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measure does not bring added information on service quality as compared to atomic delay variation. This is true in case delay variation data are stored as a time series. There are cases in which reordering may be the convenient metric. For example, reordering may be useful for detecting what is happening on lower protocol layers. One of these is end-to-end path involving transmission over links in which link layer transmission technology may cause reordering of PDUs. An example of this is wireless links with reliability enabled using link layer retransmissions. Finally, as noted earlier, certain services have inherent requirement for throughput. A CBR VoIP call is a prime example of this. For other services, the throughput requirement is not an equally black-and-white affair. The concept of utility to be discussed in 5 re ects this. Some means of estimating throughput for typical applications are discussed in 4. For the present purposes it is suf cient to note that a full description of support for continuous data transmission is typically related to variations in available throughput. CBR-type applications have a limit above which variations do not affect service quality, given that throughput measurement takes into account end-to-end delay limits, including dejitter buffering.

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A switch provides a central location for multiple LANs to connect to the network. A switch is often called an intelligent hub because of its ability to sort data. Operating at the Data Link layer (Layer 2) of the OSI Reference Model, a switch can connect multiple network segments together at a central point. When a switch receives a frame, it saves the MAC address of the originator and the port on which the frame was received. It will then use the data it collects to forward packets based on the MAC address. If it does not have the MAC address in its MAC address table, it will flood the frame out of all of its interfaces. Figure 1-14 shows an example of using a switch to forward data in a LAN.

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four-tiered herarchy of the entities that can exist in an agent-based system They start with entities, whch are inanimate objects - they have attributes (colour, weight, position) but nothing else They then define objects to be entities that have capabilities (eg tables are entities that are capable of supporting things) Agents are then defined to be objects that have goals, and are thus in some sense active; finally, autonomous agents are defined to be agents with motivations The idea is that a chair could be viewed as taking on my goal of supporting me when I am using it, and can hence be viewed as an agent for me But we would not view a chair as an autonomous agent, since it has no motivations (and cannot easily be attributed them).

Do the same thing to the other lines. When you get to the vertical line between the eyes, fade it to 40 percent so that more of the original line remains, providing some contours to the facial structure.

Figure 4-25

Specify starting width <0.0000>: .5 Specify ending width <0.5000>: .25

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Starting from this basic framework, Luck and d'Inverno go on to examine the various relationshps that might exist between agents of different types In Luck et a/ (1997), they examine how an agent-based system specified in their framework might be implemented They found that there was a natural relationship between their hierarchical agent specification framework and objectoriented systems The formal definitions of agents and autonomous agents rely on inheriting the properties of lower-level components In the Z notation, t h s is acheved through schema inclusion. . T h s is easily modelled in C++ by deriving one class from another Thus we move from a principled but abstract theoretical framework through a more detailed, yet still formal, model of the system, down to an object-oriented implementation, preserving the herarchical structure at each stage (Luck et al.

the DE-assisted receiver possessed a higher probabilityof resolving each of the multi-path contributions. Referring to Table 3.10, the CIR-estimation induced performance degradationof Structure 1 was approximately 1dB for all modulation schemes used, when compared to that of the perfect CIR estimation scenario. Similarly, an approximate degradation of 4dB was inflicted by Structure 2 in the same context. This was, because Structure 2, which utilized when compared to Structure 1, which the RKDFE, possessed a slower convergence rate, employed a RKCE as seen in Section 3.2.4.1. Thus Structure 1 provided a superior DFE coefficient estimate due to its more accurate estimation of the CIR. The degradation for the higher-order modulation schemes also higher due to their reduced Euclidean distance bewas tween neighbouring phasor constellation points, which were more susceptible to inaccurate CIR estimation. of The results summarized Table 3.10 clearly showed the superiority Structure 1,when in the Kalman algorithm was adapted throughout the duration of the entire training sequence of interval. In the next section we will examine and compare the complexity both structures used in these experiments. Perfect estimation BPSK: 2 path 3 path QPSK: 2 path 3 path QAM 16: 2 path 3 path QAM64: 2 path 3 path

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