### Table 3 Application Aperiodic Task With ISR

1991

"... In PAGE 30: ... In this case, the high-priority processing of the aperiodic request is guaranteed by the priority of the hardware interrupt (higher than all other software priorities, in Ada). The user code, then, will be slightly different ( Table3 ). In the call 18... ..."

### Table 3. Mediabench applications chosen for workload modelling.

in Adaptive Shutdown Scheduling Strategies in Chip-Multiprocessor Platforms for Future Mobile Terminals

"... In PAGE 7: ... The workload will consist of both periodic and aperiodic processes. Table3 describes briefly the selected applications with the dataset used and the performance requirements associated with each application. The dataset that accompanies the original benchmark suite has been used in all cases.... ..."

### Table 2. Aperiodic task parameters ID

### lable. In what concerns the aperiodic variables, the upper

### TABLE I Periodic, Aperiodic, and Fixed-Aperiodic Autocorrelation Biases for Selected S-Boxes

2004

Cited by 4

### Table 1: Classical algorithm for the computation of PAV (t). 2.2 Stationarity detection The stationarity detection that we consider is based on the control of the sequence of vectors Vn = P n1U. Let the row vector denote the stationary probability distribution of the Markov process X. This vector veri es A = 0 and P = . The steady state availability is given by PAV (1) = 1U: To ensure the convergence of the sequence of vectors Vn, we require that the uniformization rate veri es gt; max(?A(i; i); i 2 S) since this guarantees that the transition probability matrix P is aperiodic. We then have, for every i 2 S,

1996

"... In PAGE 11: ... For every n 0, we have 0 v0 n 1. It follows that, using the truncation step N de ned in Relation (2), we get the classical algorithm to compute the expected interval availability, by writing EIAV (t) = N X n=0 e? t ( t)n n! v0 n + e0(N); where e0(N) = 1 X n=N+1 e? t ( t)n n! v0 n 1 X n=N+1 e? t ( t)n n! = 1 ? N X n=0 e? t ( t)n n! quot;: This algorithm is basically as the one depicted in Table1 . More precisely the computation of vn in Table 1 must be followed by the the recursion (8), with v0 0 = v0, and in the last loop over j, vn must be replaced by v0 n in order to get EIAV (tj) instead of PAV (tj).... In PAGE 11: ... It follows that, using the truncation step N de ned in Relation (2), we get the classical algorithm to compute the expected interval availability, by writing EIAV (t) = N X n=0 e? t ( t)n n! v0 n + e0(N); where e0(N) = 1 X n=N+1 e? t ( t)n n! v0 n 1 X n=N+1 e? t ( t)n n! = 1 ? N X n=0 e? t ( t)n n! quot;: This algorithm is basically as the one depicted in Table 1. More precisely the computation of vn in Table1 must be followed by the the recursion (8), with v0 0 = v0, and in the last loop over j, vn must be replaced by v0 n in order to get EIAV (tj) instead of PAV (tj). 3.... ..."

Cited by 4

### Table 1: Classical algorithm for the computation of PAV (t). 2.2 Stationarity detection The stationarity detection that we consider is based on the control of the sequence of vectors Vn = P n1U. Let the row vector denote the stationary probability distribution of the Markov process X. This vector veri es A = 0 and P = . The steady state availability is given by PAV (1) = 1U: To ensure the convergence of the sequence of vectors Vn, we require that the uniformization rate veri es gt; max(?A(i; i); i 2 S) since this guarantees that the transition probability matrix P is aperiodic. We then have, for every i 2 S,

1996

"... In PAGE 11: ... For every n 0, we have 0 v0 n 1. It follows that, using the truncation step N de ned in Relation (2), we get the classical algorithm to compute the expected interval availability, by writing EIAV (t) = N X n=0 e? t ( t)n n! v0 n + e0(N); where e0(N) = 1 X n=N+1 e? t ( t)n n! v0 n 1 X n=N+1 e? t ( t)n n! = 1 ? N X n=0 e? t ( t)n n! quot;: This algorithm is basically as the one depicted in Table1 . More precisely the computation of vn in Table 1 must be followed by the the recursion (8), with v0 0 = v0, and in the last loop over j, vn must be replaced by v0 n in order to get EIAV (tj) instead of PAV (tj).... In PAGE 11: ... It follows that, using the truncation step N de ned in Relation (2), we get the classical algorithm to compute the expected interval availability, by writing EIAV (t) = N X n=0 e? t ( t)n n! v0 n + e0(N); where e0(N) = 1 X n=N+1 e? t ( t)n n! v0 n 1 X n=N+1 e? t ( t)n n! = 1 ? N X n=0 e? t ( t)n n! quot;: This algorithm is basically as the one depicted in Table 1. More precisely the computation of vn in Table1 must be followed by the the recursion (8), with v0 0 = v0, and in the last loop over j, vn must be replaced by v0 n in order to get EIAV (tj) instead of PAV (tj). 3.... ..."

Cited by 4

### Table 2: OCPT and ACPT processor service classes

1999

"... In PAGE 4: ...Table 2: OCPT and ACPT processor service classes #0F Reservation and guarantee for one-time constant processing time #28OCPT#29 and aperiodic constant processing time #28APPT#29 classes: The one-time and aperiodic CPT classes have speci#0Ccation shown in Table2 . For example, a OCPT speci#0Ccation of #28S =9hr :30min :25sec :500ms; I =100ms; PPT =20ms#29isinterpreted as that it needs no more than 20ms of processor time during the time interval from 9hr :30min :25sec :500ms to 9hr :30min :25sec : 600ms.... ..."

Cited by 74

### Table 1: Comparing Harmonic to aperiodic ratio (HNR) at the target of different sounds.

2002

Cited by 1