Data extraction algorithms

Data extraction algorithms developed for the evaluation of the real and the imaginary part of the complex refractive index - that is, of n and k - are based on various assumptions such as homogeneity and flat and parallel surfaces of the sample, dry atmosphere at the measurement, and orthogonal incidence of the THz rays. Details of this have been described elsewhere [51-54]. According to Pupeza et a. [51], the decrease in the maximum signal amplitude is given by Equation 2.11 ... 

The database is updated on an annual basis with new data extracted from the literature. This database is also the foundation of data supporting the prediction algorithms that are required to predict NMR spectral properties for chemical structures not contained within the database. 

Computer-aided automation of process design requires a whole new approach to part manufacture and assembly planning. This centres on the decision logics, formulae, algorithms and data extractions. This approach is labelled as the Generative Method [4]. Examples of such procedures are Decision Trees, Decision Tables, Axiomatic and Expert systems. Regardless of their approach they remain complex in nature and this led to the specific applications, such as those in sheet metal fabrications and electronics manufacture, where parameters are more... 

For the analysis and interpretation of the experimental results, in the first instance the software should perform a pre-processing step of raw data, extracting the required number of complete cycles and, optionally, the sequence of evenly-spaced interpolated points. Successively, the software gives forth the relevant Fourier analysis. The adopted numerical procedure is based on the above-mentioned discrete Fourier transformation (DFT) algorithm (i.e., the summation over all points in Eq. (31)). As shown in the previous paragraph, the algorithm allows any number n and any particular values of periods NA / n (frequencies,/ ) to be selected, among all the possible discrete values, for the analysis of observed data. 

The final stage of the algorithm involves data processing and data extraction from the maps which are compiled. In the course of this stage, the integrated base is created it contains all initial quantitative and qualitative information and also provides for the input of new data. For example, the overlay of the land use map on the map of physical landscapes allows one to obtain information about human-caused impacts on the land itself as well as ecological problems. 

In a series of papers, personnel from Novartis and the University of Basel in Switzerland have highlighted the pros and cons of neural networks for immediate release tablets [37-40]. In other studies neural networks have been found useful in modeling tablet formulations of antacids [41], plant extracts [42], theophylline [43], and diltiazem [44]. In a recent paper Lindberg and Colbourn [45] have used neural networks, genetic algorithms, and neurofuzzy to successfully analyze historical data from three different immediate-release tablet formulations. 

A large variety of algorithms is available for the extraction of latent vectors from rectangular data tables and from square symmetric matrices. We only discuss very briefly a few of these. 

To illustrate the application of the proposed algorithm the agrochemicals problem already presented in Chapter 4 of this textbook is revisited. It involves a completely batch operation wherein reusable water is generated from liquid-liquid extraction (product washing) operations with water as the aqueous phase in the production of three agrochemicals A, B and D. The data for the production of these products are shown in Table 5.1. These agrochemicals are produced in batch reactors. All three reactions form sodium chloride (NaCl) as a byproduct which is later removed from... 

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