Spectral Analysis - Science topic

Frequency spectrum of seismograms contain important information and depend on properties of the source (initiation), ray pathes (rock properties) and properties of receiver/recording equipment. If we know or can specify some of them we can deconvolve the seismogram and use spectrum to estimate the unknown parameters.

Dear Isaiah Adelere,

I understand that your question does not have a unique answer, and it is made in geographical units ("...I intend to use 4 half-degree aeromagnetic sheets for geothermal analysis with the intention of a 10' by 10' block size...."). I will try to help with the following explanation:

First of all, you must put in geological/structural/tectonic context, it means: if your scenario is related to a relatively high heat flow regime, the window size should be small enough to estimate the Curie point depth (CPD) because it is expected to be shallow. In contrast, if your geological scenario is considered a low heat flow, it should be large enough to estimate the deeper CPDs. It means several window sizes you must test, and the appropriate length must be selected based on the criteria of the minimal block size that does not cut off the spectral peak (Ravat et al., 2007).

On the other hand: if you have land, airborne, or satellite data, the filtering effect of anomalies could be critical, also the spectral answers. Therefore, the question is: Which is your data source for your research? Satellite magnetic data complement the existing local and regional data sets by providing a globally unified data set. It means: the local structures below the minimum wavelength content cannot be resolved in such a data set, therefore: you must choose the window size for FFT and CDPs estimation according to or in the function of your dataset.

The methodology that I recommend using to estimate Curie point depth is founded on the 2-D radial average power spectral (RAPS) analysis of magnetic anomaly data described basically by three foundational works:

The input dataset of a magnetic anomaly - the emphasis for land research - must reduce the influence:

The depth estimation from your RAPS advises you that the optimal square window dimension is about 10 times the estimated depth (Chiozzi et al., 2005). Thus, you should divide your work area/zone of interest (in km/m units) into square subareas of such as for example 300 km x 300 km to say you an accurate value, each of them overlapping with respect to each other in a step increment, for example, 100 km, and then applying the 2-D FFT RAPS method individually to get Zo and Zt. The computation requires an extensive dataset for estimating Curie point depth.

Best regards, Mario E. Sigismondi

Yeah, I have seen a majority of papers use MatLab after data acquisition. Thank you for response. I am less familiar with the MatLab coding language and software use. I guess its the next thing I need to learn!

Thanks again, Dr. Bjorkland

please just check wiki:

singular value decomposition ... generalizes the eigen decomposition of a square normal matrix ... to any m-by-n matrix

Hi,

Firstly the noise which is coming in your sample is due to scattering (lamp phenomena). Secondly sometimes lamp intensity is not so high that can excite and gives you emission. Is this peak matches one with the literature or laser based pl system.

Thank u

Good evening Aziz,

As a start point, you may use Hinze et al., 2013, Chapter 7; Gravity anomaly interpretation; pages 192 and193 and the references therein (I have attached it in a message).

I hope this would helpful,

Best regards,

Hayder

Thank you so much Jawher Makhlouf for sharing the article. I will go through it.

The authors write this: "A digital FFT (fast fourier transform) based power spectrum analysis (Welch technique, Hamming windowing function) was used to compute PSD average value of EEG with NFFT = 1024, window = 512 and 50% overlapping window. An average absolute power value of each electrode for each frequency band was calculated. An average of the pre-experimental absolute power was used to determine the individual power during rest. The power of the rest period was set as the baseline. From this reference power value, individual power changes during smartphone use were determined as the relative stimulus-related change. The relative power of each band was calculated for statistics (e.g. α/(sum of the powers from all five bands))."

From my experience and from what I can see is written, this means that they calculated the average power for all electrodes in a given band. This simplifies the analysis but looses a lot of information on location, for example, of any differences detected.

Hi Zhepeng Rui ,

First it is better to calculate the density of the power spectrum (PSD) of the signals and continue the calculations based on it. The value of the PSD is usually not negative. Delta values are usually higher than other frequency bands. Consider starting the Delta wave frequency range from 0.1 or 0.01 Hz and not from zero. If you have collected signals from several samples and the average PSD of the frequency bands in the different samples is very different, you must first normalize the PSD values calculated from each sample in each frequency band. To do this, first calculate the PSD of the data collected from each sample. Then calculate its standard deviation in each frequency band. Divide the mean values for each frequency band by its standard deviation. In this way the data is normalized. You can now use these values to calculate relative power.

