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The PTCM 8-PSK codes are interesting for satellite transmissions. For example, in the INTELSAT IDR service, a new coding system using the PTCM 2/3 8-PSK code concatenated with an ReedSolomon code is expected to improve the capacities up to 25%, based on the Standard A earth stations and INTELSAT VII space segment conditions. With the concatenation with an outer ReedSolomon code, the performance will be also largely improved over the QPSK with 3/4 punctured coding. A typical application is the satellite transmission of 139.236/155.52 Mbits/s PDH/SDH satellite interface equipment over 72 MHz transponder bandwidth. A 8-PSK trellis code of six dimension of rate 8/9 can be used as the inner code in a concatenated coding system with a Reed-Solomon outer code RS (255,239). Several such systems have been studied and tested, cf. [Ref. 21], [Ref. 22] and their cited references. Concatenated RS/PPTCM 8-PSK systems have been used also in high and variable bit-rate transmissions of HDTV signals by satellite channel [Ref. 23]. In general, for a spectral efficiency less than 3 and more than 2 bits/s/Hz, 8-PSK TCM schemes can be used. For more than 3 bits/s/Hz, trellis coded modulation schemes with QAM constellations should be considered. With increasing demand for higher bandwidth efficiency, the QAM trellis codes could find potential applications in HDTV satellite transmissions in the future. The difficulties of applying the trellis codes using QAM constellations to satellite communications is their nonconstant amplitude. More non-linear distortion occurs when passed through a non-linear amplifier such as an HPA. A precise amplitude control (AGC: automatic gain control) must be used at the receiver. A deep input back-off must be put in the HPA. However, the QAM trellis codes seem to be

This chapter discusses the myth of program trading as the primary cause of stock market trends on a day-to-day and minute-by-minute basis, and shows that what is often attributed to program trading is usually a manifestation of intermarket linkages at work. This discussion is not meant as a defense of program trading. Nor is it meant to ignore the role program trading can play in exaggerating stock market declines once they start. There are many legitimate concerns surrounding the practice of program trading which need to be addressed and corrected if necessary. However, a lot of misunderstanding exists concerning the role of program trading on a day-to-day basis. Whenever the stock market rallies, it is almost a certainty that program buying is present. It is equally certain that program selling usually takes place during a stock market selloff. Telling us that program trading is present at such times is similar to telling us that there is more buying than selling during rallies or more selling than

patients), psychiatrist (a physician who primarily treats through medications that provide temporary relief ), or both.

result of the function is the value assigned to the function s name (required).

Listing 4.25 SOAP response message using RPC-based communication. (continues)

4. Set up the configuration file. First, copy the generic LCDd.conf file into place:

Equation (1.54) expresses the delta (the hedge ratio) as the ratio of the actual price volatility of the option to the actual price volatility of the stock. Equation (1.55) is the central relation of arbitrage pricing theory. It is known as the market price of risk and is fundamental to general derivatives pricing since it is used to change probability measures in one stochastic process to an equivalent martingale measure in another process according to Girsanov s theorem discussed in section 1.8. Note, however, we have already made use of Girsanov s theorem when we moved from the real-world asset price dynamics in equation (1.12) to the risk-neutral one in equation (1.38). Changing measures allowed us to use the risk-free rate r for the drift instead of m, which is unobservable. The market price of risk relates the risk premium required by investors in the option to the option s volatility, the stock s volatility, and the risk premium on the stock. What does it tell us about the fair (arbitrage-free) price of the option Substitute the expression for mf and f from Ito s lemma into equation (1.50): f f f 1 2 2 2 f = rf + (m r )S + mS + S 2 S S 2 t S The two terms with m cancel out and we arrive at the Black-Scholes PDE: f f 1 2 2 2 f = rf + rS + S S 2 t S 2 (1.56)

[<=4] Low * 0;[>=8] High * 0; Medium * 0

XML is just as easy to use within an EJB. We ll modify the XSLT example to work as a message-driven bean. This is a useful model because you can queue up XSLT transformations that often take up a lot of processing time. In this example, our sample XSLT MDB will transform documents received in JMS messages. Our source document will once again be the order.xml, sent as a string within JMS text messages. Once transformed, the resultant HTML is saved to a directory for future access. There are several files we need to package in the EJB .jar file. In addition to the usual descriptors in META-INF, we also store resources used by the message-driven bean under the WEB-INF directory. The library .jar for dom4j, which we ll be using for our XML work, is stored here. The other resource file is our XSLT stylesheet, order_to_html.xsl, which is used by the EJB to transform incoming messages. The contents of the order_to_html.xsl file are the same as in our XSLT example earlier in this chapter. The layout for the directory is as follows: