全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用：99美元
投递稿件

查看量下载量

相关文章

更多...

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

DOI: 10.4236/jilsa.2025.172007, PP. 77-111

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

Full-Text   Cite this paper   Add to My Lib

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.

 References

[1]  Berahmand, K., Daneshfar, F., Salehi, E.S., Li, Y. and Xu, Y. (2024) Autoencoders and Their Applications in Machine Learning: A Survey. Artificial Intelligence Review, 57, Article No. 28.
https://doi.org/10.1007/s10462-023-10662-6
[2]  Chen, S. and Guo, W. (2023) Auto-Encoders in Deep Learning—A Review with New Perspectives. Mathematics, 11, Article 1777.
https://doi.org/10.3390/math11081777
[3]  Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J., Tian, J., et al. (2024) A Survey of Visual Transformers. IEEE Transactions on Neural Networks and Learning Systems, 35, 7478-7498.
https://doi.org/10.1109/tnnls.2022.3227717
[4]  Al-hammuri, K., Gebali, F., Kanan, A. and Chelvan, I.T. (2023) Vision Transformer Architecture and Applications in Digital Health: A Tutorial and Survey. Visual Computing for Industry, Biomedicine, and Art, 6, Article No. 14.
https://doi.org/10.1186/s42492-023-00140-9
[5]  Zhou, Y., Diao, X., Huo, Y., Liu, Y., Fan, X. and Zhao, W. (2024) Masked Transformer for Electrocardiogram Classification. arXiv: 2309.07136.
[6]  Faysal, A., Rostami, M., Roshan, R.G., Wang, H. and Muralidhar, N. (2024) NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication. 2024 33rd Wireless and Optical Communications Conference (WOCC), Hsinchu, 25-26 October 2024, 103-108.
https://doi.org/10.1109/wocc61718.2024.10786062
[7]  Zeng, Z., Kaur, R., Siddagangappa, S., Balch, T. and Veloso, M. (2023) From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting. 4th ACM International Conference on AI in Finance, Brooklyn, 27-29 November 2023, 82-90.
https://doi.org/10.1145/3604237.3626905
[8]  Harvey, C.J., Shomaji, S., Yao, Z. and Noheria, A. (2024) Comparison of Autoencoder Encodings for ECG Representation in Downstream Prediction Tasks. arXiv: 2410.02937.
[9]  Vafaei, E. and Hosseini, M. (2025) Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification. Sensors, 25, Article 1293.
https://doi.org/10.3390/s25051293
[10]  Irani, H. and Metsis, V. (2025) Positional Encoding in Transformer-Based Time Series Models: A Survey. arXiv:2502.12370.
https://arxiv.org/abs/2502.12370
[11]  Huan, S., Wang, Z., Wang, X., Wu, L., Yang, X., Huang, H., et al. (2023) A Lightweight Hybrid Vision Transformer Network for Radar-Based Human Activity Recognition. Scientific Reports, 13, Article No. 17996.
https://doi.org/10.1038/s41598-023-45149-5
[12]  Sana, L., Nazir, M.M., Yang, J., Hussain, L., Chen, Y., Ku, C.S., et al. (2024) Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection with Vision Transformers. IEEE Access, 12, 82443-82468.
https://doi.org/10.1109/access.2024.3404778
[13]  MacQueen, J. (1967) Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 281-297.
[14]  Ester, M., Kriegel, H.P., Sander, J. and Xu, X. (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, 2-4 August 1996, 226-231.
[15]  Liu, Y., Zhou, Y., Yang, K. and Wang, X. (2023) Unsupervised Deep Learning for IoT Time Series. IEEE Internet of Things Journal, 10, 14285-14306.
https://doi.org/10.1109/jiot.2023.3243391
