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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳安宇(An-Yeu Wu) | |
| dc.contributor.author | Li-Sheng Chang | en |
| dc.contributor.author | 張立昇 | zh_TW |
| dc.date.accessioned | 2022-11-25T05:36:12Z | - |
| dc.date.available | 2026-06-30 | |
| dc.date.copyright | 2021-11-05 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-26 | |
| dc.identifier.citation | Jain, A., Hong, L., Pankanti, S. (2000). Biometric identification. Communications of the ACM, 43(2), 90-98. Jain, Anil K., Karthik Nandakumar, and Abhishek Nagar. 'Biometric template security.' EURASIP Journal on advances in signal processing 2008 (2008): 1-17. Al-Raisi, Ahmad N., and Ali M. Al-Khouri. 'Iris recognition and the challenge of homeland and border control security in UAE.' Telematics and Informatics 25.2 (2008): 117-132. I. Odinaka, P. Lai, A. D. Kaplan, J. A. O'Sullivan, E. J. Sirevaag and J. W. Rohrbaugh, 'ECG Biometric Recognition: A Comparative Analysis,' in IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1812-1824. Labati, R. D., Muñoz, E., Piuri, V., Sassi, R., Scotti, F. (2019). Deep-ECG: Convolutional neural networks for ECG biometric recognition. Pattern Recognition Letters, 126, 78-85. J. Zhang, J. Zhang, S. Ghosh, D. Li, S. Tasci, L. Heck, H. Zhang and C.-C. J. Kuo, 'Class-incremental Learning via Deep Model Consolidation,' in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1131-1140, 2020. G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, 'Extreme learning machine: theory and applications,' Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006. H. Li, C. Chou, Y. Chen, S. Wang and A. Wu, 'Robust and Lightweight Ensemble Extreme Learning Machine Engine Based on Eigenspace Domain for Compressed Learning,' in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 12, pp. 4699-4712, Dec. 2019. Y. -C. Chuang, Y. -T. Chen, H. -T. Li and A. -Y. A. Wu, 'An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection,' in IEEE Open Journal of Circuits and Systems, vol. 2, pp. 196-209, 2021. Guang-Bin Huang, Qin-Yu Zhu and Chee-Kheong Siew, 'Extreme learning machine: a new learning scheme of feedforward neural networks,' 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004, pp. 985-990 vol.2. Guang-Bin, Huang, et al. 'On-line sequential extreme learning machine.' Computational Intelligence 2005 (2005): 232-237. S. Suthaharan, Support vector machine, Machine Learning Models and Algorithms for Big Data Classification, Springer US (2016) 207-2355. B. Zhang, J. Su and X. Xu, 'A Class-Incremental Learning Method for Multi-Class Support Vector Machines in Text Classification,' 2006 International Conference on Machine Learning and Cybernetics, 2006, pp. 2581-2585. Venkatesan, Rajasekar, and Meng Joo Er. 'A novel progressive learning technique for multi-class classification.' Neurocomputing 207 (2016): 310-321. E. Laciar, R. Jane and D. H. Brooks, 'Improved alignment method for noisy high-resolution ECG and Holter records using multiscale cross-correlation,' in IEEE Transactions on Biomedical Engineering, vol. 50, no. 3, pp. 344-353, March 2003. J. R. Annam and B. R. Surampudi, 'Inter-patient heart-beat classification using complete ECG beat time series by alignment of R-peaks using SVM and decision rule,' 2016 International Conference on Signal and Information Processing (IConSIP), 2016. Q. Zhang, D. Zhou and X. Zeng, 'HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications,' in IEEE Access, vol. 5, pp. 11805-11816, 2017. C. -L. Lee, Y. -T. Chen and A. -Y. Wu, 'A Scalable Extreme Learning Machine (S-ELM) for Class-Incremental ECG-Based User Identification,' 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, pp. 1-5. L. Goldberger Ary, N. Amaral Luis A., G. Leon, M. Hausdorff Jeffrey, I. P. Ch., G. Mark Roger, E. Mietus Joseph, B. Moody George, P. Chung- Kang and S. H. Eugene, 'PhysioBank, PhysioToolkit, and PhysioNet,' Circulation, vol. 101, p. e215–e220, 6 2000. José Antonio, and R. Rautmann. 'QRD-RLS adaptive filtering.' (2009). A. Safaei, Q. M. J. Wu, T. Akilan and Y. Yang, 'System-on-a-Chip (SoC)-Based Hardware Acceleration for an Online Sequential Extreme Learning Machine (OS-ELM),' in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 38, no. 11, pp. 2127-2138, Nov. 2019. P. K. Meher, J. Valls, T. Juang, K. Sridharan and K. Maharatna, '50 Years of CORDIC: Algorithms, Architectures, and Applications,' in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 56, no. 9, pp. 1893-1907, Sept. 200. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82117 | - |
