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Multirate control, time delay systems, missing data environments as well as limited computing and communication resources are on the grounds of these problems.
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Moreover this study shares the limitation of all surveillance systems: underreporting, missing data, timelessness and regional differences in health system.
This paper is concerned with the positive observer design for a class of discrete-time positive systems with missing data in output.
In [14] for instance, Nakoulima et al. utilized these concepts to control distributed linear systems possessing missing data.
Our aim is to show that the blocked system of a linear time-invariant system with missing data is generically zeroless and thus AR modeling approaches are sufficient in general.
The relationship between the zeros of the unblocked and blocked systems established in this paper guarantees that blocking a linear time-invariant system does not introduce new zeros and thus paves the way to show that the blocked system of a linear time-invariant system with missing data is generically zero-free.
The completeness of data reporting was not perfect in the early period of use of the reporting enquiry system, and missing data (unknown data) was therefore inevitable.
The Oakland healthcare system said the missing data weren't encrypted or password-protected.
We excluded less than 1.5% of all the data from the Real Time Monitoring system because of missing data (for example, age not known, deprivation not known).
An additional limitation of this study (and the proposed supervision system) is the missing data on secondary outcomes from patient records.
No classification system compensates for missing data and misclassifications biased towards the most easily observable conditions that may not be related to the cause of death.
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