### Table 3. Classification of M-Commerce constraints and their impact on the design process according to existing mobile devices

2002

"... In PAGE 7: ... Therefore it is necessary to classify the constraints of mobile networks and devices according to the type of mobile device since they share different characteristics and requirements. Table3 classifies the impact on the design and implementation phase of each constraint as high, medium or low for each type of mobile device. For example, a mobile service provider who wishes to offer its mobile users the ability to purchase flowers, has to take into account several constraints that affect the design and development of the m-Commerce application according to the mobile device used.... ..."

Cited by 1

### Table 1: Tight bounds on the anticipation of encoders for several (d; k)-RLL constraints.

"... In PAGE 24: ...Speci c examples In the examples below, we study three constraints, for which we obtain tight lower bounds. Our results are summarized in Table1 . The table also lists references to encoders that attain our bounds.... In PAGE 28: ...5, as it appears in [4], can be modi ed and extended to show that the (AG; n)-approximate eigenvector x in the theorem also satis es the bound kxk na. When this additional condition is taken into account, then the results in Table1 can be obtained also from this extension of the theorem. Nevertheless, Example 6.... In PAGE 29: ... = a = 2. In other words, Theorem 2.5 does not rule out anticipation 2 in this example. 4 quot;3 2 1 2 Figure 12: Graph H for Example 6.4 7 Conclusion In this work, we presented lower bounds on the anticipation of encoders for input-constrained channels|in the general case and in three particular cases of practical value that are summa- rized in Table1 . We also demonstrated the universality of the state-splitting algorithm with respect to encoders with nite anticipation: every nite-state encoder with nite anticipation can be obtained by state-splitting operations, followed by a reduction of states.... ..."

### Table 1: Tight bounds on the anticipation of encoders for several (d;; k)-RLL constraints.

"... In PAGE 24: ...Speci c examples In the examples below, we study three constraints, for which we obtain tightlower bounds. Our results are summarized in Table1 . The table also lists references to encoders that attain our bounds.... In PAGE 28: ...5, as it appears in [4], can be modi ed and extended to showthat the (A G ;;n)-approximate eigenvector x in the theorem also satis es the bound kxk n a . When this additional condition is taken into account, then the results in Table1 can be obtained also from this extension of the theorem. Nevertheless, Example 6.... In PAGE 29: ... = a =2. In other words, Theorem 2.5 does not rule out anticipation 2 in this example. 4 quot; 3 2 1 2 Figure 12: Graph H for Example 6.4 7 Conclusion In this work, we presented lower bounds on the anticipation of encoders for input-constrained channels|in the general case and in three particular cases of practical value that are summa- rized in Table1 . We also demonstrated the universality of the state-splitting algorithm with respect to encoders with nite anticipation: every nite-state encoder with nite anticipation can be obtained by state-splitting operations, followed by a reduction of states.... ..."

### Table 1. Characteristics of constraints for several algorithms

2004

"... In PAGE 11: ... Thus the complexity of the constraints we gathered was mostly relatively small and hence manageable in a short amount of time. In particular our tool is able to generate the complete sets of test cases regarding the def-use chain coverage for the algorithms mentioned in Table1 in a few seconds. This table shows what kinds of constraints occur in the example applications we have considered.... ..."

Cited by 3

### Table 1. OCL-Constraints and implementation model for Account Subsystem

2001

Cited by 15

### Table 2 Several aspectual markers and associated constraints on aspectual class.

2000

Cited by 29

### Table 1: Several demand uncertainty models based on outbound traffic constraints.

2005

"... In PAGE 4: ...Table1... ..."

Cited by 2

### Table 1: The variance (cumulative) accounted for by each eigenvector for several di erent objects, both for sparse and dense training sets.

1999

Cited by 46