### Table 3. Filtering and consistency check

2006

"... In PAGE 12: ... As noted in earlier sections, the summary Abox can be computed once, and maintained with changes to the Abox. Table3 (a) shows the effectiveness of filtering the summary ABox for the consistency detection test, and the time to perform filtering. Note that the fil- tering step is dynamic, i.... In PAGE 12: ... The filtering step can create partitions. In Table3 (a), the first number in the first column (Sin.+Mult) indicates the number of partitions with single individuals, and the second number indicates the number of partitions with multiple individuals.... In PAGE 12: ... The rest of the columns show the size of the Abox that is left after removing all partitions with single individuals. Table3 (b) shows the size of the Abox on which we had to perform the consistency check. All times for the consistency check were measured using the Pellet OWL reasoner.... In PAGE 12: ... All times for the consistency check were measured using the Pellet OWL reasoner. For those Aboxes where the filtered summary Abox in Table3 (a) was consistent, the size of the ABox was simply that in Table 3(a) . For... In PAGE 13: ... For these ontologies, we had to retrieve the image of the inconsistent partition from the original Abox. In these cases, the size shown in Table3 (b) is the image of the partition in the original Abox. Time for consistency check is provided in seconds.... In PAGE 13: ...artition in the original Abox. Time for consistency check is provided in seconds. This includes the time for the concept satisfiability check for partitions with single individuals, the time for the consistency check on the filtered summary, and the time for retrieving and checking the image of the inconsistent partition on the original Abox. As shown in Table3 (b), ST was an inconsistent ontology, but we determined this purely based on a concept satisfiability check for partitions with single individuals. We also deliberately injected an inconsistency for one of the Biopax databases (agrocyc), to check if we could detect an inconsistency that could not simply be detected by a concept satisfiability check.... ..."

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### Table 1. Impulsive noise models; envelope PDFs and LO nonlinear filters.

2002

"... In PAGE 13: ...onparametric filters. These require no explicit knowledge of the noise PDF. An example is the hardlimiter narrowband correlator (HNC) filter ([8, 10]) which is widely used in impulsive environments; y y g 1 ) ( = (4) The parametric version of the processor requires a choice of noise model, and estimation of the model parameters from the received data. The LO filters for several impulsive noise models are given in Table1 . To apply the processor, we read-in a segment of time-series data, estimate the model parameters from that segment of data,9 and then input these parameter estimates into the nonlinear filter to tune the processor.... ..."

### Table 2: Optimization results aEWF: elliptical wave filter, FIR: finite impulse response filter, BF: bandpassfilter, Edge: edge detection

1999

"... In PAGE 4: ... The running times of the optimization routine were for all examples approximately one minute (SparcSta- tion 20)6. Table2 shows the component requirement after synthesis for the original and the optimized data-flow graph. The underlying ILP-based synthesis system [9] guarantees that all computed results for both the original and trans- formed version are optimal with respect to the total costs of components.... ..."

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### Table 2: Optimization results aEWF: elliptical wave filter, FIR: finite impulse response filter, BF: bandpassfilter, Edge: edge detection

"... In PAGE 4: ... The running times of the optimization routine were for all examples approximately one minute (SparcSta- tion 20)6. Table2 shows the component requirement after synthesis for the original and the optimized data-flow graph. The underlying ILP-based synthesis system [9] guarantees that all computed results for both the original and trans- formed version are optimal with respect to the total costs of components.... ..."

### Table 1. Quality measures for the filtering of impulsive noise degraded lena image

### TABLE 1: Dimensions of the stepped impedance low pass filters

"... In PAGE 26: ...TABLE1 : Comparative study on some PLD technology Technology Typical data retention Typical erase / Typical erase / program time program cycles times EPROM Greater than 10-20 years OTP-10,000 times Some minutes UV-light / about 0.1 msec, per cell EEPROM Greater than 10-20-years Greater than 1,000- Some milliseconds per cell 10.... In PAGE 41: ...IETE TECHNICAL REVIEW, Vol 24, No 6, November-December 2007 TABLE1 : The functionality of different mechanical and vacuum components Sr.No Components To be checked 1 Window Shutters Operational 2 Transfer Mechanism Operational 3 All Valves Operational 4 Ion Pump controller Thermal cooling fan is OK Input is free from surge voltage 5 TSP Filaments are OK 6 Rotary and Turbo Pumps Oils have to be checked from time to time 7 Shutters Operational at room temperature and higher temperature.... In PAGE 66: ...Fig 6 (a) Sample image, (b) Non-iterative pyramidal median transform of sample image for three scale (a)(b) Fig 4 Interpolated image TABLE1 : Quantitative analysis of filtering method for different noiss images Salt and Pepper Gaussian noise Speckle noise Noise PSNR MSE PSNR MSE PSNR MSE level (dB) (dB) (dB) 10% 34.13 0.... ..."

### Table 6.5.1-1. Results from Figure 6.5.1.1 Illustrating Noise Effects of Matching Features.

1993

### Table 6.5.1-2. Matching Results from Figure 6.5.1.2 Illustrating Noise Effects on Disparity Range.

1993

### Table 1: Interpolation Filters: Optimal and Bilinear

"... In PAGE 4: ...Table 1: Interpolation Filters: Optimal and Bilinear and the aliasing error AE = jjH(?z)G(z)X(?z)jj2; introduced by the sampling process, where X(z) is the Z-transform of a step signal. Optimal interpolation lters G(z) can be found in this way for di erent lter lengths n, and they are given in Table1 (a) for DF=2, along with two PSNR values: PSNR1 corresponds to the PSNR obtained with a step signal, while PSNR2 corresponds to the PSNR obtained when applying steps 1 and 4 in the horizontal direction to a real image (Figure 4). Note that little PSNR improvement results from examining lters longer than n = 6.... In PAGE 4: ... Note that little PSNR improvement results from examining lters longer than n = 6. Table1 (a) also contains the PSNR performance of the bilinear interpolation lter. Its PSNR performance is about 1 dB below that of the best optimal lters.... In PAGE 5: ...the one corresponding to a PSNR improvement over no pixel averaging. As a consequence, for all values of the DF used in the following experiments, we will consider only bilinear interpolation lters, coe cients of which are given in Table1 (b). 2 3 4 5 6 7 15 20 25 30 35 Log2(Compression Ratio) Peak-Signal-to-Noise Ratio (PSNR in dB) * : H1V1 + : H2V1 + BILIN --: H2V1 + (n=6) Figure 2: MSE (PSNR) Performance Comparison between Bilinear and Optimal Interpola- tion Filters combined with Lossy JPEG 3 Image Description and Compression Assessment The lander is in the shape of a tetrahedron.... ..."

### TABLE II COMPARATIVE RESULTS IN PSNR (dB) OF FILTERING DIFFERENT IMAGES CORRUPTED BY 20% RANDOM- VALUED IMPULSES

2002

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