian based classification system for image recognition which can theoretically be modelled for signal analysis but I would like to improve the system by enhancing some of it
features like the optimizers, classifiers and the like. The best option was to make a single package out of it which might solve other problems in AI too and to make it available for other with the gnu vx license
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I think hosting it in GitHub may the way to go as pointed out by Raoul G. C. Schönhof . I undertook a similar approach sometimes back and it worked for me.
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If it's possible to be under the form of links or full name of the research papers. Thank you in advance !
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One more paper that study the classification:
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dear community, my model is based feature extraction from non stationary signals using discrete Wavelet Transform and then using statistical features then machine learning classifiers in order to train my model , I achieved an accuracy of 77% maximum for 5 classes to be classified, how to increase it ? size of my data frame is X=(335,48) , y=(335,1)
Thank you
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Use Adam optimizer with a small learning rate. i.e a learning rate of 0.0001 like that or even small. then the training accuracy improves.
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Hello! We have a project where participants engaged in reading, thinking and then responding to an ethical dilemma. We used the EMOTIV 14 channel headset to track brain activity. The reading, thinking and responding times varied across participants (they were given all the time they needed). Do you have any advice or literature about either standardizing these varying times across participants or time-varying analysis?
Many thanks,
Deyang Yu
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We can use Spectral Analysis for Neural Signals
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I have a 1D signal and I have done wavelet packet decomposition on it which is giving several sub-bands. Can I stack these sub-bands (one below other) to form a 2D matrix and hence an image representation of that 1D signal?
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Yes, you can convert 1-D signal (wave) : first convert it to data then any data can convert to image in matlab program. After that you can use wavelet decomposition pockets to image using DWT2( , , ) and same thing doing.
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Hi all, I hope everyone is doing good.
I am working on Machine Learning, I am working on EEG data for which I have to extract statistical features of the data. Using mne library I have extracted the data in a matrix form but my work requires some statistical features to be extracted.
All features which are to be extracted are given in table 2 of this paper: "Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer,". The data set I am using is dataset 2b from "http://www.bbci.de/competition/iv/".
I can't find a signal processing library. Can you suggest me any signal processing library for processing EEG signal data in Python?
Thanks to all who help.
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Aparna Sathya Murthy I came across this and tried to install this in google colab (pip install pyeeg), but it says:
ERROR: Could not find a version that satisfies the requirement pyeeg (from versions: none)
ERROR: No matching distribution found for pyeeg
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Dear community , I need your help , I'm training my model in order to classify sleep stages , after extracting features from my signal I collected the features(X) in a DataFrame with shape(335,48) , and y (labels) in shape of (335,)
this is my code :
def get_base_model(): inp = Input(shape=(335,48)) img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp) img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1) img_1 = MaxPool1D(pool_size=2)(img_1) img_1 = SpatialDropout1D(rate=0.01)(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = MaxPool1D(pool_size=2)(img_1) img_1 = SpatialDropout1D(rate=0.01)(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = MaxPool1D(pool_size=2)(img_1) img_1 = SpatialDropout1D(rate=0.01)(img_1) img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1) img_1 = GlobalMaxPool1D()(img_1) img_1 = Dropout(rate=0.01)(img_1) dense_1 = Dropout(0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1)) base_model = models.Model(inputs=inp, outputs=dense_1) opt = optimizers.Adam(0.001) base_model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc']) model.summary() return base_model model=get_base_model() test_loss, test_acc = model.evaluate(Xtest, ytest, verbose=0) model.fit(X,y) print('\nTest accuracy:', test_acc)
I got the error : Input 0 is incompatible with layer model_16: expected shape=(None, 335, 48), found shape=(None, 48)
you can have in this picture an idea about my data shape :
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So you need to code your network to get the input of size (none, 1, 48). Each feature is of dimension 1x48, while 'none' would take up the size of the number of sample points (335 in your case). Hence, your input would be of shape 335x1x48. So, modify the input layer of your network to expect input of size 1x48 instead of 335x48, and provide input as 335x1x48.
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Hello everyone,
I am trying to generate a faulty acceleration signal with SIMULINK. The inner ring of the bearing is fixed to the shaft. The bearing has 17 rolling elements. I was thinking of creating a fault in the inner ring because it is attached to the shaft. My approach was to add 17 impulses per cycle to the original measured acceleration data in order to generate a faulty signal.
I attached a picture of my Simulink model. What do you think about this approach and is my model correct so far?
10.848 * f_wheel is the Ball Pass frequency of the inner ring.
Kind regards
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Hello,
I have accelerometer data and I want to calculate the displacement, I found a software called SeismoSignal, but is mainly used to analyze the seismic signal and I want simple software for signal processing to calculate displacement and applying highpass filter and denoising processing.
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You can use OriginPro. In the Analysis, there is signal processing for several types of filters. Also, you can get the displacement from the Integrate function at Mathematics.
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Dear Colleagues, please suggest which is the best and user friendly open source software for audio signal analysis with most of the Scientific tools to audio signal analysis.
