### 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. Predators and prey for blue crab (Callinectes sapidus) across five studies (Baird and Ulanowicz, 1989; Christian and Luczkovich, 1999; Kemp et al., 1977; Odum and Heald, 1975; Peterson, 1979) after recompiling and mapping to taxa in the EcoLens database. All organisms were mentioned in only one study except for those mentioned in two studies, with (2) after the name.

"... In PAGE 7: ...iled from Cohen, 1989; Dunn et al., 2006; Jonsson et al., 2005; Myers et al., 2006; Vazquez, 2005. Original data source (publica- tion years) N webs Original N nodes Original N links Mean % species per web (SD) De- aggregated N nodes De-aggre- gated N links Distinct taxa EcoWEB (1923- 1988) 213 4064 8295 39 (31) 4503 11967 2512 Webs on the web (1989-2000) 15 1366 9412 53 (29) 1373 12056 1149 Interaction Web Database (1998- 2003) 26 2185 9882 55 (8) 2139 9882 479 Animal Diversity Web (N/A) n/a 713 3025 80 711 2165 890 Tuesday Lake 2005 2 41 127 82 (3) 101 510 71 Totals 256 8369 30741 8827 36580 5101 Example 2: Answering specific predator-prey questions What organisms are trophically linked to blue crabs, Callinectes sapidus? This species appears in five data sources which report similar though nearly exclusive suites of predators and prey ( Table2 ). Some organisms are both preda- tor and prey though each case is reported by only one study.... ..."

### Table 8: Criterion Selection Techniques Approach Description Application Objectives (#) Chromosome

"... In PAGE 23: ...A POSTERIORI TECHNIQUES 5.3 Aggregation Selection Techniques Table8 : (continued) Approach Description Application Objectives (#) Chromosome Multiobjective GA [151] (1997) Both nondominated- and roulette wheel (one objective)- based selection Transonic airfoil design (2) Mach number; Lift coe cient Binary string; Genes are air- foil parameters Multiobjective GA [109] (1998) \Two-branch quot; tourna- ment selection; Individ- uals compete in only one of 2 tournaments; Linear penalty func- tions Non-collocated control (2) Control error of Disk 1 rotational position; Same for Disk 2 Binary string; Genes are con- troller gains Multiobjective GA [42] (1998) \Two-branch quot; tourna- ment selection; Individ- uals compete once in each of 2 tournaments; External penalty func- tions Satellite constella- tion design (2) Constellation altitude; Number of satellites Binary string Multiobjective GA [31] (1998) \Two-branch quot; tourna- ment selection; Indi- viduals compete once in each of 2 tourna- ments; Scaled penalty functions Two 10-bar truss designs (2) Weight; Verti- cal displacement Binary string Parallel GA [?] (1998) \n-branch quot; tourna- ment selection; Parallel implementation; In- tegrated with XFOIL and WOPWOP codes; Penalty functions enforce constraints Airfoil optimization (2) Drag coef- cient; Overall Averaged Sound Pressure Level Binary string; Gray coded ES [90] (1998) \Predator-prey quot;model; Predators \attack quot; based on one of k objectives None (2) Numeric opti- mization Real values 5.3 Aggregation Selection Techniques Aggregation selection techniques also directly use an EA apos;s population capability.... ..."

### Table 1 summarizes the evolution of the assignments pre- sented to the students during the analyzed semesters. Oc- tave was flrst introduced in the spring semester of 2001/02, the knowledge was evaluated with a single exercise where the students were asked to simulate evolution of a predator- prey system[1]. This work had been already done using MS- Excel, so the students could compare the possibilities and difierent characteristics of each tool. Although students had

"... In PAGE 3: ...Table1 : Assignments presented to students over the semesters. found MS-Excel easier to use, they were convinced that Oc- tave was more exible and user conflgurable.... ..."

### Table 3: Nonlinear dynamic model with trend

"... In PAGE 17: ... Turning to the model of income dynamics, Table 2 gives our estimates of equation (4) without the trend (suppressing the constant term in 4).7 Table3 gives the results including the 7 The sample mean annual income is Yuan 446 per capita at 1985 prices (with a standard deviation of 264), while the corresponding mean for expenditure is Yuan 345 (standard deviation of 166).... ..."

### Table 3: Nonlinear dynamic model with trend

"... In PAGE 14: ... Turning to the model of income dynamics, Table 2 gives our estimates of equation (4) without the trend (suppressing the constant term in 4).7 Table3 gives the results including the 7 The sample mean annual income is Yuan 446 per capita at 1985 prices (with a standard deviation ... ..."

### TABLE I Fuzzy model of the nonlinear dynamic plant.

### Table 3: Generalized Impulse Responses: Average over all Models

1999

"... In PAGE 19: ... We present the results in various forms. Table3 contains all the information on the responses for the 24 month horizon. In order to help describe the various dynamic asymmetries we also present the information in graphical form.... ..."

Cited by 7

### Table 3: Generalized Impulse Responses: Average over all Models

"... In PAGE 19: ... We present the results in various forms. Table3 contains all the information on the responses for the 24 month horizon. In order to help describe the various dynamic asymmetries we also present the information in graphical form.... ..."

### Table 2. Solution time (in minutes) for nonlinear dynamic analysis

"... In PAGE 38: ... model under three different conditions: without any intermediate storage, using Oracle database to save the information at every 20 time steps, and using file system to save the information at every 20 time steps. Table2 shows the solution time for the nonlinear dynamic analysis. Since the model is fairly large and some expensive elements (fiber element) and materials (nonlinear) are used in the model, the nonlinear dynamic analysis requires a significant amount of computational time.... ..."

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