MATLAB spectrum

In all cases we have used mean spectrum as reference spectrum (other references were tested no reference, first spectrum, last spectrum but the mean spectrum is the one giving the better results). All calculations were made under Matlab 6.5 with home made functions based on Noda s theories. The negative correlations are shaded in all the two-dimensional plots. 

It makes no sense whatsoever to try to calculate the spectra of species that are known not to absorb, by applying the equation A=C+Y. Often C will be rank deficient and cannot be pseudo-inverted. Matlab will issue an error message that needs to be taken seriously. Even if C is not rank deficient, it is not reasonable to allow the calculation of the spectrum of a non-absorbing... 

We subtract the mean spectrum from each measured spectrum yp and as a result, the origin of the system of axes is moved into the mean. In the above example, it is into the plane of all spectral vectors. This is called meancentring. Mean-centring is numerically superior to subtraction of one particular spectrum, e.g. the first one. The Matlab program, Main MeanCenter, m, performs mean-centring on the titration data and displays the resulting curve in such a way that we see the zero us,3-component, i.e. the fact that the origin (+) lies in the (us ,i,us >2)-plane. 

Cross-relaxation rates and interproton distances in cyclo(Pro-Gly) from the full matrix analysis of NOESY spectrum recorded at Tm = 80 ms and T = 233 K. Cross-relaxation rates are obtained from the volumes shown in table 2 according to eq. (11) by Matlab (Mathworks Inc). Error limits were obtained from eq. (27) with Aa = 0.015 (table 2). 

Prior to analysis, the Raman shift axes of the spectra were calibrated using the Raman spectrum of 4-acetamidophenol. Pretreatment of the raw spectra, such as vector normalization and calculation of derivatives were done using Matlab (The Mathworks, Inc.) or OPUS (Bruker) software. OPUS NT software (Bruker, Ettlingen, Germany) was used to perform the HCA. The first derivatives of the spectra were used over the range from 380 cm-1 to 1700 cm-1. To calculate the distance matrix, Euclidean distances were used and for clustering, Ward s algorithm was applied [59]. 

Cross-correlation and spectral analysis have proven invaluable tools for quantifying the frequency dependent characteristics of the human subject. The cross-spectral density function, or cross-spectrum Sxyif), can be obtained from the random target x t) and random response y t) by taking the Fourier transform of the cross-correlation function Vxyir), that is, Sxyif) = Ffr yfr), or in the frequency domain via Sxy if) = X(/) y(/), or by a nonparametric system identification approach (e.g., spa.m in Matlab ). The cross-spectrum provides estimates of the relative amphtude (i.e., gain) and phase-lag at each frequency. Gain, phase, and remnant frequency response curves provide objective measures of pursuit... 

EasySpin The tools for isotropic CW-ESR in EasySpin apply to 5 = V2 species with arbitrary number of nuclei. Resonance fields are calculated exactly (no perturbation formulae). The magnetic field range is automatically determined. A least-squares fitting to an experimental spectrum can be made. The program can be downloaded at http //www.easyspin.org/. MatLab must be installed on the computer and is not provided with EasySpin. EasySpin is written and maintained by Dr. S. Stoll at the University of California, Davis. 

The power spectrum of the signal can be estimated using the pburg, pcov, pmcov, or pyulear functions in MATLAB. 

The square of Sin, a>) is often referred to as a scalogram, and it gives an estimate of the time-localized power spectrum. A MATLAB function that can be used to implement this procedure is detailed below ... 

The present authors have implemented a software tool called oplAnalyzer in MATLAB for MALDI-MS data preprocessing. To detect peaks in a mass spectrum, a search is made for locations of maximal value within a local m/z window, the size of which is 11 discrete sampUng points. This method is similar to another peak detection method [35]. For alignment, peak detection is applied to the mean spectra, analogous to the method of Karpievitch and others [34]. Subsequently, the peaks in an individual spectrum are aligned to this set of common peaks. For each common peak, its value in an individual spectrum is that of the closest detected peak in that spectrum if the distance between the common peak and the closest peak (in the m/z axis) is less than Vs Da. (A better choice is based on the actual mass accuracy of the measurement) If there is no such peak, then the m/z of the... 

In addition to commercial software products we also took advantage of custom designed software (MicrobeMS Lasch 2015) for the evaluation of our microbial mass spectra. MicrobeMS is Matlab-based and involves a specifically optimized peak detection routine. One of the key features of peak detection in MicrobeMS is a sigmoid intensity threshold function which was introduced to model the m/z dependence of the analytical sensitivity of MALDI-TOF MS. This threshold function defines intensity thresholds at each m/z value. In the MicrobeMS implementation, an intensity threshold at low m/z values is larger than at high m/z values. Another feature of the MicrobeMS peak detection routine allows to precisely define the number of resulting peaks per spectrum. This particular feature makes peak detection partially independent from the SNR which turned out to be extremely useful for subsequent classification analysis. 

NORSAR (Norway) and the University of Alicante (Spain) have developed a Matlab based tool named SELENA (Seismic Loss Estimation using a Logic Tree Approach) (Molina-Palacios Lindholm, 2006). SELENA, uses probabilistic or deterministic seismic hazard information and estimates the ground motion at a specific site using 1997 NEHRP provisions. The software computes building damages based on capacity-spectrum method of Hazus (1999). 

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