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Ideally, the second analysis stage would be able to handle identified variants of either a single sample, a family-based analysis, or a case/control study, while at the same time integrating extensive annotation with biological information and dynamic filtering.
By re-constructing the system, the dynamic filtering error system is obtained.
By re-constructing the system, the dynamic filtering error characteristics of unknown inputs and time-delays are obtained.
To solve the problem, this paper investigates the design of a dynamic filtering pattern and its generalizations.
Moreover, the technique of symbolic dynamic filtering is used to analyze the ultrasonic fluctuating signals, and extract the anomaly flow measure from the signals of ultrasonic sensor.
In addition, it has been point out the dynamic filtering performance of LZOH scheme should be better than ZOH ones due to less time-varying delays are introduced and more latest measurement information are employed.
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Fig. 2 Single-regime dynamic filtered parameter estimates using simulated data based on ("Preliminary results").
You can also choose to use different sets of tags for the pre-filter and the dynamic filter.
The combination of dynamic filter and static input output nonlinearity forms a nonlinear dynamic filter that enables a large amount of loop gain increase.
In the second layer, the Symbolic Dynamic Filter (SDF) is employed to classify the fault pattern.
The aim is to design a full order dynamic filter that can also contain quadratic terms.
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