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First, we define some terms. Aprocess produces outcomes, which are completely specified. How these arise need not be well understood, which is the case for humans producing a text. However, ambiguous outcomes are not allowed. For example, a person typing produces an identifiable sequence of characters, but someone writing longhand can create unreadable output, which is not a process in the above sense. For processes, certain groups of outcomes are of interest, which are called events. For example, if a coin is flipped five times, getting exactly one head is an event, and this consists of the five outcomes HTTTT, THTTT, TTHTT, TTTHT, and TTTTH, where H stands for heads and T for tails. Notice that each of these sequences completely specifies the result of the process. The probability of an event is a number that is greater than or equal to zero and less than or equal to one. For a simple process, probabilities can be computed exactly. For example, the probability of exactly one head in five flips of a fair coin is 5/32. However, because language is complex and ever changing, probabilities for text events are always approximate, and our interest is in computing empirical probabilities, which are estimates based on data. For the first example, we estimate the empirical probability of heads from the results of computer-simulated coin flips. The simplest method for doing this is to count the number of events and divide by the number of results. Let n (1 be the function that returns the number of times its argument occurs, then equation 4.2 summarizes the above description. P(event) = n(events) n (results) (4.2)

a very deterministic nature. For such systems, Jackson queueing networks might not be the most appropriate modelling tool. Instead, a modelling method known as the Queueing Network Analyzer (QNA) might be applied in such cases. The QNA method, as developed by Kuhn and later extended by Whitt, allows for quick analyses of large open queueing networks with fixed routing probabilities and FCFS scheduling. Its most important characteristic, though, is that it treats queueing networks in which the arrival processes need not be Poisson, and the service-time distribution need not be exponential. With QNA all arrival processes are assumed to be renewal processes characterised by their first two moments, i.e., successive arrivals are still independent of one another. Similarly, all service time distributions are characterised by the first two moments; in particular, constant services times can be dealt with, albeit approximately. Besides that, the QNA method also allows for the merging and splitting of customers, as well as for multiple customer classes. In its simplest form, it reduces to an exact analysis of Jackson queueing networks as we have seen in Section 10.3. With QNA the computational complexity is linearly dependent on the number of queueing stations. Once the traffic equations have been solved, all queueing stations can be studied individually. This advantage, however, comes not without costs; the decomposition of the overall queueing network into a number of individual queueing stations is approximate. We will discuss how good this approximation is. This section is further organised as follows. The considered queueing network class is introduced in section 10.4.1. The computational method is discussed in Section 10.4.2 and a summary of the involved approximation steps is given in Section 10.4.3.

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We are asked to construct an amplifier that will reduce a very large input voltage (i.e. , Yin ranges between 680 V) to a small output voltage in the range 'FS V. Using only two resistors, we wish to design the best possible amplifier.

How to configure the History panel That creating a snapshot preserves the current state of the image to be used later How to use the History Brush and Eraser tools to paint a previous history state onto a later state How to use the Action panel to save lots of time when editing images That Photoshop provides several built-in actions How to create and organize custom actions

efore things go too far here, let s make sure everyone s clear on the terminology. To fill a selection or a layer is to put color inside it; to stroke a selection or a layer is to put color around it. You can fill a layer or a selection or shape; you place a stroke around that filled area. Of course, you may prefer the term outline to stroke, but an outline is typically a border, a line placed around something to encase it, like a fence or a wall, to delineate it from the stuff surrounding it. Outlines are solid, uniform, and one thinks of them as being straight lines with sharp corners. A stroke, on the other hand, is more like color and/or a texture (depending on the nature of the stroke) that envelopes content, conforming to its shape rather than confining it. Further, stroke is also the PostScript term, and given the history of graphics and desktop publishing and their ties to the PostScript world, stroke also became (and remains) the term Photoshop uses, so for clarity s sake, we ll use it. This chapter will teach you to fill selections using keyboard shortcuts, how to create interesting framing effects, how to make the most of Photoshop s gradient options, and how to add an arrowhead to a curving line all in addition to the really basic things every Photoshop user needs to know. Has that filled you with excitement Then let s get started.

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