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# Spectral Analysis - Science topic

Explore the latest questions and answers in Spectral Analysis, and find Spectral Analysis experts.

Questions related to Spectral Analysis

The question is what are the final goals of doing spectral analysis and what can we infer from that?

I intend to use 4 half-degree aeromagnetic sheets for geothermal analysis with the intention of a 10' by 10' block size. But while I was going through some materials as references and citations, I got to realize the window size has to be large enough to capture the depth of the magnetic source.

I anticipate favourable answers and replies.

Thank you.

I am looking at local field potentials and brain oscillations in vitro and am wondering if the power spectral analysis can be performed in clampfit. Most manuscripts report using a custom script in Matlab concurrent with pClamp softwares. I am curious if all analysis can be performed in Clampfit.

Data need the transformation first. I do not know if this can be done.

Any help would be greatly appreciated.

I would want to know the fundamental difference between eigenvalues and singular values when applied to spectral analysis of graphs' adjacency and laplacian. As far as I know the SVDs can be worked on nonsquared matrices but adjacency and laplacians are squared matrices and they would be symmetric if the graph is undirected.

We will be very grateful for a discussion, experience sharing, possible experimental challenges for the accurate and reliable photoluminescence (PL) investigation of

**2D TMDC layers/films****(WSe**using Spectrofluorometer with a monochromatic light source (Xe arc lamp)._{2})We have performed PL spectral analysis of various WSe

_{2}films (mono- and multilayered) on different substrates (c-cut sapphire, fused silica, SiO_{2}, Si).PL properties of WSe

_{2}(and other semiconducting TMDC) are extensively investigated with a strong thickness dependence and usually broad peaks structure. The majority of the PL studies are based on a laser source configuration, while the PL analysis of TMDC**thin films**using Spectrofluorometer are very rare.However, our acquired spectrum (for all studied thin film samples) is untypical and consists of single very narrow peak at 754nm(1.64eV) with linewidth ~ 10meV at λexc = 500nm.

The samples are produced by Chemical Vapour Deposition and Thermal Assisted Conversion.

Thank you!

I'm doing spectral analysis of a signal. I compute psd.

In order to get better results and good accuracy, i use windowing.

I was wondering how to choose number of blocks in the window.

Let's assume that i'm dealing with 15000 data points, and i choose hamming window.

in the script, i write:

ham=hamming(number_of_blocks); %number_of_blocks=100? 1000?

Thank you in advance

Spector and Grant 1970 have shown that spectral analysis can be used for depth estimations of causative anomalies located at different depths. They have used aeromagnetic data. Is it valid the same formula for gravity anomalies or does it need a correction? I have searched many articles in which at least 50 % were implemented wrong. Some of the articles used half of the slopes as the depth some used 1/4pi of the slope. It is actually quite confusing how people use and misuse this method. The decay of gravity anomaly is less than magnetic so I am wondering if gravity needs any correction or derivative before using the spectral analysis for finding depths.

Different cascaded fiber structures such as single-multi-single (SMS) mode, multi-single-multi (MSM) etc. have unique spectral features due to the multimode interference (MMI) phenomena. The transmission spectrum depends on the sensing fiber length i.e., SMF and MMF for MSM and SMS, respectively at the time of fabrication (ignoring the effect of external perturbations).

How does the group dispersion phenomenon affect these spectral features?

The spectrum of one of such cascaded fiber structures (here MSM) has been attached for the reference that occurs due to the MMI phenomena.

#fiber #SMS #MSM #Groupdispersion #opticalfiber

We had a question in this article. How did they calculate a single band value representing all 64 electrodes to compare between-subject groups? Usually, a specific band (i.e., alpha) should have 64 pieces of data towards corresponding 64 electrodes.

The table shows that each participant have a single rest data for one specific band, and then research use independent t-test to compare between two groups.

Can any experts help us to understand this?

Reference:

Ding, Y., Cao, Y., Qu, Q., & Duffy, V. G. (2020). An exploratory study using electroencephalography (EEG) to measure the smartphone user experience in the short term.

*International Journal of Human–Computer Interaction*,*36*(11), 1008-1021.Zhepeng

Our research team met one question on calculating EEG relative power and absolute power at this stage.

