### Table 1. Minimum number of sensors required to cover 100% of the field using the Min-Cover ILP approach and a greedy heuristic based approach.

2003

"... In PAGE 5: ... B. Min-Cover Results Table1 presents a sampling of results for networks with the number of sensors ranging from 50 to 700 nodes. For each instance, the third column of the table contains the average number of sensors in the mini- mum cover solution for 100 randomly generated net- works using the Instance Generator.... ..."

Cited by 8

### Table 1 The Distribution of Students apos; Estimates of Wages by Field, Degree and Experience

1996

"... In PAGE 9: ... IV. VARIATIONS IN STUDENTS apos; BELIEFS ABOUT WAGES Table1 describes the distribution of students apos; beliefs about salaries for each question asked. It displays the 10th, 50th and 90th percentile wage beliefs, along with the standard deviation and the standard deviation divided by the mean.... In PAGE 10: ... Thus, it may be that a large degree of variation in beliefs about salaries in a given field or for a given level of education may mask quite uniform beliefs about relative salaries, in that some students consistently overestimate or underestimate salaries in all fields or for all levels of education. Accordingly, eleven relative starting salaries were calculated based on the questions in the middle panel of Table1 , using the starting salary for a Bachelor apos;s degree in Chemistry as the numeraire. The average of the standard deviation divided by the mean for these relative salaries was 0.... In PAGE 15: ... This finding accords with the predictions of the informal model discussed above. 14 For all of the wage questions listed in Table1 , the mean percentage error was -5.8%.... In PAGE 15: ... The median of the absolute percentage errors is a better measure of the errors typically made than is the mean of the signed errors. The average of these medians across all wage questions shown in Table1 is 19.... In PAGE 43: ... This does not appear to be the case, though. When all of the salaries in the top two panels of Table1 were converted to relative salaries using the salary of those with a Bachelor apos;s degree in chemistry as the numeraire, estimates of relative errors were quite similar, but were slightly higher in 11 of 13 cases.... ..."

Cited by 1

### Table 4. Consensus network decoding strategies using TER-based alignment on Chinese-English text translation.

2007

"... In PAGE 3: ... Next, various decoding strategies described in Section 5 were compared. The RI and RPB methods do not seem to help improve the phrase coherency in the original translations, as depicted by the degradation in BLEU scores in Table4 . On the other hand, select- ing hypotheses that have the minimum loss w.... ..."

Cited by 2

### Table VI. Estimated number of clusters by consensus clustering (CC) and by the Gap statistic, in combination with hierarchical clustering (HC) and self-organizing map (SOM). Application to gene-expression data. In parentheses is the estimated number of clusters based on visual inspection of the consensus matrices (when this differ from the one based on the consensus distribution).

Cited by 1

### TABLE I COMPARISON BETWEEN DIFFERENT COVERAGE PROTOCOLS, WHERE a2 IS THE TOTAL NUMBER OF SENSORS IN THE NETWORK, a3 THE NUMBER OF NEIGHBORS OF A GIVEN SENSOR AND a4a6a5 THE COMPUTATION REQUIRED BY THE CONTROL LAW USED. NUMERICAL COMPUTATION RESULTS BASED ON MATLAB IMPLEMENTATIONS FOR COMMON SETS OF 200 EVENTS OF DIFFERENT DISTRIBUTIONS IN A NETWORK OF 200 SENSORS. THE NUMBER OF FIXED SENSORS FOR EACH ALGORITHM IS GIVEN FOR THREE DIFFERENT REPRESENTATIVE EVENT DISTRIBUTIONS.

