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Table 4: Series Expansion Coe cients for the 2 2 Lattice

in Reduction in Computational Complexity for the Ising Problem with External Field
by R. S. Bucy, R. S. Diesposti

Table 5: Series Expansion Coe cients for the 3 3 Lattice

in Reduction in Computational Complexity for the Ising Problem with External Field
by R. S. Bucy, R. S. Diesposti

TABLE I Comparison between the polynomial phase coefficients and the Taylor series expansion coefficients

in Parametric Estimation of the Orientation of Textured Planar Surfaces
by Joseph M. Francos, Haim Permuter 2001
Cited by 2

Table 2: The fractional errors on the various f(1) computed using the series expansions up to order 10, for values of 1:05 z 1:4.

in Analytically Integrated Results for Heavy Fermion Production in Two-Photon Collisions and a High Precision
by Determination Kamal, B. Kamal, Z. Merebashvili
"... In PAGE 14: ... Adding (43) and (45) gives the series for the j = 0 total cross section and adding (44), (46) gives the series for the j = 2 total cross section. In Table2 we present the fractional error on the series for f(1)(+; +), f(1)(+; ?), f(1) unp relative to the result obtained using numerical integration, for various values of z in the region 1:05 z 1:4. For z .... ..."

Table 1: Seidel aberrations are described by power series expansion of the aberration function (n; m). The position of best focus is shifted by (Rf cos o; Rf sin o; Zf).

in VISTA Status Report
by T. Grasser, A. Hössinger, H. Kirchauer, M. Knaipp, R. Martins, R. Plasun, M. Rottinger, G. Schrom, S. Selberherr
"... In PAGE 6: ... As can be seen from (7) five terms with a + b + c = 2 exist. In Table1 a summary of the Seidel aberrations including the resulting image shift is described. Although the expansion of (7) is a general representation of the aberration function in that any aberra- tion function (n; m) can be represented, other polynomials are more suited to describe higher-order aberration terms.... ..."

Table 1: Series expansions for the zero{ eld renormalized magnetization M0 = Pm M0 m m and two{point correlation function E0 = Pm E0 m m. Only the non{zero contributions are displayed.

in Low-Temperature Series for Renormalized Operators: the Ferromagnetic Square-Lattice Ising Model
by J. Salas 1994
"... In PAGE 9: ...s odd (even). And if it is vertical, Ly = 2L ? 2 and Lx = (L + 1)=2 (Lx = (L + 2)=2). In this way we have been able to obtain the series (5a, 5b) up to order O( 30). The result is displayed in Table1 . In this algorithm we need to deal with very large numbers, much larger than the precision of the computer (32 bits in our case).... ..."
Cited by 4

Table 2 shows the results of applying the stability tests from Theorems 2, 3, 4 (using a nite series expansion of the multiplier), and the o -axis circle criterion, for various values of H1 = L in the observer-based anti-windup scheme. The corresponding multipliers X ? W (s)

in Multiplier Theory for Stability Analysis of Anti-Windup Control Systems
by Mayuresh V. Kotharey, Manfred Morari 1995
"... In PAGE 23: ... Table2 : Application of various AWBT stability conditions establishing stability for the four cases above, using Theorem 4 and the nite dimensional approximation of the multiplier, as discussed in x3.2.... ..."
Cited by 4

TABLES TABLE I. The coe cients in the expansions of nk, vk, Q, S, VQ and V for fully penetrable disks. Recall that n1 = e?4 from (29) and that v1 = V1 trivially. The series expansions for vk, VQ and VS are expressed in units of V1, the area of a single disk. The expansions for Q, S, VQ and VS are derived from (10), (12), (14) and (15), respectively. 0 1 2 3 4

in Clustering Properties of
by Dimensional Overlapping, J. Quintanilla, S. Torquato 1996
Cited by 1

Table 1 Threshold values for random K-SAT. Bold numbers are the results of the population dynamics algorithm. (0) d is the value predicted by the rst moment expansion of the cavity equations (sec. 6.3), (r) c is the result of a series expansion in quot; = 2 K of the cavity equations up to order r (secs. 6.2 and A). Note that all reported values c(K) fall between the best rigorously known upper and lower bounds.

in unknown title
by unknown authors 2006
"... In PAGE 22: ...he cavity equations up to order r (secs. 6.2 and A). Note that all reported values c(K) fall between the best rigorously known upper and lower bounds. Table1 shows the results. Since c for K = 3 is the most \prominent quot; thresh- old we spent a bit more CPU power to increase its accuracy.... In PAGE 27: ... Fig. 8 and Table1 show that the seventh order expansion gets actually very close to the numerical values. For K = 3 the deviation of the seventh order asymptotic expansion from the numerical value is less than 1%, and for K 4 this deviation is even smaller.... In PAGE 28: ... (39). The results are the values for (0) d in Table1 . The values for (0) d agree perfectly with the exact values d (within the error bars of the latter), even for K = 3, although the non-trivial distributions A(x) and B(y) that appear right above d are not -like.... In PAGE 35: ... The quality of the expansion up to seventh order can be seen in Fig. 8 and Table1 . Note that there exist also nonanalytic terms in quot;, because we dropped some corrections of order which in turn behaves as quot;1=(2 quot;).... ..."
Cited by 8

Table 1: Frequency contents of the function f1(t) The representation of a periodic function (or of a function that is de ned only on a nite interval) as the linear combination of sines and cosines, is known as the Fourier series expansion of the function. The Fourier transform is a tool for obtaining such frequency and amplitude information for sequences and functions, which are not necessarily periodic. (Note that sequences are just a special case of functions.) 2

in The Fourier Transform -- A Primer
by Hagit Shatkay Department, Hagit Shatkay, Hagit Shatkay 1995
"... In PAGE 3: ... The function f1(t) consists of sines and cosines of 3 frequencies. 1 2 3 4 5 c -6 -4 -2 0 2 4 6 1 2 3 4 5 d -6 -4 -2 0 2 4 6 1 2 3 4 5 a -6 -4 -2 0 2 4 6 1 2 3 4 5 b -6 -4 -2 0 2 4 6 Figure 1: A plot of f1(t), (d), and its components (a; b; c), for t = 0::5 Thus, the frequency analysis of f1(t), can be summarized in a table such as Table1 , which provides for each frequency of f1 the amplitude of the sine wave and of the cosine wave... In PAGE 4: ... Both are easily derived from the Taylor series expansion of cos, sin, and e . Through addition and subtraction they can be rewritten as: cos( ) = ei + e?i 2 sin( ) = ei ? e?i 2i (3) Hence, we can substitute the sin and cos expressions of equation 1 by the respective expressions of equation 3 and get: f(t) = n X k=1[Ak 2 (e2 i!kt + e?2 i!kt) + Bk 2i (e2 i!kt ? e?2 i!kt)] (4) If we denote: Ck = Ak?iBk 2 k gt; 0 Ck = Ak+iBk 2 k lt; 0 C0 = 0 !k = ?!?k k lt; 0 (5) we can again rewrite f(t): f(t) = n X k=?n[Cke2 i!kt] (6) Under this new notation we can rewrite the frequency analysis of Table1 as shown in Table 2. k Frequency (!k) Ck ?3 ?1 2 ?2 ?2 2i ?1 ?1=2 i=4 0 0 0 1 1=2 ?i=4 2 2 ?2i 3 1 ?2 Table 2: Another form of frequency contents of the function f1(t) Further manipulation of equation 6 is based on using the polar notation for complex numbers, that is: x + iy = r(cos( ) + isin( )) = rei where r = jx + iyj = qx2 + y2 and tan( ) = y x... ..."
Cited by 9
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