Choice of time-scale parameter for dead time estimation in Laguerre basis

2019 ◽  
Author(s):  
Martin Tuma ◽  
Pavel Jura
Keyword(s):  
Time Scale ◽  
Dead Time ◽  
Scale Parameter ◽  
Time Estimation ◽  
Laguerre Basis

scholarly journals Forward Curvatures on Time Scales

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
M. S. Seyyidoglu ◽  
Y. Tunçer ◽  
D. Uçar ◽  
M. K. Berktaş ◽  
V. F. Hatipoğlu
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Scale Parameter ◽  
Real Numbers

We also introduce forward curvature of a curve and give some formulas to calculate forward curvature of a curve on time scales which may be an arbitrary closed subsets of the set of all real numbers. We also introduce the length of a curve parametrized by a time scale parameter in .

Uncertainty of dead time estimation in ICP-MS

2003 ◽  
Vol 18 (5) ◽  
pp. 508-511 ◽  
Author(s):  
J. Moser ◽  
W. Wegscheider ◽  
T. Meisel
Keyword(s):  
Dead Time ◽  
Time Estimation ◽  
Icp Ms

scholarly journals The Dead Time Characterization Method of Quartz Flexure Accelerometers Using Monotonicity Number

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3123
Author(s):  
Bin Wu ◽  
Lingyun Ye ◽  
Tiantian Huang ◽  
Zhaowei Yang ◽  
Kaichen Song
Keyword(s):  
Transfer Function ◽  
Dead Time ◽  
Time Estimation ◽  
Open Loop ◽  
Experimental Result ◽  
Time Analysis ◽  
Loop Transfer Function ◽  
The Dead ◽  
Quartz Flexure Accelerometer ◽  
Least Mean Squares Algorithm

Dead time estimation is important in the design process of quartz flexure accelerometers. However, to the authors’ knowledge, the dead time existing in quartz flexure accelerometers is not well investigated in conventional identification studies. In this paper, the dead time, together with the open-loop transfer function of quartz flexure accelerometers, is identified from step excitation experiments using two steps. Firstly, a monotonicity number was proposed to estimate the dead time. Analysis showed that the monotonicity number was robust enough to measurement noise and sensitive to step excitation. Secondly, parameters of the open-loop transfer function were identified using the least mean squares algorithm. A simulation example was applied to demonstrate the validity of the proposed method. The verified method was used to test a quartz flexure accelerometer. The experimental result shows that the dead time was 500 μs.

ADAPTIVE DEAD-TIME ESTIMATION

1987 ◽  
pp. 385-389 ◽  
Author(s):  
R.M.C. De Keyser
Keyword(s):  
Dead Time ◽  
Time Estimation

scholarly journals What is all the noise about in interval timing?

2014 ◽  
Vol 369 (1637) ◽  
pp. 20120459 ◽  
Author(s):  
Sorinel A. Oprisan ◽  
Catalin V. Buhusi
Keyword(s):  
Time Scale ◽  
Scale Invariance ◽  
Fundamental Property ◽  
Time Estimation ◽  
Interval Timing ◽  
Accurate Estimation ◽  
Scale Invariant ◽  
Estimation Errors ◽  
Neural Noise ◽  
Criterion Time

Cognitive processes such as decision-making, rate calculation and planning require an accurate estimation of durations in the supra-second range—interval timing. In addition to being accurate, interval timing is scale invariant: the time-estimation errors are proportional to the estimated duration. The origin and mechanisms of this fundamental property are unknown. We discuss the computational properties of a circuit consisting of a large number of (input) neural oscillators projecting on a small number of (output) coincidence detector neurons, which allows time to be coded by the pattern of coincidental activation of its inputs. We showed analytically and checked numerically that time-scale invariance emerges from the neural noise. In particular, we found that errors or noise during storing or retrieving information regarding the memorized criterion time produce symmetric, Gaussian-like output whose width increases linearly with the criterion time. In contrast, frequency variability produces an asymmetric, long-tailed Gaussian-like output, that also obeys scale invariant property. In this architecture, time-scale invariance depends neither on the details of the input population, nor on the distribution probability of noise.

Alternating iterative regression method for dead time estimation from experimental designs

2009 ◽  
Vol 394 (2) ◽  
pp. 625-636 ◽  
Author(s):  
S. Pous-Torres ◽  
J. R. Torres-Lapasió ◽  
J. J. Baeza-Baeza ◽  
M. C. García-Álvarez-Coque
Keyword(s):  
Dead Time ◽  
Time Estimation ◽  
Regression Method ◽  
Experimental Designs

A Knowledge-based approach to dead-time estimation for process control

1995 ◽  
Vol 61 (5) ◽  
pp. 1045-1072 ◽  
Author(s):  
T. H. LEE ◽  
Q. G. WANG ◽  
K. K. TAN ◽  
S. NUNGAM
Keyword(s):  
Process Control ◽  
Dead Time ◽  
Time Estimation ◽  
Knowledge Based
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