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Asymptotic Effectiveness of the Event-Based Sampling according to the Integral Criterion

View Article: PubMed Central

ABSTRACT

A rapid progress in intelligent sensing technology creates new interest in a development of analysis and design of non-conventional sampling schemes. The investigation of the event-based sampling according to the integral criterion is presented in this paper. The investigated sampling scheme is an extension of the pure linear send-on-delta/level-crossing algorithm utilized for reporting the state of objects monitored by intelligent sensors. The motivation of using the event-based integral sampling is outlined. The related works in adaptive sampling are summarized. The analytical closed-form formulas for the evaluation of the mean rate of event-based traffic, and the asymptotic integral sampling effectiveness, are derived. The simulation results verifying the analytical formulas are reported. The effectiveness of the integral sampling is compared with the related linear send-on-delta/level-crossing scheme. The calculation of the asymptotic effectiveness for common signals, which model the state evolution of dynamic systems in time, is exemplified.

No MeSH data available.


The steady-state sampling error in the level-crossing scheme (a), the example of the event-based sampling according to the integral criterion (b). The sampled signal is the same in both plots (a) and (b), only the time scales are different.
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f2-sensors-07-00016: The steady-state sampling error in the level-crossing scheme (a), the example of the event-based sampling according to the integral criterion (b). The sampled signal is the same in both plots (a) and (b), only the time scales are different.

Mentions: By the definition, the level-crossing sampling is triggered if a temporary deviation of a signal appears. Instead, in the integral sampling, a summation of the absolute temporal sampling errors is taken into account. Let us consider, as an example, a control system where the controlled variable reaches its equilibrium state and becomes nearly constant (Fig. 2). In the level-crossing scheme, sampling sometimes may not be triggered during a long time since the signal changes are not large enough to activate the next sampling operation (Fig. 2a). If the integral criterion is used instead, the integrated error accumulated in time triggers next sampling operations, and the signal tracking becomes more accurate (Fig. 2b). Moreover, as it was mentioned, the performance of the monitoring and control system is usually defined as the integral of the absolute value of the error (IAE) [9], which directly corresponds to the definition of the integral sampling criterion.


Asymptotic Effectiveness of the Event-Based Sampling according to the Integral Criterion
The steady-state sampling error in the level-crossing scheme (a), the example of the event-based sampling according to the integral criterion (b). The sampled signal is the same in both plots (a) and (b), only the time scales are different.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3756709&req=5

f2-sensors-07-00016: The steady-state sampling error in the level-crossing scheme (a), the example of the event-based sampling according to the integral criterion (b). The sampled signal is the same in both plots (a) and (b), only the time scales are different.
Mentions: By the definition, the level-crossing sampling is triggered if a temporary deviation of a signal appears. Instead, in the integral sampling, a summation of the absolute temporal sampling errors is taken into account. Let us consider, as an example, a control system where the controlled variable reaches its equilibrium state and becomes nearly constant (Fig. 2). In the level-crossing scheme, sampling sometimes may not be triggered during a long time since the signal changes are not large enough to activate the next sampling operation (Fig. 2a). If the integral criterion is used instead, the integrated error accumulated in time triggers next sampling operations, and the signal tracking becomes more accurate (Fig. 2b). Moreover, as it was mentioned, the performance of the monitoring and control system is usually defined as the integral of the absolute value of the error (IAE) [9], which directly corresponds to the definition of the integral sampling criterion.

View Article: PubMed Central

ABSTRACT

A rapid progress in intelligent sensing technology creates new interest in a development of analysis and design of non-conventional sampling schemes. The investigation of the event-based sampling according to the integral criterion is presented in this paper. The investigated sampling scheme is an extension of the pure linear send-on-delta/level-crossing algorithm utilized for reporting the state of objects monitored by intelligent sensors. The motivation of using the event-based integral sampling is outlined. The related works in adaptive sampling are summarized. The analytical closed-form formulas for the evaluation of the mean rate of event-based traffic, and the asymptotic integral sampling effectiveness, are derived. The simulation results verifying the analytical formulas are reported. The effectiveness of the integral sampling is compared with the related linear send-on-delta/level-crossing scheme. The calculation of the asymptotic effectiveness for common signals, which model the state evolution of dynamic systems in time, is exemplified.

No MeSH data available.