### Table 2. Total flux for Problem 1

2000

"... In PAGE 14: ... Table2 . Total flux for Problem 1 (cont.... ..."

### Table 1: Coefficients ak and corresponding Riesz bounds for small N.

1999

Cited by 7

### Table 2: Definitions of terms used in the derivations.

in Partial-volume bayesian classification of material mixtures in MR volume data using voxel histograms

1998

Cited by 32

### Table 1: Lower bounds on the star network. The algorithms derived here for all of the above problems are optimal in terms of time and number of message transmissions. Some of the methods used in this sections to derive lower bounds for the commu- nications problems under consideration are similar to the methods used in [7] to derive lower bounds for similar problems on the hypercube network.

1996

"... In PAGE 14: ... The lower bounds for the algorithms with controlled degree of fault tolerance will be derived in the following sections along with the description of the algorithms. Table1 below summarizes the lower bounds for all of the above problems, with degree of fault tolerance n ? 2, and M messages transmitted to each node. By tn we denote the quantity n!(n + 2... ..."

Cited by 13

### TABLE V DEFINITIONS OF TERMS USED IN THE DERIVATIONS.

in Partial-Volume Bayesian Classification of Material Mixtures in MR Volume Data using Voxel Histograms

1998

Cited by 1

### TABLE V DEFINITIONS OF TERMS USED IN THE DERIVATIONS

1998

Cited by 1

### TABLE V DEFINITIONSOF TERMS USED IN THE DERIVATIONS.

in Partial-Volume Bayesian Classification of Material Mixtures in MR Volume Data using Voxel Histograms

### Table 2. Rules for deriving valid term judgements

2003