### Table 8-5. Median Filter simulation results including partial redundancy unrestricted alias analysis.

2003

"... In PAGE 91: ... 8.2 Results Table8 -1, Table 8-2, and Table 8-3 compare the simulation results of the baseline compiler, partial redundancy elimination, and instruction scheduling. Each table also provides the simulation time when partial redundancy elimination and instruction scheduling are both applied.... In PAGE 91: ...46% 892,353 23.11% partial scheduling combined Table8 -1. IFFT baseline simulation results.... In PAGE 91: ...36% 289,837 7.35% partial scheduling combined Table8... In PAGE 92: ...61% 8,099,096 3.62% partial scheduling combined Table8 -3. Matrix Multiply baseline simulation results.... In PAGE 93: ...95% 26.91% partial w / aa combined Table8 -4. IFFT simulation results including partial redundancy unrestricted alias analysis.... In PAGE 94: ...11% 35.53% partial w/ aa combined Table8 -6. Matrix Multiply simulation results including partial redundancy unrestricted alias analysis.... ..."

Cited by 3

### Table 8-2. Median Filter baseline simulation results.

2003

"... In PAGE 91: ... 8.2 Results Table8 -1, Table 8-2, and Table 8-3 compare the simulation results of the baseline compiler, partial redundancy elimination, and instruction scheduling. Each table also provides the simulation time when partial redundancy elimination and instruction scheduling are both applied.... In PAGE 91: ...46% 892,353 23.11% partial scheduling combined Table8 -1. IFFT baseline simulation results.... In PAGE 92: ...61% 8,099,096 3.62% partial scheduling combined Table8 -3. Matrix Multiply baseline simulation results.... In PAGE 93: ...95% 26.91% partial w / aa combined Table8 -4. IFFT simulation results including partial redundancy unrestricted alias analysis.... In PAGE 93: ...49% 15.22% partial w/ aa combined Table8 -5. Median Filter simulation results including partial redundancy unrestricted alias ... In PAGE 94: ...11% 35.53% partial w/ aa combined Table8 -6. Matrix Multiply simulation results including partial redundancy unrestricted alias analysis.... ..."

Cited by 3

### Table 2. Axioms for recursion

2007

"... In PAGE 7: ... This constant is denoted by hXjEi. The additional axioms for recursion are given in Table2 . In this table, we write htXjEi for tX with, for all Y 2 V(E), all occurrences of Y in tX replaced by hY jEi.... ..."

### Table 4: Popularity of filters for recursion depth 1 and 2 in psample.

2007

"... In PAGE 7: ... To detect popular filters, we run the algorithm of Section 4.1 on the observed routes of psample (see Table4 ). Using a maximum recursion depth of 1 and 2 in our algorithm reveals the impact of the recursion depth on the popularity of the identified filters.... In PAGE 7: ...Table 4: Popularity of filters for recursion depth 1 and 2 in psample. Table4 provides further details about the popularity of filters. There are some locations for filtering which seem to be very pop- ular.... In PAGE 8: ... We compare business relationships with our candidate filters as follows: as a first step, popular locations for filtering are identified. According to Table4 , 5% of the filters found are useful for more than 8,000 prefixes. We select those filters and obtain a total of 2,290 popular filters (see Table 5).... ..."

Cited by 2

### Table 5 : Times required for filter reconfiguration and writing partial bitstreams for filter

2001

"... In PAGE 10: ........... 71 Table5 : Times required for filter reconfiguration and writing partial bitstreams for filter banks of varying number of taps. The partial bitstream file size is also reported for each configuration.... ..."

Cited by 3

### Table 4: Popularity of filters for recursion depth 1 and 2 in psample.

2007

"... In PAGE 7: ... To detect popular filters, we run the algorithm of Section 4.1 on the observed routes of psample (see Table4 ). Using a maximum recursion depth of 1 and 2 in our algorithm reveals the impact of the recursion depth on the popularity of the identified filters.... In PAGE 7: ...hat large recursion depths add a lot of noise, i.e., they identify can- didate filters at locations which are unlikely to be related to the mismatches we try to fix. Table4 provides further details about the popularity of filters. There are some locations for filtering which seem to be very pop- ular.... In PAGE 8: ... We compare business relationships with our candidate filters as follows: as a first step, popular locations for filtering are identified. According to Table4 , 5% of the filters found are useful for more than 8,000 prefixes. We select those filters and obtain a total of 2,290 popular filters (see Table 5).... ..."

Cited by 2

### Table 4: Popularity of filters for recursion depth 1 and 2 in psample.

2007

"... In PAGE 7: ... To detect popular filters, we run the algorithm of Section 4.1 on the observed routes of psample (see Table4 ). Using a maximum recursion depth of 1 and 2 in our algorithm reveals the impact of the recursion depth on the popularity of the identified filters.... In PAGE 7: ...hat large recursion depths add a lot of noise, i.e., they identify can- didate filters at locations which are unlikely to be related to the mismatches we try to fix. Table4 provides further details about the popularity of filters. There are some locations for filtering which seem to be very pop- ular.... In PAGE 8: ... We compare business relationships with our candidate filters as follows: as a first step, popular locations for filtering are identified. According to Table4 , 5% of the filters found are useful for more than 8,000 prefixes. We select those filters and obtain a total of 2,290 popular filters (see Table 5).... ..."

Cited by 2

### Table 2 MAE results for median filtering

"... In PAGE 7: ... Salt-and-pepper noise is commonly cleaned in image pro- cessing by median filtering and we have compared this tra- ditional technique to the proposed approach. Table2 shows the MAE results for two increasing size median filters. Com- paring these results with those presented in Table 1 indicates seemed performances using at least 1 image to select the win- dow and 1 image to train the W-operator for the case of Fig- ure 8.... In PAGE 7: ...1.7% (from 0.00046 to 0.00036). Figures 12(c) and 12(d) illustrate the results obtained by applying median with feed- backing to the Figures 10(e) and 10(f) respectively. Finally, Table 4 shows the results for the resulting images with MAE given by Table2 taken as input to the median operation. The application of the feedback operation more than once does not change significantly the results.... ..."

### Table 3 Median Filter Linear Transform Shearing

2001

Cited by 3