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SMO has been designed to be easier to use and more ef cient than its predecessor DMO (Distributed Management Objects). For example, when returning the collection of databases for a server, SMO will only return the name and the schema for the databases on the server. DMO would return the fully instantiated collections of every database on that server, taking time to both populate that entire collection and increase the network bandwidth required to pass it over the wire. Before using SMO within PowerShell, the SMO assembly must be loaded into the environment. If PowerShell is started from within SQL Server Management Studio or from SQL Server Agent, the sqlps.exe program is run, not basic PowerShell. Sqlps.exe is discussed later in this chapter. When running the native PowerShell environment interactively, the following commands are required:

. . .M

You can import the MySQLdb module as follows:

Classically, the angular momentum of a particle can be written as l r p. (Section 1.6.2). From this classical expression, we can write down the classical components of the vector l: lx ypz zpx ly zpx xpz lz xpy ypx (E.62)

Another extension of step 5 consists of adding a second modality to the processing tree, to obtain dynamic data fusion. Here we use the IntensityEdges modality, described in Section 4.4.1, which for feature-level matching is implemented in the following way: 1. IntensityEdges::preProcess. Perform edge detection of the image. We will use a combination of the Sobel lter and the Canny edge map, so that edges are localized with both position and orientation information. 2. IntensityEdges::sampleModelFeatures. Sample uniformly distributed contour points from the visible model contours, retaining their object coordinates for subsequent re-projection. This is executed only once per frame, at the average pose predicted. 3. IntensityEdges::matchFeatureLevel. Re-project and match model points to the preprocessed edges by searching along the normals of the respective edges, up to a distance given by the validation gate. Here, we can also choose whether to restrict to search to the nearest neighbor only, yielding a single association hypothesis, or to keep multiple hypotheses within the validation gate and use a non-Gaussian likelihood, for more robustness. In OpenTL, contour sampling is done on the GPU and implemented into the lower-level class opentl::modelprojection::ContourSampler, which may be shared by different contour-based modalities such as CCD.

$ sed s/test/trial/w test data5 This is a trial line. This is a different line. $ cat test This is a trial line. $

data value may look different from other values in the variable, the data may, in fact, be correct, so it is important to consult with an expert. Problems can also arise when data from multiple sources is integrated and inconsistencies are introduced. Different sources may have values for the same variables; however, the values may have been recorded using different units of measurement and hence must be standardized to a single unit of measurement. Different sources of data may contain the same observation. Where the same observation has the same values for all variables, removing one of the observations is the most straightforward approach. Where the observations have different values, choosing which observation to keep is more challenging and best decided by someone who is able to assess the most trusted source. Other common problems when dealing with integrated data concern assessing how up-to-date the observations are and whether the quality is the same across different sources of data. Where observations are taken from different sources, retaining information on the source for future reference is prudent.

Also, in some cases a service provider network may contain multiple ASs, hence requiring the use of interprovider mechanisms.

(4.5-6)

and nally, we de ne the Hu moments: I1 = 20 + 02 I 2 = ( 20 02 ) + ( 2 11 )

1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 Ver = 1 Diagnostic State P F C A D R My Discriminator Your Discriminator Desired min Tx Interval Required min Rx Interval Required min Echo Rx Interval Optionally, authentication field may present: 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 Authentication Type Authentication Length Authentication Data ... ... Detect Multi Length