跳到主要內容

臺灣博碩士論文加值系統

(3.236.84.188) 您好!臺灣時間:2021/08/02 19:10
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:林軒宇
研究生(外文):Hsuan-Yu Lin
論文名稱:應用於光譜感測器陣列之盲目光強度不均勻及錯誤感測器修正演算法
論文名稱(外文):Blind Nonuniformity Correction and Faulty Sensor Detection Methods for On-Chip Spectrum Sensor Array
指導教授:張正春張正春引用關係
口試委員:蔡佩芸郭天穎譚旦旭
口試日期:2012-07-24
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:72
中文關鍵詞:脈衝雜訊光不均勻性盲目修正演算法微型光譜儀光譜感測器陣列錯誤感測器
外文關鍵詞:impulsive noiseinput nonuniformityblind correction algorithmminiature spectrometerspectrum sensor arrayfaulty sensor
相關次數:
  • 被引用被引用:0
  • 點閱點閱:177
  • 評分評分:
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:0
以濾波器陣列製成的光譜感測元件已經被認為是實現微型光譜儀一個可靠的架構,藉由使用多個具有不同頻譜響應的光譜感測器,可以判斷被測物光譜的特性。然而由於低成本且微型的輸入介面,輸入光對於每個感測器的強度可能會不平均。除此之外,每個感測器的輸出都可能包含來自CMOS感測器的脈衝干擾。
未預期的光強度不均勻性將對量測頻譜造成碩大的影響,雖然精緻的光學儀器可以抑制這個問題,我們仍使用演算法處理這個問題因為(1)即便是精緻的光學儀器,仍會有5%至10%的光不均勻性,(2)相較之下,現今透過演算法解決這個問題會使成本下降。因此我們提出一個迭代性的盲目修正演算法以解決這個光強度不均的問題。只要光強度在每個感測器之間是平順變化,這個被提出的演算法便可在沒有任何其他資訊的狀況下解決問題。使用這個被提出的演算法無論對模擬還是在實測的數據都有顯著的改善。
脈衝性的干擾常因為傳輸錯誤、感測器的功能不正常、記憶體存取的問題還有在類比轉數位時的時序問題而產生。在大多光譜感測器陣列的應用領域中,這樣的干擾會大幅影響系統效能,所以修正這些被干擾的感測器是必須且急需的。使用在編碼理論中「線性區塊碼」的概念,這篇論文提出一個修正錯誤感測器的方法。

Filter-array spectrum sensors have been a promising structure that can be used to realize miniature spectrometers or spectrometers on-a-chip. By using multiple spectrum sensors with different spectral responses, the spectrum of a measurement object can be characterized. However, due to the low-cost and miniature design of the input optic interfaces, the intensity of the input light shining onto the imager of the sensor array may not be uniform. In addition, the sensor outputs may contain impulsive perturbation from the CMOS imager.
The unmodeled input nonuniformity could lead to a severe distortion in the spectrum measurement. Although the input nonuniformity can be alleviated by introducing dedicated input optic interface, we are interested in tackling this issue from an algorithmic perspective because (1) dedicated optics could still render 5%-10% intensity variation, and (2) the cost of computation power in electronics is potentially much lower than the cost of optics nowadays. Accordingly, we propose an iterative blind correction algorithm to solve the input light nonuniformity issue. The algorithm is based on the assumption that variation of input light intensity shall change smoothly, and hence requires no additional information. With the proposed iterative blind correction algorithm, significant improvement on the quality of spectrum reconstruction is obtained in both simulation and experimental studies.
Impulsive perturbations often occur due to transmission errors, malfunctioning pixel elements in the camera sensors, faulty memory locations, and timing errors in analog-to-digital conversion. It should be noticed that most of the spectrum sensor applications are sensitive to sensor readout perturbation. If the impulsive perturbation exists, it reduces the system performance dramatically, so it is imperative and even indispensable to correct these faulty pixels. Based on the concept of linear block code in coding theory, this thesis proposes an approach to correct the faulty pixels.

摘 要 i
ABSTRACT ii
誌 謝 iii
CONTENTS iv
List of Tables vi
List of Figures vii
Chapter 1 INTRODUCTION 1
1.1 Preface 1
1.2 Motivation 2
1.3 Organization 4
Chapter 2 BACKGROUND KNOWLEDGE 5
2.1 Introduction to Micro-Spectrometer 5
2.2 Spectrum Reconstruction & Potential Issues 12
2.2.1 Spectrum Reconstruction Using Adaptive Regularization 12
2.2.2 Potential Issues 14
Chapter 3 ITERATIVE BLIND NONUNIFORMITY CORRECTION ALGORITHM 16
3.1 Introduction 16
3.2 System of Interest 19
3.3 Proposed Method 25
3.4 Results 29
3.5 Conclusions 39
Chapter 4 FAULTY SENSOR DETECTION METHOD 40
4.1 Introduction 40
4.2 System of Interest 42
4.3 Real-valued Error Correction Using the Concept of Linear Block Code 47
4.4 Proposed Method 49
4.5 Results 53
4.6 Conclusions 64
Chapter 5 CONCLUSIONS AND FUTURE WORKS 65
5.1 Conclusions 65
5.2 Future Works 66
REFERENCES 67



[1]R. F. Wolffenbuttel, “State of the art in integrated optical microspectrometers”, IEEE Trans. on Instrumentation and Measurement, vol. 53, no. 1, pp. 197-202, Feb. 2004.