When we integrated all negative and positive amplitude/power data in five EEG bands (delta, theta, alpha, beta, gamma), a few relative power results became huge (i.e., 440%(44.44) or even over 1000%). We thought these values were abnormal results. The reason is that the integration result of five EEG bands with negative and positive power values could be 1 or 2 as the denominator, but the numerator could be very large for the integration of one specific band(i.e., delta). The relative power calculation is (sum of spectral power in the band)/(sum of spectral in all bands)

The attached image showed some negative and positive spectral power values.

Therefore, we would like to ask whether we need first to transfer negative value to absolute value to consider relative power or absolute power. Normally, the relative power should be around 0-100%.

Can experts help us? Could experts please share some references with us?

The general consensus about the brain and various neuroimaging studies suggest that brain states indicate variable entropy levels for different conditions. On the other hand, entropy is an increasing phenomenon in nature from the thermodynamical point of view and biological systems contradict this law for various reasons. This can be also thought of as the transformation of energy from one form to another. This situation makes me think about the possibility of the existence of distinct energy forms in the brain. Briefly, I would like to ask;

Could we find a representation for the different forms of energy rather than the classical power spectral approach? For example, useful energy, useless energy, reserved energy, and so on.

If you find my question ridiculous, please don't answer, I am just looking for some philosophical perspective on the nature of the brain.

Thanks in advance.

Hello everyone! I'm doing a study based on comparing spectral indices from Sentinel-2/MSI and Landsat-8/OLI data. The Sentinel-2/MSI - Level-1C are provided on TOA reflectance data, and Level-2A on BOA reflectance data. However, Landsat-8/OLI data are provided only on BOA reflectance through Collection 2, Level 2. Considering that I have to convert the Landsat-8/OLI Collection 2, Level 1 data to TOA reflectance, do I need to do another type of pre-processing in all other data?

Is there any automated peak finder program for mineral identification with Raman data ?

Or any data set as peak library or spectral library in this field.

I know some websites that have some data but I need a program software or a good data set as library for mineral identification.

Thanks every body.

We can measure exchangeable K, Ca using flame photometer. Is it possible to analyse these ion using spectral analysis

Hello dear Researchers,
I need guidance related to derampdemod. working with single SLC_IW data, after applying derampdemod signal encountered with stripes.
https://sentinels.copernicus.eu/documents/247904/1653442/Sentinel-1-TOPS-SLC_Deramping.pdf/b041f20f-e820-46b7-a3ed-af36b8eb7fa0?t=1533744826000 1
Anyone who worked with this process is given in the above link, actually, I need to produce figure.2. spectrum after derampdemod.
I have attached one intensity image and after derampdemod I encountered stripes.
Looking forward to your advice.

I am looking for a software which can take a txt file of a spectra obtained from an OES device, detect the peaks, and qualify the element/atom that is associated to the peak with some probability. Does anyone have experience with any such software?

I am trying to compare a time series data by these two methods: Holo-Hilbert spectral analysis and Hilbert-Huang transformation.

I regularly doing ATR-FTIR analysis from several mineral oil and surfactant, lately i discover a strange and unknown peak appear (1000-1300 cm-1) which is not belong to the material absorbance (i'm pretty sure because I have previous measured spectra from the same sample).

In the attachment, I put Fig 1 as the comparison of previous measurement and the current spectra.

I did a background scan and follow with scanning empty sample, and I can get straight line without any obvious noises. (fig 2)

Is there anyone having the same issue or know what happen with the spectra result?

In the other hand, i found this file (Fig 3) appear in my storage folder which I don't know where its coming from.

Is there any relation between this file and the issue in my FTIR spectra?

Kindly advice, thanks!

nb: I use Perkin Elmer FTIR

I want to compare the experimental measurement of the spectrum at river confluence with the theoretical model. Can we apply von Karman spectral model for river flows, particularly at the shear layer? Please suggest other models, if any, with references.

**Hi I'm looking for some mineral and rock spectra repositories to use in the interpretation of remote sensing images and spectral mapping for mineral prospecting and research.**

Typical positive congo red spectral assay for binding to amyloids would be an observation of spectral shift from 498nm (CR only) to 540nm (in presence of amyloids). However I've tried the both the spectral assay and the birefringence assay (http://www.assay-protocol.com/biochemistry/protein-fibrils/the-congo-red-birefringence-assay) with an amyloid sequence AB(27-32) peptide that forms amyloid fibers, the spectra I've got does not shift to 540nm, there was only an increase in absorbance at 498nm. Under polarized light microscopy the fibers do appear to be apple green and birefringent after staining with Congo Red though. What is wrong? Or is it normal to get an increase in intensity of the spectra instead of spectral shift?

