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Nitro PDF Reader allows you to create PDF files from over 300 different formats. Comment, review , and collaborate. Fill and save PDF forms. Extract text and ...

You can use this technique whether you use XMLTV or DataDirect to fetch your programming data. You can also run these commands any time after your initial import of icons. It will update any icons that have changed, add icons for new channels, and so on.



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KAILATH (1994), LMS and backpropagation are minimax filters; in Neural Computation and Learning, V Roychowdhurys, K Y Siu, and A Orlitsky, Eds, Kluwer Academic Publishers, pp, 425-447 B HASSIBI, H SAYED,AND T KAILATH A (1996), Hm optimality of the LMS algorithm: IEEE Trans Signal Process, vol 44, no 2, pp 267-280 B HASSIBI, H SAYED A AND T KAILATH (1999),Indejinite Quadratic EstimationandConrrol: A Unified Approach to H 2 and 7-lm Theories, SIAM, PA S HAYKIN (1991), Adaptive Filter Theory, 2nd edition, Prentice-Hall, Englewood Cliffs, NJ S , HAYKIN (1996), Adaptive Filter Theory, 3rd edition, Prentice-Hall, Englewood Cliffs, NJ S HAYKIN (2000), Adaptive Filter Theory, 4th edition, Prentice-Hall, Englewood Cliffs, NJ S HAYKIN, H SAYED, R ZEIDLER, P WEI, AND P YEE (1997), Adaptive tracking of linear A J time-variant systems by extended RLS algorithms, IEEE Trans Signal Process, vol 45, no 5 , pp, 11181128 G .





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Class reading: Kinny and Georgeff (1997). This article describes arguably the first agent-specific methodology. For a class familiar with 00 methodologies, it

HEINIG N D K ROST (1998), Representations of Toeplitz-plus-Hankel matrices using trigonometric A transformations with application to fast matrix-vector multiplication, Linear Algebra and Its Applications, V O ~2751276, pp 225-248 S J W HELTON AND 0 MERLINO (1998), Classical Control Using UrnMethods, SIAM,PA H V HENDERSON S R SEARLE AND (1981) On deriving the inverse of a sum of matrices, SIAM Review, vol 23, pp 53-60 R I R HESTENES (1975) Optimization Theory, Wiley, New York N J HIGHAM (1996), Accuracy and Stability of Numerical Algorithms, SIAM, PA DHIRSCHAND W J WOLF(1970), A simple adaptive equalizer for efficient data transmission, IEEE Trans Commun Tech, vol 18, no 1, pp 5-12 Y C Ho (1963), On the stochastic approximation method and optimal filter theory, J Math Anal Appl, v01 6, pp 152-154 hl L HONE AND DG .

i (i+1)

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MESSERSCHMITT (1984), Adaptive Filters: Structures, Algorithms, and Applications, Kluwer Academic, Boston, MA R A HORNA N D C R JOHNSON MatrixAnalysis, Cambridge University Press, Cambridge, Eng(1987), land R A HORNAND C R JOHNSON (1994) Topics in Matrix Analysis, Cambridge University Press, Cambridge, England L L HOROWITZ AND K D SENNE (1981), Performance advantage of complex LMS for controlling narrowband adaptive arrays, IEEE Trans Acoust Speech Signal Process, vol 29, no 3, pp 722-736 A HOUACINE (1991), Regularized fast recursive least squares algorithms for adaptive filtering, IEEE Trans Sig nal Process, vol 39, pp 860-870 A HOCACINE G DEMOMENT AND (1986) Chandrasekhar adaptive regularizer for adaptive filtering, Pmc ICASSP, Tokyo, Japan, pp 2967-2970 A S HOUSEHOLDER (1953) Principles of Numerical Analysis, McGraw Hill, New York..

Since p(t) is a parabola, its discriminant must be nonpositive,

2. Using temporal variation of the pupil function or parallel nondegenerate optical systems to increase the dimensionality or sampling range of the focal volume 3. Applying generalized sampling and estimation strategies to infer W from discrete measurements on S. These strategies are not exclusive and are often applied in combination. All three strategies are improved by design and coding of the aperture function to facilitate particular applications. Given that coding, sampling, and inversion strategies for Eqn. (6.52) are the focus of much of the remainder of this text, we cannot hope to fully analyze the possibilities in this section. We do, however, brie y overview examples of the rst two basic strategies. The rst strategy focuses on reconstruction of the cross-spectral density arising from remote incoherent objects, as described by Eqn. (6.21). For such incoherent objects the cross-spectral density reduces to a 4D function over (Dx, Dy, q, n) and reduces Eqn. (6.52) to S(x, y, z, n) 4 2 2 lz W(Dx, Dy, q, n)B(Dx, Dy, q) (6:53)

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