Education

1. Ph.D., Department of Electrical and Computer Engineering, University of Illinois at Urbana - Champaign, USA, May 2002
2. M.S., Applied Mathematics, and Computer Science, Peking University, China, July 1996
3. B.S., Mathematics, Peking University, China, July 1994

Biography

 Dr. QiangCheng received the BS and MS degrees from the College of Mathematical Science at Peking University, China, and the PhD degree from the Department of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign. Currently, he is an associate professor at the Biomedical Informatics Institute and the Department of Computer Science at the University of Kentucky. He previously worked as a faculty fellow at the Air Force Research Laboratory, Wright-Patterson, OH, and a senior researcher and senior research scientist at Siemens Medical Solutions, Siemens Corporate Research, Siemens Corp., Princeton, NJ. His research interests include data science, machine learning,pattern recognition, artificial intelligence, and biomedical informatics. He has published about 100 peer-reviewed papers in various premium venues including IEEE TPAMI, TNNLS, TSP, NIPS, CVPR, ICDM, AAAI, ICDE, CIKM, KDD, ACM TIST, and TKDD. He has a number of international patents issued or filed with Southern Illinois University Carbondale, IBM T.J. Watson Research Laboratory, and Siemens Medical.

Research Interest

Data science, machine learning, pattern recognition, artificial intelligence, and biomedical informatics.

Scientific Activities

Publications

A. Books

1. Proceedings of IEEE 2012 5th International Congress on Image and Signal Processing (CISP 2012), Editors: Q. Chen, Q. Cheng, Y. Li, T. Zhang, and L. Wang. IEEE Press. IEEE Catalog Number CFP1294D-CDR. ISBN 978-1-4673-0963-9.

B. Articles in Professional Journals:

1. Kang, Z., Peng, C., and Cheng, Q. (2017). Kernel driven similarity learning. Neurocomputing, accepted. (doi: 10.1016/j.neucom.2017.06.005)
2. Peng, C., Kang, Z., and Cheng, Q. (2017). Integrating feature and graph learning with low-rank representation. Neurocomputing, 249: 106-116. (doi: 10.1016/j.neucom.2017.03.071)
3. Peng, C., Kang, Z., Fei, X., Chen, Y., and Cheng, Q. (2017). Image projection ridge regression for subspace clustering. IEEE Signal Processing Letters, 24(7): 991-995. (doi: 10.1109/LSP.2017.2700852/) Page 7 of 26