| dc.description.abstract | "如今，用戶身份識別 (User identification) 對於授權的計算機訪問和遠程個人數據使用中扮演越來越重要的角色。由於隱私和便利性，虹膜、指紋和臉部識別等基於生物特徵的用戶身分識別已成為我們日常生活中的主流方法。但是，大多數生物特徵辨識方法都可以被模仿或被人工破解。然而，新型的生物特徵辨識技術可以緩解以上問題，例如心電訊號 (ECG)，是基於內在生理訊號而不是傳統的外在生物特徵。此外，由於在智慧工廠中的作業員必須穿著無塵裝備，因此非常適合使用心電訊號身份識別系統的。 另外，在現實的智慧工廠中，必然會有新員工的加入，因此，必須在現有的辨識系統上增加辨識人數。然而，如果為了該新員工而重新學習整個辨識系統，會造成額外的運算資源因而不切實際。為了能夠有效地學習新進員工的資訊，也就是在機器學習系統裡面的新的類別，我們利用類增量學習 (Class-incremental learning, CIL) 的方法來實現我們的辨識系統。此外，心電訊號感測器 (ECG sensor) 通常會安裝於邊緣裝置 (Edge devices) 上，並且需要在邊緣裝置上做長期監測。因此，我們必須利用輕量級的分類器來實現辨識系統。在此論文，我們挑選極限學習機 (Extreme learning machine, ELM) 來當作輕量的分類器，並針對極限學習機提出支援類增量學習之演算法。最後，為了能進一步降低此輕量分類器在邊緣裝置的能耗，我們設計一個高能效且支援類增量學習的極限學習機硬體架構。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T05:36:12Z (GMT). No. of bitstreams: 1 U0001-2010202102335900.pdf: 3503230 bytes, checksum: 341b4923f1a1db05451302733a9be8ae (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 vii 摘要 ix ABSTRACT xi CONTENTS xiii LIST OF FIGURES xvi LIST OF TABLES xix Chapter 1 Introduction 1 1.1 Biometric-based User Identification 1 1.2 Challenges of ECG-based Biometric User Identification 3 1.3 Motivation and Main Contributions 5 1.4 Thesis Organization 7 Chapter 2 Review and Related Works 9 2.1 Review of Extreme Learning Machine (ELM) 9 2.1.1 Introduction of ELM 9 2.1.2 Introduction of Online Sequential ELM (OS-ELM) 10 2.2 Related Works of Class-incremental Learning (CIL) 12 2.2.1 CIL for Multi-class Support Vector Machine (SVM) 12 2.2.2 Progressive ELM 14 2.3 Summary 18 Chapter 3 ELM Algorithm for Class-incremental ECG-based User Identification 19 3.1 Overview of the ECG-based User Identification System 19 3.1.1 ECG Raw Data Pre-processing 20 3.1.2 Identification Model 21 3.2 Overview of the Proposed Scalable ELM Algorithm 22 3.2.1 Proposed Scalable ELM (S-ELM) Algorithm 22 3.2.2 Mathematical Proof for S-ELM 25 3.3 Simulation Result 27 3.3.1 Simulation Settings 27 3.3.2 Comparison between Proposed S-ELM and Progressive ELM 28 3.4 Summary 29 Chapter 4 Hardware-Friendly ELM Algorithm 31 4.1 Issues of Hardware Mapping of ELM Learning Algorithm 31 4.2 Overview of the Proposed QRD-ELM Algorithm 32 4.2.1 Proposed QRD-ELM Algorithm 32 4.2.2 Systolic Array of Proposed QRD-ELM Algorithm 37 4.3 Overview of Proposed S-QRD-ELM Algorithm 39 4.3.1 Introduction of Proposed S-QRD-ELM Algorithm 39 4.4 Simulation Results 42 4.4.1 Comparison between OS-ELM and QRD-ELM 42 4.4.2 Comparison between S-ELM and S-QRD-ELM 44 4.5 Summary 45 Chapter 5 Hardware Architecture Mapping of the Proposed QRD-ELM Engine 47 5.1 Prior Work of Hardware Implementation for ELM 47 5.2 Overview of Hardware Architecture of Proposed QRD-ELM Engine 49 5.2.1 Overall Architecture of Proposed QRD-ELM Engine 49 5.2.2 Overview of Main Operating Modules 50 5.2.3 Architecture Mapping of Each Operation Process 59 5.3 Fixed-point and CORDIC Analysis 61 5.4 Summary 63 Chapter 6 Conclusion 65 6.1 Main Contributions 65 6.2 Future Direction 66 REFERENCE 69 | |
| dc.language.iso | en | |
| dc.subject | 硬體架構 | zh_TW |
| dc.subject | 心電訊號身份識別 | zh_TW |
| dc.subject | 類增量學習 | zh_TW |
| dc.subject | 極限學習機 | zh_TW |
| dc.subject | ECG-based user identification | en |
| dc.subject | Hardware architecture | en |
| dc.subject | Extreme learning machine | en |
| dc.subject | Class-incremental learning | en |
| dc.title | 適用於心電訊號身分識別之輕量極限學習機引擎 | zh_TW |
| dc.title | Lightweight Extreme Learning Machine (ELM) Engine for ECG-based Biometric User Identification | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 盧奕璋(Hsin-Tsai Liu),劉宗德(Chih-Yang Tseng),沈中安 | |
| dc.subject.keyword | 心電訊號身份識別,類增量學習,極限學習機,硬體架構, | zh_TW |
| dc.subject.keyword | ECG-based user identification,Class-incremental learning,Extreme learning machine,Hardware architecture, | en |
| dc.relation.page | 71 | |
| dc.identifier.doi | 10.6342/NTU202103909 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-27 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2026-06-30 | - |
| 顯示於系所單位： | 電子工程學研究所 | |
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