[16]  Shan, L., Li, Y., Jiang, H., Zhou, P., Niu, J., Liu, R., et al. (2022) Abnormal ECG Detection Based on an Adversarial Autoencoder. Frontiers in Physiology, 13, Article 961724.
https://doi.org/10.3389/fphys.2022.961724
[17]  Xin, Q., Xu, Z., Guo, L., Zhao, F. and Wu, B. (2024) IoT Traffic Classification and Anomaly Detection Method Based on Deep Autoencoders. Applied and Computational Engineering, 69, 64-70.
https://doi.org/10.54254/2755-2721/69/20241511
[18]  Lomoio, U., Vizza, P., Giancotti, R., Petrolo, S., Flesca, S., Boccuto, F., et al. (2025) A Convolutional Autoencoder Framework for ECG Signal Analysis. Heliyon, 11, e41517.
https://doi.org/10.1016/j.heliyon.2024.e41517
[19]  Roy, M., Majumder, S., Halder, A. and Biswas, U. (2023) ECG-NET: A Deep LSTM Autoencoder for Detecting Anomalous ECG. Engineering Applications of Artificial Intelligence, 124, Article ID: 106484.
https://doi.org/10.1016/j.engappai.2023.106484
[20]  Shah, A., Singh, D., Mohamed, H.G., Bharany, S., Rehman, A.U. and Hussen, S. (2025) Electrocardiogram Analysis for Cardiac Arrhythmia Classification and Prediction through Self Attention Based Auto Encoder. Scientific Reports, 15, Article No. 9230.
https://doi.org/10.1038/s41598-025-93906-5
[21]  Zhang, S., Fang, Y. and Ren, Y. (2024) ECG Autoencoder Based on Low-Rank Attention. Scientific Reports, 14, Article No. 12823.
https://doi.org/10.1038/s41598-024-63378-0
[22]  Bank, D., Koenigstein, N. and Giryes, R. (2023) Autoencoders. In: Rokach, L., Maimon, O. and Shmueli, E., Eds., Machine Learning for Data Science Handbook, Springer, 353-374.
https://doi.org/10.1007/978-3-031-24628-9_16
[23]  Dong, Y., Zhang, M., Qiu, L., Wang, L. and Yu, Y. (2023) An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention. Micromachines, 14, Article 1155.
https://doi.org/10.3390/mi14061155
[24]  Faysal, A., Boushine, T., Rostami, M., Roshan, R.G., Wang, H., Muralidhar, N., Sa-hoo, A. and Yao, Y.D. (2025) Denomae: A Multimodal Autoencoder for Denoising Modulation Signals. arXiv: 2501.11538.
[25]  Zhang, W., Xue, K., Yao, A. and Sun, Y. (2024) CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network. Electronics, 13, Article 3408.
https://doi.org/10.3390/electronics13173408
[26]  Cai, M. and Zeng, Y. (2024) MAE-EEG-Transformer: A Transformer-Based Approach Combining Masked Autoencoder and Cross-Individual Data Augmentation Pre-Training for EEG Classification. Biomedical Signal Processing and Control, 94, Article ID: 106131.
https://doi.org/10.1016/j.bspc.2024.106131
[27]  Tschannen, M., Bachem, O. and Lucic, M. (2018) Recent Advances in Autoencoder-based Representation Learning. arXiv: 1812.05069.
[28]  Kingma, D.P. and Welling, M. (2013) Auto-Encoding Variational Bayes. arXiv: 1312.6114.
[29]  Vaid, A., Jiang, J., Sawant, A., Lerakis, S., Argulian, E., Ahuja, Y., et al. (2023) A Foundational Vision Transformer Improves Diagnostic Performance for Electrocardiograms. npj Digital Medicine, 6, Article No. 108.
https://doi.org/10.1038/s41746-023-00840-9
[30]  Liu, T., Si, Y., Yang, W., Huang, J., Yu, Y., Zhang, G., et al. (2022) Inter-Patient Congestive Heart Failure Detection Using ECG-Convolution-Vision Transformer Network. Sensors, 22, Article 3283.
https://doi.org/10.3390/s22093283
[31]  Jamil, S. and Roy, A.M. (2023) An Efficient and Robust Phonocardiography (PCG)-Based Valvular Heart Diseases (VHD) Detection Framework Using Vision Transformer (VIT). Computers in Biology and Medicine, 158, Article ID: 106734.