Best regards

Hi,

The mitochondrion in cells is a powerhouse of energy. There are some articles on the topics of your interest:

Jeffery KJ, Rovelli C. Transitions in Brain Evolution: Space, Time and Entropy. Trends Neurosci. 2020;43(7):467-474. doi:10.1016/j.tins.2020.04.008

Lynn CW, Cornblath EJ, Papadopoulos L, Bertolero MA, Bassett DS. Broken detailed balance and entropy production in the human brain. Proc Natl Acad Sci U S A. 2021;118(47):e2109889118. doi:10.1073/pnas.2109889118

Carhart-Harris RL. The entropic brain - revisited. Neuropharmacology. 2018;142:167-178. doi:10.1016/j.neuropharm.2018.03.010

Sen B, Chu SH, Parhi KK. Ranking Regions, Edges and Classifying Tasks in Functional Brain Graphs by Sub-Graph Entropy. Sci Rep. 2019;9(1):7628. Published 2019 May 20. doi:10.1038/s41598-019-44103-8

Tobore TO. On Energy Efficiency and the Brain's Resistance to Change: The Neurological Evolution of Dogmatism and Close-Mindedness. Psychol Rep. 2019;122(6):2406-2416. doi:10.1177/0033294118792670

Raichle ME, Gusnard DA. Appraising the brain's energy budget. Proc Natl Acad Sci U S A. 2002;99(16):10237-10239. doi:10.1073/pnas.172399499

Matafome P, Seiça R. The Role of Brain in Energy Balance. Adv Neurobiol. 2017;19:33-48. doi:10.1007/978-3-319-63260-5_2

Engl E, Attwell D. Non-signalling energy use in the brain. J Physiol. 2015;593(16):3417-3429. doi:10.1113/jphysiol.2014.282517

Kang J, Jeong SO, Pae C, Park HJ. Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics. Hum Brain Mapp. 2021;42(11):3411-3428. doi:10.1002/hbm.25442

You can use surface reflectance datasets for both, Sentinel and Landsat-8, which area already atmospherically corrected.

Google Earth Engine Snippet:

Sentinel image Snippet: ee.ImageCollection("COPERNICUS/S2_SR")

Lt 8 image Snippet:ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")

In order to compare these two datasets using spectral indices, as a preprocessing step, you have to harmonize them. You can read more about this process in these articles.

A GEE tutorial that might help.

Good luck.

The best software and database is "Know-it-all" but it is not free.

Here the collection of resources wich could be used to analyse Raman spectrs it include mention early RRUFF an many more: https://www.researchgate.net/post/Free-Database-with-Raman-spectra

Dear Ashar Ahmad

You can measure the exchangeable cations using Ammonium Molebdate solution under spectroscopy.

Roland Akiki Muhammad Amjad Iqbal Hi Roland and Amjad,

Would you please let me know how can I apply de-ramping and re-ramping on S1 data? I have access to GAMMA and ESA SNAP, but couldn't find any module to re-ramp the data—Is there any open-source or freely available matlab/python code for these two processes?

Many thanks,

With free optical spectroscopy software http://Spectragryph.com, you can load your txt file(s), activate "Peak labels", choose "LIBS" label type and have peak labels with element names shown automatically. With the "Peaks & FHWM" function, you can create peaks lists with peak positions and element names, also for a batch of spectra at once.

Repeat the attached screenshot search and follow the details. Best wishes David Booth

The logical thing is to think that the sample has changed (it has reacted, degraded, etc ...) Knowing what sample it is would help to see what may be the cause of this spectrum change ...

I am not familiar with Von Karman model use in Rivers, or even wind turbulence. River confluences can be very complex, and actually for rivers with floodplains, the confluence location may shift for flows below bankfull, and above bankfull. And braided streams might even have multiple confluences shifting with flow, and channel shifting due to excessive sediment, woody or organic debris accumulations, etc. Whether HEC RAS or Rivermorph models might be helpful to your questions, I don’t know. My advice if you want to collect data would be to purchase or find assistance from water scientist, agency or university that has a Doppler flow meter, that is able to collect flow depth, velocity details as it is pulled across the specific cross sections of interest. In relatively clear rivers, Green LiDAR might also be helpful in remotely collecting some channel morphology and associated data. During drought or dry periods, LiDAR might also be helpful in revealing some exposed channel complexities, although the LiDAR pulses are absorbed by water.

USGS High Resolution Spectral Library https://www.usgs.gov/centers/gggsc/science/usgs-high-resolution-spectral-library

JPL Spectral Library https://speclib.jpl.nasa.gov/documents/jpl_desc

Also see http://www.planetary.brown.edu/pds/other.html

Also had such problem. I use Congo Red for studying amyloids in a biofilm matrix. With comparable visualization of the staining density, I found that 540 nm works best for me, no more, no less.