2. Peng, C., Kang, Z., Hu, Y., Cheng, J., and Cheng, Q. (2017). Robust graph regularized nonnegative matrix factorization for clustering. ACM Transactions on Knowledge Discovery from Data, 11(3): 1-30 (doi: 10.1145/3003730, Article No: 33).
3. Peng, C., Kang, Z., Hu, Y., Cheng, J., and Cheng, Q. (2016). Nonnegative matrix factorization with integrated graph and feature learning. ACM Transactions on Intelligent Systems and Technology, 8(3): 1-29. (doi: 10.1145/2987378, Article No: 42).
4. Peng, C., Cheng, J., and Cheng, Q. (2016). A supervised learning model for high-dimensional and large-scale data. ACM Transactions on Intelligent Systems and Technology, 8(2): 1-30. (doi: 10.1145/2972957, Article No: 30)
5. Peng, C., Kang, Z., Yang, M., and Cheng, Q. (2016). Feature selection embedded subspace clustering. IEEE Signal Processing Letters, 23 (7): 1018-1022. (doi: 10.1109/LSP.2016.2573159)
6. Yang, M., Chen, S.B., Liu, W., Kang, Z., Peng, C., Xiao, M., and Cheng, Q. (2016). On identifiability of 3-tensors of multilinear rank (1, Lr, Lr). Big Data and Information Analytics (BDIA), American Institute of Mathematical Sciences, 1(4): 391-401. (doi: 10.3934/bdia.2016017)
7. Tezcan, J., Hazirbaba, Y.D., and Cheng, Q. (2016). A kernel-based mixed effect regression model for earthquake ground motions. Advances in Engineering Software, accepted and forthcoming. (doi: 10.1016/j.advengsoft.2016.06.002)
8. Kang, Z., Peng, C., and Cheng, Q. (2015). Robust subspace clustering via smoothed rank approximation. IEEE Signal Processing Letters, 22 (11): 2088–2092.
9. Kang, Z., Peng, C., Cheng, J., and Cheng, Q. (2015). LogDet rank minimization with application to subspace clustering. Computational Intelligence and Neuroscience, vol. 2015, Article ID 824289, 10 pages, 2015. (doi:10.1155/2015/824289, open access)
10. Tezcan, J., Cheng, J., and Cheng, Q. (2015). Modeling and prediction of nonstationary ground motions as time-frequency images. IEEE Trans. Geoscience and Remote Sensing, accepted.
11. Goodson III, W., et al. (Cheng, Q. with altogether 138 authors worldwide) (2015). Assessing the carcinogenic potential of low dose exposures to chemical mixtures in the environment: The challenge ahead. Carcinogenesis, 36 (Suppl 1): S254-S296 (doi:10.1093/carcin/bgv039).Narayanan, K., Ali, M., Barclay, B., Cheng, Q., et al. (with altogether 30 authors Page 8 of 26
12. worldwide) (2015). Disruptive environmental chemicals and cellular mechanisms that confer resistance to cell death. Carcinogenesis, 36 (Suppl 1): S89-S110 (doi:10.1093/carcin/bgv032).
13. Zhou, H. and Cheng, Q. (2015). A scalable projective scaling algorithm for l-p loss with convex penalizations. IEEE Trans. Neural Networks and Learning Systems, 26(2): 265-276 (doi: 10.1109/TNNLS.2014.2314129).
14. Cheng, Q., Zhou, H., Cheng, J., and Li, H. (2014). A minimax framework for classification with applications to images and high dimensional data. IEEE Trans. Pattern Analysis and Machine Intelligence, 36(11): 2117-2130 (doi: 10.1109/TPAMI.2014.2327978).
15. Huang, X., Yang, Y., Wang, G., Cheng, Q., and Du, Z. (2014). Highly conserved RNA pseudoknots at the Gag-pol junction of HIV-1 suggest a novel mechanism of -1 ribosomal frameshifting. RNA, 20(5): 1-7 (doi: 10.1261/rna.042457.113).
16. Cheng, Q., Tezcan, J., and Cheng, J. (2014) Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine. Pattern Recognition Letters, 40: 88-95.
17. Huang, X., Cheng, Q., and Du, Z. (2013). A genome-wide analysis of RNA pseudoknots that stimulate efficient -1 ribosomal frameshifting or readthrough in animal viruses. BioMed Research International, vol. 2013, Article ID 984028, 1-15 (doi:10.1155/2013/984028).
18. Huang, X., Cheng, Q., and Du, Z. (2013). Possible utilization of -1 ribosomal frame shifting in the expression of a human SEMA6C isoform. Bioinformation, 9(14): 736-738.
