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姓名	黃士庭(Shih-ting Huang)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	應用於時頻聚類盲源分離之正交匹配追蹤及稀疏字典訓練晶片架構設計 (VLSI Architecture Design for Blind Source Separation Based on Time-Frequency Masking and Dictionary Training with Orthogonal Matching Pursuit)
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摘要(中)	盲訊號源分離在各個領域如今是一個很重要的研究方向，例如：生物訊號處理，腦科學，多媒體訊號處理…等，在語音訊號處理更是一個很好的研究主題。而在解盲訊號源分離這個問題上，是需要很大的運算量，所以在未來如果要即時應用下，實現成超大型積體電路是一個很好的選擇。本論文主要採用的演算法是時頻聚類之盲訊號源分離演算法，主要將訊號的特徵擷取出來後，利用聚類演算法將訊號分離，來得到我們想要的訊號。在這邊我們利用壓縮感測重建並加強分離之訊號，並且在壓縮感測中提出一個在面積上較有效率的Orthogonal Matching Pursuit硬體架構。而在壓縮感測演算法中，我們必須利用一個訓練好的字典來完成其重建訊號的動作，在這邊我們是利用K-SVD演算法來訓練字典。此演算法在各個領域也是一個重點的研究方向，例如：影像處理，訊號去噪…等，而這個演算法運算量一樣龐大，若我們想即時的運用，實現成超大型積體電路勢在必行。所以在這邊我們也提出了一個K-SVD的硬體架構，利用此硬體架構，可以有效的減少利用K-SVD所花費的時間。
摘要(英)	Now, blind source separation (BSS) is a very important research direction in various fields, For example, Biomedical Signal Progressing, brain science, multimedia signal processing, etc. And in audio domain, it is a good theme, too. In the paper, we design a BSS VLSI architecture which based on time-frequency masking. We fetch the feature for each time-frequency points and cluster them to get the separation signal. In the architecture, we add the sharing multiplication, and modify it to achieve more area- efficiency. On the other hand, when we use the algorithm to solve the problem, we must use a trained dictionary, and the training time is so long. To solve the question, we also design a VLSI architecture based on K-SVD algorithm. We use the architecture of orthogonal matching pursuit we designing to implement the K-SVD, and we also design an Acceleration & Lock Unit to decrease the power and the clock rate.
關鍵字(中)	★ 盲源分離 ★ 稀疏字典訓練	關鍵字(英)	★ Orthogonal Matching Pursuit ★ Blind Source Separation ★ K-SVD
論文目次	摘要……………………………………………………………………………ii abstract………………………………………………………………iii 圖目錄………………………………………………………………………iv 表目錄 ……………………………………………………………………vi 章節目次 ………………………………………………………………vii 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目的 2 1.3 論文架構 2 第二章 盲訊號源分離簡介 4 2.1 簡介(Introduction) 4 2.2 混合模型(Mixing Model) 4 2.2.1 旋積混合模型(Convolutive Mixtures Model) 4 2.2.2 即時混合模型(Instantaneous Mixing Model) 5 2.2.3 在頻率域上的旋積混合 6 2.3 Over and Under-determined 6 2.4 兩個輸入兩個輸出系統 7 2.5 分離原理 8 2.5.1 Independent Component Analysis(ICA) and BSS 9 第三章 時頻聚類盲訊號源分離之VLSI架構 9 3.1 概觀時頻聚類盲訊號源分離 9 3.2 特徵參數選取與其VLSI架構 13 3.2.1 特徵參數選取演算法 13 3.2.2 特徵參數選取之VLSI架構 13 3.3 K-Means分群演算法與其VLSI架構 15 3.3.1 可硬體式K-Means演算法 15 3.3.2 K-Means硬體架構 18 3.4 壓縮感測與其VLSI架構 20 3.4.1 壓縮感測演算法 20 3.4.2 OMP(Orthogonal Matching Pursuit)硬體架構 23 第四章 K-SVD稀疏字典訓練VLSI架構 34 4.1 字典訓練與其VLSI架構 34 4.1.1字典訓練-K-SVD演算法 34 4.2 K-SVD之VLSI架構 38 4.2.1 Matrix Acceleration Unit之硬體架構 41 4.2.2 Omega Unit之VLSI架構 43 4.2.3 SVD和Update Unit之VLSI架構 44 第五章 實驗結果 49 5.1 時頻聚類盲訊號源分離之VLSI架構 49 5.1.1 電路環境的設置 49 5.1.1 電路的合成與Layout 52 5.2 K-SVD之字典訓練之VLSI架構 54 第六章 結論及未來研究方向 57 參考文獻 59
