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

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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.

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The principle of the integral send-on-delta algorithm based on the “upward” event-driven architecture. The messages are broadcasted when the IAE reaches a prespecified threshold.
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f3-sensors-07-00016: The principle of the integral send-on-delta algorithm based on the “upward” event-driven architecture. The messages are broadcasted when the IAE reaches a prespecified threshold.

Mentions: A detection of triggers (i.e. events that cause the sampling operation) in the integral sampling can be accomplished using pure analog circuitry. An alternative solution is the use of the compound architecture for data acquisition where the continuous-time signal is first periodically oversampled. Next, on the top of the time-triggered acquisition, the event-triggered communication and processing activities are implemented, i.e. a detection of a trigger over a set of periodic samples is provided (Fig. 3) [17,18]. In other words, the periodic oversampling is followed by the low resolution difference quantificator. Such a compound approach, where the asynchronous events are presynchronized by background periodic sampling, is called the “upward” event-driven architecture [17,18].


Asymptotic Effectiveness of the Event-Based Sampling according to the Integral Criterion
The principle of the integral send-on-delta algorithm based on the “upward” event-driven architecture. The messages are broadcasted when the IAE reaches a prespecified threshold.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3756709&req=5

f3-sensors-07-00016: The principle of the integral send-on-delta algorithm based on the “upward” event-driven architecture. The messages are broadcasted when the IAE reaches a prespecified threshold.
Mentions: A detection of triggers (i.e. events that cause the sampling operation) in the integral sampling can be accomplished using pure analog circuitry. An alternative solution is the use of the compound architecture for data acquisition where the continuous-time signal is first periodically oversampled. Next, on the top of the time-triggered acquisition, the event-triggered communication and processing activities are implemented, i.e. a detection of a trigger over a set of periodic samples is provided (Fig. 3) [17,18]. In other words, the periodic oversampling is followed by the low resolution difference quantificator. Such a compound approach, where the asynchronous events are presynchronized by background periodic sampling, is called the “upward” event-driven architecture [17,18].

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.