2004

Cited by 3

### TABLE I COMPARISON BETWEEN DIFFERENT COVERAGE PROTOCOLS, WHERE s IS THE TOTAL NUMBER OF SENSORS IN THE NETWORK, n THE NUMBER OF NEIGHBORS OF A GIVEN SENSOR AND Cc THE COMPUTATION REQUIRED BY THE CONTROL LAW USED. NUMERICAL COMPUTATION RESULTS BASED ON MATLAB IMPLEMENTATIONS FOR COMMON SETS OF 200 EVENTS OF DIFFERENT DISTRIBUTIONS IN A NETWORK OF 200 SENSORS. THE NUMBER OF FIXED SENSORS FOR EACH ALGORITHM IS GIVEN FOR THREE DIFFERENT REPRESENTATIVE EVENT DISTRIBUTIONS.

2004

Cited by 3

### Table 4.3: Parameter Estimates Obtained Using Filtered Data, Two Vector Sensors

2004

### Table 4.4: Parameter Estimates Obtained Using Filtered Data, Accelerometer-Only Sensor Suite

2004

### Table 2. Numerical filter specification for an estimate based on five points. The derivative is being calculated for the point at location 0 .

2000

"... In PAGE 44: ... The temporal derivative is one-dimensional and requires only a convolution with the derivative filter to compute fy. Table2 below gives the numerical values used for differentiation based on a five-point filter [36].... In PAGE 46: ... This provides a total of n2 total flow measurements (vectors in Figure 33) for the network. For the current study, 25 flow measurements are sufficient and 5x5 pixel patches will be used henceforth, allowing the filters detailed in Table2 to be used. This patch size is also the one illustrated in Figure 33.... ..."

Cited by 16

### Table 14-1: CyberGrasp sensors.

"... In PAGE 3: ... DATA ACQUISITION The development of haptic devices is in its infancy. We have focused our research and experiments on the CyberGrasp exoskeletal interface and accompanying CyberGlove, which consists of 33 sensors ( Table14 -1). We use the CyberGrasp SDK to write handlers to record sensor data for our experiments whenever a sampling interrupt occurs.... In PAGE 3: ... We term each of these 10 letters a sign. The 22 sensor values (excluding sensors 23 to 33 in Table14 -1) are recorded in a log file for each sign made by a subject, termed as a session. Each session log file con- tains thousands of rows of sensor values sampled at some frequency, which depends on the sampling technique used.... In PAGE 14: ...92). Table14 -2 illustrates a comparison among the techniques. Table 14-2: Overall classification error.... In PAGE 14: ....92). Table 14-2 illustrates a comparison among the techniques. Table14 -2: Overall classification error. Error Standard Derivation C4.... In PAGE 14: ...e., C, G, and H ) quite well (see Table14 -3). Note that although C4.... In PAGE 15: ... Understanding of User Behavior in Immersive Environments Chapter 14 Bayesian Classifier decides based on probability distribution of the input samples, it tends to perform quite well overall despite intuitive variations in performance of signs by different subjects. Table14 -3: Best recognition technique for each sign. A B C D E F G H I L C4.... In PAGE 15: ...N C4.5 C4.5, B C4.5 Table14 -4: Nearest neighbors for each sign in multidimensional space. Nearest Farthest Avg.... In PAGE 15: ...693604 L G D A E H C I F B 2.697530 As illustrated in Table14 -8,1 we see that the best classifier for a sign is not necessarily the one that confuses the sign with fewer other signs. The decision to choose a classifier from given classifiers becomes very much application-dependent.... In PAGE 16: ...253 Bayesian Classifier and an appropriate I/O design we can achieve an acceptable perform- ance. Table14 -5: C4.5 Precision and recall.... In PAGE 16: ... 0.00 0.00 0.00 6.66 6.66 0.00 6.66 0.00 0.00 80.00 Table14 -6: Bayesian precision and recall. A B C D E F G H I L A 86.... In PAGE 17: ... Understanding of User Behavior in Immersive Environments Chapter 14 Table14 -7: Neural network precision (standard deviation) and recall. A B C D E F G H I L A 71.... In PAGE 17: ...57, 9.32 Table14 -8: Number of other signs with which each sign is confused for different classifiers. Sign Bayesian C4.... ..."