With Facebook’s name changing to Meta, the metaverse concept has become popular again. There are many indications that the current fashionableness of the metaverse is not driven by technical factors, rather related to the public hype. To clarify the reasons for the increasing popularity of the concept, this study develops a model based on embodied social presence theory. We surveyed 292 metaverse users, and analyzed the obtained data using partial least squares structural equation modeling. The results show that the main (...) technical factors influencing the metaverse popularity do not significantly predict users’ embodied presence and embodied co-presence, while users’ imagination positively predicts their embodied presence in the metaverse and positively influences users’ willingness to continuously participate through the multiple mediating effects of embodied presence and co-presence. The results of this study confirm, to some extent, that user imagination triggered by public opinion drives the popularity of the metaverse. (shrink)

We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning, developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies ridges of the overlapped multicomponent from a time-frequency representation. The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first (...) extraction is decided using the framework of the intrinsic mode polynomial chirp transform, which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications. (shrink)