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, including 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.

Edy Portmann explains why it is important for schools to reinforce the scientist that lies within every child. He talks about intelligent learning systems and how they can be used to build collective intelligence, as well as to encourage students' creativity and help them learn to work together to solve problems. Sabine Gysi: In discussions of the digital transformation in education, skeptics often complain that reality is being pushed aside in favor of the digital. Does it make sense to look at the "real" world and the digital world as opposites? Edy Portmann: I've heard some teachers say that technological tools are "artificial."