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Figure 6.24. SEM images of CuInS2 lms grown by AACVD: (a) porous, rough, dull lms, and (b) smooth, shiny, dense lms.

(13.14) the self-standardized influence function of the estimator T ( 2 ) we can bound the , influence function of the asymptotic level by bounding the self-standardized influence function of T(2,. Moreover, maximizing the asymptotic power at the model is equivalent to maximizing the noncentrality parameter 6, which in turn is equivalent to minimizing the asymptotic variance V(22)of T ( 2 2 ) . Therefore optimal boundedinfluence tests can be obtained by finding a $-function defining an Ill-estimator T such that T(2) minimum asymptotic variance under a bound on the selfhas standardized influence function. The solution of this minimization problem can be found in Hampel et al. (1986), Section 4.4b. Examples of such tests are given for example in Heritier and Ronchetti (1994) and Heritier and Victoria-Feser (1997).

then we de ne it as a large PCB. Equation (15.A.5) is the de nition of a large-sized PCB electrically. It should be noted that the electric wavelength of a runner on the PCB, , depends on the width of the runner or its related characteristic impedance, Zo, since

Then, the total undesired voltage or the system undesired voltage across ZL is 1 ZL Gk +1OIPm,k

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