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Consequently, from expressions (10.A.33), (10.A.35), and (10.A.36), we have gg1 = Then,
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where the subscript 0 indicates that this is the 0th iteration. We have
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Here is the base implementation class for all database-specific classes:
Figure 45-2: SQL Server Management Studio Metadata tab The developer of a cube may choose to group dimension hierarchies into folders, also shown in Figure 45-2. Folders provide a handy way to organize long lists of hierarchies and have no effect on the structure of the cube or how MDX is written.
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Locking resources in relational databases means the database administrators or application developers must decide whether to lock an attribute, a tuple, or a complete table (i.e. relation). Since the basic data model for ontologies is much richer (especially due to hierarchical relationships between concepts, between relations, and between instances and concepts) the decision of what a lock entails is more complex. The most simple answer would be to lock the complete ontology with all its components. But this solution is ruled out since it would disallow any kind of concurrency and distributed collaboration. Another simple answer would be to lock the resources that are to be modi ed within a transaction; for example, the resource X in the transaction that states that concept X has a super-concept Y. Apparently, for this transaction, concept Y should also be locked since a new sub-concept for Y is de ned. Thus, the second simple approach seems to lock too few resources. Due to hierarchical relationships between concepts locking a concept X implies read-locks for all super-concepts of X and all their super-concepts, recursively. A read-lock marks a resource as being read-only, that is, modi cations to it are currently disallowed. If a read-lock for at least one superconcept cannot be achieved X will not be locked and the BOT fails. Thus, no operations may modify X. Read-locks can be available to multiple clients at the same time without con ict. If a client is the only one that read-locked a resource, the client can achieve a stricter (write) lock. Other clients cannot. The reason why a lock propagates from one resource to another in the ontology can be seen in the following example scenario. Assume, X is a sub-concept of Y and Y has a slot A with range Y. Assume, we want to restrict the value range of A for X from Y to X. Thus, in the BOT we just lock the concept X and call the appropriate operation on X. Before we send the commit
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(a) If x(n) has unity variance (Rxx (0) = 1), calculate Rxx (1), Rxx (2) and Rxx (3). (b) Use the Levinson Durbin algorithm (see [Haykin 1991] or [http://ccrma.stanford.edu/ jos/lattice/Levinson_Durbin_algorithm.html ] to calculate the predictor coef cients a (p) for j = 1, . . . , p and the prediction error V (p) for p = 3. 10. A sequence {xn } has been formed as an MA (Moving Average) sequence by using a stationary white random sequence un according to x(n) = u(n) + u(n 1) with zero-mean and unity variance. (a) Determine the autocorrelations RXX (0), RXX (1) and RXX (2). (2) (2) (b) Determine the optimum predictor coef cients a1 and a2 and the associated error measure (2) 2 of a second order MEV predictor (p = 2). (c) In an application, x(n) is to be predicted using only a rst-order predictor (p = 1). Deter(l) mine the predictor coef cient a1 and the associated error measure (l) in this case. 11. Considering narrowband speech representation (fs = 8000 Hz), human beings are known to produce typically 1 formant per kHz, resulting in 4 formants (or poles) to code. With this knowledge, we would normally choose a linear prediction lter of order 8, which is capable of modeling all the formants. Despite that, typically 10 lter coef cients are used to describe the LP lters. Give an explanation. 12. To make linear prediction analysis adapt to the quasi-stationary speech source, the autocorrelations from stochastic linear prediction can be replaced by short-time estimates of the ACF, calculated over samples inside a window starting at sample index m
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Dip coating is another low-cost solution deposition technique that is commonly used in the semiconductor industry, mainly to coat irregular and complex shapes. In this process (Fig. 2.7b),8 the complex part or substrate is immersed into the coating solution and the lm is formed by either removing the substrate vertically from the solution or draining the solution from the reservoir at a constant rate. Dip coating can be performed employing either continuous or batch processing but entails the same key stages: immersion, withdrawal, lm formation, solvent evaporation, and continued drainage of the excess solution. The physics behind lm formation in the dip-coating process involves a balance among the viscous drag, gravitational forces, and surface tension in the concavely shaped meniscus.89,90 Various factors affecting the thickness of the lm formed are reviewed by Brinker and Scherer.1 According to Brinker and Hurd,89 the thickness of the lm formed is determined by
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