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Figure 6.1 shows a schematic overview of the most essential HSDPA RRM algorithms at the RNC and the Node B. At the RNC, the new HSDPA algorithms include HSDPA resource allocation, admission control, and mobility management. In this context, HSDPA resource allocation refers to the functionality that allocates power and channelization codes to the Node-B for HSDPA transmission in each cell. HSDPA admission control is di erent from the Release 99 dedicated channel (DCH) admission control algorithms, since HSDPA relies on a shared channel concept. Mobility management for HSDPA is also a new functionality, since data are only transmitted from one cell to the user equipment (UE) at a time, and e ective Node B bu er management is needed during handovers due to the distributed architecture. The new HSDPA RRM algorithms at the RNC are further described in Section 6.1.1. At the Node B, a new high-speed downlink shared channel (HS-DSCH) link adaptation functionality is needed to adjust the HS-DSCH bit rate every transmission time interval (TTI), depending on the user s reception quality. High-speed shared control channel (HS-SCCH) power control is needed to minimize the power overhead while guaranteeing reliable reception. Finally, the high-speed medium access control (MAC-hs) packet scheduler in the Node B controls how often admitted HSDPA users are served on the HS-DSCH. A welldesigned MAC-hs packet scheduler is able to maximize cell capacity while ensuring

1989; Channon, Hemsley & de Silva, 1988; Cooper & Fairburn, 1992a; Green, McKenna & de Silva, 1994; Long, Hinton & Gillespie, 1994). (For reviews, see Vitousek, 1996, and Williamson, Mathews & McLeod, 1996.) Data from these Stroop experiments are dif cult to interpret; they may be assessing state-like salient concerns instead of stable attentional biases and they do not provide de nitive evidence of biases in attention (Vitousek, 1996). Nevertheless, they are at least consistent with the cognitive-behavioural theories described above. There have been no studies with patients with anorexia nervosa using the dichotic listening task or lexical decision tasks although such studies have been done in people with bulimia nervosa (Schotte, McNally & Turner, 1990; see later) and women with body dysphoria (Fuller, Williamson & Anderon, 1995). Cognitive-behavioural theories suggest that personally salient information concerning shape and weight will be more readily encoded and recalled than neutral stimuli. People with body dysphoria have been shown to recall more body-related words and dif culty recalling thinness words than those low in body dysphoria (Baker, Williamson & Sylve, 1995; Watkins et al., 1995). A memory bias for words concerning fatness was found in a mixed group of patients with eating disorders (Sebastian, Williamson & Blouin, 1996) and an explicit memory bias for anorexia-nervosa related words was recently demonstrated in a small sample of patients with anorexia nervosa compared to non-dieting controls (Herman et al., 1998). This nding could not be attributed to the valence of the words. In summary, such experimental data have provided support for the leading cognitivebehavioural model of anorexia nervosa (Garner & Bemis, 1985; Vitousek & Hollon, 1990). However, as with the case of the questionnaire studies, the experimental data do not provide particular support for any one speci c model as opposed to another.

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Consider Figures 58a and c where we can easily see that cases such as c and c play an important role in affecting the overall competence distribution in the group Therefore, it is important to detect such cases (which are called weak links in the following discussion) for possible identi cation of smaller competence groups, such as G1 ; G2 , and G3 in Figure 58c, those are having more evenly distributed cases The competence of these smaller groups can then be computed using equations (519) to (521) It is worth noting that the competence of weak links can be considered to be their respective individual coverage, which re ects the relation among the several new groups A new way of computing group competence based on this principle is described next 553.

clearly hinders the applicability of these approaches for research in context awareness as the used tool de nes the sensors and platform that needs to be used for testing. For this reason, BeTelGeuse, a tool for Bluetooth data gathering [38], has been developed. Since BeTelGeuse has been implemented using J2Me, it is platform-independent. In addition, BeTelGeuse can be extended to use any type of sensor. The main features of BeTelGeuse are summarised in Table 4.1. More details about BeTelGeuse can be found at the website [13]. 4.2.3 Simulation Currently there are three main research directions in simulation of context-aware systems. One of the directions is to simulate physical sensor measurements. For example, [51] suggests

1 Competence Error In general, competence groups such as G1 and G2 in Figure 58a do not necessarily have the same strictly uniform distribution, and the weak-link case c is not necessarily a pivotal case (a case that cannot be solved by other cases) To deal with this situation, GroupDensity G1 [which is assumed to be equal to GroupDensity G2 ] can be replaced by the average group density of groups G1 and G2 , which can be denoted by GroupDensity Gi ; i 2 f1; 2g Let GroupDensity Gi GroupDensity G be denoted by GroupDensity The concept of quasi-uniform distribution can be used to describe the case-base distributions that are close to uniform distribution As mentioned, the other assumption that c is a pivotal case in the example is not necessarily true in many cases.

Handbook of Psychology in Legal Contexts, Second Edition Edited by D. Carson and R. Bull. C 2003 John Wiley & Sons, Ltd.

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