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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 丁建均 | |
dc.contributor.author | Chi-Lin Kuo | en |
dc.contributor.author | 郭起霖 | zh_TW |
dc.date.accessioned | 2021-06-17T01:34:25Z | - |
dc.date.available | 2020-08-03 | |
dc.date.copyright | 2017-08-03 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-01 | |
dc.identifier.citation | [1] S. H. Nawab and T. F. Quatieri, ‘’Short time Fourier transform,’’ in Advanced Topics in Signal Processing, pp. 289-337, Prentice Hall, 1987.
[2] M. J. Bastiaans, ‘’Gabor’s expansion of a signal into Gaussian elementary signals,’’ Proc. IEEE, vol. 68, pp. 594-598, 1980. [3] S. C. Pei and J. J. Ding, ‘’Relations between Gabor transform and fractional Fourier transforms and their applications for signal processing,’’ IEEE Trans. Signal Processing, vol. 55, no. 10, pp. 4839-4850, Oct. 2007. [4] P. Boggiatto, G. De Donno, and A. Oliaro, ‘’Two window spectrogram and their integrals,’’ Advances and Applications, vol. 205, pp. 251-268, 2009. [5] S. G. Mallat and Z. Zhang. ‘’Matching pursuit with time-frequency dictionaries,’’ IEEE Trans. Signal Processing, vol. 41, issue 12, pp. 3397-3415, 1993. [6] A. Bultan. ‘’A four-parameter atomic decomposition of chirplets,’’ IEEE Trans, Signal Processing, vol. 47, no. 3, pp. 731-745, Mar. 1999. [7] H. Zhu, S.N. Zhang, and H.C. Zhao. ‘’Single-channel source separation of radar fuze mixed signal using advanced adaptive decomposition,” Acta Phys. Sin. vol. 63(5), 058401, 2014. [8] Y. Zhou, X. Wang, Y. Tian, and D. Zhou. ‘’A novel time-frequency atomic dictionary for radar intra-pulse modulation signal sparse representation,’’ APMC, pp. 6-9, Dec. 2015. [9] H. Zou, Q. Dai, R. Wang, and Y. Li. ‘’Parametric TFR via windowed exponential frequency modulated atoms,’’ IEEE Trans, Signal Processing, vol. 8, no. 5, pp. 140-142, May. 2001. [10] S. Ghofiani, D.C. McLernon, and A. Ayatollahi. ‘’Comparing Gaussian and chirplet dictionaries for time-frequency analysis using matching pursuit decomposition,’’ ISSPIT, Dec. 2003. [11] A. Bultan and O. Arikan, ‘’A parallelized matching pursuit algorithm for the four-parameter chirplet decomposition,’’ IEEE-SP International Symposium, pp. 421-424, Oct. 1998. [12] J. C. O’Neill and P. Flandrin, ‘’Chirp hunting,’’ IEEE-SP International Symposium. pp. 425-428, Oct. 1998. [13] Y. Wang and Y. Jiang, ‘’Modified adaptive chirplet decomposition and its efficient implementation,’’ ICSP, vol. 1, pp. 16-26, Nov. 2006. [14] X. Wang, J. Liu, H. Meng, and Y. Liu, ‘’Novel atomic decomposition algorithm for parameter estimation of multiple superimposed Gaussian chirplet,’’ IET Radar Sonar Naving, vol. 5, no. 8, pp. 854-861, 2011. [15] S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee. ‘’Compressive sensing: form theory to applications, a survey,’’ Journal of Communications and Networks, vol. 15, no. 5, pp. 443-456, Oct. 2013. [16] Cleve Moler. ‘’Magic Reconstruction: Compressed Sensing,’’ MathWorks