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Although AppleScript programs look easy to read and are nicely formatted by the Script Editor (as shown in the preceding code), don t allow yourself to be seduced: AppleScript is a real programming language, with real syntax rules that can be unforgiving if you don t follow them exactly. AppleScript unquestionably is more inviting than conventional languages like C and Objective-C, but it still presents a barrier to many users, who can run AppleScripts but not write or modify them. But wait there s something new! Automator, a new feature freshly unwrapped for Mac OS X Tiger, is designed to fill the gap for those clever folks who can t or don t want to script but still want to make repeated tasks easier to perform. Automator lets users streamline jobs by stringing together individual actions, then saving and reusing the set of actions. Using Automator involves no scripting or programming. Figure 13-1 shows what you see when you run the Automator application and start putting steps together. Automator creates a high level of automation that didn t exist before Tiger. I ll spend the rest of this chapter discussing Automator and its machinations.

, Babkes & Weiss, 1999; Brustad, 1996; C t , 1999; Fredricks & Eccles, 2005) Furthermore, the issue of psychological control on the part of parents may be reflected in the research on parents level of directiveness (Averill & Power, 1995; Power & Woolger, 1994), as well as the research on children s sport commitment and burnout (eg, Raedeke, 1997; W M Weiss & Weiss, 2003) Finally, the degree to which parents provide autonomy support to their children in regard to their sport behavior has been linked to children s and adolescents tendencies to discontinue their sport participation, to devalue sport, and to experience higher levels of burnout in sport contexts (eg, Coakley, 1992; C t , 1999; Stuart, 2003) This research on individual aspects of these parenting styles has indicated that these are important dimensions of effective parenting However, as Pomerantz et al.

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In this chapter, we discuss and review various standard and emerging coding, packetization, and transmission technologies that are needed to support voice transmission using the IP technologies. Limitations of the current technologies and some possible extensions or modi cations to support high-quality that is, near-PSTN grade real-time voice communications services using IP are then presented. VOICE SIGNAL PROCESSING For traditional telephony or voice communications services, the base-band signal between 0.3 and 3.4 KHz is considered the telephone-band voice or speech signal. This band exhibits a wide dynamic amplitude range of at least 40 dB. In order to achieve nearly perfect reproduction after switching and transmission, this voice-band signal needs to be sampled as per the Nyquist sampling criteria at more than or equal to twice the maximum frequency of the signal. Usually, an 8 KHz (or 8000 samples per second) sampling rate is used. Each of these samples can now be quantized uniformly or nonuniformly using a predetermined number of quantization levels; for example, 8 bits are needed to support 2 8 or 256 quantization levels. Accordingly, a bit stream of (8000 8) or 64,000 bits/sec (64 Kbps) is generated. This mechanism is known as the pulse code modulation (PCM) encoding of voice signal as de ned in ITU-T s G.711 standard [1], and it is widely used in the traditional PSTN networks.

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(2005) point out in their recent chapter, more global or comprehensive research is needed to examine these individual parenting dimensions in one larger study Thus, future researchers in the sport and exercise psychology field may want to include the more global parenting style measures in their research to examine parental influences in the sport setting Parental Inf luences: Conclusions and Future Directions Research indicates that parents have a significant effect on their children s performance, behavior, and psychosocial responses in the sport and physical activity settings Their influences appear to follow, at least in part, the paths specified in Figure 312 However, additional research on these hypothesized links is needed First, more developmentally based research studies are needed Given the research and theory (see, eg, Fry, 2001;.

We now show a direct connection between gambling and data compression, by showing that a good gambler is also a good data compressor. Any sequence on which a gambler makes a large amount of money is also a sequence that can be compressed by a large factor. The idea of using the gambler as a data compressor is based on the fact that the gambler s bets can be considered to be his estimate of the probability distribution of the data. A good gambler will make a good estimate of the probability distribution. We can use this estimate of the distribution to do arithmetic coding (Section 13.3). This is the essential idea of the scheme described below. We assume that the gambler has a mechanically identical twin, who will be used for the data decompression. The identical twin will place the same bets on possible sequences of outcomes as the original gambler (and will therefore make the same amount of money). The cumulative amount

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