An HMM-based coarse-to-fine framework constructs radio maps from unlabeled RSS sequences in unidirectional corridor environments, reporting 8.96 dB MAE and enabling 3.33 m KNN localization accuracy.
On spectral clustering: Analysis and an algorithm
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Bridge and interface gluing operations between multi-agent subsystems modify the Fiedler eigenvalue of the graph Laplacian, thereby altering consensus convergence rates.
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Survey-Free Radio Map Construction via HMM-Based Coarse-to-Fine Inference
An HMM-based coarse-to-fine framework constructs radio maps from unlabeled RSS sequences in unidirectional corridor environments, reporting 8.96 dB MAE and enabling 3.33 m KNN localization accuracy.
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Effect of Graph Gluing on Consensus in Networked Multi-Agent Systems
Bridge and interface gluing operations between multi-agent subsystems modify the Fiedler eigenvalue of the graph Laplacian, thereby altering consensus convergence rates.