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In science and engineering, observations are usually not the quantities to be measured themselves but are related to these quantities. Throughout the book, it will be assumed that a mathematical model of this relation is available. More precisely, we will assume that this model is a known parametric function and that the quantities to be measured are parameters of this function. Under these assumptions, measurement is estimating parameters of mathematical models of observations. In Section 2.2, examples of purposes of estimating parameters of mathematical models are given and the notions of target parameters and nuisance parameters are introduced. The models presented in the examples are deterministic and exact. They describe observations as exact function values. The traditional parameter estimation methods introduced in Section 2.3 are based on the existence of such errorless observations. The most important difficulty with these methods is that errors in the measured parameters cannot be explained since errors in the observations are absent. This difficulty vanishes if statistical parameter estimation methods are used. These methods, introduced in Section 2.4, use an exact deterministic model for the expectations of the observations instead of for the observations themselves. The observations are modeled as sample values of stochastic variables that fluctuate around the expectations. Section 2.4 is concluded by a description of advantages of this statistical approach, especially the possibility to compute the errors in the measured parameters as a result of the fluctuations of the observations.

Image Adjust Invert is just about the only color mapping command that retains the rich diversity of color in an image. (The Hue/Saturation command also retains color diversity under specific conditions.) For example, if you apply the Invert command twice in a row, you arrive at your original image without any loss in quality. When you re working on a full-color image, the Invert command simply inverts the contents of each color channel. This means the command produces very different results when applied to RGB, Lab, and especially CMYK images. Color Plate 17-1 shows the results of inverting a single image in each of these modes. The RGB and Lab images share some similarities, but you ll find all kinds of subtle differences if you study the backgrounds and the basic colors of the faces. Inverting in CMYK is much different. Typically, the Invert command changes most pixels in a CMYK image to black. Except in rare instances such as in night scenes the black channel contains lots of light shades and few dark shades. So when you invert the channel, it becomes extremely dark. To reverse this effect, I inverted only the cyan, magenta, and yellow channels in the right example of Color Plate 17-1. (I did this by inverting the entire image and then going to the black channel Ctrl+4 and pressing Ctrl+I again.) Although this approach is preferable to inverting the black channel, it prevents the blacks in the hair and shadows from turning white (which would be the only portions even remotely light had I inverted the black channel as well).

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The Ethernet transceiver speci es the pinout for three signals, transmit, receive, and collision detect, whereas the IEEE 802.3 standard provides for an added signal of control out (which is not used). Although the pin assignments are such that a cable manufactured for either standard would work with the other standard s transceiver, it is not recommended due to differences used in signal grounding. Vampire taps may not be located any closer together than 2.5 meters with a maximum of 100 taps per network segment. Network segments can be combined with the use of repeaters to increase the overall combined network length to 2,500 meters. The characteristic impedance of 10BASE5 cable is 50 ohms.

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The output of the data model process is a series of tables, each of which contains keys and attributes. The normal output produces numerous tables, each with only a modicum of data. While there is nothing wrong (per se) with lots of little tables, there is a problem from a performance perspective. Consider the work the program must do to interconnect the tables dynamically, as shown in Figure 3-26. In Figure 3-26, a program goes into execution. First, one table is accessed, then another. To execute successfully, the program must jump around many tables. Each time the program jumps from one table to the next, I/O is consumed, in terms of both accessing the data and accessing the index to find the data. If only one or two programs had to pay the price of I/O, there would be no problem. But when all programs must pay a stiff price for I/O, performance in general suffers, and that is precisely what happens when many small tables, each containing a limited amount of data, are created as a physical design. A more rational approach is to physically merge some of the tables so that minimal I/O is consumed, as shown in Figure 3-27. Now the same program operates as before, only it needs much less I/O to accomplish the same task.

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