https://doi.org/10.1016/j.compbiomed.2023.106734
[32]  Banville, H., Alberga, L., Engemann, D.A., Gramfort, A. and Sabbagh, D. (2021) Uncovering the Structure of EEG Data Using Self-Supervised Learning. arXiv: 2007.16104.
[33]  Telangore, H., Azad, V., Sharma, M., Bhurane, A., Tan, R.S. and Acharya, U.R. (2024) Early Prediction of Sudden Cardiac Death Using Multimodal Fusion of ECG Features Extracted from Hilbert-Huang and Wavelet Transforms with Explainable Vision Transformer and CNN Models. Computer Methods and Programs in Biomedicine, 257, Article ID: 108455.
https://doi.org/10.1016/j.cmpb.2024.108455
[34]  Nejedly, P., Kremen, V., Lepkova, K., Mivalt, F., Sladky, V., Pridalova, T., et al. (2023) Utilization of Temporal Autoencoder for Semi-Supervised Intracranial EEG Clustering and Classification. Scientific Reports, 13, Article No. 744.
https://doi.org/10.1038/s41598-023-27978-6
[35]  Deldadehasl, M., Jafari, M. and Sayeh, M.R. (2025) Dynamic Classification Using the Adaptive Competitive Algorithm for Breast Cancer Detection. Journal of Data Analysis and Information Processing, 13, 101-115.
https://doi.org/10.4236/jdaip.2025.132006
[36]  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
[37]  Jang, J., Kim, T.Y., Lim, H. and Yoon, D. (2021) Unsupervised Feature Learning for Electrocardiogram Data Using the Convolutional Variational Autoencoder. PLOS ONE, 16, e0260612.
https://doi.org/10.1371/journal.pone.0260612
[38]  Afify, H.M., Mohammed, K.K. and Ella Hassanien, A. (2023) Novel Prediction Model on OSCC Histopathological Images via Deep Transfer Learning Combined with Grad-Cam Interpretation. Biomedical Signal Processing and Control, 83, Article ID: 104704.
https://doi.org/10.1016/j.bspc.2023.104704
[39]  Lan, Z., Sourina, O., Wang, L., Scherer, R. and Muller-Putz, G. (2017) Unsupervised Feature Learning for EEG-Based Emotion Recognition. 2017 International Conference on Cyberworlds (CW), Chester, 20-22 September 2017, 182-185.
https://doi.org/10.1109/cw.2017.19
[40]  Wen, T. and Zhang, Z. (2018) Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals. IEEE Access, 6, 25399-25410.
https://doi.org/10.1109/access.2018.2833746
[41]  Sawano, S., Kodera, S., Setoguchi, N., Tanabe, K., Kushida, S., Kanda, J., et al. (2024) Applying Masked Autoencoder-Based Self-Supervised Learning for High-Capability Vision Transformers of Electrocardiographies. PLOS ONE, 19, e0307978.
https://doi.org/10.1371/journal.pone.0307978
[42]  Li, Z., Li, S. and Yan, X. (2023) Time Series as Images: Vision Transformer for Irregularly Sampled Time Series.
https://proceedings.neurips.cc/paper_files/paper/2023/file/9a17c1eb808cf012065e9db47b7ca80d-Paper-Conference.pdf
[43]  Ni, J., Zhao, Z., Shen, C., Tong, H., Song, D., Cheng, W., Luo, D. and Chen, H. (2025) Harnessing Vision Models for Time Series Analysis: A Survey. arXiv: 2502.08869.
[44]  Zhang, S., Zhou, J., Ma, X., Pirttikangas, S. and Yang, C. (2024) Tsvit: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery. Applied Sciences, 14, Article 10781.
https://doi.org/10.3390/app142310781
[45]  Tarasiou, M., Chavez, E. and Zafeiriou, S. (2023) ViTs for SITS: Vision Transformers for Satellite Image Time Series. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, 17-24 June 2023, 10418-10428.
https://doi.org/10.1109/cvpr52729.2023.01004
[46]  Kara, D., Kimura, T., Liu, S., Li, J., Liu, D., Wang, T., et al. (2024) FreqMAE: Frequency-Aware Masked Autoencoder for Multi-Modal IoT Sensing. Proceedings of the ACM Web Conference 2024, Singapore, 13-17 May 2024, 2795-2806.