Yes, in order to measure the line flux (and subsequently - luminosity) you need to subtract the continuum. There is a simple explanation for this. Imagine that your galaxy has 10 times more stars but the same SFR. Then the continuum level will go up by a factor of 10, so if you don't subtract it you would measure SFR 10 times higher, which would be wrong.

By the way, check the SDSS catalogues - maybe your galaxy already has the H alpha flux measurement.

For JONSWAP, please check:

https://www.researchgate.net/publication/339272195_Development_of_submarine_cable_hydrodynamic_loads_calculation_program_using_MATLAB

For simple PM, please check pages 6 to 8:

Mrs/Miss Haraźna,

Do you know the reliability of these so called predictions of NMR spectra?

There is a plenty of software for prediction of mass spectra as well. However, a comparative analysis with experiment shows a dramatic lack of accuracy between theory and experiment.

Such software are very useful to only educational purpose. Because of, all important and really observed both NMR and mass spectrometric phenomena are unable to be accounted. Thus, the so-call predicted spectra produce very illustratively the fundamental basick knowledge.

An additional comment on: RG represent forum for exchange of knowledge at a highly specialized professional level. Very frequently, many participants are unable to distinguish between highly specialized technical information and low specialized information of general or popular character. The latter one is typical for the public press. Owing to the fact that the comments on RG are inaccessible to the mass reader or to the communities as whole, this means that RG does not represent forum for distribution of knowledge at a general public level.

Not sure if it has stainless steel, but a good reference to have saved nonetheless.

First of all it depends to the material studied, and for me i guess that IR is more better then UV cause its wavelength is bigger than UV.

Utkarsh Singh There is an esthetic reason in why a mathematical method is of interest in signal processing:

-a beautiful algorithm is well articulated, says what it does in few instructions, and does it in a stable and reliable manner

-this hints to the underlying algebra

With powerful and minimal computation, we go deep into algebra structures: group, rings, fields (see references on Evariste Galois as the inventor of "group" as we know it)

-Fourier transform is an interesting invention: it allows to decompose a signal into resonating modes (as for piano music: you produce a sound at frequency F, but also its harmonic NxF...). Naturally there is the aliasing question and the Nyquist theorem for reconstruction

There are many more time-frequency representations: Fourier, Laplace, discrete or continuous, cosine transform, wavelet transform, etc.

The interesting feature of discrete algorithms for those transforms is that you can implement a butterfly structure.

The key idea is to replace a very large number of multiplications (in brute force "non-esthetic" programming) by a smaller number of additions.

This idea worked for me for developing a codec system using underlying GF(n) properties.

See this patent:

The regularity in the processing and the efficiency of the representation go hand in hand.

Let me go back to a very basic mathematical method: the Gram-Schmidt decomposition: take a sequence of n vectors v(1),..., v(n), and the matrix of cross-products m(i,j)=<v((i),v(j)>. The Gram-Schmidt method diagonalises this matrix. It extracts eigenvalues, and eigen vectors. In frequency terms, it extracts modes (resonating modes present in the signal).

This algorithm highlights the efficiency side of the representation: it's projecting the signal onto something found "in itself", call it principal components if you want.

There are only two reasons for choosing a technique in engineering:

-(i) it addresses the problem completely

-(ii)it's economically implementable.

Both criteria are equally important and a good way to find these is to look for elegant, esthetic solutions (minimal and complete at the same time).

Does it help?

Isam Alkhalifawi Sir, these adaptive echo cancellation techniques don't help me in my scenario. My echo is non-stationary and the same sound is being produced with delay by multiple speakers, not just 1 speaker.

I suggest you follow

Roland Yonaba Aline de Matos Valerio Roland questioned himself as to whether the method is relevant. It is not. Methods such as SVM or Mahalanobis distance or Random Forest rely on a data distribution, thus the covariance that is derived from the many pixels in a region of interest taken for training. With the endmembers, you have only one example of each endmember spectrum. SAM and other methods can work with the endmembers because they are just calculating a distance in spectral space between each image pixel spectrum and the endmember spectrum.

Chris Gueymard Thanks for the suggestion. Yes, I have been thinking to use SMARTS. But I was also curious to know if there are any other transpositional models that transform the spectral irradiance from global horizontal to tilted plane.

Hi Ravi

I would recommend to start checking the frequency band over which you perform the SMSIM simulation. This kind of short period trough could be due to high-pass filtering - or absence of HF computation - at frequencies around 5-20 Hz. Have a look at the Fourier spectra of your SMSIM acceleration time series and compare it with the expected typical shape (log-log scale).

