Limits on the search rate of a pseudonoise sliding correlator synchroniser due to self-noise and decorrelation

1984 ◽  
Vol 131 (7) ◽  
pp. 742
Author(s):  
R.F. Ormondroyd ◽  
V.E. Comley
Keyword(s):  
Search Rate ◽  
Sliding Correlator

scholarly journals Global analysis of a predator–prey model with variable predator search rate

2020 ◽  
Vol 81 (1) ◽  
pp. 159-183 ◽  
Author(s):  
Benjamin D. Dalziel ◽  
Enrique Thomann ◽  
Jan Medlock ◽  
Patrick De Leenheer
Keyword(s):  
Global Analysis ◽  
Search Rate ◽  
Predator Prey ◽  
Predator Prey Model ◽  
Prey Model

Application of High Performance Parallel Computing Based on GPU

2013 ◽  
Vol 411-414 ◽  
pp. 585-588
Author(s):  
Liu Yang ◽  
Tie Ying Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Parallel Computing ◽  
Parallel Computation ◽  
High Performance ◽  
Search Process ◽  
Search Rate ◽  
Swarm Optimization ◽  
Path Search ◽  
Parallel Feature ◽  
Time And Space Complexity

This paper introduces parallel feature of the GPU, which will help GPU parallel computation methods to achieve the parallelization of PSO parallel path search process; and reduce the increasingly high problem of PSO (PSO: Particle Swarm Optimization) in time and space complexity. The experimental results show: comparing with CPU mode, GPU platform calculation improves the search rate and shortens the calculation time.

scholarly journals Two case studies of very long-term retention

2021 ◽  
Author(s):  
Ashleigh M. Maxcey ◽  
Richard M. Shiffrin ◽  
Denis Cousineau ◽  
Richard C. Atkinson
Keyword(s):  
Case Studies ◽  
Search Rate ◽  
Conscious Awareness ◽  
Original Order ◽  
Term Retention ◽  
Long Term Retention

AbstractHere, we present two case studies of extremely long-term retention. In the first, Richard C. Atkinson (RCA) had learned word sequences during experiments for his dissertation. Sixty-seven years later, RCA relearned the same words either in the original order or in a scrambled order. RCA reported no conscious awareness that the words were those used in the dissertation, but his relearning was considerably better for the words in the original order. In the second case study, Denis Cousineau had searched displays of objects for the presence of a target. The targets and foils had been novel at the beginning of training, and his search rate improved markedly over about 70 sessions. After 22 years, retraining showed retention of much of this gain in rate of search, and the rate was markedly faster than search for new objects with the same structure as the trained set. We consider interpretations of these case studies for our understanding of long-term retention.

Realization of a Sliding-Correlator-Based Continuous-Wave Pseudorandom Binary Phase-Coded Radar Operating in $W$ -Band

2016 ◽  
Vol 64 (10) ◽  
pp. 3302-3318 ◽  
Author(s):  
Reinhard Feger ◽  
Heinz Haderer ◽  
Herman Jalli Ng ◽  
Andreas Stelzer
Keyword(s):  
Continuous Wave ◽  
Binary Phase ◽  
Sliding Correlator

scholarly journals Separating spatial search and efficiency rates as components of predation risk

2012 ◽  
Vol 279 (1747) ◽  
pp. 4626-4633 ◽  
Author(s):  
Nicholas J. DeCesare
Keyword(s):  
Predation Risk ◽  
Resource Selection ◽  
Population Level ◽  
Search Rate ◽  
Proportional Hazard ◽  
Predator Prey ◽  
Multiple Components ◽  
Predation Efficiency ◽  
Aggregative Response ◽  
Spatial Hazard

Predation risk is an important driver of ecosystems, and local spatial variation in risk can have population-level consequences by affecting multiple components of the predation process. I use resource selection and proportional hazard time-to-event modelling to assess the spatial drivers of two key components of risk—the search rate (i.e. aggregative response) and predation efficiency rate (i.e. functional response)—imposed by wolves ( Canis lupus ) in a multi-prey system. In my study area, both components of risk increased according to topographic variation, but anthropogenic features affected only the search rate. Predicted models of the cumulative hazard, or risk of a kill, underlying wolf search paths validated well with broad-scale variation in kill rates, suggesting that spatial hazard models provide a means of scaling up from local heterogeneity in predation risk to population-level dynamics in predator–prey systems. Additionally, I estimated an integrated model of relative spatial predation risk as the product of the search and efficiency rates, combining the distinct contributions of spatial heterogeneity to each component of risk.

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