https://doi.org/10.1145/3589334.3645346
[47]  Langarica, S. and Núñez, F. (2023) Contrastive Blind Denoising Autoencoder for Real Time Denoising of Industrial IoT Sensor Data. Engineering Applications of Artificial Intelligence, 120, Article ID: 105838.
https://doi.org/10.1016/j.engappai.2023.105838
[48]  Haseeb, J., Mansoori, M., Hirose, Y., Al-Sahaf, H. and Welch, I. (2022) Autoencoder-based Feature Construction for IoT Attacks Clustering. Future Generation Computer Systems, 127, 487-502.
https://doi.org/10.1016/j.future.2021.09.025
[49]  Xin, Y., Chen, H. and Xie, L. (2021) Efficient Noisy Data Transmission Using Denoising Autoencoder in Internet of Things. 2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xi’an, 17-19 August 2021, 1-6.
https://doi.org/10.1109/icspcc52875.2021.9565090
[50]  Aloul, F., Zualkernan, I., Abdalgawad, N., Hussain, L. and Sakhnini, D. (2021) Network Intrusion Detection on the IoT Edge Using Adversarial Autoencoders. 2021 International Conference on Information Technology (ICIT), Amman, 14-15 July 2021, 120-125.
https://doi.org/10.1109/icit52682.2021.9491694
[51]  Yao, M., Tao, D., Gao, R. and Qi, P. (2025) Anomaly Detection for MEC Enabled Hierarchical Industrial IoT with Transformer Enhanced Variational Auto Encoder. IEEE Transactions on Industrial Informatics, 21, 40-48.
https://doi.org/10.1109/tii.2024.3421600
[52]  Wasswa, H., Nanyonga, A. and Lynar, T. (2024) Impact of Latent Space Dimension on IoT Botnet Detection Performance: Vae-Encoder versus Vit-Encoder. 2024 3rd International Conference for Innovation in Technology (INOCON), Bangalore, 1-3 March 2024, 1-6.
https://doi.org/10.1109/inocon60754.2024.10511431
[53]  Chen, J., Wong, W. and Hamdaoui, B. (2024) Unsupervised Contrastive Learning for Robust RF Device Fingerprinting under Time-Domain Shift. ICC 2024—IEEE International Conference on Communications, Denver, 9-13 June 2024, 3567-3572.
https://doi.org/10.1109/icc51166.2024.10622173
[54]  Bai, J., Wang, Y., Xiao, Z. and Alazab, M. (2022) RffAe-S: Autoencoder Based on Random Fourier Feature with Separable Loss for Unsupervised Signal Modulation Clustering. IEEE Transactions on Industrial Informatics, 18, 7910-7919.
https://doi.org/10.1109/tii.2022.3171349
[55]  Chen, Y., Wang, J., Wang, X., Cao, Y. and Yang, Q. (2023) Unsupervised Learning for Human Sensing Using Radio Signals. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Waikoloa, 3-8 January 2022, 1091-1100.
[56]  Shi, Y., Xu, H., Zhang, Y., Qi, Z. and Wang, D. (2024) GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder. IEEE Transactions on Cognitive Communications and Networking, 10, 94-106.
https://doi.org/10.1109/tccn.2023.3318414
[57]  Gupta, R., Sharma, P. and Desai, A. (2023) Spectravit: Unsupervised Spectrum Anomaly Detection Using Vision Transformers. Proceedings of the ACM SIGCOMM Workshop on AI for Networks, 45-51.
[58]  Lee, S. and Park, J. (2024) Self-Supervised Modulation Recognition via Time-Frequency Masked Autoencoding. IEEE Wireless Communications Letters, 13, 789-793.
[59]  Kim, J., Cho, S., Hwang, S. and Choi, Y. (2023) Automatic LPI Radar Waveform Recognition Using Vision Transformer. 2023 IEEE International Radar Conference (RADAR), Sydney, 6-10 November 2023, 1-6.
https://doi.org/10.1109/radar54928.2023.10371014
