### Table 1: Distortion Rate Function Values from Blahut Algorithm

"... In PAGE 6: ... By Laplacian data, we mean that the underlying density is f#28x#29=#281=2#29e ,jxj We created a special MATLAB function #5Claplace quot; #28given in Chapter 9#29 to generate pseudorandom samples simulating output data samples from the Laplacian memoryless source. The D#28R#29values we obtained are given in Table1 below. Each of the #0Cfteen D#28R#29values in the table was obtained byaveraging 10 runs of the function dist rate func on pseudorandomly generated data vectors of length 100; 000.... In PAGE 6: ...f length 100; 000. We rounded o#0B each D#28R#29value to the nearest one-tenth of a decibel. For the same three sources, Jayant and Noll #5B4#5D tabulated the D#28R#29values for R =1; 2; 3. The D#28R#29values in Table1 for R =1; 2; 3 agree with the Jayant-Noll values. Table 1: Distortion Rate Function Values from Blahut Algorithm... In PAGE 6: ...Note from Table1 that uniform data is easiest to compress, followed by Laplacian data. The Gaussian data was the hardest to compress, bearing out our remark at the end of Section 10.... ..."

### Table 1. Comparison of results between grids with and without diagonals. New results

1994

"... In PAGE 2: ... For two-dimensional n n meshes without diagonals 1-1 problems have been studied for more than twenty years. The so far fastest solutions for 1-1 problems and for h-h problems with small h 9 are summarized in Table1 . In that table we also present our new results on grids with diagonals and compare them with those for grids without diagonals.... ..."

Cited by 11

### Table 5. Same as Table 1, but using the metallicity-dependent CO-H2 prescription of Arimoto et al. (1996). The last column shows the relative increase in h i as compared with the stan- dard case X = 3:68

1997

### Table 1{Performance bounds for zero propagation delay algorithms Class of Scheduling Range of Property P3 Property P2 Property P1 Algorithms Throughput k N k

1997

"... In PAGE 13: ...3 For gt; 12, S 6, and n 3, no scheduling algorithm in the class CONTIN- UOUS STATIC has any property P1{P4. Table1 summarizes the throughput and delay characteristics of the scheduling algorithms pre- sented in this and the previous section. The last three columns list the upper bounds for k N k,... ..."

Cited by 45

### Table 2. Speedup in Worst-Case Execution Time for Optimized Virtual Table Algorithm

"... In PAGE 5: ... However, for the OVTA, the optimiza- tion over VTA depends completely on the characteristics of the generator polynomial chosen. Table2 shows the improvement over the VTA for several different polyno- mials (refer to Section 4 for a description of CRC32sub8 and CRC32sub16) . Note that for the particular CRC24 and CRC32 polynomials we used for our experiments, the OVTA has no improvement at all over the VTA.... ..."

### Table 1 shows the classification of safety strategies, clearly showing the details. We have already developed cybernetic actuators (Ikuta et al. 1991) and non-contact magnetic gear (Ikuta, Makita, and Arimoto 1991), which have force limiting functions as safety designs. Other strategies have been devised, such as force limiting equipment using electrorheological fluid (Saito and Sugimoto 1997), force control, shock absorption cover (Suita et al. 1995), and chamfering, etc. Thusfar,littleresearchhasbeencarriedoutonsafetyevalu- ation methods; some of the research to date measures the dan- ger inherent in different actuator arrangements (Dohi 1996) and safety in human control (Saito et al. 1996).

"... In PAGE 3: ... 1996). Table1 . Classification of Safety Strategies International safety standards have defined safety as free- dom from unacceptable risk of harm , and thus estimate only the risk of harm (International Organization for Standardiza- tion 1990).... ..."

### Table 1. The best parameters for tting / gx=ry

1997

"... In PAGE 11: ...11 Table 5. Same as Table1 , but using the metallicity-dependent CO-H2 prescription of Arimoto et al. (1996).... ..."

### TABLE l Parameter values fitted by the optimization algorithms for the test problem*

1976