Under the assumption of perfect channel estimation, decision-directed (DD) techniques were proposed for joint CFO/SFO estimation and tracking [21] and for phase noise and residual frequency offset compensation [25] in OFDM systems.
Random selection methods are assumed in the statistical methods used for estimation, decision-making and drawing inferences in enumerative studies.
This survey focuses on some aspects of estimation and decision fusion.
The anchor method is D1MD0 configuration with ConvME when comparing the quality-complexity performance of various combinations of motion estimation, mode decision, and distortion metric together.
Results are benchmarked against more traditional methods under consideration for commercial applications including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density estimation, and decision trees.
Some related work has been done for direct estimation and decision feedback equalization in [32], but more investigation is needed to find solutions for inverse control.
We evaluate infection state classifiers based on Naive Bayes, Naive Bayes with kernel density estimation, and decision trees, in combination with features including infection betweenness centrality and different centrality measures.
The proposed approach forms a multi-layered monitoring framework for measuring QoS at both application and infrastructure levels targeting trigger events for runtime adaptability of resource provisioning estimation and decision making.
It can be used for nearly any forecasting, estimation, or decision-making problem [13].