[2]C. P. Bacon, Y. Mattley, and R. Defrece, “Miniature spectroscopic instrumentation: applications to biology and chemistry,” Review of Scientific Instrument, vol. 75, pp. 1-16, 2004.
[3]J. M. Eichenholz and J. Dougherty, “Ultracompact fully integrated megapixel multispectral imager,” in Proceedings of SPIE, vol. 7218, pp. 721814/1-721814/10, Feb. 2009.
[4]P. Vines, C. H. Tan, and J. P. R. David, “Quantium dot infrared photodetectors with highly tunable spectral response for an algorithm based spectrometers,” in Proceedings of SPIE, vol. 7660, pp. 76602D/1-76602D/10, Orlando FL, Apr. 2010.
[5]P. Parrein, A. Landragin-Frassati, and J. M. Dinten, “Reconstruction method and optimal design of an interferometric spectrometer,” Applied Spectroscopy, vol. 63, no. 7, pp. 786-790, Jan. 2009.
[6]S.-M. Liu, “The development of a portable spectrophotometer for noncontact color measurement,” IEEE Trans. Instrumentation and Measurement, vol. 53, no. 1, pp. 155-162, Feb. 2004.
[7]“CS-200 Luminance and color meters,” [Online] Available:http://www.konicaminolta.com/instruments/products/display/luminance-color-meters/cs200/index.html [Accessed: Sept. 25, 2011].
[8]G. Themelis, J.-S. Yoo and V. Ntziachristos, “Multispectral imaging using multiple-bandpass filters,” Optics Letters, vol. 33, pp. 1023-1025, May 1. 2008.
[9]R. Rubio, J. Santander, J. Fonollosa, L. Fonseca, I. Gracia, C. Cane, M. Moreno, and S. Marco, “Exploration of the metrological performance of a gas detector based on an array of unspecific infrared filters,” Sensors and Actuators B, vol. 116, pp. 183-191, 2006.
[10]S.-W. Wang, C. Xia, X. Cheng, W. Lu, M. Li, H. Wang, W. Zheng, and T. Zhang, “Concept of a high resolution miniature spectrometer using an integrated filter array,” Optics Letter, vol. 32, 632-634, 2007.
[11]U. Kurokawa, B. I. Choi, and C.-C. Chang, “Filter-based miniature spectrometers: spectrum reconstruction using adaptive regularization,” IEEE Sensors Journal, vol. 11, no. 7, pp. 1556-1563, Jul. 2011.
[12]R. Z. Morawski, A. Miekina, “Improving absorbance spectrum reconstruction via spectral data decomposition and pseudo-baseline optimization,” IEEE Trans. Instrumentation and Measurement, vol. 58, no. 3, Mar. 2009.
[13]L. Szczecinski, R. Z. Morawski, and A. Barwicz, “Original-domain Tikhonov regularization and non-negativity constraint improve resolution of spectrophotometric analyses,” Measurement, vol. 18, pp. 151-157, 1996.
[14]I. Andreadis and G. Louverdis, “Real-time adaptive image impulse noise suppression,” IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 3, pp. 798- 806, June 2004.
[15]Y. Dong, R. H. Chan, and S. Xu, “A Detection Statistic for Random-Valued Impulse Noise,” IEEE Transactions on Image Processing, vol. 16, no. 4, pp. 1112-1120, April 2007.
[16]E. Abreu, M. Lightstone, S. K. Mitra, and K. Arakawa, “A new efficient approach for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Image Processing, vol. 5, no. 6, pp. 1012-1025, Jun 1996.
[17]S. F. Liang, S. M. Lu, J. Y. Chang, and C. T. Lin, “A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 863-873, Aug. 2008.
[18]W. Luo, “An efficient detail-preserving approach for removing impulse noise in images,” IEEE Signal Processing Letters, vol. 13, no. 7, pp. 413-416, July 2006.
[19]H. Yu, L. Zhao, and H. Wang, “An Efficient Procedure for Removing Random-Valued Impulse Noise in Images,” IEEE Signal Processing Letters, vol. 15, pp. 922-925, 2008.