I have downloaded SDSS specra data of the dwarf galaxies for my study. I am studying strongest emission lines. I am bit confused, whether we need to perform a baseline correction beofore the measurement or not. Need your help. Thanks.

I am interested to perform spectral analysis of a structure under random waves. could anyone suggest me a book or an example that starts from wave spectrum (such as

**, P-M etc) to RAO. A complete example from formulation to numerical evaluation.**

*JONSWAP spectrum*Dear All!

Is there a software in which I will make NMR prediction of compounds in deuterated acetontrile, acetone or methanol ? In mestrenova I can make only predictions in chloroform, dmso or water.

Thank you so much for your help!

Is there any information about refraction indices and extinction coefficients for some types of Stainless Steel?

I need information concerning the penetration and reflection capacity of ultraviolet and infrared radiation wavelengths on different most common materials.

Edit.

Can somebody recommend a book to learn about? I'm especially interested in spectrogroscopy with a city environment materials and albedo.

In several discussions, I have often come across a question on the 'mathematical meaning of the various signal processing techniques' such as Fourier transform, short-term fourier transform, stockwell transform, wavelet transform, etc. - as to what is the real reason for choosing one technique over the other for certain applications.

Apparently, the ability of these techniques to overcome the shortcomings of each other in terms of time-frequency resolution, noise immunity, etc. is not the perfect answer.

I would like to know the opinion of experts in this field.

Hi,

I am working on an echo removal project. So far, I have successfully identified the far-end signal of length 21 ms at a sampling rate of 48000Hz whose echo is present in my near-end signal of 21ms. I did it using Echo Detection and Delay Estimation using a Pattern Recognition Approach and Cepstral Correlation .

Now, I want to remove that

**far-end echoed signal**from my**near-end signal**which contains(echoed signal of farend and voice).Things I tried:

- Time-domain subtraction of PCM signals. i.e output[n] = near_end[n] - far_end[n]
- Spectral Subtraction technique Eliminate Signal A from Signal B. Even Ephraim-Malah

In both, I am not getting the expected result as for spectral subtraction I read that

**It works well when there is static noise or one signal is stationary**. For non-stationary signals, it doesn't work well.What are the other techniques to remove the echo in my scenario? Since I have identified the far-end chunk whose echo is present in the near end chunk, I just want to remove it from near end chunk.

I was reading "Time-Series Anomaly Detection Service at Microsoft" (https://arxiv.org/pdf/1906.03821.pdf) in these days, and I got some problems for the programming part.

The first picture shows the algorithm, the general idea is to perform the fast fourier transform for a time series sequence, calculate the spectral residual and perform inverse fast fourier transform at the end. When I checked the official code of this paper, before performing the inverse fast fourier transform, the transformed signal ('trans' in the code) was multiplied by the spectral residual and then divided by its amplitude (line 212 - 215 in the second picture) which is confused. If someone can explain about this part? Thanks.

We are trying to map land cover classes on a watershed. We have selected training sites (during a field campaign in early 2017) and extracted their spectral profiles based on a Landsat 8 image acquired at the time of field surveying.

In order to assess the land cover changes, we wanted to map the same cover classes at a previous year. Since our training sites might not be relevant, we wanted to perform supervised classification using endmembers spectra instead of ROIs. When importing those spectra inside ENVI's Endmember Collection toolbox, it appears that only Spectral Angle Mapper and Spectral Information Divergence classifiers could be used. Common algorithms such as Maximum Likelihood or Mahalanobis distance fail, returning the following error message :

Problem: the selected algorithm requires that the collected endmember spectra all contain an associated covariance. ENVI is unable to continue because some of the endmembers collected to not have their covariance.

Could anyone help here ? Actually is our method relevant ? How can we possibly perform supervised classification using Maximum Likelihood/ Mahalanobis classifiers on some older satellite images ?

I am working with the spectral irradiance on different planes. The readings are available on a global horizontal and plane of the array of 45 degrees. I have a PV module inclined vertically and I want to apply the transpositional model to calculate the spectral irradiance at a vertical plane. I am looking for a reliable model, which I haven't come across, unfortunately.

I believe, spectral albedo would be the deciding factor on these models, even more so compared to the broadband albedo. Can someone suggest any spectral irradiance transpositional models? That'd be a great help.