Thanks and Regards
N Das
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Thanks dear @Shahin Mardani, @Mila Ilieva, @Mani Entezami for your response to my discussion.
Dear @Mani, I will install the sonicvisualiser . Thanks for your support.
Best Wishes
Regards
N Das
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In MOSFET small signal analysis, we can relate charge (Q) and capacitance (C) using the formula Cij = dQi/dVj. Is the voltage term (dVj) calculated across the electrode terminals 'i' and 'j' ? Or is it between electrode terminal 'j' and GND?
For example: Cgs = dQg/dVs; where g=gate and s=source. Is this voltage (dVs) between gate and source? or between source and ground?
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You aare right in specifying that the capacitance is expressed by the dielectric between the two interfaces with conductors, and I would add that its value is specific of the dielectric itself. For example, the capacitance of the gate oxide is of course epsilon_ox x thickness / gate area. The fact is that in any case, the answer to the original question is that the voltage drop which charge the capacitance is that applied to the dielectric interfaces, not between one of the interfaces and ground. Of course, if one of the interfaces is grounded, one can say that dV is between the other interface and ground, but i is just a special case. It is semantically true when a PowerMOSFET is operated in static regime (away from swithching phases) and the source electrode is kept at ground potential, so the gate-source capacitance is named a gate-to-ground parameter just because the source electrode is grounded.
Of course we agree on the meaning of dynamically active parasitic terms playing a role in switching speed and power, such as Ciss, Coss, Crss, but again the (time-dependent) voltage drop across the involved capacitors (e.g. Cgd) applies across the same capacitors which, by the way, are dynamically involving not only an oxide, but also a modulated depletion zone which behaves as a dielectric between the two conductive materials (as in the case of Cds) or are in series to an oxide capacitor (as in the Cgs - when surface inversion is not reached - or Cgd)
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Suppose that X1, X2 are random variables with given probability distributions fx1(x), fx2(x).
Let fy(x) = fy( fx1(x) , fx2(x) ) be a known probability distribution of "unknown" random variable Y. Is it possible to determine how the variable Y is related to X1 and X2?
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Dear all,
When using tonyplot, I can get the I-V curve without any problems, but when I try to add small signal analysis after gate voltage sweep (ac freq=1e6) in Silvaco Atlas it shows zero Cgd for all gate voltages. I was wondeing if there is a way to plot the C-V curve correctly.
Thank You in advance.
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Maryam
May I give a hint that you may use to solve the problem.
When you want to calculate or measure some physical parameter you must stick to its definition relation. Specifically here, you have
cgd= d QGD/dVGD
In small signal notation
cgd= qgd/ vgd
So accordingly:
You have to set certain DC operating point
apply a small signal voltage vgd in series with the DC bias,
Then measure or calculate qgd.By dividing qgd by vgd you get cgd.
You can also calculate the current igd
Then you you can get igd/vgd= jwcgd which is the susceptance of the cgd.
Best wishes
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I need some help with VISSIM. I have modeled an intersection where I would like to use no-changing lane rules near 100 ft of the traffic signal. Picture 1 shows intersection without applying lane change restriction, where vehicle 1 and vehicle 2 are changing lane near the traffic signal.
However, after applying no-lane change near the traffic signal, I found the picture 2 for EB direction. Actually, those two vehicles from picture 2 would like to turn left, but they are in no turning section. Therefore, they are not moving.
How may I apply no-lane changing near an intersection?
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Hi,
You shall make the connector sufficienctly long (say 100ft as per your requirement), and enable the NoLnChRAllVehTypes and NoLnChLAllVehTypes parameters in the link properties for the connector. Also, to prevent vehicles from having to make last minute decisions on changing the lane to follow a desired route, introduce the vehicle route decision point well upstream of the connector.
Hope this helps.
Best wishes,
Abdhul
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Hi All,
I have an audio database consisting of various types of signals and I'm planning to extract features from the audio signal. So I would like to know whether it's a good idea to extract basic audio features (eg MFCC, Energy ) from the audio signal with a large window (Let's say 5s width 1s overlap) rather than using conventional small frame size (in ms). I know that the audio signal exhibits homogeneous behavior in a 5s duration.
Thanks in advance
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Due to the dynamic nature of audio signals, features calculated from large window sizes becomes an average value over the window rather than instantaneous values. On the other extreme, for window sizes less than 5 ms there might too few samples to give a reliable evaluation.
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Can anybody give details about how NIR spectra is related to glucose absorption in the sense of wavelength?
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Infrared radiation induces molecular vibrations as a result of which different bonds absorb light at different frequencies. Glucose for example is a hydrocarbon which consists of C-H, O-H, C-C, C=O functional groups which absorb photons with the right energy to excite overtone and combinations of fundamental molecular vibrations. Therefore, glucose is capable of absorbing NIR light. However, NIR absorption features are low in magnitude and highly overlapping in nature.
References
Hope that helps. Best of luck!!
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Is the introduction of labeling bad while detecting small molecules? If yes, what are the major disadvantages of using labels for signal amplification in the detection of small molecular weight ( <1 kDa ) compounds?