參考文獻	[1] Hyvärinen, E. Oja, Independent component analysis: Algorithms and applications, Neural Networks 13 (2000) 411–430. [2] S. Roberts and R. Everson, Independent component analysis : Principles and Practice., Cambridge University Press, 2001. [3] S. C. Douglas, Malay Gupta, Hiroshi Sawada, and Shoji Makino, “Spatio-Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures,” IEEE Transactions Audio, Speech and Language Processing, Vol. 15, No. 5, pp. 204–215, 2007. [4] H. Saruwatari, T. Kawamura, T. Nishikawa, A. Lee, and K. Shikano,“Blind source separation based on a fast-convergence algorithm combining ICA and beamforming,” IEEE Trans. Audio, Speech, Lang.Process., vol. 14, no. 2, pp. 666–678, Mar. 2006. [5] A. Belouchrani, and M. G. Amin, “Blind source separation based on time-frequency signal representations”, IEEE Trans. on Signal Processing, vol.46(11), pp.2888-2897, Nov. 1998. [6] S. Winter, W. Kellermann, H. Sawada, and S. Makino,“MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and ℓ1- norm minimization,” EURASIP Journal on Advances in Signal Processing, vol. 2007, 2007, article ID 24717. [7] P. Bofill, “Underdetermined blind separation of delayed sound sources in the frequency domain,” Neurocomputing, vol. 55, pp. 627–641, 2003. [8] P. Bofill and M. Zibulevsky, “Underdetermined blind source separation using sparse representations,” Signal Process., vol. 81, pp. 2353 – 2362, Jun. 2001. [9] Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, “Underdetermined blind source separation based on sparse representation,” IEEETrans. Signal Process., vol. 54, no. 2, pp. 423–437, Feb. 2006. [10] A. Aissa-El-Bey, K. Abed-Mraim and Y. Grenier, “Blind separation of underdetermined convolutive mixtures using their time-frequency representation,” IEEE Trans. Audio, Speech, Lang. Process., vol. 15, pp. 1540 – 1550, Jul. 2007. [11] A.J. Bell, T.J. Sejnowski, “Blind separation and blind deconvolution: an information-theoretic approach,” 1995 International Conference on Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., vol.5, no., pp.3415-3418 vol.5, 9-12 May. 1995. [12] K.S. Cho, S.Y. Lee, “Analog CMOS implementation of nonholonomic ICA algorithm with automatic offset compensation,” 2003. Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, vol.1, no., pp. 279- 282 vol.1, 14-17 Dec. 2003. [13] J. Park, K. Muhammad, K. Roy, “High-performance FIR filter design based on sharing multiplication,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol.11, no.2, pp.244-253, April. 2003. [14] V. G. Reju, S. N. Koh and I. Y. Soon, “Underdetermined convolutive blind source separation via time-frequency masking,” IEEE Trans. Audio, Speech, Lang. Process., vol. 18, pp. 101 – 116, Jan. 2010. [15] S. Araki, H. Sawada, R. Mukai and S. Makino, “Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors,” Signal Process., vol. 87, pp. 1833 – 1847, Feb. 2007. [16] R. Lyons, “Another contender in the arctangent race," IEEE Signal Processing Magazine,vol.21, no.1, pp. 109-110, Jan. 2004. [17] Hu, Y.H.; , "CORDIC-based VLSI architectures for digital signal processing," Signal Processing Magazine, IEEE , vol.9, no.3, pp.16-35, July 1992. [18] T.-W. Chen and S.-Y. Chien, “Bandwidth adaptive hardware architecture of K-means clustering for video analysis,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 18, no. 6, pp. 957–966, Jun. 2010. [19] K. S. Gurumoorthy, A. Rajwade, A. Banerjee, and A. Rangarajan, “A method for compact image representation using sparse matrix and tensor projections onto exemplar orthonormal bases,” IEEE Trans. Image Process., vol. 19, no. 2, pp. 322-334, Feb. 2010. [20] J. C. Yang, J. Wright , T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861-2873, Nov. 2010. [21] W. S. Dong, L. Zhang, G. M. Shi, and X. L. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838-1857, July 2011. [22] Y. Li, S.-I Amari, A. Cichocki, and C. Guan, “Probability estimation for recoverability analysis of blind source separation based on sparse representation,” IEEE Trans. Inf. Theory, vol. 52, no. 7, pp. 3139-3152, July 2006. [23] M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, Dec. 2006. [24] M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Processing Mag., vol. 25, no. 2, pp. 72-82, Mar. 2008. [25] M. Marim, E. Angelin, and J.-C. Olivo-Marin, “A compressed sensing approach for biological microscopic image processing,” IEEE Int. Symp. Biomedical Imaging, 2009, pp. 1374-1377. [26] L. Zhu, Y. L. Zhu, H. Mao, and M. H. Gu, “A new method for sparse signal denoising based on compressed sensing,” Int. Symp. Knowledge Acquisition and Modeling, 2009, pp. 35-38. [27] J. J. Han, O. Loffeld, K. Hartmann, and R.Wang, “Multi image fusion based on compressive sensing,” Int. Conf. Audio Language and Image Processing, 2010, pp. 1463-1469. [28] J. Wu, F. Liu, L. C. Jiao, and X. D. Wang, “Compressive sensing SAR image reconstruction based on Bayesian framework and evolutionary computation,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 1904-1911, July 2011. [29] N. Yu, T. Qiu, F. Bi, and A. Wang, “Image features extraction and fusion based on joint sparse representation,” IEEE J. Selected Topics Signal Process., vol.5, no. 5, pp. 1074-1082, Sept. 2011. [30] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, Feb. 2008. [31] M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for Sparse Representation,” IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311–4322, Nov. 2006. [32] S. G. Mallat and Z. F. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993. [33] J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory, vol. 53, pp. 4655-4666, 2007. [34] D. Needell and R. Vershynin, “Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit,” IEEE J. Selected Topics Signal Process., vol. 4, no. 2, pp. 310-316, Apr. 2010 [35] P. B.Denyer and D. Renshaw, “VLSI Signal Processing; A Bit-Serial Approach,"Addison-Wesley Longman Publishing Co., Boston, MA, USA, 1985. [36] J. C. Wang, Y. C. Chen, and S. T. Huang, “VLSI design for time-frequency clustering based convolutive BSS,” to be submitted. [37] J. C. Wang, Y. C. Chen, S. T. Huang, et al. “VLSI design for orthogonal matching pursuit,” to be submitted. [38] J. C. Wang, Y. C. Chen, and S. T. Huang, “A convolutive BSS system based on time-frequency clustering and compressive sensing,” invention patent to be applied. [39] J. C. Wang, S. C. Huang, and Y. C. Chen, “VLSI design for Infomax based convolutive BSS,” to be submitted. [40] J. C. Wang, S. C. Huang, and Y. C. Chen, “A convolutive blind source separation system,” invention patent to be applied. [41] R. Brent, F. Luk, and C. Van Loan, "Computation of the singular value decomposition using mesh-connected processors", J. VLSI Comput. Syst., vol. 1, pp.242 -270 1984 [42] Q. Zhang and B. Li "Discriminative K-SVD for dictionary learning in face recognition", Proc. IEEE Conf. Computer Vision Pattern Recognition, pp.2691 -2698 2010 [43] J. R. Cavallaro , M. P. Keleher , R. H. Price and G. S. Thomas "VLSI Implementation of a CORDIC SVD Processor", Proc. 8th Biennial University/Government/Industry Microelectronics