19. Tesorero, R.A., Yu, N., Wright J.O., Svencionis J.P., Cheng, Q., Kim, J., and Cho, K. (2013). Novel regulatory small RNAs in Streptococcus pyogenes. PLoS One, 8(6): e64021 (doi:10.1371/journal.pone.0064021).
20. Huang, X., Du, Z., Cheng, J., and Cheng, Q. (2013). PKscan: A program to identify H-type RNA pseudoknots in any RNA sequence with unlimited length. Bioinformation, 9 (9): 440-442.
21. Zhou, H., Cheng, Q., Yang, H., and Xu, H. (2012). A multi-scale Bayesian hypothesis testing approach for the analysis of prepulse inhibition test. American Journal of Medicine and Medical Sciences, 2(4):80-84, July. (doi: 10.5923/j.ajmms.20120204.04).
22. Tezcan, J. and Cheng, Q. (2012). A nonparametric characterization of vertical ground motion effects. Earthquake Engineering & Structural Dynamics, 41: 515-530 (doi: 10.1002/eqe.1142).Page 9 of 26
23. Tezcan, J. and Cheng, Q. (2012). Support vector regression for estimating earthquake response spectrum. Bulletin of Earthquake Engineering, 10(4): 1205-1219.
24. Tezcan J and Cheng Q (2012). Relevance vector machines for earthquake response spectra. American Transactions on Engineering and Applied Sciences, 1 (1): 25-39.
25. Zhou, H., Cheng, Q., Yang, H., and Xu H. (2011). Weighted kernel density estimation of the prepulse inhibition test. Journal of Computer Science, 7 (5): 611-618.
26. Cheng, J., Sayeh, M., Zargham, M., and Cheng, Q. (2011). Real-time vector quantization and clustering based on ordinary differential equations. IEEE Trans. Neural Networks, 22(12): 2143-2148.
27. Yang H-J, Wang L, Cheng Q, Xu H. (2011). Abnormal behaviors and microstructural changes in white matter of juvenile mice repeatedly exposed to amphetamine. Schizophrenia Research and Treatment, Special Issue of Oligodendrocytes in Schizophrenia.vol. 2011, Article ID 542896, 11 pages, doi:10.1155/2011/542896. http://www.hindawi.com/journals/sprt/2011/542896/
28. Cheng, Q., Zhou, H., and Cheng, J. (2011). The Fisher-Markov selector: Fast selecting maximally separable feature subset for multi-class classification with applications to high-dimensional data. IEEE Trans. Pattern Analysis and Machine Intelligence, 33(6), 1217-1233.
29. Tezcan, J., Piolatto, A., and Cheng, Q. (2010). A direct mapping approach to ground motion estimation. Current Development in Theory and Applications of Computer Science, Engineering and Technology, 2(1): 1-11.
30. Bellinger, J., Hassan, R., Rivers, P., Cheng Q., Williams E., and Glover, S. (2010). Specialty care use in US patients with chronic diseases. Int. J. Environ. Res. Public Health. 7:975-990. (doi:10.3390/ijerph7030975)
31. Tezcan, J., Cheng, Q., and Cekic, A. (2010). Concrete strength estimation using relevance vector machines. Journal of Materials Science and Engineering with Advanced Technology. 2(1): 61-76.
32.  Cheng, Q. (2010). A sparse learning machine for high-dimensional data with applications to microarray gene analysis, IEEE/ACM Trans. Computational Biology Bioinformatics. 7(4): 636-646.
33. Cheng Q, Cheng J (2009) Sparsity optimization for multivariate feature screening for gene expression analysis, Journal of Computational Biology, 16(9): 1241-1252.Page 10 of 26
34. Cheng Q (2009): Generalized embedding of multiplicative watermarks, IEEE Trans. Circuit and Systems for Video Technology, 19(7): 978-988.
35.  “Toward actively defending from denial of service attacks in UMTS-WLAN,” H. Qu, Q. Cheng, E. Yaprek, and L.-Y. Wang, Ubiquitous Computing and Communication Journal, vol.3, no.3, pp. 1-11, July 2008.
36. “An efficient compression method for multiplanar reformulated biomedical images,” Q. Cheng and M. Zargham, Int. Journal of Functional Informatics and Personalized Medicine, Special Issue for IEEE 7th BIBE, vol.1, pp 68-79, Feb. 2008.
37. “A novel distributed sensor positioning system using the dual of target tracking,” L. Zhang, Q. Cheng, Y. Wang, and S. Zeadali, IEEE Trans. Computers, vol. 57, no. 2, pp. 246-260, Feb. 2008.