[60]  Kayacan, Y.E. and Erer, I. (2024) A Vision-Transformer-Based Approach to Clutter Removal in GPR: DC-VIT. IEEE Geoscience and Remote Sensing Letters, 21, 1-5.
https://doi.org/10.1109/lgrs.2024.3385694
[61]  Ghosh, R. and Bovolo, F. (2024) URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised Vit with Contrastive Feature Learning Framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 15512-15524.
https://doi.org/10.1109/jstars.2024.3447879
[62]  Yu, T., Han, F., Zhang, L. and Zou, B. (2024) Multimodal Slot Vision Transformer for SAR Image Classification. 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Zhuhai, 22-24 November 2024, 1-6.
https://doi.org/10.1109/icsidp62679.2024.10869269
[63]  Xin, X., Li, M., Wu, Y., Li, X., Zhang, P. and Xu, D. (2024) PolSAR-MPIformer: A Vision Transformer Based on Mixed Patch Interaction for Dual-Frequency PolSAR Image Adaptive Fusion Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 8527-8542.
https://doi.org/10.1109/jstars.2024.3386854
[64]  Li, X., Chen, S., Zhang, S., Zhu, Y., Xiao, Z. and Wang, X. (2024) Advancing IR-UWB Radar Human Activity Recognition with Swin Transformers and Supervised Contrastive Learning. IEEE Internet of Things Journal, 11, 11750-11766.
https://doi.org/10.1109/jiot.2023.3330996
[65]  Muzeau, M., Frontera-Pons, J., Ren, C. and Ovarlez, J.P. (2024) SAFE: A SAR Feature Extractor Based on Self-Supervised Learning and Masked Siamese ViTs. arXiv: 2407.0085.
[66]  Li, S., Li, W., Huang, P., Zheng, M., Tian, B. and Xu, S. (2025) MTBC: Masked Vision Transformer and Brownian Distance Covariance Classifier for Cross-Domain Few-Shot HRRP Recognition. IEEE Sensors Journal, 25, 16440-16454.
https://doi.org/10.1109/jsen.2025.3550584
[67]  Feng, Y., Han, B., Wang, X., Shen, J., Guan, X. and Ding, H. (2024) Self-Supervised Transformers for Unsupervised SAR Complex Interference Detection Using Canny Edge Detector. Remote Sensing, 16, Article 306.
https://doi.org/10.3390/rs16020306
[68]  Shi, Y., Qiao, L., Shu, Y., Li, B., Xiao, B., Li, W., et al. (2024) Semi-Supervised FMCW Radar Hand Gesture Recognition via Pseudo-Label Consistency Learning. Remote Sensing, 16, Article 2267.
https://doi.org/10.3390/rs16132267
[69]  Xiang, Y., Guo, J., Chen, M., Wang, Z. and Han, C. (2023) Mae-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals. Sensors, 23, Article 7869.
https://doi.org/10.3390/s23187869
[70]  Dal Corso, J. and Bruzzone, L. (2024) An Approach to Semantic Segmentation of Radar Sounder Data Based on Unsupervised Random Walks and User-Guided Label Propagation. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-19.
https://doi.org/10.1109/tgrs.2024.3458188
[71]  Kahya, S.M., Yavuz, M.S. and Steinbach, E. (2023) Reconstruction-based Out-Of-Distribution Detection for Short-Range FMCW Radar. 2023 31st European Signal Processing Conference (EUSIPCO), Helsinki, 4-8 September 2023, 1350-1354.
https://doi.org/10.23919/eusipco58844.2023.10290040
[72]  Jiang, Y., Shi, S., Zhang, F. and Huang, W. (2024) Radar Pre-Sorting Algorithm Based on Autoencoder and LSTM. AEU—International Journal of Electronics and Communications, 187, Article ID: 155535.
https://doi.org/10.1016/j.aeue.2024.155535
[73]  Liang, X., Chen, B., Chen, W., Wang, P. and Liu, H. (2022) Unsupervised Radar Target Detection under Complex Clutter Background Based on Mixture Variational Autoencoder. Remote Sensing, 14, Article 4449.
https://doi.org/10.3390/rs14184449
