### Table D.1: Description of some of the sensors to use in future activity recognition systems

2003

### Table 3: The triplet values for the optimal sensor placements and the statistics of the performance measures for the two models. Measures Pfail (%) T (sec) E (mm) Sm

"... In PAGE 18: ... 2 as shown in Figure 8 nds the optimal sensor placement as shown in Figure 9. The triplet values for the optimal sensor placement and the statistics of estimated perfor- mance measures for the two models are shown in Table3 . Object recognition for the model No.... ..."

### Table 3: The triplet values for the optimal sensor placements and the statistics of the performance measures for the two models. Measures Pfail (%) T (sec) E (mm) Sm

"... In PAGE 18: ... 2 as shown in Figure 8 nds the optimal sensor placement as shown in Figure 9. The triplet values for the optimal sensor placement and the statistics of estimated perfor- mance measures for the two models are shown in Table3 . Object recognition for the model No.... ..."

### TABLE 1 SENSOR PLACEMENT CONSTRAINTS Constraint

2002

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### Table 1: The percentage of failed trails Pfail (%) and the average computation time T (sec) for N =1000, 5000 and 10000 under ve di erent sensor placements. Sensor N = 1000 N = 5000 N = 10000

"... In PAGE 13: ... One problem is how many trials should be done to evaluate a sensor placement. Simulation results of 1000, 5000 and 10000 trials under ve di erent sensor placements are shown in Table1 . The percentage of failed recognition trials and the recognition time are almost the same regardless of the number of trials.... In PAGE 14: ...Ri; ti) for i = 1; . . . ; N is obtained as E = 1 N N Xi=1 ei: (10) The probable error E of the position error estimate E is de ned as E v u u u u t 1 N N Xi=1(ei)2 ? (E)2 N : (11) The probable error E is inversely proportional to the square root of the number of trials N, which is regarded as a characteristic of a Monte Carlo method. Given an object as shown in Figure 1, and a set of transformations, an estimated average position error and its probable error under the ve sensor placements from Table1 are... ..."

### Table 1: The percentage of failed trails Pfail (%) and the average computation time T (sec) for N =1000, 5000 and 10000 under ve di erent sensor placements. Sensor N = 1000 N = 5000 N = 10000

"... In PAGE 13: ... One problem is how many trials should be done to evaluate a sensor placement. Simulation results of 1000, 5000 and 10000 trials under ve di erent sensor placements are shown in Table1 . The percentage of failed recognition trials and the recognition time are almost the same regardless of the number of trials.... In PAGE 14: ...Ri; ti) for i = 1; . . . ; N is obtained as E = 1 N N Xi=1 ei: (10) The probable error E of the position error estimate E is de ned as E v u u u u t 1 N N Xi=1(ei)2 ? (E)2 N : (11) The probable error E is inversely proportional to the square root of the number of trials N, which is regarded as a characteristic of a Monte Carlo method. Given an object as shown in Figure 1, and a set of transformations, an estimated average position error and its probable error under the ve sensor placements from Table1 are... ..."

### Table 3. Deterministic sensor deployment results for several placement strategies.

"... In PAGE 9: ...In addition to random deployments, we have studied the effects of several regular, deterministic sensor placement strategies on exposure. Table3 lists the exposure and path lengths for several such strategies of sensor deployment using the 1/d2 (K=2) and 1/d4 (K=4) sensing models, IA and IC intensity models, and varying number of sensors. In the cross deployment scheme, sensors are equally spaced along the horizontal and vertical line that split the square field in half.... ..."

### Table 3. Deterministic sensor deployment results for several placement strategies.

"... In PAGE 9: ...In addition to random deployments, we have studied the effects of several regular, deterministic sensor placement strategies on exposure. Table3 lists the exposure and path lengths for several such strategies of sensor deployment using the 1/d2 (K=2) and 1/d4 (K=4) sensing models, IA and IC intensity models, and varying number of sensors. In the cross deployment scheme, sensors are equally spaced along the horizontal and vertical line that split the square field in half.... ..."

### Table 2. Active vs. passive capabilities of sensors and effectors Sensors Effectors

"... In PAGE 5: ...2 Active and passive components The key components for situating DSSs are sensors and effectors. In Table2 two types of these com- ponents, as well as their capabilities and functions are presented. The examination of the basic capa- bilities of sensors and effectors reveals the fact that some of them are more advanced than the oth- ers.... ..."

### Table 11. Classification by Recognition of Omitted Explanatory Variables.

"... In PAGE 11: ... Nevertheless, some of these variables have been omitted because they are simply unavailable. The classification in Table11 is by recognition of omitted explanatory variables, where the recognition is explicitly stated in the study. Such an explicit recognition of omitted explanatory variables is used primarily as a check of consistency against the number of proxy variables used.... In PAGE 11: ... Such an explicit recognition of omitted explanatory variables is used primarily as a check of consistency against the number of proxy variables used. Of the 50 studies in Table11 , exactly three-fifths did not explicitly recognise that any variables had knowingly been omitted, with the remaining 20 studies Table 10. Classification by Number of Political Explanatory Variables.... In PAGE 13: ...ariables used give mean numbers omitted of 2.45 and 2.72, respectively, a median of 2 in each case, and a mode of 1 in each case. By comparison with Table11 , in which 13 of the 20 studies explicitly recognised the omission of a single explanatory variable, Table 13 shows that only 7 studies used a single proxy variable. Otherwise, the results in Tables 11 and 13 are reasonably similar.... ..."

Cited by 4