Do you have similar troughs whatever the site conditions ? What is the "kappa" value you consider ?

Hoping it will help, all the best

https://dsp.stackexchange.com/questions/53717/how-to-make-a-power-spectral-density-plot-in-r

these two units do not have a linear scaling relationship. rather, this depends on wavelength. wavenumber ν [cm-1] relates to wavelength λ [nm] as v = 10^7/λ. when integrating the same radiances (in their appropriate units) over the same finite spectral interval (in either wavenumber or wavelength), the answer has to be the same. from the differential dv/dλ = -10^7/λ^2, it follows that dv = -10^7/λ^2 dλ, and therefore [W/cm^2/sr/cm^-1] = [W/cm^2/sr/nm] * 10^7 / λ^2.

Yes, we have synthesized the SnO2 nanoparticles by chemical co-precipitation method. we have made the FL life time measurement to study effect solvents and calcination temperature on life time of carriers of synthesized sample. Refer our article "Strctural and Optical properties of Zirconium Oxide nanopaticles : effect of calcination temperature " which we have published recently in Nano Express. We will publish articles on life measurement on SnO2 nanoparticles soon.

Best of luck.

First of all what is the purpose of the programme? I mean, what is the subject?

If you study about Astronomy, I suggest iSpec analysis programme.

This can be another choise for astronomical use --> https://hesperia.gsfc.nasa.gov/rhessi3/software/spectroscopy/spectral-analysis-software/index.html

I have made somce applications in finance. Could you help for comments and discussion on my results ?

the formula sheet

Try with ASGS website..

Dear Truman Prevatt

Thanks for the files, I hope leads me to better understanding.

You can try spectroscopy Ninja. it is open source https://www.effemm2.de

You can use some chemical probes such as APF (aminophenyl fluorescein) or SOSG (singlet oxygen sensor green). You measure probes' fluorescence before and at the end of the irradiation. Fluorescence enhancement will indicate ROS or signlet oxygen generation.

The pre-treatments you use will depend on the sample set: noise, level(s) of analyte, number of samples, ranges, etc. In many cases, it is a case of start small (Absorbance data) and add treatments, as needed. Just remember, every treatment can hide/generate peaks and will have an effect on sensitivity.

Dear Researcher,

Your question is interesting. Unfortunately I do not know the answer but I will follow your question and if you find something interesting, I attach it.

I'm also struggling the interpretation of my GC-MS results. that's the problem of not a chemist.

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.

Hi Jessica,

You may make an inquiry at Alfa Chemistry, they offer kinds of chemicals for research use.

https://www.alfa-chemistry.com/inquiry

Neither of those polymers are too hard to obtain good ATR spectra. If you do the experiments carefully and run control samples you should be able to see the chemistry changes. There are a few things to keep in mind. A diamond ATR will allow you to apply a lot more pressure to the sample and using a high pressure clamp will help. Depending on the amount of treatment, you may need to be careful of the penetration depth. If the ATR element you use gets too deep a penetration, the bulk material may "wash out" the spectrum of the treatment. And if you are planning to compare to library spectra make sure you take into account (or correct for) the wavelength dependent depth of penetration. I would strongly suggest reading this

https://www.piketech.com/files/pdfs/PIKE_ATR_Theory-Applications.pdf

as primer on ATR.

..hmm..conceptionally you are right, but what would happen to signals so small that they remain completely below the baseline (see e.g. duplett of duplett (dd) and singulett (s) left and right of your signal of interest - if you were interested in those)...I'd regard such a approach "dangerous" - and would recommend properly processed spectra!

Alfred

First what are you measuring. BP has two componets, systolic and diastolic. What have you got? where is the cuff that measures blood pressure. IMO you need to look at where you get your data before you do any supermathematics. Then you might want consult an expert in chemometrics. I would suggest: Dr. Peter Harington, Chemistry and Biochemistry Dept, OHIO University, USA. Google this name to get full contact information and the ask his advice or perhaps even invite him to work with you. You may mention my name. Good luck, David Booth

If the length of the simulations are only one month, the only spin-up that needs to be accounted for is the atmospheric one, which is typically only a few days. If I understand well, you are nesting two domains, the first one at 27km resolution (which is about the same as ERA5), and the second one at around 9km. In this situation, I would suggest to use 5 days spin-up in the beginning of each nested integration (so adding up both spin-up would lead to 10 days), and then begin the one-month simulation.

Maybe you could find the following paper of interest :

@Maria Ignacia Devoto,

Using MATLAB, https://www.mathworks.com/help/pde/ug/basics-of-the-finite-element-method.html

There is free software which will do this, as well as adjustment and filtering, for you.