[74]  Wang, Y., Hernández, H.H., Albrecht, C.M. and Zhu, X.X. (2025) Feature Guided Masked Autoencoder for Self-Supervised Learning in Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 321-336.
https://doi.org/10.1109/jstars.2024.3493237
[75]  Chan-To-Hing, H. and Veeravalli, B. (2024) FUS-MAE: A Cross-Attention-Based Data Fusion Approach for Masked Autoencoders in Remote Sensing. IGARSS 2024—2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, 7-12 July 2024, 6953-6958.
https://doi.org/10.1109/igarss53475.2024.10642424
[76]  Wang, J., Li, Y., Quan, D., Hou, B., Wang, Z., Sima, H., et al. (2024) MAPM: PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens. Remote Sensing, 16, Article 4280.
https://doi.org/10.3390/rs16224280
[77]  Ali, A. and Fan, Y.Y. (2017) Unsupervised Feature Learning and Automatic Modulation Classification Using Deep Learning Model. Physical Communication, 25, 75-84.
https://doi.org/10.1016/j.phycom.2017.09.004
[78]  Shevitski, B., Watkins, Y., Man, N. and Girard, M. (2023) Digital Signal Processing Using Deep Neural Networks: Evaluating the Effectiveness of Hybrid Autoencoder/ Transformer Models for RF Data. arXiv: 2109.10404.
[79]  Qu, Y., Lu, Z., Zeng, R., Wang, J. and Wang, J. (2025) Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction. IEEE Transactions on Vehicular Technology, 74, 4192-4207.
https://doi.org/10.1109/tvt.2024.3486079
[80]  Zayat, A., Hasabelnaby, M.A., Obeed, M. and Chaaban, A. (2024) Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunities. IEEE Communications Magazine, 62, 88-94.
https://doi.org/10.1109/mcom.002.2300257
[81]  Li, G., Lin, C.C., Zhang, X., Ma, X. and Guo, L. (2024) Adversarial Robust Vit-Based Automatic Modulation Recognition in Practical Deep Learning-Based Wireless Systems. 2025 IEEE Symposium on Security and Privacy (SP), San Francisco, 12-15 May 2025, 30.
[82]  Zhao, J., Cheng, Q., Wang, H. and Yao, Y. (2024) ViT-MAE Based Foundation Model for Automatic Modulation Classification. 2024 33rd Wireless and Optical Communications Conference (WOCC), Hsinchu, 25-26 October 2024, 50-54.
https://doi.org/10.1109/wocc61718.2024.10786055
[83]  Xia, Y., Xiong, Y. and Wang, K. (2023) A Transformer Model Blended with CNN and Denoising Autoencoder for Inter-Patient ECG Arrhythmia Classification. Biomedical Signal Processing and Control, 86, Article ID: 105271.
https://doi.org/10.1016/j.bspc.2023.105271
[84]  Wang, D., Lin, M., Zhang, X., Huang, Y. and Zhu, Y. (2023) Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network. Sensors, 23, Article 7281.
https://doi.org/10.3390/s23167281
[85]  Martinez, C., Nguyen, L. and Patel, A. (2023) RF-Vit-Gan: Unsupervised RF Signal Synthesis Using Vision Transformers. NeurIPS Workshop on Deep Learning for Physical Sciences 2023, 15 December 2023, New Orleans.
[86]  Kim, J., Lee, H. and Park, S. (2025) Radar Anomaly Detection with Multi-Resolution Vision Transformers. IEEE Transactions on Signal Processing, 73, 1234-1245.
[87]  Huang, Z., Denman, S., Pemasiri, A., Fookes, C. and Martin, T. (2025) Radar Signal Recognition through Self-Supervised Learning and Domain Adaptation. arXiv: 2501.03461.
[88]  San Martin, G., López Droguett, E., Meruane, V. and das Chagas Moura, M. (2018) Deep Variational Auto-Encoders: A Promising Tool for Dimensionality Reduction and Ball Bearing Elements Fault Diagnosis. Structural Health Monitoring, 18, 1092-1128.
https://doi.org/10.1177/1475921718788299

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133