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For a given size label, the effect on molecular properties will depend on the size of the molecule you want to label. This is even true for isotope effects, they are much stronger for 2H/1H than for 13C/12C. In addition, for a small molecule the chances that the label sterically interferes with binding of the molecule into the pocket of its receptor are much larger than for, say, a big protein.
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The Fourier transform does not give information about the local time–frequency
characteristics of the signal which are especially important in the context of nonstationary
signal analysis. Various other mathematical tools like the Wavelet transform,
Gabor transform,Wigner–Ville distribution, etc. have been used to analyze such
kind of signals. The Fractional Fourier transform (FrFT) which is a generalization
of the integer order Fourier transform can also be used in this context
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Its probable easiest to illustrate the difference with examples. Try to visualise your time signal as being the x-axis of a graph, and the Fourier domain (frequency domain) as being the Y-axis of a graph. This is exactly how you would represent a STFT. The Fourier transform operation is a mapping that "rotates" your data from the time axis to the frequency one.
As a side note, you may find that the STFT does what you seem to require in that is allows you to look at the changing frequency content as a function of time.
Now ask yourself what happens if you don't fully rotate by 90 degrees from time to frequency - you end up somewhere in between. This is what a fractional Fourier transform does; it takes you to a domain that is neither frequency or time but between the two
Note that the concept of frequency applies when your basis functions are (co)sinusoids. However we could use a different set of basis functions, that rather than expanding and contracting by changing thier frequency, are stretched by a scaling factor. You could use any family of pulses from the wide variety availableand decompose your signals into pulses of different scales and amplitudes; this is a wavelet transform. Clearly when looking to decompose against a wavelet base, frequency is now an irrelevant concept
Hope that helps
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I'm working on Bipolar shaper amplifier and solving some circuits.
As shown in attachment the drain of two Mosfet pairs are shorted. Does that serve as AC ground in small signal analysis?
I've made its small signal model but it seems to be incorrect.
Can anybody plz comment in it how to perceive the short between 2Mos pair in differential amplifier?
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Dear Adeel,
Hope you are well,
If M4a and M4b are biased in a consatnt current source then they will act as emitter follower. When one applies equal differential input signals at M2 a and M2b then they will cancel at the drains of M4a andb.
This means that the short can be considered virtual ac ground.
Best wishes
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I have a reference time series and main data set(Similar sampling rate) which contains multiple instance of reference signal. Applying Cross Correlation (xcorr - Matlab) and from the highest xcorr values I have extracted multiple signals instance from the main data set.
It occurs some times the list include slightly different signals and I wanted to keep/remove those signal by again comparing with the reference signal by finding whether this matches or not. Any efficient way to do that ?
Reference snapshot attached.
Regards
Sreeraj
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The length of data result is 2xN-1 (N size of the original signal). The cross correlation result than can be displayed on [-N, N]. You will get maximal value when two signals are more similar. For example, if y=x (t-t0), then we get maximum of xcorr (x, y) at"t0". Cross correlation can be used to measure the delay between two similar signals... Please refer to this link for more details : https://fr.mathworks.com/help/matlab/ref/xcorr.html
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I have conducted an experiment in which the impulse was exerted on sound bowl (it was hit by a hammer). As a result I have obtained acceleration from accelerometer on 3 axis, calculated net acceleration ( sqrt(x^2+y^2+z^2) ) and tried to obtain frequency components of the response. What I need to do is to identify input impulse but I have no idea on how to do that and I'm kind of walking blindly. I would appreciate any ideas/references.
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Accelerometer gives system response. I think you need input signal( excitation source ) or hammer signal. To measure system response you should connect hammer cable to the FFT device. Actually you measure a transfer function. As you know transfer function is output/input. you may get input from transfer function itself. The following link may help you:
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Hi,
I am trying to generate eye diagram for a particular signal along with defined Eye mask. But cannot find any reference for how to integrate Eye Mask along with Matlab Eye Diagram Object ? Any one have any information ?
First diagram is the matlab eye diagram generated to which I would like to add Eye mask to look like the second diagram.
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Thank You Muhammad Ali for the suggestion. But I am afraid I am looking also for a way to pictorially represent Eye Mask Along with generated Eye diagram to get a quick glance on the performance. Now uploaded expected waveform.
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I'm wondering:
The spectrogram gives a limited information about the non-stationary signal, but it is enough to do a classification method? Is there any predefined "names" for the shape and behavior of the spectrogram? Where (on the spectrograms) is the fundamental frequency (F0)?
We have found a time-frequency behaviour in plant signals
See full spectrograms below
Any inputs will be very appreciated. Thank you
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In order to obtain a steady state or equilibrium state or to obtain roots or maxima/minima, we need a first derivative or making saddle point approximation or the Laplace approximation for the high dimension integrals. The easy way is then to perform Bayesian parametric classification of maximum likelihood estimation. Data may be pre-process via PCA, and finally predictions via multivariate density estimation over parametric modelling.
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I am facing a problem in Simulink.
When I want to write a bus signal to workspace in Simulink, I fail.
