The rapid growth of unlabeled time-series data in domains such as wireless
communications, radar, biomedical engineering, and the Internet of Things (IoT)
has driven advancements in unsupervised learning. This review synthesizes
recent progress in applying autoencoders and vision transformers for
unsupervised signal analysis, focusing on their architectures, applications,
and emerging trends. We explore how these models enable feature extraction,
anomaly detection, and classification across diverse signal types, einschließlich
electrocardiograms, radar waveforms, and IoT sensor data. The review highlights
the strengths of hybrid architectures and self-supervised learning, while
identifying challenges in interpretability, scalability, and domain
generalization. By bridging methodological innovations and practical
applications, this work offers a roadmap for developing robust, adaptive models
for signal intelligence.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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