W. Proß, M. Otesteanu, F. Quint


In this paper we propose an extension of the Estimation-Decoding algorithm for the decoding of our Data Matrix Code (DMC), which is based on Low-Density-Parity-Check (LDPC) codes and is designed for use in industrial environment. To include possible damages in the channel-model, a Markov-modulated Gaussian channel (MMGC) was chosen to represent everything in between the embossing of a LDPC-based DMC and the camera-based acquisition. The MMGC is based on a Hidden-Markov-Model (HMM) that turns into a two-dimensional model when used in the context of DMCs. The proposed ED2D-algorithm (Estimation-Decoding in two dimensions) is implemented to operate on a 2D-LDPC-Markov factor graph that comprises of a LDPC code’s Tanner-graph and a 2D-HMM. For a subsequent comparison between different barcodes in industrial environment, a simulation of typical damages has been implemented. Tests showed a superior decoding behavior of our LDPC-based DMC decoded with the ED2D-decoder over the standard Reed-Solomon-based DMC.


  1. Bahl, L. R., Cocke, J., Jelinek, F., and Raviv, J. (1974). Optimal decoding of linear codes for minimizing symbol error rate. IEEE Transactions on Information Theory, 20(2):284-287.
  2. Eckford, A. W. (2004). Low-density parity-check codes for Gilbert-Elliott and Markov-modulated channels. PhD thesis, University of Toronto.
  3. Gallager, R. G. (1962). Low density parity check codes. IRE Transactions on Information Theory, 1:21-28.
  4. Garcia-Frias, J. (2004). Decoding of low-density paritycheck codes over finite-state binary markov channels. IEEE Transactions on Communications, 52(11):1841.
  5. Hu, X. Y., Eleftheriou, E., and Arnold, D. M. (2005). Regular and irregular progressive edge-growth tanner graphs. IEEE Transactions on Information Theory, 51(1):386-398.
  6. ISO/IEC (2000). 16022:2000(e) information technology - international symbology specification - data matrix.
  7. Johnson, N. L. (1949). Systems of frequency curves generated by methods of translation. Biometrika,36(1- 2):149.
  8. Johnson, N. L., Kotz, S., and Balakrishnan, N. (1994). Continuous univariate distributions. A Wiley-Interscience publication. Wiley, New York, 2. ed. edition.
  9. MacKay, D. and Neal, R. (1995). Good codes based on very sparse matrices. Cryptography and Coding, pages 100-111.
  10. Proß, W., Quint, F., and Otesteanu, M. (2010). Using pegldpc codes for object identification. In Electronics and Telecommunications (ISETC), 2010 9th, pages 361- 364.
  11. Ratzer, E. A., editor (2002). Low-density parity-check codes on Markov channels, Proceedings of 2nd IMA Conference on Mathematics and Communications, Lancaster, U.K.
  12. Tanner, R. M. (1981). A recursive approach to low complexity codes. IEEE Transactions on Information Theory, 27:533-547.
  13. Wadayama, T., editor (2000). An iterative decoding algorithm of low density parity check codes for hidden Markov noise channels, Proceedings of International Symposium on Information Theory and Its Applications, Honolulu, Hawaii, USA.
  14. Woodland, J. N. and Silver, B. (1949). U.S. Patent No. 2,612,994. Washington, DC: U.S. Patent and Trademark Office.

Paper Citation

in Harvard Style

Proß W., Otesteanu M. and Quint F. (2011). ESTIMATION-DECODING ON LDPC-BASED 2D-BARCODES . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011) ISBN 978-989-8425-72-0, pages 34-39. DOI: 10.5220/0003457400340039

in Bibtex Style

author={W. Proß and M. Otesteanu and F. Quint},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011)
SN - 978-989-8425-72-0
AU - Proß W.
AU - Otesteanu M.
AU - Quint F.
PY - 2011
SP - 34
EP - 39
DO - 10.5220/0003457400340039