News&Notes, pp. 1-4, 2010. [17] D. Donoho, ‘’Compressed sensing,’’ IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006. [18] M. Wakin, ‘’An introduction to compressive sampling,’’ IEEE Signal Process. Mag., 2008. [19] E. Candes, J. Romberg, and T. Tao, ‘’Stable signal recovery from incomplete and inaccurate measurements,’’ Commun. Pure Applied Math., vol. 59. No. 8, pp. 1207-1223, 2006. [20] E. Candes and J. Romberg, ‘’Sparsity and incoherence in compressive sampling,’’ Inverse Problems, vol. 23, p. 969, 2007. [21] E. Candes, ‘’The restricted isometry property and its implications for compressed sensing,’’ Comptes Rendus Mathematique, vol. 346, no. 9-10, pp. 589-592, 2008. [22] E. Candes and J. Romberg, ‘’Practical signal recovery from random projections,’’ IEEE Trans. Signal Process., 2005. [23] J. Tropp and A. Gilbert, ‘’Signal recovery from random measurements via orthogonal matching pursuit,’’ IEEE Trans. Inf. Theory, vol. 53, no. 12, p. 4655, 2007. [24] T. Bluemensath and M. Davies, ‘’Iterative hard thresholding for compressed sensing,’’ Applied and Computational Harmonic Analysis, vol. 27, no. 3, pp. 265-274, 2009. [25] D. Donoho, A. Maleki, and A. Montanari, ‘’Message-passing algorithms for compressed sensing,’’ Proc. Nat. Academy Sci., vol. 106, no. 45, p. 18914, 2009. [26] E. Candes and B. Recht, ‘’Exact matrix completion via convex optimization,’’ Foundations Computational Math., vol. 9, no. 6, pp. 717-772, 2009. [27] S. Chen, D. Donoho, and M.Saunders, ‘’Atomic decomposition by basis pursuit,’’ SIAM Rev., vol. 43, no. 1, pp. 129-159, 2001. [28] A. Gilbert, S. Muthukrishnan, and M. Strauss, ‘’Improved time bound for near-optimal sparse Fourier representations,’’ in Proc. SPIE, vol. 5914, p. 59141A, 2005. [29] A. Gilbert, M. Strauss, J. Tropp, and R. Vershynin, ‘’Algorithmic linear dimension reduction in the l1 norm for sparse vectors,’’ Arxiv preprint cs/0608079, 2006. [30] R. Chartand, ‘’Exact reconstruction of sparse signals via nonconvex minimization,’’ IEEE Signal Process. Lett., vol. 14, no. 10, pp. 707-710, 2007. [31] R. Chartand and W. Yin, ‘’Iteratively reweighted algorithms for compressive sensing,’’ in Proc. IEEE ICASSP, 2008. [32] W. Yin, S. Osher, D. Goldfarb, and J. Darbon, ‘’Bergman iterative algorithms for l1-minimization with applications to compressed sensing,’’ SIAMJ. Imaging Sci., vol. 1, no. 1, pp. 143-168, 2008. [33] http://billor.chsh.chc.edu.tw/sound/zoo.htm [34] Y. C. Pati, R. Rezaiifar, P. S. Krishnaprasad, 'Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition', Proc. 27th Annu. Asilomar Conf. Signals Systems and Computers, vol. 1, pp. 40-44, 1993-Nov. [35] T. E. Prieto, J. B. Myklebust, R. G. Hoffman, E. G. Lovett, and B. M. Myklebust. ‘’Measures of postural steadiness: differences between healthy young and elderly adults.’’ IEEE Transactions on Biomedical Engineering, vol. 43, 1966, pp. 956–966. [36] Z. Liang, R. Clark, A. Bryant, J. Quek, and Y.H. Pua. ‘’Neck musculature fatigue affects specific frequency bands of postural dynamics during quiet standing’’ Gait Posture, vol. 39, 2014, pp. 397-403. [37] J.R. Chagdes, S.Rietdyk, J.M. Haddad, H.N. Zelaznik, A. Raman, C.K. Rhea, and T.A. Silver. ‘’Multiple timescales in postural dynamics associated with vision and a secondary task are revealed by wavelet analysis.’’ Exp Brain Res., 197(2009), pp. 297-310. [38] J. Treleaven, R. Murison, G. Jull, N. LowChoy, and S. Brauer. ‘’ Is the method of signal analysis and test selection important for measuring standing balance in subjects with persistent whiplash?’’ Gait Posture, vol. 21, 2005, pp. 395-402. [39] J. Quek, S.G. Brauer, R. Clark, and J. Treleaven. ‘’ New insights into neck-pain-related postural control using measures of signal frequency and complexity in older adults.’’ Gait Posture, vol.39, 2014, pp. 1069-1073. [40] F. Hlawatsch and G.F. Boudreaux-Bartels. ‘’Linear and quadratic time frequency signal representations.’’ Proc. IEEE, vol. 9, 1992, pp. 21-67. [41] M. J. Bastiaans, ‘’Gabor’s expansion of a signal into Gaussian elementary signals,’’ Proc. IEEE, vol. 68, 1980, pp. 594-598. [42] S. C. Pei and J. J. Ding, ‘’Relations between Gabor transforms and fractional Fourier transforms and their applications for signal processing,’’ IEEE Trans. Signal Processing, vol. 55, no.10, Oct. 2007, pp.4839-4850. [43] P. Boggiatto, G. De Donno, and A. Oliaro, ‘’Two window spectrogram and their integrals,’’ Advances and Applications, vol. 205, 2009, pp251-268. [44] Chi-Wei Wang, ‘’ Introduction to Machine Learning’’, NTU-DISP, available from http://disp.ee.ntu.edu.tw/tutorial.php. [45] C. C. Chang and C. J. Lin, “LIBSVM -- A library for SVM,” available from http://www.csie.ntu.edu.tw/~cjlin/libsvm/ [46] “Examples for SVM using Matlab codes,” available from http://djj.ee.ntu.edu.tw/SVMExamples.zip. [47] ‘’Support vector machine’’, Wikipedia. [48] ‘’K-nearest neighbors algorithm’’, Wikipedia. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67488 | - |
dc.description.abstract | 對於信號處理領域來說,時頻分析一直是一項重要的分析工具。在這篇碩士論文中,我們將會運用時頻分析方法去實作出兩項有關信號處理的應用。第一項應用是針對於聲音訊號去做壓縮;第二項應用是設計一個分類器可以有效將兩組不同性質的醫學平衡信號種類分開。
對於聲音訊號壓縮的研究中,我們藉由Matching pursuit和Compressive sensing 的概念而達到壓縮的目的。對於Matching pursuit來說,運算時間與效能會相互影響使得我們必須做出衡量,但由於運算時間過長的問題導致這領域的許多研究學者不喜歡使用Matching pursuit,因此我們提出一個演算法藉由修改Matching pursuit與結合Compressive sensing的概念來達到在不影響效能的情形下還能有效降低運算時間的成效。模擬結果將顯示相較於以往的Matching pursuit來說,對於運算時間上我們確實有顯著的貢獻,且對於壓縮結果來說,比現今廣為人知的MP3壓縮格式還能達到更低的資料量。 在有關肢體平衡的醫學領域上,Myelopathy和Radiculopathy是兩種影響肢體平衡的疾病,我們藉由Signal processing與Generalized spectrogram兩項與時頻分析有關的手法去計算出肢體平衡信號在時間與頻率上的參數特徵,結合統計學上的Z-score與K-value對這些統計參數設計出能用於分類兩類疾病的分類器,更進一步引入KNN演算法對這個分類器做出準確率上的改良,模擬結果將顯示出我們設計出來的分類器與現今廣為人知的SVM分類器比較下,是具有較佳的準確率的。 | zh_TW |
dc.description.abstract | Time-frequency analysis is an important tool for signal processing. In this thesis, we use the concepts of time-frequency analysis to implement two topics, one is for vocal signal compression, another is for medical signal classifier design.