The next time I use a bus selector to select some signals in the bus and then write into the workspace. But I also fail this time with the error: 'The selected signal is invalid since it refers to a bus element within an array of subbuses'.
When I want to use the busselector to select the subbuses from the former busselector, I cannot find other signals.
So how to write a bus signal to a workspace?
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Hello
I found this answer in mathwork website, I hope it is useful for you:
There are several ways to store bus signals in the MATLAB workspace. Please choose one of the following three approaches:
1. Starting with Simulink 7.9 (R2012a), the 'To Workspace' block can be used to store bus signals of any mixed data when using the MATLAB 'Timeseries' format. For more information on this, please refer to the following link:
2. To store bus signals in Simulink 7.6 (R2010b) in the MATLAB workspace as a structure with the same hierarchy and signal names, data logging can be used as follows:
1) Right click on the desired bus signal and select Signal Properties.
2) In the Signal Properties window, check the box for 'Log signal data'.
3) In the model's Configuration Parameters dialog box, select Data Import/Export in the left pane.
4) Select the Signal logging checkbox and specify the variable name where the bus signal will be stored.
The logged signal variable will be of the form:
variablename.busname.signalname
3. Unfortunately, the above methods do not work for models compiled with Real-Time Workshop. This is because Real-Time Workshop does not support signal logging. An alternative method can be used as a workaround, however it is less direct. The following steps demonstrate how to create a MATLAB structure from a bus signal's flattened array and the accompanying model.
1) Save the bus signal to a MAT-file using the To File block and select 'MAT-file logging' under Configuration Parameters>Real-Time Workshop>Interface>Data exchange
2) Compile and run the model and executable, respectively, to generate the MAT-file with the bus signal stored as an array.
3) From the Simulink model, use the following code to create a Simulink bus object:
busInfo = Simulink.Bus.createObject(mdlName, blkName); num_el = eval([busInfo.busName '.getNumLeafBusElements']); elemList = eval([busInfo.busName '.getLeafBusElements']);
4) Create an array of Timeseries objects that capture the signal data from your MAT-file:
load MyFile % generated in the second step for i = 1:num_el size = elemList(i).Dimensions; ts{i} = timeseries(data(i+1:i+size,:)',data(1,:)'); end
5) Finally, propagate the Simulink bus object with the above timeseries using the CREATESTRUCTOFTIMESERIES method:
MYBUS = Simulink.SimulationData.createStructOfTimeseries(busInfo.busName,ts);
End of answer
Bast Regards
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I want to get a time-frecuency spectogram using windowed burg and lomb-scargle method. As long as I know they calculate the psd for a segment of time. But for shot signal(less than 5 min of length). The recommended window sizes are bigger than the singal length so I get only a psd for the whole signal. So what window size should I use in order to get a 5 min time frequency spectogram for a 5 min signal.
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The window size of the STFT should be short enough to maintain the stationarity of the signal. If the frequency characteristics change in a window, you can set the window size shorter. Check the periodicity of signal in the time domain, and and determine the window size short enough to catch the periodicity.
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The signals for induction motor current are plotted in frequency domain using MATLAB.
I attached the plot for more explanation.
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Hi
I was going through different methods to implement serial decoding for Flexray analogue electrical signal using Matlab. Any Suggestion or useful reference much appropriated !
Reference waveform from external tool attached. The electrical waveform is represented by 8bit(0x83)
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Dear Sreeraj,
In order to preserve the waveform shape you have to perform waveform decoding. It is sometimes called pulse code modulation.
Such encoding has two parameters:
The sampling frequency fs which must be greater than or = 2fmax where fmax is the maximum frequency contained in the waveform.
The other parameter is the sample amplitude quantization which is determined by the allowed quantization noise. So generally very sample is quantized bu an n bits. As you toled n= 8,
Then you have only to determine fs by determining fmax.
This can be determined empirically or by the frequency analysis of the signal using FFT with oversampling.
Then one cut the bandwidth to the dominant frequency components.
If you take fs greater than 2fmax the reconstruction of the waveform will simpler.
Best wishes
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By calculating the distance between two antennas and then taking the 'fft' of the received signal, how the speed of the signal can be calculated?
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In free space the speed of the signal in form of electromagnetic wave is the speed of light which is the speed of propagation.
But i think you mean to calculate the bit rate or the symbol rate.
For this you use the Shannon limit of the channel capacity
C= w log2(1 +S/N ), w is the bandwidth and N is the noise of the receiver.
Here the signal power S is the received power = Th transmitted power/4pi R^2,
where R is the distance between the two antennas provided that the two antennas are in light of site.
Best wishes
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I have been doing research on extracting information like acceleration, distance, etc. from an analogue signal by performing analogue signal processing on any type of signal. I haven't found any certain techniques which would help me in providing a formula or any sort of information which would lead to extracting information by analogue signal processing. If anyone is aware of analogue signal processing, could you please enlighten me if it is even possible to do this?
After spending hours, I am at a stage where I feel like it's not even possible to do this.
