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Table 5.2 compares the important features of TinyOS, Contiki and Mantis with respect to concurrency, code size and ease of learning. TinyOS is highly optimized to achieve a small code size, but is hard to program, while Mantis is a more traditional structure of a layered multi-threaded operating system, which leads to the largest code size, but simpli es programming. Contiki by design combines exibility with low memory footprint. While for non-expert programmers, TinyOS is tricky to program [42], Mantis simpli es programming. The same is true for protothreads available in the Contiki operating system. However, Mantis large memory footprint makes it impossible to implement it on systems with limited memory such as some PIC micro-controllers, to which TinyOS has been ported [50]. Smaller memory is both more cost-ef cient and energy-ef cient. For example, micro-controllers featuring less RAM have also been used for sensor nodes that solely rely on energy scavenged from the environment [54]. Other sensor node operating systems - SOS. The SOS operating system [33] is very similar to Contiki, particularly the design emphasis on dynamically loadable modules, motivated by the need for code updates during deployment of a sensor network. Like Contiki, SOS consists of dynamically-loaded modules and a small kernel. The kernel implements messaging, and dynamic memory and module loading and unloading at runtime. In SOS, modules are position independent binaries implementing a speci c function. Applications are

by and against people from cultural minority groups, adopt a minority perspective in their interviewing practice (see French, 1987; Mildren, 1997). At present, there is a relative paucity of discussion in psycho-legal arenas about the varied cultural and contextual needs of clients from minority groups. Similarly, there is a concomitant lack of guidance about the speci c implications of these concerns, and the numerous ways that forensic professionals should be sensitive to them. While legal research and commentary reveals an ever-increasing number of cases where the processes followed in eliciting evidence from minority defendants has been questionable (e.g. see Aldridge and Wood, 1998; Criminal Justice Commission, 1996; Cronheim and Schwartz, 1976; Lawrie, 1999; Manson, 1997; Mildren, 1997; Roy, 1990), most legal psychology textbooks and training courses fail to address issues related to multicultural assessment or interviewing practice (Powell and Bartholomew, in press; Young, 1989). Despite these numerous indicators of need, coherent recommendations about how practitioners can accommodate these differences have not been forthcoming. This chapter seeks to address these concerns by offering an overview of the critical elements required to obtain relevant and accurate information from forensic clients who are from cultural minority groups. These elements, which form the acronym PRIDE , include:

I don t just mean a demographic mix by gender, age, and race, although that helps. Teams benefit from the contributions from people of different

Downlink [MHz] 2620-2690 New 3G band 2110-2170 Mainstream WCDMA band 1930-1990 PCS band in USA and Americas 2110-2155 New 3G band in USA and Americas 1805-1880 Europe, Asia and Brazil 1845-1880 Japan 925-960 869-894 875-885

1999), 5.

Figure 6-3: A negative shock to aggregate demand. Y*

where the positive sign is used when the path C is closed in the left-half plane and the negative sign is applied when C is closed in the right-half plane. For N < M, we have RN;M z

If we used the Black Scholes model to value this option, we would get a value for this put of 5.34. Merton and Perold showed that, as long as the liabilities remain relatively constant, you can approximate the option value by the following simple formula* 0.4 (Asset Value) (Volatility of Asset Value) ( T ) Plugging in the values for asset value, volatility of asset value, and time from above 0.4 100 0.14 1 = 5.60 The actual value of the option is 5.34; the approximation gives me 5.60.

Reliable and energy-ef cient data transfer which is application-dependent for improved performance is required. No doubt collaboration between sensor nodes will increase the reliability of the transferred data in the WSN. However, the overhead introduced by cooperation along with the additional energy consumption should be at a reasonable level. Indeed, in order to avoid being suboptimal, protocols should be developed according to particular application requirements. Data fusion issues that require further investigation are the associated overhead of the fusion process and the heterogenity of data due to different sensor sources. Also, a high-level descriptive language oriented towards the collective model of solving tasks is necessary in order to implement distributed collaborative WSN applications. This language should support DCAs and provide real-time guarantees for energy-ef cient operations.

# /usr/sbin/exportfs -v /export/myshare client.example.com(ro,wdelay,root_squash,no_subtree_check) /mnt/public <world>(rw,wdelay,root_squash,no_subtree_check)