In our proposed method for vocal signal compression, we use the concepts of the matching pursuit algorithm and the compressive sensing to implement it. In the matching pursuit algorithm, there is a trade-off between the computation time and the performance. The problem of the computation time often makes people do not want to use it. Hence, we propose the method which modify the matching pursuit algorithm and the compressive sensing to reduce the computation time and do not affect the performance. The simulation results will show that we have a significant improvement in operational efficiency and the better performance than some audio compression format. In medical field of postural steadiness, cervical myelopathy and cervical radiculopathy are common disease that affect the balance of limb. We use the signal processing methods and the generalized spectrogram to calculate a variety of time and frequency domain measures of postural steadiness between myelopathy and radiculopathy under both eyes-open and eyes-closed. We utilize that information to observe the difference between myelopathy and radiculopathy. We design the statistical method which combine the concepts of the Z-score and the K-value to determine that the subject belongs to which type and use KNN (K Nearest Neighbor) algorithm to judge fuzzy case which statistical method can not judge. The simulation results will show that we have a better performance than SVM classifier. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T01:34:25Z (GMT). No. of bitstreams: 1 ntu-106-R04942096-1.pdf: 3158866 bytes, checksum: c432b14a0e61e5ea0d0bf39bf3775577 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 Background Review 3 2.1 Time-Frequency Distribution [1]– [4] 3 2.1.1 Short-time Fourier Transform (STFT) [1] 3 2.1.2 Rectangle Short-time Fourier Transform (Rec-STFT) [1] 3 2.1.3 Gabor Transform [2], [3] 5 2.1.4 Generalized Spectrogram [4] 6 2.2 Matching Pursuit 8 2.2.1 Introduction [5] 8 2.2.2 Dictionary and Atom [5] 9 2.2.3 Decomposition Scheme [5] 12 2.2.4 Other Time-Frequency Atoms [5]-[14] 13 2.3 Compressive Sensing 18 2.3.1 Introduction [15]- [19] 18 2.3.2 Restriction 19 2.3.3 Acquisition Model [15] 20 2.3.4 Reconstruction Model [15] 21 2.3.5 Reconstruction Algorithms [15] 23 2.3.6 Applications of Compressive Sensing [15] 25 Chapter 3 Proposed Method 26 3.1 Concepts 26 3.2 Principal Components Prediction 26 3.3 Memory-Matching Pursuit (MMP) 34 3.4 Pseudo Compressive Sensing (PCS) 38 3.5 Encoder 44 Chapter 4 Simulation Results 47 Chapter 5 Summary of Vocal Signal Compression 60 Chapter 6 Medical Signal Processing 63 6.1 Introduction 63 6.2 Background 64 6.2.1 Subjects Testing and Data Acquisition 64 6.2.2 Basic Definitions 65 6.2.3 Related Concepts [40]- [48] 65 Chapter 7 Proposed Method 70 7.1.1 Training Model [35]- [39] 70 7.1.2 Classification 74 Chapter 8 Simulation Results 77 8.1 Simulation Results of Medical Signal Processing 77 8.2 Summary of Medical Signal Processing 80 REFERENCE 84 Vocal Signal Compression 84 Medical Signal Processing 87 | |
dc.language.iso | en | |
dc.title | 時頻應用於醫學信號分類器設計與聲音信號壓縮 | zh_TW |
dc.title | Time-Frequency Applications for Medical Signal Classifier Design and Compressive-Based Vocal Signal Compression | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 許文良,張榮吉,王家慶 | |
dc.subject.keyword | 時頻分析,分類器,聲音信號壓縮,壓縮感知, | zh_TW |
dc.subject.keyword | Time-Frequency Analysis,Classifier,Vocal Signal Compression,Compressive Sensing, | en |
dc.relation.page | 88 | |
dc.identifier.doi | 10.6342/NTU201702323 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-08-02 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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