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Naghibi and Behnia[18] have investigated the problem of waveform design for target classification and estimation in the presence of clutter using a MMSE estimator for widely separated and closely spaced antenna configurations.
Data collection and estimation in the lifecycle system is a time- and cost-consuming process.
The focus of this work is on symbol-wise demodulation to efficiently perform detection and estimation in the frequency domain.
The performance analysis and estimation in the simulation and implementation domains are supported, necessitating no design and implementation of HW co-processing blocks for the partitioning evaluation.
All these aspects render BST a powerful structure for model implementation and estimation in the face of uncertainty.
Application 1: Simulation and estimation in the simulated stochastic one-compartmental pharmacokinetic model Application 1: Simulation and estimation in the simulated stochastic one-compartmental pharmacokinetic model As a first application of stochastic mixed effects modeling, we used a stochastic one-compartmental pharmacokinetic model for a simulation study.
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We set out our data, assumptions and estimations in the web-based Appendix.
It is shown that detection and estimation in two domains can improve the accuracy probability of estimating evidently.
This approach has the benefits of effective outlier detection and robust estimation in the presence of outliers, and in health applications, that quantile estimates can reflect risk factors.
In conclusion, collision detection can prevent reconstructing impossible poses and it improves the position and motion estimation in the out-of-plane direction.
PREDICT has demonstrated its effectiveness for expertise capture, reliability, and performance estimation in the nuclear weapons program and for concept system development in the automotive industry.
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