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For this technique to work there has to be a clear division between the clean files and the dirty files; if both types occupy the same space you would not be able to distinguish between them. For example, if a clean file is in the range of 0 to 50 and a dirty file is in the range of 100 to 150, then they occupy different space and it is easy to detect them. If both clean and dirty files have values in the range of 0 to 50, then they occupy the same space and hidden data is hard to detect. By analyzing the graph in Figure 8.16 you can see that when you calculate the average there is a clear division between clean and dirty files. Any value above 175 indicates that a file has data embedded in it, and any value below that indicates a clean file. Looking at the skew graph in Figure 8.18 and the differential values graph in Figure 8.21 you will notice some overlap, but you cannot determine with 100 percent accuracy whether a given file has data embedded in it. With Figure 8.21 if a value is above 150, then you know the file is dirty or contains hidden information. If a file has a value below 50, then you know it is clean and it does not contain hidden information. With variation, kurtosis, and average deviation graphs, there is no distinct break point, so these are not good tests for universal stego detection.

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Data Mining Methods and Models naturally ts the role of textbook for an introductory course in data mining. Instructors will appreciate the following: r The presentation of data mining as a process r The white-box approach, emphasizing an understanding of the underlying algorithmic structures: Algorithm walk-throughs Application of the algorithms to large data sets exercises Hands-on analysis r The logical presentation, owing naturally from the CRISP DM standard process and the set of data mining tasks r The detailed case study, bringing together many of the lessons learned from both Data Mining Methods and Models and Discovering Knowledge in Data r The companion Web site, providing the array of resources for adopters detailed above Data Mining Methods and Models is appropriate for advanced undergraduateor graduate-level courses. Some calculus is assumed in a few of the chapters, but the gist of the development can be understood without it. An introductory statistics course would be nice but is not required. No computer programming or database expertise is required.

This application is available on the companion CD-ROM. You ll find two versions on the CD: one for Excel 2000 (using OWC 9.0), and one for Excel 2002 (using OWC 10.0).

230 Acquisition and Tracking of GNSS Signals processing of the two receivers This is possible because the two receivers are not too far from each other and given the fact that the same satellite is considered for both receivers (see Fig 726 for details) The double difference is the difference of simple differences, for two satellites It allows the clock bias of the receivers to be removed because the differences are carried out at the same instant for the two satellites (this aspect is fundamental; see Fig 727 for details) The triple difference (see Fig 728) allows the removal of the integer ambiguities common to the preceding measurements (refer to the previous section for details), where only the changing cycles are accounted for.

This is achievable by considering the difference of two double differences, at two successive measurement instants (this is the basis of the carrier phase measurement technique) Furthermore, as one knows that the propagation errors are at least two orders of magnitude higher than the accuracy being searched for (around 1 m where one is seeking centimeter accuracy), there is the need for a method to eliminate these effects thoroughly This can only be achieved by a local differential method, such as the one described above for troposphere effects,27 and by the use of a dualfrequency method for removing the ionosphere effects In summary, it is possible to state that code phase based measurements allow an accuracy in the few meters range and are a compromise of basic techniques, both physical measurements and acceptable modeling.

When dealing with more accurate positioning, there is the need for advanced methods, both in terms of measurement techniques and also physical methods, in order to remove all error factors28 Another interesting approach of local and regional augmentation systems concerns the high accuracy carrier phase measurement networks intended to provide professionals with a high density of reference base stations, allowing centimeter accuracy positioning measurements in real time, over large areas Such networks are based on speci c so-called kinematic techniques and are available in many countries They are usually operated by private companies that offer differential local accurate services29 The four main techniques are as follows: The Static Geodesic Mode This consists in having xed geodesic receivers carrying out carrier phase measurement and resolving ambiguity The positioning is relative and requires enough time in order to solve the ambiguity.

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