Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915355032551424 |
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| author | Usama, Muhammad Jang, Hee-Deok Shanbhag, Soham Sung, Yoo-Chang Bae, Seung-Jun Chang, Dong Eui |
| author_facet | Usama, Muhammad Jang, Hee-Deok Shanbhag, Soham Sung, Yoo-Chang Bae, Seung-Jun Chang, Dong Eui |
| contents | This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations by focusing on valid data features. Our approach is evaluated across three anomaly detection algorithms and consistently outperforms two baseline methods. Detailed ablation studies further support these findings. Furthermore, we introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%. The source code and data used in this study are available at https://github.com/Usama1002/learning-latent-representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18288 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals Usama, Muhammad Jang, Hee-Deok Shanbhag, Soham Sung, Yoo-Chang Bae, Seung-Jun Chang, Dong Eui Machine Learning This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations by focusing on valid data features. Our approach is evaluated across three anomaly detection algorithms and consistently outperforms two baseline methods. Detailed ablation studies further support these findings. Furthermore, we introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%. The source code and data used in this study are available at https://github.com/Usama1002/learning-latent-representations. |
| title | Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.18288 |