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Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals, to demonstrate the effectiveness of our method. Combining MLPHVG and support vector machine, we detect epileptic seizures from the EEG signals recorded from healthy subjects and epilepsy patients and the classification accuracy is 100%. In addition, we derive MLPHVGs from oil-water two-phase flow signals and find that the average clustering coefficient at different scales allows faithfully identifying and characterizing three typical oil-water flow patterns. These findings render our MLPHVG method particularly useful for analyzing nonlinear time series from the perspective of multiscale network analysis.

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Increasingly advanced technology allows the monitoring of complex systems from a wide variety of perspectives. But the exploration of such systems from a multi-channel sensor information viewpoint remains a complicated challenge of ongoing interest. As a development of modality transition theory, we first present a novel multiplex network-based model for multichannel sensor information fusion. Toward this aim, projection network and weighted network measures, including average weighted clustering coefficient and graph energy, are exploited both to implement data mining and quantitatively characterize the studied system. In particular, as a validation, the model is tested on spatial-temporal sensor measurements acquired from oil-water flow experiments. The results suggest that our analytical framework allows for the efficient characterization of the spatial flow behaviors underlying the transition of different flow patterns.