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I think so. The most acceptable method is to convert the measured non-electric quantity (acceleration, distance, etc.) into an electrical quantity (voltage, current, resistance). After receiving the analog electrical signal corresponding to the measured value, you can further work on the processing of this signal. First of all, it will be necessary to choose the appropriate type of sensor (accelerometer, piezo sensor, displacement sensor, etc.). The analog signal received from the sensor will most likely have to be amplified, and then it can be processed using an analog-to-digital converter. But the easiest way is to convert the signal from the sensor into electrical voltage with a value proportional to this value and then using the capabilities of a conventional multimeter, you can control this value. If the received DC signal, then using a multimeter you can easily control the voltage in the range of 1 mV - 1000V. If the AC voltage is less than 200mV, then it must be amplified so that it can be measured with a multimeter.
I wish you success
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Is it possible to visualize such high frequencies in distribution networks with the conventional signal processing techniques?
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Dear Utkarsh,
another way to measure harmonics over 2 kHz is to use a spectrum analyzer. There are many products on the market.
Sincerely,
Bystrík
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Hi,
I am new to EEG signal processing. I am now working on the DEAP dataset to classify EEG signals into different emotion catagories.
My inputs are EEG samples of shape channel*timesteps. The provider of dataset has already removed artifects.
I use convolution layers to extract features and use a fully connected layer to classify. I also use dropout.
I shuffle all trails(sessions) and split the dataset into trainning and testing sets. I get resonable accuracy on the trainning set.
However the model is anable to generalize accross trails.
It overfits. Its performance on the test set is just as bad as a random guess(around 50% accuracy for low/high valance classification).
Is there any good practice for alleviate overfitting in that senario?
Another thing bothers me is that when I search for related literature, I find many paper also give an around 50% accuracy.
Why are results from EEG emotion classifcation so bad??
I feel quite helpless now. Thank you for any suggestions and reply!
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Hi, Ge.
I have applied the DEAP dataset for EEG emotion classification before. The situation you mentioned also occurred in my research. From my perspective, here are some suggestions:
Firstly, Compared with the classification model you used, I think the input features are more significant. Maybe you could pay more attention about the feature extraction(Such as: PSD, based on frequency domain; HOC, on time domain; Discrete Wavelet Transform, on time-freq domain), selection and confusion parts, which was included the channel selection in terms of the different emotion categories.
Secondly, about overfitting issue, I thought it's kind of unsuitable to use the DNN model for this DEAP dataset unless you could increase the mount of data. Maybe you could give the data segmentation part(cutting the epochs) a shot.
Finally, in terms of accuracy problem, I suggest you double check which emotion estimation method was used in the specific paper. There are two aspects for EEG emotion classification: one is based on Valence-Arousal plane, which was learned from Speech emotion recognition; another is for the specific emotions(such as: angry, happy,sad,etc). So, be careful about the baseline you used to compare.
Regards
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Hi I am confused about the concept of signal quality index. what is signal quality index? what is the relation between signal quality index and signal strength? can we determine signal strength from signal quality index?
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As Martín Martínez Villar stated, I don't think there is a unique definition of a signal quality index. However, if you consider for example the field of (biomedical) signal processing, by a signal quality index you usually mean a value between 0 and 1 (or 0 and 100%) that indicated how "good" (free from noise and other artifacts) the signal of interest is.
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Brain signals Analysis for fMRI images.
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From the literature I am familiar with, I can say that the most numerous are studies of brain activity in the reading process. This is probably due to the great interest in his disorders and the problems of dyslexia. I have practically never encountered such studies of brain signals when performing various mathematical tasks. At the same time, the claim is that the left hemisphere dominates this type of operation.
In your study, you should consider the different involvement of the brain departments in solving arithmetic (non-verbal) and verbally-assigned (text-based) tasks. I'm sure you'll find a difference in the organization of brain signals in these two types of tasks.
I wish you success and look forward to seeing the results of your experiment!
Neli
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I analysis four ethanolamine compounds (Mono-ethanolamine, Diethanolamine and methyl diethanolamine (MDEA) and Piperazine) via triple quadrupole LCMSMS.The column is proshell 120 EC-C18 and Acetonitrile and 5 mM ammonium acetate are organic and aqueous phases respectively. The method is used to analysis water sample via direct injection. Recently, I have got high background signal for MDEA and Piperazine in the blank sample. Any suggestion to solve this issue. Thanks in advance.
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Thanks for your reply. I am looking for a robust method via LCMSMS to specifically analysis Alkanoamines that do not show false positive signals for blank samples.
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Someone who is in a coma is unconscious and will not respond to voices, other sounds, or any sort of activity going on nearby. however; in this case I'm wondering if any brain activity yet causes some senses to work.
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when a person is in coma you should not say what you would not say when the person is awake.
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I am developing an application to estimate the distance to a BLE Beacon using its RSSi values measured from a Mobile Phone. But when I started to collect data I could see that they varied so much that I cannot possibly estimate the distance using the values. Even though I implemented mean and median filters with various window sizes, the RSSI values are highly varying. Is this a common problem with RSSI values ? Is there a way to eliminate/filter out these variations so that I can input them to a distance modal ? Help is greatly appriciated.
