### Table 4. Performance of SFQ with stationary and non-stationary best-effort arrivals.

1999

"... In PAGE 6: ... We effectively induce a total of ten peak bursty arrival peri- ods throughout the duration of the simulation. Table4 illus- trates the performance measures for experiments 4 to 6. The non-stationary process has no effect on the missed deadlines for Canyon.... In PAGE 6: ... The non-stationary process has no effect on the missed deadlines for Canyon. The slowdown of both short and long jobs be- comes higher than the one shown in Table4... In PAGE 9: ...tributed. A-SFQ behaves almost identically to SFQ with the Canyon workload (see Table4 ). The multimedia demand is so low that the work-conserving behavior of SFQ is enough for the system to balance the CPU proportions among the two application classes.... ..."

Cited by 10

### Table 5. Performance of A-SFQ under stationary and non-stationary best-effort arrivals.

1999

"... In PAGE 8: ... 4.2 Performance of A-SFQ under Stationary ar- rivals of Best-effort Jobs Table5 presents the performance measures for experi- ments 1 to 3.... ..."

Cited by 10

### Table 1: Recursive least L normed errors training algorithm. If the lter is to be used in a non{stationary signal environment, the partition H may need to be periodically updated. To account for the changing signal statistics an exponential \forgetting quot; factor can easily be added to the sum of L normed estimate errors. The sum to be minimized is now

1994

"... In PAGE 20: ... The sum to be minimized is now E (M; H) = M X n=1 (M?n)jd(n) ? FP(x(n); H)j ; (42) where 2 (0; 1] is the \forgetting quot; factor. The recursive permutation lter training algorithm that minimizes E (M; H) in (42) is sum- marized in Table1 . The algorithm initially sets the permutation lter to the median lter, and then updates the decision vector according to each new observation.... ..."

Cited by 5

### Table 1. Methods of analysis for deterministic systems subjected to random loading (MCS: Monte Carlo Simulation, Stat.: Stationary, lin.: linear, N-St.: Non-stationary, FWN: Filtered white noise)

"... In PAGE 3: ....r.t. practical application. Tables 1 and 2 provide an overview of the applicability of the various methods available, where Table1 refers to deterministic and Table 2 to stochastic structural properties, respectively. In the following, a brief description and discussion of the various methods in view Table 1.... ..."

### Table 2. Methods of Analysis of systems with uncertain properties and random loading. (MCS: Monte Carlo Simulation, Stat.: Stationary, lin.: linear, N-St.: Non-stationary)

"... In PAGE 3: ....r.t. practical application. Tables 1 and 2 provide an overview of the applicability of the various methods available, where Table 1 refers to deterministic and Table2 to stochastic structural properties, respectively. In the following, a brief description and discussion of the various methods in view Table 1.... ..."

### Table 1: Word Accuracy (in %) in simulated non-stationary noises, achieved by the sequential Monte Carlo method in comparison with baseline without noise com- pensation, denoted as Baseline, and noise compensation assuming stationary noise, denoted as Stationary Compensation. Experiment Baseline Stationary # samples = 60 # samples = 120

2002

"... In PAGE 6: ... Second, the larger driving noise variance V will make faster convergence but larger estimation error of the method. In terms of recognition performance, Table1 shows that the method can efiectively improve system robustness to the time-varying noise. For example, with 60 samples, and the environment driving noise variance V set to 0.... ..."

Cited by 4

### Table 2: 5 APPLICATION TO NON-STATIONARY POISSON PROCESSES

1998

Cited by 2

### Table 3: Non-Stationary Exponential Distribution Experiment: Exponential Cases

### Table 8. FPGA resource utilization for uni-variate non-stationary growth model implementation.

"... In PAGE 15: ...ystem.............................................................................................................. 80 Table8 .FPGA resource utilization for uni-variate non-stationary growth model implementation.... ..."