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During each sampling instance, red stars indicate sampling sensors while black stars indicate non-sampling sensors.
At each sampling instance, the probabilities that the process operates under the effect of an assignable cause are updated using Bayes' theorem.
All three chart parameters, i.e., the time until the next sampling instance, the sample size and the control limit are adaptive and depend on these probabilities.
In the context of WSN for example, the set Ω specifies the collection of sensors that are active at each specific sampling instance.
The model uses an economic (or an economic/statistical) optimization criterion for the time to the next sampling instance, the size of the next sample, as well as the control limits of the inspection.
In this work, we proposed a distributed sampling-centralized recovery scheme where due to various design choices and physical constraints, only a small subset of the entire set of measurements is collected during each sampling instance.
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Main Outcome Measure: Average angular displacement magnitude between successive sampling instances for the ankle, knee, hip, and trunk.
The sequence reconstruction properties are determined by the corresponding channel output patterns given by the signs at the sampling instances.
The (M+1) sampling instances within this time interval are indicated by the vertical bars on the x-axis.
By allocating aperiodic sampling instances for a digital control loop, a self-triggered controller is able to utilize network resources more efficiently than conventional sampled-data systems.
The model predictive controller is based on the continuous process model, but the optimisation of the control performance index is made at discrete sampling instances.
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