I have observed that there are usually a dip in the spectral ordinate (Sa/g) at short period (<0.1s) for the ground motions obtained using stochastic simulation (SMSIM). I have attached a figure highlighting this. The same can be observed for the spectra obtained using some GMPEs (which are developed using synthetic ground motions).

However, I have seen this dip to be absent in the median spectra (Sa/g) of horizontal component obtained using recorded ground motions or the spectra obtained using GMPEs (which are developed using recorded ground motions).

I believe this dip is due to spectral De-amplification for which is higher in medium/stiff soil site and covered in SMSIM. What is the reason that this dip is not observed in recorded data?

However, this may also lead to inconsistency in GMPEs for the same region generated using real ground motions and synthetic ground motions.

I am particularly new in this area and would really appreciate some answers.

I want to plot the lower and upper confidence with the spectral density plot to find the significance of the peaks. Any help would be appreciated.

It's how to to convert spectral radiance from W/cm^2/sr/nm to W/cm^2/sr/cm-1. Fisrt one is the radiance represented by wavelength and second one is represented by wavenumber.

If it possibility to take Metal doped nano SnO

_{2. }Please guide me for measurementsSpectra collected during chemo metric experiments like tit-ration stop flow reaction etc

I use Spectral Kurtosis and Kurtogram to study the turbulence of financial markets. I would like your advice concerning the intuition behind Spectral Kurtosis and Kurtogram.

- Financial Crisis

- Kurtogram

- Spectral Kurtosis

In estimating the texture distribution of asphalt mixtures, i have managed to use the discrete fourier transform on my idealized surface profile and obtained my spectral power density.

However the process of Transforming constant bandwidth spectral data to constant-percentage

bandwidth spectral data has proved challenging i would appreciate assistance in understanding and progressing from my current stage.

I have attached the excel sheet and an image of the formulas from ISO 13473-4

I know there are free current satellites such as Landsat, sentinel and planet. but i want to know whats the new satellites which provide free images with high resolution.

According to the articles, we use fourier transform to compute power spetra density(psd) in spectral analysis ,

Sm = psd = lim {2|Ym|^2 /( N * delta) }, m=1,2,3,..., N / 2

psd is a function of frequency ( Fm = m / N),

In the logarithmic graph, Fm is expressed in terms of Sm.

Sm ~ Fm ^(- Beta) ---> log Sm ~ log Fm^(- Beta) ---> Beta (spectral exponent) = - log Sm / log Fm

the relation of spectral exponent with fractal dimension :

Beta = 5 - 2 D ----> D = (Beta - 5) / 2

for example : 0 < Beta < 1 ---> 0 < 5 - 2 D < 1--> 2 < D < 2.5

Answer range for fractal dimension(FD) : 2 < D < 2.5

I want to know what is the direct impact of the fractal dimension on the analysis of the Rossler system?

In other words, How can I connect this answer range(FD) to the analytical solution of Rossler system?

Does anybody know a free online UV-Visible spectra database for inorganic salts, e.g. NaNO3, NaNO2, NH4Cl, K3PO4?

I hope there are some databases for UV/Vis spectra where raw data could be downloaded. Like this, Fourier Transform Infrared (FTIR) Reference Spectra https://www3.epa.gov/ttn/emc/ftir/refnam.html

Hi i am a Medical Physics PhD student, im interested in Radiolysis and production of free radicals, i am very curious is to collect information is there any possibility to measure the free radicals, singlet oxygen in vivo and vitro. My idea is to measure the spectral changes.I would appreciate suggestions or possibilities if any.

Is there any standard procedure/sequence of tools to process the hyperspectral tabular data before PLSR regression modeling.

example of tools are 1) De-resolve 2) second derivative 3)normalize 4) de-trending 5)baseline etc.

Application is for field spectroradiometer data of soil and crop.

Or the sequence of tools differ for different datasets ?

Cavity modes are a consequence of

*constructive interference*of EM waves in a particular region. It is common knowledge that the (frequency) linewidth of a cavity resonance is broadened by both homogeneous (energy loss/damping) and inhomogeneous (non-uniform environment) effects. The linewidth due to homogeneous damping is inversely proportional to the lifetime of the cavity mode. I believe that 'integral intensity' of the resonance uniquely defines how much energy is within the mode, and that the integral intensity and the linewidth in combination uniquely define the amplitude of the mode.I am curious about resonances of

*destructive interference.*They can also be characterized by a linewidth, integral intensity, and an amplitude. However, in this case, the resonance corresponds to the absence of EM modes at a particular frequency.**What do linewidth, integral intensity, and amplitude indicate for a resonance of destructive interference?**It should be pretty similar, because both cavity modes and destructive interference resonances are the consequence of interference, the only difference is whether the interference is constructive or destructive.Maybe there are some texts of spectral analysis or characterization of resonances that could help clarify this?

