Correlation algorithm

By choosing the proper correlation algorithm, it is possible to realise sensitive filters for other types of defects (e.g. corrosion). Fig. 5.2 shows an example for the suppression of signals which do not exhibit the expected defect stmcture (Two parallel white lines near upper central rim portion of Fig. 5.2). The largest improvement in SNR is obtained here by using the expression (ai ai+x /ai+yj), since for a gradiometric excitation, one expects the crack response to show two maxima (a, aj+x) with a minimum (a m) in the centre (see Fig. 5.3). 

Image analysis is an important aspect of many areas of science and engineering, and imaging will play an important role in characterizing self-assembled structures as well as in on-line process control. Development of effective noise identification and suppression, contrast enhancements, visualization, pattern recognition, and correlation algorithms should be co-opted where possible and adapted to the analysis of self-assembled structures. 

Shock E. L. and Helgeson H. C. (1988). Calculations of the thermodynamic and transport properties of aqueous species at hight pressures and temperatures Correlation algorithms for ionic species and equations of state predictions to 5Kb and 1000°C. Geochim. Cosmochim. Acta, 52 2009-2036. 

A technique, also known as fluctuation correlation spectroscopy, that uses a correlation algorithm to detect similarity of a fluctuating signal occurring over a period of time. This property aids in the kinetic characterization of fluctuations in light scattering, or other electromagnetic... 

MacCoss MJ, Wu CC, Liu H, Sadygov R, Yates JR 3rd. A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal Chem 2003 75 6912-6921. 

A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal. Chem. 

A method developed by Eng and Yates uses a MS/MS cross-correlation algorithm called SEQUEST . - In this method, an unknown protein is digested using trypsin and the resulting peptides are used to collect product MS-MS scans. These MS-MS scans are used to search a protein database for a match within the expected MS-MS data from the known tryptic peptides. 

This value is similar to the value of -509.3 J-K -mol reported by [76VAS/LYT]. It is very much lower than the value (-416.3 J-K -mor ) estimated by [88SHO/HEL] from a correlation algorithm based on ionic radius and charge. Additionally, the molar entropy of Zr is lower than that of the actinides U ... 

In principle, correlation at even shorter time-scales is possible by including the micro times of both modules in the calculation of the correlation function. The problem with this approach is that the micro time scales of the modules may be slightly different, and the macro time transitions may be shifted due to different transit times in the detectors, cables and TCSPC modules. Correcting all these effects is extremely difficult, yet not impossible. Suitable calibration and correlation algorithms were developed by S. Felekyan, R. Ktihuemuth, V. Kudryavtsev, C. Sandhagen, and C.A.M. Seidel, Universitat Dortmund. 

These systems have shown themselves to be capable of identifying raw materials which are variable enough to cause downstream processing problems even though the more sensitive but more specific traditional types of analyses have found the materials to be acceptable. Often this approach is taken further and a prediction model developed whereby a qualitative model, using conformity or correlation algorithms checks the identity (ID) then a quantitative model uses the same data to predict a value of actual physical parameters such as moisture, concentration, and even particle size [4]. 

Correlation algorithms have been applied to detect and locate the small discontinuities [5], An intrinsic limitation of this technique is the attenuation and dispersion of the reflected signal that can limit the maximum distance of wire fault detection and can affect the accuracy of fault location. 

Figure 6.3 Background estimation with a correlator algorithm designed to reduce the perturbation due to peaks. The top line is the original spectrum. The seven steps in arriving at the final estimate on the bottom line are illustrated. The vertical scale is logarithmic, with each spectrum displaced vertically for clarity. (Reprinted by courtesy of EG G ORTEC.)...

Figure 1 Initial Silage description of auto-correlation algorithm.

Johnson SR, Josephs JL, Claus B, Langish RA. Automated regional assignment of metabolic modification using cross correlation algorithms, maximum common substructure analysis, and MS/MS spectral libraries. Abstracts of Papers. Paper presented at the 232nd ACS National Meeting, San Francisco, CA, September, 10-14,2006. 

Surface Forces Apparatus. The experiments were carried out with a modified version of the Mk 3 SFA (Surforce, Santa Barbara, CA). 2 The instrument is equipped with a fully automated data acquisition and evaluation program based on the fast spectral correlation algorithm and is mounted in a box with precise temperature control. All experiments were carried out at 25 °C. 

The simple and robust nature of NNC and the linear natnre of its pattern-correlation algorithms mean that it is well suited to geometric parallelization. This is exploited within NNC/NVD DAISY to permit the system to scale seamlessly to tens of thousands of taxa, making optimal use of available computing resources. An appropriate microcluster hardware architecture for efficient geometric parallelization of DAISY and similar systems is discussed by O Neill et al. (2003). 

FIGURE 7.2 Operation of the NNC pattern correlation algorithm. The unknown U is compared with each training set image in turn. The identity of the unknown is the same as the training set image Tj for which the affinity a j is maximized. 

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