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Dear Ravindu,
It seems that your channel from the transmitter to the receiver is relatively rapidly varying with time possibly due to multi path fading. The channel could be a Rayleigh scattering channel.
The solution could be to fit the RSSI with Rayleigh channel response from which you can get the average received power that can be considered a function of the link length. The other solution which is a practical solution is to calibrate the RSSI with link distance.
If you give more description of the communication channel one can propose a suitable method. One of the best method is to see for a free line of sight path between the beacon and the receiver.
Best wishes
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Hi everybody, I'm actually doing my master thesis in Biomedical Engineering about pulse oximeter.
I have to consider a real time system for my device. To compute the SpO2 from PPG signal, I filtered my signal with a low pass filter (to obtain DC component) and a BPF (to obtain AC component) .
I used FIR filter because of real time system and linear phase .
However, all the article about real time system which I have read, use IIR Butterworth filter because they have lower order and better result.
So, my question is: what is the better way to proceed? I can use higher order FIR filter (only way to obtain good AC and DC signal quality)?
Thanks for your answer.
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As per my understanding since you have to select a band limited signal and reject DC and high frequency noise. You do not require "perfect liner " phase filter, as per my understanding.
FIR filter has bulkey hardware, so your pulse oximeter will be costly as well as bulkey.
If cost and compactness, are not an issue, then only go for FIR
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How can implementation an Adaptive Dictionary Reconstruction for Compressed Sensing of ECG Signals and how can an analysis of the overall power consumption of the proposed ECG compression framework as would be used in WBAN.
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Thid is in class of machine learning in youtubre
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I have a number of questions regarding EEG signal analysis. Even after looking in a number of websites I am confused regarding it's usage. Please suggest with clear explanation.
1) Suppose, I have an eeg data with 100 Hz sampling frequency, recorded for 30 seconds. This means that we have 30 * 100 = 3000 samples of data. For these 3000 samples of data, in order to plot it's graph against time, how do I calculate the time data from this ? Do I simply divide the sample points by 3000 which makes (time_at_1_sec = 1/3000, time_at_2_sec = 2/3000 etc) ? Or Do i divide the sample points by 100 (time_at_1_sec = 1/100, time_at_2_sec = 2/100 etc) And continue with (1+1/100) after it reaches 100 sample points ?
2) While calculating alpha, beta, delta bands power, do we need to average the final value of power spectrum from all the channels of eeg data ?
3) After cleaning the noise using FFT and obtaining the final spectra, how to separate the individual band power from the final data ? Is there any function ?
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I am not into this field but at least I could answer the first question for the time being :)
the time axis:
t = (sample index/total number of samples) * total sampling time
or, t = sample index / sampling rate
so for example, the sample #300 occurs at t = 300/100 = 3 s
I hope you get a complete answer to your questions as soon as possible.
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I have complex values of a periodic signal which is clearly visible in time domain. But I want to find its frequency content ? FFT is not working with me and I am looking for alternate ways to solve the problem.
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One of the other method other than the DFT or FFT is the using a band pass filter bank. As the filter bandwidth decreases and its sharpness increases it can resolve the frequency components in the signal. The DFT and FFT are equivalent to using using filter bank.
In order to get a correct representation in the frequency domain you have to properly sample your signal with a sampling frequency fs>= fmax the highest frequency contained in the waveform. The other condition is that have to take sufficient length of the waveform or a time window which is long enough to resolve the lowest frequency in the signal. Increasing the sampling frequency or the window time time T will lead to increase the size of the FFT transform and increases the computational load.
These are the two parameters which control the resulting obtained fft analysis results.
Best wishes
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Hello,
I need guidance to find harmonics of complex and real time sound captured by four Microphone array?
I have data set of drone flying operation, i have localized sound sources that have drone sound and noises.
I want to detect drone because most of frequency components are consisting of drone, by estimating harmonic frequencies i can make guess for drone sound.
Fs = 32000.
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I designed function of above algorithm but i'm unable to specify the threshold frequency Ft and N.. i just know sampling frequency of drone sound data. Arpit Jain
function f=fre_harmonic(s,fl,fr,ft,N,fs)
%fs: sampling frequency
FFT_s=abs(fft(s,N));
fl1=ceil(fl*fs/N); %Transform Frequency to Points + inf
fr1=floor(fr*fs/N); %Transform Frequency to Points - inf
[v,f(1)]=max(FFT_s(fl1:fr1));
for n=2:N
fl1=ceil((n/(n-1)*f(n-1)-ft)*fs/N);
fr1=floor((n/(n-1)*f(n-1)+ft)*fs/N);
[v,f1]=max(FFT_s(fl1:fr1));
f(n)=f1+fl1;
end
Kindly help me so that i may get able to complete above mentioned process in main question.
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In most grinding applications, the grinding and dressing is still done depending on the experience and know-how of a skilled operator. To overcome the difficulties of experience-dependent grinding and dressing, a systematic method which can measure the status of the working wheel surface and evaluate the dressed wheel surface is necessary. A methodology could help to detect the extent of wheel loading based on secondary vibration signals. These vibration signals are captured by the use of a Piezoelectric Sensor placed around the Grinding Spindle Housing. The signals are captured for various conditions of the wheel and the results are used to form a regression model. The Lab VIEW software is used to perform the signals analysis. This online monitoring of the grinding wheel based on vibration signals is an effective method to implement in the case of mass production.
