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4.6 EARLIEST DUE DATE In classic earliest due date EDD. w8, 9x scheduling, we assign each packet a deadline, and the scheduler serves packets in the order of their deadlines. If the scheduler is overcommitted, then some packets miss their deadlines. Obviously, with EDD, packets assigned deadlines closer to their arrival times receive a smaller delay than packets assigned deadlines farther away from their arrival times. Delay EDD w9x is an extension of EDD that specifies the process by which the scheduler assigns deadlines to packets. A delay-EDD scheduler reserves bandwidth at a session s peak rate. The scheduler sets a packet s deadline to the time at which it should be sent had it been received no faster than its peak rate. Thus, every packet from a session obeying the peak-rate constraint receives a hard delay bound, which is independent of its bandwidth reservation. Therefore, delay EDD separates the bandwidth and delay bounds, but at the cost of using peak-rate allocation, giving up temporal statistical multiplexing gain. Jitter EDD w10x extends delay EDD to provide a delay-jitter bound i.e., a bound on the maximum delay difference between two packets.. Jitter EDD incorporates a delay-EDD scheduler preceded by a delay-jitter regulator. After a packet has been served at each server, a field in its header is stamped with the difference between its deadline and the actual finishing time. A regulator at the entrance of the next server holds the packet for this period before it is made eligible to be scheduled. A scheduler implementing delay-jitter regulation can remove the effect of queuing delay variability in the previous node, thus avoiding the burstiness n n built up within the network. More precisely, if a k and e k are the arrival and eligibility times for the kth packet at the nth node, respectively, then
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Theorem C.4 Let the real symmetric M x M matrix V be positive definite and let P be a real M x N matrix. Then, the N x N matrix PTVP is real symmetric and positive semidefinite. The matrix PTVP is positive definite if and only if P is nonsingular:
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In Figure 3-3, draw lines connecting the corresponding terms.
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Eyedroppers Brightness bar Curve tools Figure 17-21: The Curves dialog box lets you distribute brightness values by drawing curves on a graph.
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The CramCr-Rao inequality (4.108) states that the difference of cov ( ~ ( w )~(w)) , , which is the covariance matrix of any unbiased estimator ~ ( wof the functions p(B), and the ) Cram&-Rao lower bound matrix is a positive semidefinite matrix. We have seen that the diagonal elements of such a matrix are nonnegative. This implies that any diagonal element of cov ( ~ ( w )~, ( w )is larger than or equal to the corresponding diagonal element of the ) CramCr-Rao lower bound matrix. However, the diagonal elements of the covariance matrix are the variances of the estimators T L ( W ) , e = 1,. . . , L. Therefore, any of these variances is larger than or equal to the corresponding diagonal element of the Cramtr-Rao lower bound matrix. This implies that no unbiased estimator can be found that is more precise than a hypothetical unbiased estimator that attains the CramCr-Rao lower bound, that is,
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Whether you pick the case, the motherboard, or the CPU first, these three items usually come first during the selection process. That s because the CPU/motherboard combination must fit the case, and what the motherboard can do dictates the kinds of expansion cards you must purchase to assemble a complete, working Media Center PC. The power consumption and attendant heat output of the CPU also has an impact on case selection (hotter-running CPUs generally do better in bigger cases with more room for active ventilation components and airflow for cooling). Or, if you prefer, you can stand this logic on its head and decide you want a small compact case, and pick a micro-ATX motherboard with a Pentium M or Athlon CPU that doesn t require extreme cooling. Because we wanted to hit all bases for potential Media Center PC platforms, we decided to pick three distinct configurations, each based around a specific CPU. At the same time, we also tried hard to pick components that would be as quiet as possible to keep noise output down. To establish a level playing field for memory, we decided to outfit all systems with 1GB of RAM (which maxes out two-slot micro-ATX motherboards when using 512MB DIMMs, and a modest amount for those mobos with three or four DIMM slots; this is also sufficient RAM for Media Center PCs to work within).
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ython is a rich and powerful language, but also one that is easy to learn. This chapter gives an overview of Python s syntax, its useful data-types, and its unique features. As you read, please fire up the Python interpreter, and try out some of the examples. Feel free to experiment, tinker, and wander away from the rest of the tour group. Everything in this chapter is repeated, in greater detail, in later chapters, so don t worry too much about absorbing everything at once. Try some things out, get your feet wet, and have fun!
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In our sh example we saw how using multiple features could lead to improved recognition. We might imagine that we could do better if we had several component classi ers. If these categorizers agree on a particular pattern, there is no di culty. But suppose they disagree. How should a super classi er pool the evidence from the component recognizers to achieve the best decision Imagine calling in ten experts for determining if a particular sh is diseased or not. While nine agree that the sh is healthy, one expert does not. Who is right It may be that the lone dissenter is the only one familiar with the particular very rare symptoms in the sh, and is in fact correct. How would the super categorizer know when to base a decision on a minority opinion, even from an expert in one small domain who is not well quali ed to judge throughout a broad range of problems
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CREATE DATABASE UserDb; USE UserDb; -- Create a table. CREATE TABLE People ( FirstName VARCHAR(5) LastName VARCHAR(40) Salary DECIMAL(10,2) PRIMARY KEY (LastName, FirstName) );
Figure 10-13: The Add to Panel window.
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