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If the signal transition between repeater Ri and Rj is very fast (that is, dt is very small), double differences of DS!A, t, and Dprop ( rst, second, and third terms in the above equation respectively) will be negligible and the double pseudo range difference will nally be reduced to Dji Dd dj 2 di. At the end of each cycle we have the following system: D12 d1 d2 D23 d2 d3 D34 d3 d4 D41 d4 d1 : Note that the sum of rst three equations gives the last one. This is the reason only three differences are retained from the four: for example the rst three. Expressing distances di by the coordinates of receiver (xr, yr, zr) and those of repeaters Ri (xRi, yRi, zRi, i 1, . . . , 4), it follows that D12 d1 d2 q (xR1 xr )2 (yR1 yr )2 (zR1 zr )2 q (xR2 xr )2 (yR2 yr )2 (zR2 zr )2 D23 d2 d3 q (xR2 xr )2 (yR2 yr )2 (zR2 zr )2 q (xR3 xr )2 (yR3 yr )2 (zR3 zr )2 D34 d3 d4 q (xR3 xr )2 (yR3 yr )2 (zR3 zr )2 q (xR4 xr )2 (yR4 yr )2 (zR4 zr )2 D41 d4 d1 q (xR4 xr )2 (yR4 yr )2 (zR4 zr )2 q (xR1 xr )2 (yR1 yr )2 (zR1 zr )2 (10:12) (10:11)

The packaging of muscle-based foods is necessary to ensure that such products reach the consumer in a condition that satis es his or her demands on a number of levels, namely: nutrition, quality, safety, and convenience, as well as the ability to deliver a product shelf life that will endure the stresses of handling, transportation, storage, sale, and consumer contact. However, in order for such products to be truly commercially successful, consumer desires and demands must be addressed and met with respect to the sensory properties of such products, before other quality dimensions become relevant (Chambers and Bowers 1993). The three sensory properties by which consumers most readily judge meat quality are: appearance, texture, and avor (Liu et al. 1995). Each food product category presents its own unique challenges in this regard, and meat is no different. Unfortunately, fresh meat color is short-lived and surface discoloration that occurs during chilled and frozen storage is considered a sign of unwholesomeness and product deterioration (Faustman and Cassens 1990). The bright, cherry-red color of fresh beef is used by consumers as an indicator of meat quality (Cassens et al. 1988; Kennedy et al. 2004). In red meats, consumers relate the bright red color to freshness, while discriminating against meat that has turned brown in color (Hood and Riordan 1973; Morrissey et al. 1994). It is because of such sensory quality changes in fresh meat

two models have as similar a dimensionality as possible; that is, the threshold value is determined such that the two eigenspaces include the same amount of information on training samples. Compared with the Batch PCA, the proposed Chunk IPCA has slightly lower classi cation accuracy; however, the difference is not signi cant. This means that the proposed method ensures the quasi-optimality in the construction of eigenspace without keeping past training samples. The computation costs of the eigenspace update are estimated by measuring the CPU time using a MATLAB function. In Table 7.2b, the CPU time of Fixed Eigenspace corresponds to the time required to construct an initial feature space. As you can see in Table 7.2b, the computation costs of Chunk IPCA are signi cantly reduced against both Original IPCA and Batch PCA, especially for large data sets. However, for the Advertisement data set, Chunk IPCA has no advantage over Original IPCA, in which an intermediate eigenproblem is solved for every training sample. The main reason for this is that the computation costs of the eigen-axis selection could be expensive for largedimensional data because the large-scale matrix multiplication is needed many times for all eigen-axis candidates. However, this can be alleviated by introducing a more sophisticated search algorithm (e.g., binary search) in the eigen-axis selection. The experimental results here suggest that the proposed method possesses excellent scalability unless the number of attributes is too large.

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For the lower bound, let S be a random variable with a binomial distribution with parameters n and p. The most likely value of S is S = np . This can easily be veri ed from the fact that P (S = i + 1) n i p = P (S = i) i+11 p (11.47)

This example exposes an inconsistency in Excel s object model. To change the font size of header or footer text, you must use a string that contains a special formatting code. In the example above, &8 is the code for 8-point font. Ideally, there should be a Font object available for page headers and footers. To find out the other formatting codes available, consult the online help (or record a macro while you access the Page Setup dialog box). When testing BeforePrint event-handlers, you can save time (and paper) by previewing rather than actually printing.