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Munir, M., Siddiqui, S.A., Dengel, A. and Ahmed, S. (2019) DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series. IEEE Access, 7, 1991-2005.
https://doi.org/10.1109/access.2018.2886457

has been cited by the following article:

• TITLE: Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications

  AUTHORS: Hossein Ahmadi, Sajjad Emdadi Mahdimahalleh, Arman Farahat, Banafsheh Saffari

  KEYWORDS: Unsupervised Learning, Autoencoders, Vision Transformers, Time-Series Analysis, Signal Processing, Representation Learning, Anomaly Detection, Wireless Signals, Biomedical Signals, Radar, IoT

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.17 No.2, May 28, 2025

  ABSTRACT: 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.