[20]H. Ibrahim, N. S. P. Kong, and T. F. Ng, “Simple adaptive median filter for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Consumer Electronics, vol. 54, no. 4, pp. 1920-1927, November 2008.
[21]W. Wang and P. Lu, “An Efficient Switching Median Filter Based on Local Outlier Factor,” IEEE Signal Processing Letters, vol. 18, no. 10, pp. 551-554, Oct. 2011.
[22]“Spectroscopy - Wikipedia, the free encyclopedia” [Online]. Available: http://en.wikipedia.org/wiki/Spectroscopy [Accessed: June. 15, 2012]
[23]“Definition of a Spectrometer | eHow.com” [Online]. Available: http://www.ehow.com/facts_5972582_definition-spectrometer.html [Accessed: June. 15, 2012]
[24]J.F. James and R.S. Sternberg, The Design of Optical Spectrometers. Chapman and Hall Ltd., 1969.
[25]“The spectrometer technology of OtO – Optical mechanism” [Online]. Available: http://www.otophotonics.com/english/Technology_01.asp [Accessed: June. 15, 2012]
[26]R. F. Wolffenbuttel, “State-of-the-Art in integrated optical microspectrometers,” IEEE Transactions on Instrumentation and Measurement, vol.53, no.1, pp. 197-202, Feb.2004.
[27]K. Kodate, Y. Komai, and K. Okamoto, “Compact spectroscopic sensor using an arrayed waveguide grating,” 2007 IEEE/LEOS International Conference on Optical MEMS and Nanophotonics, 2007, pp. 159-160.
[28]“Spectrometer - Wikipedia, the free encyclopedia” [Online]. Available: http://en.wikipedia.org/wiki/Spectrometer [Accessed: June. 15, 2012]
[29]F. Horst et al., “Echelle grating WDM (de-)multiplexers in SOI technology, based on a design with two stigmatic points,” in Proceedings Silicon Photonics and Photonic Integrated Circuits, 2008, pp. 69960R-8.
[30]Z. Xia, A. A. Eftekhar, M. Soltani, B. Momeni, Q. Li, M. Chamanzar, S. Yegnanarayanan, and A. Adibi, “High resolution on-chip spectroscopy based on miniaturized microdonut resonators,” Optics Express, vol.19, pp. 12356–12364, 2011.
[31]B. Momeni, E. S. Hosseini, M. Askari, M. Soltani, and A. Adibi, “Integrated photonic crystal spectrometers for sensing applications,” Optics Communications, vol. 282, no. 15, pp. 3168–3171, Aug. 2009.
[32]S.-W.Wang, C. Xia, X. Cheng, W. Lu, M. Li, H.Wang, W. Zheng, and T. Zhang, “Concept of a high resolution miniature spectrometer using an integrated filter array,” Optics Letters, vol. 32, pp. 632–634, 2007.
[33]C. L. Lawson and R. J. Hanson, Solving Least Squares Problems. Englewood, NJ: Prentice-Hall, 1974.
[34]L. Szczecinski, R. Z. Morawski, and A. Barwicz, “Variational algorithms of measurand reconstruction based on entropy-like criteria,” J. Chemometrics, vol. 12, no. 6, pp. 397–403, Nov. 1998.
[35]R. Z. Morawski, “Spectrophotometric applications of digital signal processing,” Measurement Science Technology, vol. 17, no. 9, pp. R117–R114, Sep. 2006.
[36]F. S. V. Bazan, “Fixed-point iterations in determining the Tikhonov regularization parameter,” Inverse Problems, vol. 24, pp. 1–15, 2008.
[37]Lu, S. V. Perverzev, and R. Ramlau, “An analysis of Tikhonov regularization for nonlinear ill-posed problems under a general smoothness assumption,” Inverse Problems, vol. 23, pp. 217–230, 2007.
[38]L. Wu, “A parameter choice method for the Tikhonov regularization,” Electronic Transactions on Numerical Analysis, vol. 16, pp. 107–128, 2003.
[39]P. C. Hansen, “Analysis of discrete ill-posed problems by means of the L-curve,” SIAM Review, vol. 34, no. 4, pp. 561–580, Dec. 1992.
[40]G. H. Goulub, M. Heath, and G.Wahba, “Generalized cross validation as a method for choosing a good ridge parameter,” Techometrics, vol. 21, no. 2, pp. 215–223, May 1979.
[41]A. Mi˛ekina and R. Z. Morawski, “Incorporation of the positivity constraint into a Tikhonov-method-based algorithm of measurand reconstruction,” in Proceedings International IMEKO-TC1&TC7 Colloquium, London, U.K., Sep. 1993, pp. 299–304.
[42]R. Z. Morawski and A. Miekina, “Improving absorbance spectrum reconstruction via spectral data decomposition and pseudo-baseline optimization,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 3, pp. 691–697, Mar. 2009.