Thanks!

-Ryan

I have send my extracts for GCMS analysis. The problem is I don't really know how to classify it. I am also not good in chemistry. I have read papers and researchers always group their components. Do I need to go through each compounds one by one and identify whether this one fall under what group?

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.

I am looking to make an Echinochrome A calibration curve for microplate visual spectral analysis of S. purpuratus coelomic fluid.

We want to show the surface changes to PET and PLA plastics (in pulverized form) as a proof of the UV treatment. From what I understand ATR is useful for spectral analysis of a solid, but I read that it cannot be used for "hard" polymers/powders. Are PET and PLA compatible with this method or is there something else I should look into?

I am new to NMR analysis.

What I wish to do is an untargeted approach (i.e. no compound identification), using standardized spectral binning.

This should be possible and in theory pretty straight forward in the CHENOMX software.

However, the values in the output file seems to be based on both the above and below baseline 'peaks' , I upload here an example which I hope illustrates the issue.

What am I doing wrong? I will greatly appreciate any feedback to this question.

We are currently trying to perform spectral analysis on blood pressure with matlab. The device we used(Caretaker, Biopac) provides time position and amplitude of the peak according to the PDA model. The inter-peak interval was calculated by subtracting the time position of successive peak.

However, the interval between peaks were not uniform. We found that the process called interpolation should be performed before using FFT. So, instead of using FFT, we tried Lomb-Scargle periodogram(Reference: https://www.mathworks.com/help/signal/examples/spectral-analysis-of-nonuniformly-sampled-signals.html). The problem is, we don’t know how to calculate the blood pressure variability of interested bandwidth based on Lomb-Scargle periodogram. As a quick fix, we used trapz function on matlab to integrate the area under the curve of interested bandwidth.

But we are not sure about the validity of analyzing process. Is it OK to use trapz function on matlab to calculate the BPV of interested bandwidth on Lomb-Scargle periodogram?

I want to perform a WRF simulation, but I am unsure whether I should spinup as I am also doing nudging. So I have these questions basically.

1. Should I do model spinup if I am doing nudging along with it?

2. Several studies show different spinup time. What will be the optimal spinup period?

3. Whether spectral or analysis nudging is best for simulating precipitation in a very complex terrain?

Dear all, I would like to do the following:

Calculate the heat source due to the absorption of radiation within a layer that absorbs radiation. I would like to solve that at a spectral level. Meaning that the absorption coefficient of the material will be wavelength dependent. At the same time, I would like to fix a source light with an spectral distribution. The idea is to use FEM approach.

To start simple, I would like to release a density of rays perpendicular to the material and account for the reflection at the interface due to different refractive indexes. After that calculate the heat source to to absorption. After that, I would like to add a stack of layers with different materials (refractive indexes) and calculate the heat source in each of the layers while the light travels through the different mediums. Once I achieve this, I would like to calculate the transference of heat (by conduction, convection and radiation).

I was thinking in COMSOL, although I do not find it intuitive and I am having trouble using the "Ray Heating" interface. Any one can make recommendations for software using FEM simulations or knows how to approach the optical-thermal model in COMSOL?

Best wishes!

Dear all,

Let me briefly go through the problem I am facing.

Currently, I have data ( of ground acceleration) obtained from the "seismic accelerograph instrument system" which was placed at the basement of the building and the plot is shown below. According to the plot, it is showing a random waveform up to a certain time and it starts decaying (damping occurs). However, it again gets another waveform (sinusoidal, as shown in the figure) after 300 sec. It looks very unusual to me. I suspect the sinusoidal part to be a building response. But I couldn't decide whether my assumption is valid or not.

So, my questions are:

- Is there anything (books/journals/published or unpublished thesis/lecture notes) that talks about the limitations of the time period which we are supposed to make while plotting the ground motion data?
- Is there any specific guidelines or any thumb-rule to determine whether the certain waveform is coming from the earthquake motion or is a building response? Normally, what I do is- I consider the random waveform as an "earthquake response" and a sinusoidal waveform as a "building response". Is it the correct way or is there another way we need to look at?
- My confusion arises when I saw a portion of "sinusoidal" wave before there is damping. In the figure, it is shown under the "orange" box. So, is it acceptable if I make a statement like-
**the presence of sinusoidal wave along with the random wave is due to the fact that the sensors recorded the both "earthquake and building response" at a time?** - If No, how can it be justified? If Yes, how do I correct this problem?

