### Table 3 The Angular Spectrum

"... In PAGE 6: ... In the C` power spectrum this should show up as a constant C` = C0 contribution, which is found by using the e ective rms con- tributed by a random distribution of point sources, rms2 = P` (2` + 1)C0W`=(4 ), and the known beam window function W`. For results quoted in the units of Table3 , this leads to an estimated... In PAGE 7: ... 7. Power Spectra The results are summarized in Table3 and in Figure 2. The sources of uncertainty that are included in the errors quoted in Table 3 include sample variance and statistical noise.... In PAGE 7: ... Power Spectra The results are summarized in Table 3 and in Figure 2. The sources of uncertainty that are included in the errors quoted in Table3 include sample variance and statistical noise. The former dominates at ` lt; 600 and the latter at higher `.... In PAGE 7: ... These combine to produce an un- certainty in the e ective beam of 13%. This un- certainty is not included in the errors quoted in Table3 , as its e ect is to produce an overall tilt to the spectrum. The amplitude of the tilt cor- responding to the 1 sigma uncertainty that is as- signed to the e ective beam width is illustrated in Figure 2.... ..."

### Table 8: Effect of load on spectrum

2003

"... In PAGE 10: ...Table 8: Effect of load on spectrum The above observation indicates that although the load reduces the overall packet rate by the attack tool our technique for gener- ating spectral signature is robust to load and thus can be used to identify repeated attacks. Table8 summarizes the quality of the signature matches under load conditions. The entries in the table match the spectral signatures of the Linux testbed machines, with and without load.... ..."

Cited by 1

### Table 1. Discrete spectrum and estimated errors for sample A.

### Table 2. Discrete spectrum and estimated errors for sample B.

### Table 3. Discrete spectrum and estimated errors for sample C.

### Table 4. Revised KM Spectrum and Applications

2003

"... In PAGE 11: ... Since asset improvement is normally done using computer-based statistical techniques, but does not transform the asset into a different form, it belongs to the left of Asset Management but to the right of Analytical KM in the spectrum. On the basis of this, we suggest a revised version of the KM spectrum ( Table4 ). We also suggest revisions to Table 2 and Table 3, which are presented in list form below.... In PAGE 13: ... The implications of this are various: we see that Binney has not tried to develop any new knowledge about KM approaches, but has simply analysed existing ones. This might imply that there are more KM approaches waiting to be identified or developed; however, the fact that one or other of the approaches covers nearly all the KM approaches identified in Section 2 suggests that the set of approaches in the KM spectrum (or at least, in the revised spectrum shown in Table4 ) is nearly or fully complete. We also see that the applications, technologies, and mappings to knowledge management strategies identified in Tables 2-4 are crucial to the task of selecting an appropriate KM strategy.... ..."

Cited by 3

### Table 1. SW Estimation Techniques

2001

Cited by 9

### Table 3: Relationship of the Haar Spectrum and Output Probabilities

2000

"... In PAGE 9: ... The following table contains symbols for each of the Haar coe cients, Hi, values that indicate the size of the constituent function range, i and n, and probability expressions that evaluate whether the function to be transformed and the constituent function simultaneously evaluate to logic-0 (denoted as pm0), or evaluate to logic-1 (denoted as pm1). Using the notation introduced in Table3 , we can write an algebraic equation to compute the value of the kth Haar spectral coe cient in terms of the output probabilities used to compute pm0 and pm1. Hk = 2n?i[2(pm0 + pm1) ? 1] (13) 7 Probability Extraction from Graphs Techniques for the extraction of output probability values from generalized graphs are given here.... ..."

Cited by 1

### Table 12: High redshift AGN candidates selected using the ultra steep spectrum technique.

2006

"... In PAGE 15: ... The other 4 USS sources could be high redshift AGN. These candidates are listed in Table12 . Followup observations of these candidates are required to determine if these sources are HzRGs.... ..."