From my point of view, it can be considered, but the end result will be inaccurate and approximate because it will depend on many variables as Professor Abdelhalim Zekry mentioned it above,

For more details please take a look at the attached files

https://arxiv.org/pdf/1412.3935.pdf

http://www.soda-pro.com/web-services/meteo-data/monthly-means-solar-irradiance-temperature-relative-humidity

https://hal-mines-paristech.archives-ouvertes.fr/hal-01676634/file/2018_basics_solaire_wald_v1.pdf

https://hal.archives-ouvertes.fr/hal-00295214/document

Hello Elmira:

I recommend you to use gridded glas coverslips. You can search Google for "gridded glass coverslips" to find the type and brand that suits you.

By asd device you will get .asd file and you can convert that ascii asd file to txt file with help of viewspcpro software. Then import that data into excel sheet.then you can calculate any SVI.

When it comes to the determination of an absolute valence of the band gap, the usefulness of these Tauc plots is very limited. You can use it for a qualitative study, in your case maybe something like "X has a band gap lower than Y", but keep in mind that, depending in the quality of your data and also on what you know about your material, you may introduced an error of 200-300meV easily.

As Jürgen said, use other quantitative methods to substantiate your result. Spectroscopic ellipsometry and modelling is usually working well for me.

There is no difference between the number of PP and RR intervals to perform short-term HRV analysis. Both types of information represent essentially the same process - time intervals between heart contractions. As to the number of intervals needed for the analysis, it depends on what HRV parameters you want to calculate. For frequency-domain analysis, it would be more accurate to determine the length of recording in seconds rather than the number of intervals due to the nature of spectral analysis (assumed that FFT analysis will be used). It should be enough to record PP intervals for 5 minutes to estimate HF and LF. But it would be better to record intervals for 10 or more minutes to reliably estimate VLF.

Use lasers and an a monochromatic and control the signal aliasing ( frequency sampling) .Examples

http://www.eas.uccs.edu/~mwickert/ece2610/lecture_notes/ece2610_chap4.pdf

http://www.umsl.edu/~orglab/documents/IR/IR2.html

Matlab

https://www.mathworks.com/help/signal/examples/practical-introduction-to-frequency-domain-analysis.html

Hi

The methodology (code) is not different from HRV analyses having RR-interval from ECG.

fs = 4;

time = (Rtimes(1):1/fs:Rtimes(end));

IBIint = interp1(Rtimes,IBIs,time','spline')';

y = detrend(IBIint, 'linear'); % you can apply different detrending methods

window = min(300*fs, length(y));

noverlap = window/2;

nfft = max(256,2^nextpow2(window));

if rem(nfft, 2) == 0

DFT = (nfft/2)+1;

else

DFT = (nfft+1)/2;

end

% you can decide which DFT you want for example DFT = 0:0.001:2;

% for AR metod

order = 20; % you can use different order

[Pyy, f] = pburg(y,order,DFT,fs, 'onesided');

% for FFT method

[Pyy, f] = pwelch(y,window,noverlap,DFT,fs,'PSD','onesided');

VLF_ab = sum(Pyy(f >= 0.003 & f < 0.04));

LF_ab = sum(Pyy(f >= 0.04 & f < 0.15));

HF_ab = sum(Pyy(f >= 0.15 & f < 0.4));

Dear Mohammend,

Nowadays there are many softwares to get a good spectrum.But the softwares used in the spectrometers themselves are the best. You should get academic version of the softwares to manipulate your datas. Usually the academic version is free.

ACDLabs used to have a free academic edition. I use it for standard NMR data analysis. Though I have no idea what metabolomics you can give it a shot (attached here)

A physical law is always good but I thing that an empirical correlation that you found under your experimental conditions with your specific materials is better. When I do FTIR-ATR experiments I prefer my calibrations over external ones like PNNL and I would prefer using it also over theoretical ones (but maybe that's just me)... good luck

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.

Interesting

In my opinion when response spectrum analysis is used, the problem of evaluating Von Mises stresse or perming linearization is tricky. The stress tensor at a point resulting from RS analysis has components which (1) do not occur simultaneously and (2) have lost a physical sign. So folks...

Following

Different related software's can be used for building ANN predictive model using NIR spectra. My suggestion are Unscrambler and IBM modeler. Before ANN modeling, PCA should be done to reduce large spectra variables to PC1, PC2 .... . after calibrating ANN, you have predicted values by calibrated model and you have also reference values for each sample. Now use RPD index to realizing the goodness of your calibrated model.

Try "Spectragryph" from Spectroscopy Ninja. The software accepts 61 file formats. https://www.effemm2.de/