38. “Unconfined e-healthcare system using UMTS-WLAN,” H. Qu, Q. Cheng, and E. Yaprek, Int. Journal of Modeling and Simulation, vol. 26, no. 3, pp. 261-270, 2006.
39. “Performance analysis and error exponents of asymmetric watermarking systems,” Q. Cheng, Y. Wang, and T.S. Huang, Signal Processing, vol. 84, no. 8, pp. 1429-1445, Aug. 2004.
40. “Robust optimum detection of transform-domain multiplicative watermarks,” Q. Cheng and T.S. Huang, IEEE Trans. on Signal Processing, Special Issue for Data Hiding in Digital Media and Secure Content Delivery, vol. 51, no. 4, pp. 906-924, April 2003.
41.  “An additive approach to transform-domain information hiding and optimum detection structure,” Q. Cheng and T.S. Huang, IEEE Trans. on Multimedia, vol. 3, pp. 273-284, Sept. 2001.
42. Patents and Disclosures“Top-N Recommender System via Matrix Completion,” Zhao Kang and Qiang Cheng, Provisional Patent Application Number 62/259,303, filed by Southern Illinois University, Carbondale, IL, November 24.
43. “Scalable Message Passing for Ridge Regression,” Hongbo Zhou and Qiang Cheng, Provisional Patent Application, Serial No: 61/788,107, filed by Southern Page 11 of 26
44. Illinois University, Carbondale, IL, March 15, 2013. Formal Patent application filed March 15, 2014.“Method and Apparatus of Efficient Transmission of Multiple-Plane-Reformulated Images for High Quality Diagnosis,” Q. Cheng, et al., patent filed, Siemens Medical, Siemens Corp., Princeton, NJ, 2007.
45. “Interframe Visual Redundancy Reduction Method In the Presence of Zooming, Rotation, and Translation for Remote Visualization Service,” Q. Cheng, et al., invention disclosure, Siemens Medical, Siemens Corp., Princeton, NJ, 2006.
46. “Fast JPEG-LS Compression Method,” S. Smita, Q. Cheng, et al., patent filed in USA, Germany, and China, Siemens Medical, Siemens Corp., Princeton, NJ, 2006.
47. “Methods and System for Windowing and Leveling Compressions for Interactive Remote Client/Server Visualization of 3D Medical Object,” Q. Cheng, et al., patent filed in USA, Germany, and China, Siemens Medical, Siemens Corp., Princeton, NJ, 2006.
48. “Providing Representative Image Information” Q. Cheng and S. Ovens, US Patent 8238678, Siemens Medical Solutions (US), issued Aug. 2012.
49. “Systems and Methods of Inter-Frame Compression - I,” Q. Cheng, Michael Pisot, and Min Xie, US Patent 8121419, Siemens Medical Solutions (US), and Siemens Aktiengesellschaft (DE), Issued on Feb. 21, 2012.
50. “Systems and Methods of Inter-Frame Compression - II,” Q. Cheng, Michael Pisot, and Min Xie, US Patent 8121420, Siemens Medical Solutions (US), and Siemens Aktiengesellschaft (DE), Issued on Feb. 21, 2012.
51. “Systems and Methods of Inter-Frame Compression - III,” Q. Cheng, Michael Pisot, and Min Xie, US Patent 8170354, Siemens Medical Solutions (US), and Siemens Aktiengesellschaft (DE), Issued on May 1, 2012.
52. “Spread Spectrum Signaling for Speech Watermarking,” Q. Cheng and Jeffrey S. Sorensen, US Patent 6892175, IBM T. J. Watson Research Center, Yorktown Heights, Issued on May 10, 2005.
53. “Image Watermarking Using Pyramid Transforms,” Q. Cheng and T. Huang, disclosure to Yamaha Corp., 2001.
54. “Method and Apparatus for Multiplicative Watermarks in Image and Video Processing,” Q. Cheng and T. Huang, disclosure to Yamaha Corp., 2001.Page 12 of 26
55. Qu, H., Wang, L. Y., Klaus, C.M., Cheng, Q., Yaprak, E., and Wang, H. (2011). Wireless Telemedicine System: An Accurate, Reliable and Secure Real-time Health Care. Telemedicine Techniques and Applications, pp. 71-99. Edited by G. Graschew and S. Rakowsky. InTech Publishing House.
56. “Digital rights management for e-content and e-technologies,” Wang, Y., Cheng, Q., Cheng, J., and Huang, T.S. (2006). Encyclopedia of E-Commerce, E-Government, and Mobile Commerce, Ed. Mehdi Khosrow-Pour, pp. 210-216. Hershey, PA: Idea Group Reference.