Symposium, pp.256 -260 1989 [44] N. D. Hemkumar , K. Kota and J. R. Cavallaro "CAPE-VLSI implementation of a systolic processor array: Architecture, design and testing", Proc. 9th IEEE Biennial University/Government/Industry Microelectronics Symp., pp.64 -69 1991 [45] Z. Liu, K. Dickson. J. McCanny “CORDIC Based Application Specific Instruction Set Processor for QRD/SVD”, Proc. 37th Asilomar Conference on Signals, Systems & Computers, vol.2, pp.1456-1460, 2003 [46] Weiwei Ma, M. E. Kaye, D. M. Luke and R. Doraiswami, “An FPGA-Based Singular Value Decomposition Processor,” CCECE Conf., pp. 1049-1050,May. 2006 [47] W. Yue , K. Cunningham , P. Nagvajara and J. Johnson "Singular value decomposition hardware for MIMO: State of the art and custom design", Proc IEEE ReConFig, pp.400-405, 2010 [48] P. M. Szecowka and P. Malinowski "CORDIC and SVD implementationin digital hardware", Proc. Int. Conf. MixedDesign of Integrated Circuits and Systems (MIXDES), pp.237 -242, 2010 [49] L. M. Ledesma-Carrillo, E. Cabal-Yepez, R. de J Romero-Troncoso, A. Garcia-Perez, R. A. Osornio-Rios and T. D. Carozzi, “Reconfigurable FPGA-Based Unit for Singular Value Decomposition of Large m x n Matrices,” Proc IEEE ReConFig, pp. 345-350,Dec. 2011 [50] A. Ahmedsaid, A. Amira, A. Bouridane, "Improved SVD Systolic Array and Implementation on FPGA", Proc. IEEE FieldProgrammable Technology, pp35-42, 2003. [51] Emre Telat Ar and I. Emre Telatar. “Capacity of multi-antenna gaussian channels,” Europ. Trans. Telecommun., vol. 10, pp.585 -596 1999 [52] Li Hui, Y. H. Shen and S. Z. Chen, “A Robust On-line Blind Separation Algorithm with Dynamic Source Number Based on Neural Network,” Networks Security Wireless Communications and Trusted Computing (NSWCTC )Conf., vol. 1, pp. 99-102,April 2010 [53] L. W. Kang, C. Y. Hsu, H. W. Chen, C. S. Lu, C. Y. Lin, and S. C. Pei, "Feature-Based Sparse Representation for Image Similarity Assessment", IEEE Transactions on Multimedia , Oct. 2011. [54] Q. Li, H. G. Zhang, J. Guo, B. Bhanu and A. Le, “Reference-Based Scheme Combined With K-SVD for Scene Image Categorization,” IEEE Signal Processing Letters , pp. 67-70, Jan. 2013. [55] M. S. Koh and E. Rodriquwz-Marek“Turbo Inpainting Iterative K-SVD with a New Dictionary,” IEEE Multimedia Signal Processing, pp. 1–6, Oct. 2009. [56] V. Abolghasemi, S. Ferdowsi and S. Sanei, “Sparse Multichannel Source Separation Using Incoherent K-SVD Method,” IEEE Statistical Signal Processing Workshop (SSP),pp. 477–480, Jun. 2011. [57] M. Protter and M. Elad "Image sequence denoising via sparse and redundant representations", IEEE Trans. Image Process., vol. 18, no. 1, pp.27 -35 2009 [58] Shan Bin, Hao Wei and Zhao Rui, “Infrared Image De-noising Based On K-SVD Over-complete Dictionaries Learning,” Image and Signal Processing (CISP) International Congress, pp. 316–320, Oct. 2012. [59] V. Patel, Y.shi, P. M. Thompson and A. W. Toga, “K-SVD For HARDI Denoising,” 8th International Symposium on Biomedical Imaging, pp.1805-1808,April 2011. [60] J. C. Wang, S. T. Huang, and Y. C. Chen, “VLSI architecture design of OMP for CS-based speech,” to be submitted. [61] Y. C. Chen, “VLSI architecture design for Blind Source Separation based on Infomax and Time-frequency Masking,” MS Thesis, NCU, July 2012.
指導教授	王家慶(Jia-Ching Wang)	審核日期	2013-8-26
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