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Finally, psychosocial factors may also contribute to an individual s resistance or vulnerability to the promotion of a subclinical pathogenic or pathophysiologic process or the onset of clinically manifest disease. Subclinical processes that have been associated with psychosocial factors include coronary artery stenosis and related disease processes (Schneiderman et al., 2001), glucose control and insulin resistance (Frenzel, McCaul, Glasgow, & Schafer, 1988; Schneiderman et al., 2001), neoplastic cell growth (Antoni, 2003a), and viral infections (Antoni, 2003b; Cohen et al., 1991). Psychosocial factors implicated as contributing to these processes include affective disorders and distress states (Frasure-Smith, Lesperance, & Talajic, 1993; Herbert & Cohen, 1993a, 1993b; Schneiderman et al., 2001); anger and hostility (Dembroski & Costa, 1987); appraisals of self-efficacy or personal control (Cohen & Edwards, 1989; O Leary, 1992); pessimism (Byrnes et al., 1998; Peterson, Seligman, & Valliant, 1988; Scheier & Carver, 1985); various coping strategies such as denial, avoidance, and emotional suppression (Holahan & Moos, 1986; Ironson et al., 1994; Pettingale, Greer, & Tee,

Eigenvalues or latent roots of a polynomial matrix: The eigenvalues or latent roots of the polynomial matrix P(x) are the root.s of th~' polynomial de! P(x). Latent vector of a polynomial matrix: Let P(x) be a polynomial lIlatrix with eigenvalue A. A vector II such that P(A)v 0 is called a latent vector of P(x). Regular polynomial matrix: The (Tn X Tn) polynomial matrix P( x) ::: Po + PIJ' + ... + Pr,t r is railed regular if P r is nonsingular. Monic polynomial matrix: The (m x m) polynomial matrix P( x) ::: Po + PIJ' + '" + PrX r is monic if Pr :::: 1m. Unimodular or invertible polynomial matrix: The (711 x JIl) polynomial Illatrix P(J') is said t.o be unimodular or invertihle if dt'l P(J') ('onstant::j: O. that is. det P(J') is a constant function. Left and right multiples of polynomial matrices: The (til x tl) polynolllialll1atrix prJ') satisfying P(x) :::: Q(x)T(x) for polynomial matrices Q(J') (1/1 X It) and 1'(J.:) (h x n) is called a left multiple of T(J') and a right mult.iple of Q(x). Left quotient and left divisor: Consider polynomial matrices

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The most common example of generation-based stego is where you use an overt file to create a fractal image. A fractal image has critical mathematical properties, but it is essentially a collection of patterns and lines in different colors. You could use your covert message to determine the angle, length, and color of each line. Figure 6.2 shows an example of a fairly complex fractal that was created using generation-based stego. The important thing to keep in mind in using generation-based stego techniques is that the image that is generated has to fit the profile of the person using it to avoid detection. If somebody has never received a fractal file before, and he or she starts receiving dozens of such images, it may attract attention. With generation-based stego the file created must have some predictable pattern that could be coded into an algorithm. For example, with a fractal the angles and length of the lines can be fed into an algorithm that is based on the input text and creates the overt image. A picture of something as realistic as a car cannot easily be created with a generation-based technique. In most cases, random-looking images like fractals and English text are the two main media for generation-based stego.

Fundamentals of Telecommunications. Roger L. Freeman Copyright 1999 Roger L. Freeman Published by John Wiley & Sons, Inc. ISBNs: 0-471-29699-6 (Hardback); 0-471-22416-2 (Electronic)

vary a lot in real situations In mobile wireless networks that operate over extensive periods of time, the characteristics of the network are likely to change, and hence it is advisable to use communication algorithms that do not require knowledge of network topology, or even of its size On the other hand, in more stable situations it may be better to use the most efficient centralized algorithms In our presentation, we focused attention on the design of algorithms and on mathematical analysis of their performance A more experimental approach to broadcasting in wireless networks can be found, eg, in [40] and in the literature therein We conclude this chapter by proposing a short list of open problems This list is by no means complete, and the choice of problems reflects personal interests of this author.

FN Cost $28.40 4 0.5% 8 0.9%

The order of problems corresponds to the order of the relevant material in this chapter, and some of them were already mentioned in the respective parts of the exposition We refer the reader to the appropriate sections for information on the related results known to date 1 Find a centralized deterministic broadcasting algorithm working in asymptotically optimal time for arbitrary radio networks, assuming that the central monitor has complete knowledge of the graph 2 Find a distributed deterministic broadcasting algorithm working in asymptotically optimal time for arbitrary radio networks of unknown topology 3 Find a distributed deterministic broadcasting algorithm working in asymptotically optimal time under the assumption that every node knows the part of the radio network at (graph) distance at most r from it 4 Find a distributed randomized broadcasting algorithm working in asymptotically optimal time for arbitrary radio networks of unknown topology 5.

Establish the exact trade-off between time and cost (number of transmissions) of deterministic broadcasting for arbitrary radio networks of unknown topology..