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Of course, vibration signal analysis is one of the most important methods for condition monitoring because it always contains the essential information of the system. For a defective element, an increase in the vibration energy may occur.
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Some of the books have always been used as a standard reference in that particular field. I am looking for such book on active components design theory and analysis; and that could have system level implementation/examples/models.
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Hello,
Practical Rf Circuit Design for Modern Wireless Systems, Volume II: Active Circuits ( by Rowan Gilmore (Author), Les Besser (Contributor) )
An Introduction To RF Circuit Design For Communication Systems 1st Edition
( Roger C. Palmer (Author) )
Thanks
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The enclosed is a sample of nECG file for an ECG signal analysis project. However, here I can not open it... although I tried so many interpreters and also wrote programs...
Can u help me? Thanks in advance!!
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Did you also try BioSig? http://biosig.sourceforge.net/
The BioSig project also contains code to load HL7aECG-files.
The nECG-file you provided is a zipped XML-file. If you use Windows just open it with e.g. 7-Zip and extract the xml-file. Next, open the xml-file with e.g. Notepad. You can then read all the information that is contained in the file.
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I have two sets of oscillator populations A and B with different frequencies and amplitudes; for instance population A has higher frequency and lower amplitude while population B has lower frequency and higher amplitude. If I mix the two populations in a given ratio, the entire network synchronizes and results in oscillation of some arbitrary amplitude and frequency. Note that I can record oscillation of the entire network as a single time series but not oscillations of individual oscillators within the network. Assuming that the network of A and B oscillators do not instantaneously synchronize, but exhibit unstable and non-stationary oscillation for a short duration (let me refer this time window as transients) before a steady state oscillation is reached, are there methods to extract information regarding the nature of coupling or coupling function between the oscillators from such data? I was wondering if I can use Hilbert Transformation to obtain instantaneous amplitude and frequency values to see how the frequency and amplitude evolve through transients before reaching a steady state or if there are other methods to do so? Thank you!
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I think that it is important to give a precise description of the physical model of the system of oscillators and accordingly based on the physical model you can develop the mathematical model with independent and performance functional parameters. Then you can investigate how do you determine the model parameters which means which measurements you need to get the system unknowns from observing its parameters.
Best wishes
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In telecommunications, a transmission system is a system which transmits a signal from one place to another. The signal can be an electrical, optical or radio signal.
Can we consider some of the human body systems as transmission systems and then model it using telecommunications' concepts for better understanding?
If we do, can someone please provides some examples of these systems and determines their basic elements(message, transmitter, medium and receiver)?
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Dear Mahdi,
This question is an interesting one as it invokes the analogy between the electrical communication systems and the signal transmission in the human body.
Any communications system consists of information sources, transmitters, transmission medium receiver and communication destination.
At first i would like to speak about the signal transmission medium in the human body. The main medium of the transmission in the human body is the water.
Water is a dipolar material and serves as a solvent for the substances supplied to the human body. It solves the slats including sodium chloride and forms an electrolyte capable to conduct electricity by its positive sodium ions and negative chlor ions. So, the electricity conduction is an ionic conduction. The generation of electrical signals is by electrochemical effect.
The system responsible for the sensation is the nervous systems where it generates the electrical signals in form of electrical pulses and transfer it from the a part of the body to the brain or from the brain to an intended part of the body. The brain is responsible for processing, taking actions and storing the signal in its memory cells. Th humans tried to mimic the function of the nervous system by introducing the so called Neural network.
The information is generated by sensors at skin of the human body. It is generated also by the ears and eyes. All of these sensors work as transducers converting the nonelectrical signals int electrical signals conducted by the Nerves to the central spinal cord then to the brain and back from the brain to the different organs to control them.
So the brain can be considered a source an destination of the information. It also stores and process the information to take decisions.
Signals also are generated by the transducers and some of them work as a destination. The communication system can be considered wire line one transmitting base band signals directly through conducting wires.
Best wishes
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Dear all,
I used oscilloscope to measure voltages and got data from two channels, each channel has time data and values data. now, I want to calculate magnitude and angle as ( A ∠±θ ) for each channel and magnitude and phase shift between two voltage channels as  ( A ∠±θ ) by using MATLAB.
DATA file in attachment.
Could you help me to do that?
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Question: What is the formula for the phase of a sine wave? There is no phase of a sine wave. A sine wave has no phase. A phase can only develop between two sine waves. Two sine waves are mutually shifted in phase, if the time points of its zero passages do not coincide. = http://www.sengpielaudio.com/calculator-timedelayphase.htm
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Muscle tissue does not normally produce electrical signals during rest. So its expected that the value Amplitude in mV will be roughly 0. However when muscles are stiff is when your muscles feel tight and you find it more difficult to move than usual, especially after rest. You may also have muscle pains, cramping, and discomfort. Cramps, which acts like muscle stiffness, can occur when muscles are unable to relax properly due to myosin fiber's not fully detaching from actin filaments. In skeletal muscle, ATP must attach to the myosin heads for them to disassociate from the actin and allow relaxation — the absence of ATP in sufficient quantities means that the myosin heads remains attached to actin. So will there be an expected amplitude in mV well greater than 0, maybe 3 to 5 mV range.