57. “E-Health security and privacy,” Wang, Y., Cheng, Q., and Cheng, J. (2006). Encyclopedia of E-Commerce, E-Government, and Mobile Commerce, Ed. Mehdi Khosrow-Pour, pp. 385-390. Hershey, PA: Idea Group Reference.
58. “Integrated selective encryption and data embedding for medical images,” Y. Wang, Q. Cheng, and J. Tan, in E-Health Paradigm Shift: Perspectives, Domains and Cases, Wiley: Jossey-Bass, 2005.
59. “Unconfined mobile Bluetooth telemedicine for empowered healthcare,” Q. Cheng, H. Qu, Y. Wang, and J. Tan, in E-Health Paradigm Shift: Perspectives, Domains and Cases, Wiley: Jossey-Bass, 2005.Papers and Presentations at Professional Meetings:
60. Kang Z, Peng C, Yang, M, and Cheng Q (2017). Exploiting nonlinear relationships for top-N recommender systems, The 8th IEEE International Conference on Big Knowledge (ICBK 2017). August 2017, Hefei, China.
61. Peng C, Kang Z, and Cheng Q (2017). Subspace clustering via variance regularized ridge regression, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2017). July 21-27, Honolulu, HI.
62. Kang Z, Peng C, and Cheng Q (2017). Clustering with adaptive manifold structure learning, The 33rd IEEE International Conference on Data Engineering (ICDE 2017). April 19-22, San Diego, CA.
63. Kang Z, Peng C, and Cheng Q (2017). Twin learning for similarity and clustering: A unified kernel approach, The 31st AAAI Conference on Artificial Intelligence (AAAI-17). Feb. 4-9, San Francisco, CA. (with an acceptance rate of 24.6%). Page 13 of 26
64. Peng C, Kang Z, and Cheng Q (2016). A fast factorization–based approach to robust principal component analysis. IEEE International Conference on Data Mining (IEEE ICDM 2016). Dec. 13-15, Barcelona, Spain. (with an acceptance rate of 19.6%)
65. Peng C, Kang Z, Yang M, and Cheng Q (2016). RAP: Scalable RPCA for low-rank matrix recovery. The 25th ACM International Conference on Information and Knowledge Management (ACM CIKM 2016). October 24-28, Indianapolis, IN. (with an acceptance rate of 28%).
66. Kang Z, Peng C, Yang M, and Cheng Q (2016). Top-N recommendation on graphs. The 25th ACM International Conference on Information and Knowledge Management (ACM CIKM 2016). October 24-28, Indianapolis, IN. (with an acceptance rate of 28%).
67. Kang Z, Peng C, and Cheng Q (2016). Top-N recommendation with novel rank approximation. 2016 SIAM International Conference on Data Mining (SDM 16). May 5-7, Miami, FL. (with an acceptance rate of 26%).
68. Kang Z, Peng C, and Cheng Q (2016). Top-N recommender system via matrix completion. The 30th AAAI Conference on Artificial Intelligence (AAAI-16). Feb. 12-17, Phoenix, AZ. (with an acceptance rate of 26%).
69. Kang Z, Peng C, and Cheng Q (2015). Robust PCA via nonconvex rank approximation. IEEE International Conference on Data Mining (IEEE ICDM 2015). Nov. 14-17, Atlantic City, NJ. (Student Travel Award of $550 from the ICDM Award Committee; with an acceptance rate of 8.4%)
70. Kang Z, Peng C, and Cheng Q (2015). Robust subspace clustering via tighter rank approximation. The 24th ACM International Conference on Information and Knowledge Management (ACM CIKM 2015). October 19-23, Melbourne, Australia. (Student Travel Award of $1,100 from the ACM CIKM Award Committee; with an acceptance rate of 18%).
71. Peng C, Kang Z, Li H, Cheng Q (2015). Subspace clustering using log-determinant rank approximation. Proc. the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM KDD 2015). August 10-13, Sydney, Australia. (Student Travel Award of $1,000 from the SIG KDD Award Committee; with an acceptance rate of 19%).
72. Bai L, Tezcan J, Cheng J, Cheng Q (2013). A multiway model for predicting earthquake ground motions. Proc. the 14th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2013). July 1 - 3, Honolulu, Hawaii, USA.
73. Cheng C, Bi L, Cheng J, Soltanian-Zadeh H, Cheng Q (2012). Discriminative features for interictal epileptic discharges in intracerebral EEG signals. Proc. the 5th IEEE Int. Congress on Image and Signal Processing (CISP 2012), jointly with the 5th IEEE Int. Conf. on BioMedical Engineering and Informatics (BMEI 2012). Oct. 16-18, Chongqing, China.
