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The return loss generally improves for larger aperture horn arrays because the fundamental Floquet modes and the dominant waveguide mode (the TE01 mode) match well for a larger aperture As shown in Figure 82b, the return loss for the bore-sight beam is better than 19 dB for the 149 0 square horn array The return loss curves have several dips and peaks, and some of them appear as sharp spikes These dips and peaks are primarily due to the grazing lobes, also known as end-fire lobes In Figure 82a the peak near the 23 scan angle on the E-plane is due to the grating lobe at the 90 location This grating lobe corresponds to the TM( 1 0) Floquet mode (to avoid a negative subscript, we place the mode indexes inside parentheses).

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The grazing lobe causes strong mutual coupling1 between the elements, which in this case leads to scan blindness because the return loss becomes near 0 dB The scan angle associated with a grazing lobe can be easily identified from the circle diagram of the array shown in Figure 83 A grazing lobe occurs when the scan location on the kx ky -plane lies on a Floquet mode circle For rectangular grids with cell size a b, the scan angles at E H,.

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Riley, J. (1955). Solving Systems of Linear Equations with a Positive De nite, Symmetric but Possibly Ill-Conditioned Matrix. Mathematical Tables and Other Aides to Computation 9, 96 101. Ripley, B. D. (1987). Stochastic Simulation. New York: John Wiley & Sons. Ripley, B. D. (1988). Uses and Abuses of Statistical Simulation. Mathematical Programming 42, 53 68. Ripley, B. D. (1990). Thoughts on Pseudorandom Number Generators. Journal of Computational and Applied Math 31, 153 63. Ritter, T. (1986). The Great CRC Mystery. Dr. Dobb s Journal of Software Tools 11, 26 34, 76 83. Rivest, R. (1992). RFC 1321: The MD5 Message-Digest Algorithm. Internet Activities Board. Robbin, A. and Frost-Kumpf, L. (1997). Extending Theory for User-Centered Information Services: Diagnosing and Learning from Error in Complex Statistical Data. Journal of the American Society of Information Science 48(2), 96 121. Robert, C. P. (2001). The Bayesian Choice: A Decision Theoretic Motivation, 2nd ed. New York: Springer-Verlag. Robert, C. P. and Casella, G. (1999). Springer-Verlag. Monte Carlo Statistical Methods. New York:

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Sample u from u[0 :1] If a( , ) > u, move to ; otherwise, remain Here we assume that is very small such that A is a trapping set requiring a large number of generated u values to obtain the condition that the Metropolis Hastings acceptance ratio de ned in (510), a( , ), is greater than a generated u This means that the chain will be trapped in A for a long time Now if a( , ) for all A falls below the uniform random number generator s smallest possible value, A is now an absorbing state because there cannot be a u < a( , ) giving a successful jump Young et al (1998) nd a related problem that is equally distressing but easily observed.

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where y is a T 1 vector of the dependent variable, X is a T p matrix of p regressors where the rst column consists of all ones if an intercept is present in the model. The matrix X has rank p if the regressors are not collinear. If the rank of X is de cient, the regression coef cients b1, b2, . . . , bp, which are the individual elements of the p 1 vector b, cannot be estimated. The vector u is a T 1 vector of independent and identically distributed (iid) true unknown errors with elements ut and unknown possibly nonnormal true distribution function F satis es E(u) = 0, E(uu ) = s2I; that is, they have mean zero and variance s2.