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As is well known, the Discrete Fourier Transform (DFT) is one of the main tools for digital signal analysis in electrical engineering and related fields. Now the question is:
Do you think the the Discrete Fourier Transform is deeply understood in the Geospatial community?
Although DFT was used in several areas of geospatial sciences such the Filtering of Digital Terrain Models, digial image processig, pattern recognition, its theoretical principles and their implications may not be deeply understood in the geospatial community.
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Dear Gamal,
welcome,
Any analog function f(t) or f(x) which is a function of time or position in the sense they vary with time or position or both when drawn in a linted time span or position range one depicts the variations of the amplitude of the function with time and position. Such variations may be called as the profile of the function or its waveform. Such look is abstract and complex.
The scientists were convinced that such complex shapes an be constructed from a unitary periodic wave forms which they discovered to be the sinusoidal waves or the complex exponential signals. So, it is so that any waveform in t or x or any variable is composed from its basic sinusoidal components. So, any waveform can be resolved into its constituting cosinusoidal weaves or complex exponential functions. These sinusoidal components or vibrations can be represented in the frequency domain as they are extending from - infinity to plus infinity. Such representation is called the frequency spectrum of the signal. It is normally designated by X(f).
So, one has devised two equivalent descriptions of the signal, the time domain description and the frequency domain description where the real domain in which the signals happens is the time domain while the frequency domain is the domain where the signal is analysed to its fundamental sinusoidal components.
The Fourier devised the Fourier transform FT to convert the signal from the time domain to the frequency domain. This transform is reversible which means that if one has the frequency components of the signal one can get the time domain signal.
There is great benefit to analyse the signal and get its frequency spectrum. One of great benefit is to determine its frequency range and its bandwidth. One can frequency limit this signal and filter it. One can synthesize it from its frequency components.
So assume that one has x(t) transformed it by FT to X(f),
This from is called analog form which the values are continuous with time.
For digital signal processing one has to transform the analog signal into digital form which can xd= x[n], which is a sequence of values of the x called digitized samples. The sampling rate fs must be accomplished according the Nyquist rate fs>= 2fm where fm is the maximum frequency in x(t),
The Fourier transform of this digital signal x[n] is called the Digital Fourier Transform DFT, X[k] where k is the sample index in the frequency domain.
Th DFT can be considered the sampled frequency domain.
Since DFT is time consuming one has invented the fast Fourier transform FTT to save calculations in DFT.
The most important point is that one can resolve any complex waveform or function into its frequency spectrum content for sake of signal analysis , synthesis and filtering.
Best wishes
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Hi everyone.
There's a MEMS sensor which has an accelerometer giving tri-axial angular velocity and linear acceleration. I wanted to measure the total kinetic energy which has 2 parts: translational and rotational part. K=0.5*m*V^2+0.5*Iw^2. I have the omegas (w) so I can measure the rotating part but I need to estimate the Eulerian framework velocities to get the translating part of the kinetic energy. I think I need to convert the Lagrangian acceleration values to Eulerian framework then with a filter (or an integral) Eulerain velocity can be estimated.
I'd be appreciate it if anyone could help me with the route to estimating velocity (Eulerian) to have the translational part for Kinetic energy.
Thanks
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Dear George Dishman Thanks for consideration.
but this lecture couldn't help since it's about introducing the Lagrangian and Eulerian derivatives. the thing is I need to change the reference frame from body reference to inertial. there are some methods like Quaternion, Euler angles,...
but I need an expert view on it if there's a need to use any filters and how to handle drift issue, noise and these stuff
thanks
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Please share me any information, links or papers about this subject.
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Raw Data or Annotation File Format
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If yes how it is done?
and what details we can obtain using this method?
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You may find it helpful to distinguish between "dream" as content and "dreaming" as process. At this point recording techniques show only the correlates of the dreaming process. The distinction between dream and dreaming is well established.
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In today’s world using technology, we can get signals of the brain. the question is can we process these signals and obtain meaningful content? (for example, when the target brain is thinking to a number we show that number in a computer)
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Not yet, but we can right now define normal from abnormal brain response
We can also define ill brain tissue responsible of excessive discharge for example for epilepsy and other neurological disease
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Different people have different biological properties like DNA ,finger prints, iris and so on.
We’re looking for a mechanism that can identify people according to their cerebral frequencies and nervous system.
Is it possible to recognize these differences?
And do you know about unique parameters of the brain frequencies?
Have you ever read something about it?
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I am not a person in this field, but I guess this could be a very good criteria for the person identification together with other established parameters. There are several waves which individually or and in combination should be used as a candidate to evaluate and set up as a benchmark for your aim.