Thank you so much.

I would like to calculate the monthly average of the representative solar spectrum for each month based on minute spectral irradiance measurements. Can I consider this hypothesis? Has anyone made this assumption?

Hi everyone,

Usually finding the sample position in bright field is easy but when we change the microscop to the spectroscopy setting, my sample got lost. Does anybody has a solution for this problem?

Thanks alot for your help

should the reflectance data be resampled to a sensor?

I have examined two compounds band gap having same pyrochlore type structure but one contained carbonate group in the vacant place of pyrochlore structure having negative band gap (-0.22 eV) but other one have no carbonate group with band gap 0.2 eV.

Why this compound exhibited negative band gap?

Could carbonate group influence the band gap?

The minimum number of RR intervals required for reproduce several metrics derived from HRV (e.g., HF power) can be found in the literature (Richards et al., 2010). However, what about PPG signals and the Pulse Rate Variability? Since PPG was proposed as a surrogate of ECG for the analysis of HRV, is there a minimum number of PP intervals required for reliable and accurate derivation of the PRV?

1) I would like to ask you about a criteria from your experience to help me decide on the values constituting the background noise that I should suppress from my signals while doing data analysis of data acquired via spectrometer (Intensity counts and wavelength). If I have 4/5 spectra I want to make them start from zero.

2) Is there a formula (set of formulas) that take (s) into account the dependency of the spectrum (wavelength drift) with respect to frequency, voltage and temperature ?

Thank you very kindly and in advance for your contributions.

Where could I find a MATLAB code for estimating the PRV-high frequency (0.04 - 0.15 Hz) power from a PPG signal? Thanks in advance!

proper tool for converting the spectral data and ocr tool for it and if any case studies regarding it

I am working in the field of metabolomics, we don't have any licensed software, I tried even the MNOVA free trial software. Kindly suggest me any other free software for NMR data analysis in metabolomic research.

I'm trying to use Mettler Toledo's IC Quant software to generate calibration models for the reaction components. I run my reactions at high temperature and pressure, since I cannot collect the reaction standards needed for the calibration at the reaction temperature, I prepare the reaction standards first by running the reactions to different conversions of my limiting reagent (so that I have different concentrations for the components). I then collect the spectrum of these reaction standards at room temperature and use these spectra and the measured GC-FID concentrations for multivariate data analysis.

Now the problem is, the absorptions are becoming less intense with increasing temperature. Hence when I try to apply the calibration model (built using reaction standards collected at 25 C) to the real-time reaction spectra collected at the reaction temperature of 140 C, I see a significant offset in the predicted concentrations from that of its actual value (the predicted concentration have negative values). I also notice that the temperature dependence is linear in the range that I tested (25 - 140 C). I'd like to to know if there is a standard procedure to apply the temperature correction to the spectra collected at a different temperature in real-time to get accurate predictions for concentrations.

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.

can anybody please help me determine how i can obtain the zonal acceleration from reference PGA of 0.323 for class site B?

I made a response spectrum analysis in Ansys Workbench and I would need to know the linearized stress components for further validation, but only the Von-Mieses stresses are available in solution toolbar.

Has someone solution for this? Maybe user defined results, PRSECT command, or export the results of RS analysis to structural one?

I am trying with these, but I could not figured it out yet...

Thanks!

Jozsi

Hi

I want to know the exact functional groups for my FTIR Spectrum , the peaks were located at 1048,1073,1239,1256,1276,1333,1428,1554,1638,2854,2926,2961 &3421

I have the data of total dissolved soilds of apple as references (y-variable).

I also have near-infared spectra data as predictors (x-variables).

I have the

*StatSoft Statistica*software for the analysis.I have been scanning UV-Vis spectra on a Genesys 10S & have saved the *.ss data files onto a USB. However, my computer is unable to open them. The VisionLite 5 software from Thermo Scientific costs around $700, so is not really an option for me...

Could someone help me - which software do I need to read lsjpg/lsjpi file formats? This seems to be a proprietary format produced by thermal imaging cameras.

I can't find much information about them on the internet and its proving hard work tracing the equipment/camera used to produce them as well.

Any guidance with an existing program or methodology to decipher them would be most helpful.