[43]C. C. Chang and H. N. Lee, “On the estimation of target spectrum for filter array based spectrometers,” Optics Express, vol. 16, no. 2, pp. 1056–1061, Jan. 2008.
[44]T. C. Lin and P. T. Yu, “Thresholding noise-free ordered mean filter based on Dempster-Shafer theory for image restoration,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 53, no. 5, pp. 1057- 1064, May 2006.
[45]Z. Xu, H. R. Wu, B. Qiu, and X. Yu, “Geometric Features-Based Filtering for Suppression of Impulse Noise in Color Images,” IEEE Transactions on Image Processing, vol. 18, no. 8, pp. 1742-1759, Aug. 2009.
[46]K. Hirakawa and T. W. Parks, “Image denoising using total least squares,” IEEE Transactions on Image Processing, vol. 15, no. 9, pp. 2730-2742, Sept. 2006.
[47]A. Bosco, K. Findlater, S. Battiato, and A. Castorina, “A noise reduction filter for full-frame data imaging devices,” IEEE Transactions on Consumer Electronics, vol. 49, no. 3, pp. 676- 682, Aug. 2003.
[48]G. M. Morris, T. R. M. Sales, S. Chakmakjian, and D. J. Schertler, “Engineered diffusers for display and illumination systems: design, fabrication , and applications,” Yountville Conference, Yountville, CA USA, Jun. 2006.
[49]D. L. Perry and E. L. Dereniak, “Linear theory of nonuniformity correction in infrared staring sensor,” Optical Engineering, vol. 32, no. 8, pp. 1853-1859, Aug. 1993.
[50]M. D. Lasarte, J. Pujol, M. Arjona, and M. Vilaseca, “Optimized algorithm for the spatial nonuniformity correction of an imaging system based on a charge-coupled device color camera,” Applied Optics, vol. 46, no. 2, pp. 167-174, Jan. 2007.
[51]B. M. Ratliff and M. M. Hayat, “An algebraic algorithm for nonuniformity correction in focal-plane arrays,” Opt. Soc. Am. A, vol. 19, no. 9, pp. 1737-1747, Sep. 2002.
[52]R. C. Hardie, M. M. Hayat, E. Armstrong, and B. Yasuda, “Scene-based nonuniformity correction with video sequences and registration,” Applied Optics, vol. 39, no. 8, pp. 1241-1250, Mar. 2000.
[53]S. N. Torres, J. E. Pezoa, and M. M. Hayat, “Scene-based nonuniformity correction for focal plane arrays by the method of the inverse covariance form,” Applied Optics, vol. 42, no. 29, pp. 5872-5881, Oct. 2003.
[54]H.-X. Zhou, H.-L. Qin, Y.-B. Jian, B.-J. Wang, and S.-Q. Liu, “Improved Kalman-filter nonuniformity correction algorithm for infrared focal plane arrays,” Infrared Physic and Technology, vol. 51, pp. 528-531, Jun. 2008.
[55]T. Xu, Y.-K. Wu, X. Luo, and L. J. Guo, “Plasmonic nanoresonators for high-resolution colour filtering and spectral imaging,” Nature Communications, vol.1, no. 5, pp.1-5, Aug. 2010.
[56]C.-C. Chang, N.-T. Lin, U. Kurokawa, and B. I. Choi, “Spectrum reconstruction for filter-array spectrum sensor from sparse template selection,” Optical Engineering, vol. 50, no. 1 pp. 114402/1-114402/7, Nov. 2011.
[57]“Error detection and correction.” Internet: http://en.wikipedia.org/wiki/Error_detection_and_correction, [April. 12, 2012].
[58]M.N.V. Jose, M.S.S. Dorabella, and P.J.S.G. Ferreira, “Error Detection with Real-Number Codes Based on Random Matrices,” Digital Signal Processing Workshop, 12th - Signal Processing Education Workshop, 4th, Sep. 2006, pp. 526-530.
[59]E. Candes and T. Tao, “Decoding by linear programming,” IEEE Transaction on Information Theory, vol. 51, no. 12, pp. 4203–4215, Dec. 2005.
[60]J. T. Marshall, “Coding of real-number sequences for error correction: A digital signal processing problem,” IEEE Journal on Selected. Areas in Communications, vol. 2, no. 2, pp. 381–392, Mar. 1984.
[61]A. Zanko and A. Leshem,“Analog Product Codes Decodable by Linear Programming” IEEE Transaction on Information Theory, vol. 58, no. 2, pp. 509-518, Feb. 2012.
[62]B. K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM Journal on Computing., vol. 24, no. 2, pp. 227–234, 1995.
[63]D. L. Donoho and X. Huo, “Uncertainty principles and ideal atomic decomposition,” IEEE Transaction on Information Theory, vol. 47, no. 7, pp. 2845–2862, Nov. 2001.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top