74. Zhou H, Cheng Q (2011). O(N) implicit subspace embedding for unsupervised multi-scale image segmentation. Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR 2011), pp.2209-2215. June, Colorado Springs, CO.
75. Zhou H, Cheng Q, She Z (2010): Reparameterization based consistent graph-structured linear programs. Proceedings of The 25th ACM Symposium on Applied Computing (ACM-SAC 2010), pp. 974-978. Sierre, Switzerland, March. (Student Travel Award of $500 from ACM-SAC Award Committee).
76. Zhou H, Cheng Q (2010). Sufficient conditions for generating group level sparsity in a robust minimax framework. Proceedings of Twenty-Fourth Annual Conf. on Neural Information Processing Systems (NIPS 2010), pp. 2577-2585. Vancouver, Canada, Dec
77. Zhou H, Cheng Q, Yang HJ, Xu H (2010). Weighted kernel density estimation of the prepulse inhibition test. Proc. IEEE Int. Workshop on Healthcare Informatics Services (HIS 2010), in The 6th World Congress on Services, pp. 291-297. Miami, FL.
78. Yu N, Cho KH, Cheng Q, Tesorero RA (2009): A hybrid computational approach for the prediction of small non-coding RNAs from genome sequences. Proc. IEEE Int. Conf of Embedded and Ubiquitous Computing (EUC’09), Sept., Vancouver, Canada.
79. Zhou H, Cheng Q, Zargham M (2009): Fast fusion of medical images based on Bayesian risk minimization and pixon map. Proc. IEEE Int. Conf of Computational Science and Engineering (CSE’09), Sept., Vancouver, Canada.
80. Zhou H, Yang HJ, Xu H, Cheng Q (2009): A new computational tool for the post session analysis of the prepulse inhibition test in neuralscience. Proc. IEEE Int. Conf of Computational Science and Engineering (CSE’09), Sept., Vancouver, Canada.
81. Qu H, Cheng J, Cheng Q, Wang LY. (2009). WiFi-based telemedicine system: signal accuracy and security. Proc. of IEEE Int. Conf of Computational Science and Engineering (CSE’09), volume 2, pp.1081~1085, Sept., Vancouver, Canada.
82.  “An efficient compression method for multiplanar reformulated biomedical images,” Q. Cheng and M. Zargham, Proceedings of Int. Symposium on Bioinformatics and Bioengineering (BIBE’07), Cambridge, MA, Oct. 2007.
83. “Landscape (T): A robust and low-cost sensor positioning system using the dual of target tracking,” L. Zhang and Q. Cheng, Proceedings of Int. Conf. Distributed Computing in Sensor Systems (DCOSS2006), San Francisco, CA, June 2006.
84. “Landscape (3D): A robust sensor localization scheme for sensor networks over 3D terrains,” L. Zhang, X. Zhou and Q. Cheng, Proceedings of IEEE Conf. Local Computer Networks (LCN), Tampa, FL, Nov. 2006.
85. “Landscape: A high performance distributed positioning scheme for outdoor sensor networks,” L. Zhang, Q. Cheng, Y. Wang, and S. Zeadali, Proceedings of IEEE Int. Conf. Wireless and Mobile Computing, Networking and Communications (WiMob2005), Montreal, Canada, August 2005.
86. “SNR analysis for phased-array MRI,” Y. Wang, Q. Cheng, and J. Cheng, Proceedings of International Conference on Acoustic, Speech, and Signal Processing (ICASSP '05), Philadelphia, 2005
87. “Enhancing Bluetooth security with covert channel signaling,” H. Qu and Q. Cheng, Proceedings of IEEE and IFIP Int. Conf. on Wireless Communications Networks, June 2004.
88. “Unconfined mobile Bluetooth nursing and daily data collection,” X. Luo and Q. Cheng, IEEE Consumer Communications and Networking Conf. (CCNC '04), Las Vegas, Nevada, Jan. 2004.
89. “Health information integrating and size reducing,” X. Luo and Q. Cheng, Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference (MIC '03), Portland, OR, Oct. 2003.
90. “A lossless data embedding scheme for medical images in application of e- diagnosis,” X. Luo, Q. Cheng, and J. Tan, Proceedings of Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS '03), Cancun, Mexico, Sept. 2003.