Electrical Capacitance Volume Tomography Static Imaging by Non-Optimized Compressive Sensing Framework

Nur Afny Catur Andryani, Dodi Sudiana, Dadang Gunawan

Abstract


Electrical capacitance volume tomography is a volumetric tomography technique that utilizes capacitance and fringing to capture behavior or perturbation in the sensing domain. One of the crucial issues in developing ECVT technology is the reconstruction algorithm. In practice, ILBP is most used due to its simplicity. However, it still presents elongation errors for certain dielectric contrasts. The high undersampling measurement of the ECVT imaging system, which is mathematically defined as an undetermined linear system, is one of the most challenging issues. Compressive sensing (CS) is a framework that enables the recovery of a sparse signal or a signal that can be represented as sparse in a certain domain, by having a lower dimension of measurement data compared to the Shanon-Nyquist theorem. Thus, mathematically, this framework is promising for solving an undetermined linear system such as the ECVT imaging system. This paper discusses the possibility of developing an ECVT imaging technique for static objects based on a CS framework. Based on the simulation results, Non-optimized CS does not completely succeed in providing better ECVT imaging quality. However, it does provide more localized imaging compared to ILBP. In addition, by having fewer requirements for the measurement data dimension, the CS framework is promising for reducing the number of required electrodes.


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References


Li, Y. & Holland, D.J., Fast and Robust 3D Electrical Capacitance Tomography, Measurement Science and Technology, 24(10), pp. 105-406, 2013.

Warsito, W., Marashdeh, Q.M. & Fan, L.S., Electrical Capacitance Volume Tomography, IEEE Sensor Journal, 7(4), pp. 525-535, 2007.

Saputra, A., Taruno, W.P., Baidillah, M.R. & Handoko, D., Combined Feed-Forward Neural Network and Iterative Linear Back Projection for Electrical Capacitance Volume Tomography, in Asia-Pacific Conference on Computer Aided System Engineering (APCASE), pp. 102-106, 2014.

Donoho, D.L., Compressed Sensing, IEEE Transactions on Information Theory, 52(4), pp. 1289-1306, 2006.

Xinjie, W., Guoxing, H., Jingwen, W. & Chao, X., Image Reconstruction Method of Electrical Capacitance Tomography Based on Compressed Sensing Principle, Measurement Science and Technology, 24(7), pp. 075-401, 2013.

Qiuwei, L., Zhihui, Z., Si, T., Liping, C. & Gang, L., Projection Matrix Optimization Based on SVD for Compressive Sensing Systems, in 32nd Control Conference (CCC), pp. 4820-4825, 2013.

Warsito, W., Marashdeh, Q.M., Fan, L.S. & Teixeira, F.L., A Multimodal Tomography System Based on ECT Sensors, IEEE Sensors Journal, 7(3), pp. 426-433, 2007.

Yang, W.Q. & Lihui, P., Image Reconstruction Algorithms for Electrical Capacitance Tomography, Measurement Science and Technology, 14, p. R1, 2003.

Taruno, W.P., Baidillah, M.R., Sulaiman, R.I., Ihsan, M.F., Yusuf, A. & Widada, W., Brain Tumor Detection using Electrical Capacitance Volume Tomography (ECVT), in 6th international IEEE/EMBS Conference on Neural Engineering (NER) , pp. 743-746, 2013.

Taruno, W.P., Baidillah, M.R., Sulaiman, R.I., Ihsan, M.F., Yusuf, A., Widada, W. & Alzufri, H., A Novel Sensor Design for Breast Cancer Scanner Based on Electrical Capacitance Volume Tomography (ECVT), in IEEE SENSORS, pp. 1-4, 2012.

Marashdeh, Q.M., Teixeira, F.L. & Liang-Shih, F., Adaptive Electrical Capacitance Volume Tomography, IEEE Sensors Journal, 14(4), pp. 1253-1259, 2014.

Qaisar, S., Bilal, R.M., Iqbal, W., Naureen, M. & Sungyoung, L., Compressive Sensing: From Theory to Applications, a Survey, Journal of Communications and Networks, 15(5), pp. 443-456, 2013.

Mohades, M.M., Mohades, A. & Tadaion, A., A Reed-Solomon Code Based Measurement Matrix with Small Coherence, IEEE Signal Processing Letters, 21(7), pp. 839-843, 2014.

Oey, E., Projection Matrix Design for Compressive Sensing, in International Conference on Electrical Engineering and Informatics (MICEEI), pp. 124-129, 2014.

Qianru, J., Huang, B., Dan, L. & Xincai, H., Alternative Optimization of Sensing Matrix and Sparsifying Dictionary for Compressed Sensing Systems, in 9th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 510-515, 2014.

Wenjie, Y., Qiang, W. & Yi, S., Shrinkage-Based Alternating Projection Algorithm for Efficient Measurement Matrix Construction in Compressive Sensing, IEEE Transactions on Instrumentation and Measurement, 63(5), pp. 1073-1084, 2014.

Donoho, D.L. & Elad, M., Optimally Sparse Representation in General (Nonorthogonal) Dictionaries via ℓ1 Minimization, Proceedings of the National Academy of Sciences, 100(5), pp. 2197-2202, 2003.

Wenjie, Y., Qiang, W., Yi, S. & Zhenghua, W., Measurement Matrix Construction Algorithm for Sparse Signal Recovery, in IEEE International Conference on Instrumentation and Measurement Technology Conference (I2MTC), pp. 1051-1056, 2013.

Ravishankar, S., MR Image Reconstruction From Highly Under Sampled k-Space Data by Dictionary Learning, IEEE Transaction on Medical Imaging, 30(5), pp. 1028-1041, 2011.




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2016.10.3.4

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