COMPRESSIVE SENSING BY COLPITTS CHAOTIC OSCILLATOR FOR IMAGE SENSORS

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 2 (June 2015) > List of articles

COMPRESSIVE SENSING BY COLPITTS CHAOTIC OSCILLATOR FOR IMAGE SENSORS

Li Liu * / Peng Yang / Jianguo Zhang / Guifen Wei

Keywords : Compressive sensing, image sensors, chaos, Colpitts oscillator.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,225-1,243, DOI: https://doi.org/10.21307/ijssis-2017-804

License : (CC BY-NC-ND 4.0)

Received Date : 01-February-2015 / Accepted: 23-April-2015 / Published Online: 01-June-2015

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ABSTRACT

Compressive sensing uses simultaneous sensing and compression to provide an efficient image acquisition technique and it has been demonstrated in optical and electrical image sensors. To guarantee exact recovery from sparse measurements, specific sensing matrix, which satisfies the Restricted Isometry Property (RIP), should be well chosen. Toeplitz-structured chaotic sensing matrix constructed by Logistic map has been proved to satisfy RIP with high probability. In this paper, we propose that chaotic sequence sampled from Colpitts oscillator can also be used to generate Toeplitz-structured chaotic sensing matrix. Simulation results show that the proposed Colpitts chaotic sensing matrix has similar performance to random matrix or other chaotic matrix for exact reconstructing compressible signals and images from fewer measurements.

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REFERENCES

[1]Z. Y. Xiong, X. P. Fan, S. Q. Liu, Y. Z. Li, and H. Zhang, ―Performance analysis for dct-based coded image communication in wireless multimedia sensor networks‖, International Journal on Smart Sensing and Intelligent Systems, Vol. 6, No. 1, 2013, pp. 120-135.
[2]B. Wu, Y. P. Feng, and H. Y. Zheng. ―Posterior belief clustering algorithm for energy-efficient tracking in wireless sensor networks‖, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 3, 2014, pp. 925-941.
[3] L. K. Lai, T. S. Liu, ―Design of auto-focusing modules in cell phone cameras‖, International Journal on Smart Sensing and Intelligent Systems, Vol. 4, No. 4, 2011, pp. 568-582.
[4] E. Candès, J. Romberg and T. Tao, ―Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information‖, IEEE Transactions on Information Theory, Vol. 52, No. 2, 2006, pp. 489-509.
[5] E. Candès and T. Tao, ―Near optimal signal recovery from random projections: Universal encoding strategies‖, IEEE Transactions on Information Theory, Vol. 52, No. 12, 2006, pp. 5406-5425.
[6] D. Donoho, ―Compressed sensing‖, IEEE Transactions on Information Theory, Vol. 52, No. 4, 2006, pp. 1289-1306.
[7] M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly and R. G. Baraniuk, ―Single pixel imaging via compressive sampling‖, IEEE Signal Processing Magazine, Vol. 25, No. 2, 2008, pp. 83-91.
[8] Y Wu, P Ye, I. O. Mirza, G. R. Arce and D. W. Prather, ―Experimental demonstration of an optical-sectioning compressive sensing microscope(CSM)‖, Optics Express, Vol. 18, No. 24, 2010, pp. 24565-24578.
[9] R. Baraniuk and P. Steeghs, ―Compressive radar imaging‖, IEEE Radar Conference, pp. 128-133, Boston, MA, 17-20 April 2007.
[10] M. Zhang and A. bermak, ―Compressive acquisition CMOS image sensor—From algorithmic solution to hardware implementation‖, IEEE Transactions on Very Large Scale Integration Systems, Vol. 10, No.3, 2010, pp. 490-500.
[11] M. R. Dadkhah, M. J. Deen and S. Shirani, ―Block-based compressive sensing in a CMOS image sensor‖, IEEE Sensors Journal, Vol. 14, No. 8, 2012, pp. 2897-2909.
[12] R . F. Marcia and R. M. Willett, ―Compressive coded aperture superresolution image reconstruction‖, Proc. of ICASSP, pp. 833-836, Las Vegas, NV, March 31-April 4 2008.
[13] J. Haupt and R. Nowak, ―Signal reconstruction from noisy random projections‖, IEEE Transactions on Information Theory, Vol. 52, No. 9, 2006, pp. 4036-4048.
[14] J. A. Tropp, W. B. Wakin, M. F. Duarte, D. Baron and R. G. Baraniuk, ―Random filters for compressive sampling and reconstruction‖, Proc. of ICASSP, pp. 872-875, Toulouse, France, 14-19 May 2006,
[15] J. Romberg, ―Compressive sensing by random convolution‖, SIAM Journal on Imaging Sciences, Vol. 2, No. 4, 2009, pp. 1098-1128.
[16] N. L. Trung, D. V. Phong, Z. M. Hussain, H. T. Huynh, V. L. Morgan and J. C. Gore, ―Compressed sensing using chaos filters‖, Proc. of ATNAC, pp. 219–223, Adelaide, SA, 7-10 December 2008.
[17] V. Kafedziski and T. Stojanovski. ―Compressive sampling with chaotic dynamical systems―. Proc. of 19th TELFOR, pp. 695-989, Belgrade, Yugoslavia, 22-24 November 2011.
[18] L. Yu, J. P. Barbot, G. Zheng and H. Sun, ―Compressive sensing with chaotic sequence‖, IEEE Signal Processing Letters, Vol. 17, No. 8, 2010, pp. 731–734.
[19] L. Yu, J. P. Barbot, G. Zheng and H. Sun, ―Toeplitz-structured chaotic sensing matrix for compressive sensing‖, Proc. of CSNDSP, pp. 229-233, Newcastle upon Tyne, 21-23 July 2010.
[20] G. Sengupta, T.A.Win, C. Messom, S. Demidenko and S.C. Mukhopadhyay, ―Defect analysis of grit-blasted or spray printed surface using vision sensing technique‖, Proceedings of Image and Vision Computing NZ, Nov. 26-28, 2003, Palmerston North, pp. 18-23.
[21] M. Frunzete, Y. Lei, J. Barbot and A. Vlad, ―Compressive sensing matrix designed by tent map, for secure data transmission‖, Proc. of SPA, pp. 1-6, Poznan, 29-30 September 2011.
[22] G. Chen, Q. Chen, D. Zhang and D. Zhou, ―The characteristic of different chaotic sequences for compressive sensing‖, Proc. of 5th CISP, pp. 1475-1479, Chongqing, China, 16-18 October, 2012.

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