### Table 1: Parameters of the closed-form approximations for bandwidth, capacity, and transmission power at SNR0 =

in On the Relationship Between Capacity and Distance in an Underwater Acoustic Communication Channel

### Table 3 Composite and superstrate configurations: accuracy of the closed-form expressions used.

"... In PAGE 7: ...ubstrate. Detailed formulas can be found in [15]. Different multi-layer composite and superstrate build- ups within our technology set have been tested. Their parameters are collected in the following Table3 . The accuracy results for the these configurations are shown in the similar form as for single dielectric configuration and are presented in Table 4 and Fig.... ..."

### Table 1: M/G/1 PSFFA Models responds to the functional relationship x = G1( ) of Eqn. (3) and in this case it can be inverted in a closed form to yield

1996

"... In PAGE 3: ... (7) can be solved numeri- cally for the time varying behavior of the average num- ber in the system. Table1 lists some special cases of the M/G/1 PSFFA for various common service time distributions namely: D - deterministic service times with C2 s = 0; Ek - Erlang-k distributed service times with C2 s = 1=k; k 1; and M - exponentially dis- tributed service times with C2 s = 1. Note that for the special case of the M/M/1 queue, the service distribu- tion is exponential with C2 s = 1 which results in the expression for (t) in Eqn.... In PAGE 4: ...Table1 are given in Figures 1, 2, and 3 for the M=D=1; M=E2=1 and M=M=1 models respectively. In Figures 1, 2, and 3, the average number in the queueing system x is plot- ted versus time for the nonstationary tra c (t) = 0:5+0:4 sin(0:2(t+20))1 with mean service rate = 1:0 and initial condition x(0) = 0:1.... ..."

Cited by 11

### Table 1. Primitives/kernels compatibility chart: ( ) closed-form solution exists, ( ) no closed-form solution, (1) integral yields in- finite value, (elliptic) a solution is expressed via elliptic integrals.

1999

"... In PAGE 4: ... More on practices of modeling with implicit surfaces at PDI may be found in Beier (1990). In order to produce implicit points and line segments, as Table1 demonstrates, all five kernels may be employed. Computational cost analysis is needed to choose the most effective implementation.... In PAGE 5: ... Unfortunately, there is one serious problem that makes polynomial kernels difficult to implement for higher-dimensional primitives. Table1 shows the existence of closed form expressions for the definite integrals that are computed over the whole volume of each primitive. The limited range of influence of the polynomial kernels make the integration domain posi- tion dependent.... ..."

Cited by 11

### Table 7.1: Loss probabilities for uniform deadlines, derived from numeric convolution (C), numeric inversion of the Laplace transform (L), closed-form expression (F) or closed-form inversion of the Laplace transform (I)

1990

Cited by 10

### TABLE I CLOSED-FORM EXPRESSIONS FOR pM(k|i), E[bi,j] AND E[b2 i,j]

2006

### Table 1: Closed Form Analytical Expressions for the gradi- ent and Hessian of the cost function and constraints Cost function - JW

in TIME DOMAIN OPTIMIZATION TECHNIQUES FOR BLIND SEPARATION OF NON-STATIONARY CONVOLUTIVE MIXED SIGNALS

### Table 2a: Closed Form Expressions For Bayes Factor Under Conjugate Prior: w(y) = e y Likelihood Prior Bayes Factor

1994

"... In PAGE 19: ...Table2 b: Closed Form Expressions For Bayes Factor Under Conjugate Prior: w(y) = y or y( ) = y(y ? 1):::(y ? + 1) Likelihood Prior Bayes Factor Gamma( ; ) Gamma( ; ) ?( + )?( + )(y+ ) ?( )?( + + ) Exp0l ( ) Gamma( ; ) ?( +1)?( +1)(y+ ) ?( + +1) Normal( ; 1) Normal( ; 2) Use Tierney-Kadane Approximation Poisson( ) Gamma( ; ) ?(y+ ) ?(y+ ? )y( )(1+ ) Neg.Bin.... ..."

Cited by 5

### Table 1. Closed-form solutions for I d ( )

1999

Cited by 14

### Table 2b: Closed Form Expressions For Bayes Factor Under Conjugate Prior: w(y) = y or y( ) = y(y ? 1):::(y ? + 1) Likelihood Prior Bayes Factor

1994

"... In PAGE 18: ...Table2 a: Closed Form Expressions For Bayes Factor Under Conjugate Prior: w(y) = e y Likelihood Prior Bayes Factor Gamma( ; ) Gamma( ; ) Use Tierney-Kadane Approximation Exp0l ( ) Gamma( ; ) e? y ? (y+ ) Normal( ; 1) Normal( ; 2) exp[12 2(1 ? ?1) + ( (y) ? y)] where (y) = ( 1 1+ 2 ) 0 + 2 1+ 2 y ?1 = 2=( 2 + 1) Poisson( ) Gamma( ; ) (e + 1+ ) ( e + (1+ )e )y Neg.Bin.... ..."

Cited by 5