Publications

Information Theory for Artificial Intelligence
  1. E.-H. Yang and S. M. Hamidi, “Coded deep learning: framework and algorithms,” IEEE Transactions on Information Theory, Vol. 71, No. 11, pp. 8959 – 8976, Nov. 2025  (https://doi.org/10.1109/TIT.2025.3610095).
  2. L. Ye, S. M. Hamidi, and E.-H. Yang, “Towards undistillable models by minimizing conditional mutual information,” TransactionsonMachineLearningResearch, pp. 1 – 27, June, 2025 (https://openreview.net/pdf?id=jVABSsD4Vf). Code: https://anonymous.4open.science/r/CMIM-605C/utils.py
  3. E.-H. Yang, S. M. Hamidi, L. Ye, C. Tan. and B. Yang, “Conditional mutual in-formation constrained deep learning for classification,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 36, No. 8, pp. 15436 – 15448, Aug. 2025.
  4. S. Chen, K. Zheng, A. H. Salamah, and E.-H. Yang, “Differentiable JPEG-based input perturbation for knowledge distillation amplification via conditional mutual informa-tion maximization,” Proc. of the Fourteenth International Conference on Learning Representations (ICLR 2026), Rio de Janeiro, Brazil, April 23 - 27, 2026 (24 pages):  https://openreview.net/pdf/57eb9afde3c509080d5402259a798b6f89b5435e.pdf
  5. S. M. Hamidi, B. Liang, and E.-H. Yang, “Coupled data and measurement space dynamics for enhanced diffusion posterior sampling,” Proc. of the Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, USA, Dec. 2 - 7, 2025 (39 pages).
  6. E.-H. Yang and S. M. Hamidi, “Coded deep learning: Framework and preliminary results,” Proc. of the 2025 IEEE Int. Symp. Information Theory (ISIT 2025), Ann Arbor (Michigan), USA, June 22 - 27, 2025 (6 pages).
  7. S. M. Hamidi and E.-H. Yang, “Conditional mutual information based diffusion pos-terior sampling for solving inverse problems,” Proc. of the 2025 IEEE Int. Symp. Information Theory (ISIT 2025), Ann Arbor (Michigan), USA, June 22 - 27, 2025 (6 pages).
  1. S. M. Hamidi and E.-H. Yang, “Score-based manifold projection for diffusion-based inverse problems,” Proc. of the 2025 IEEE Int. Symp. Information Theory (ISIT 2025), Ann Arbor (Michigan), USA, June 22 - 27, 2025 (6 pages).
  2. T. Sivakaran and E.-H. Yang, “Leveraging conditional mutual information to improve large language model fine-tuning for classification,” Proc. of the 2025 IEEE Int. Symp. Information Theory (ISIT 2025), Ann Arbor (Michigan), USA, June 22 - 27, 2025 (6 pages).
  3. A. H. Salamah, K. Zheng, Y. Liu, and E.-H. Yang, “JPEG inspired deep learning,” Proc. of the Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore, April 24 - 28, 2025 (24 pages): https://openreview.net/pdf?id=te2IdORabL
    Code: https://github.com/AhmedHussKhalifa/JPEG-Inspired-DL
  4. X. Zhong, B. Chen, H. Fang, X. Gu, S.-T. Xia, and E.-H. Yang, “Going beyond feature similarity: Effective dataset distillation based on class-aware conditional mutual infor-mation,” Proc.oftheThirteenthInternationalConferenceonLearningRepresenta-tions (ICLR 2025), Singapore, April 24 - 28, 2025 (18 pages): https://openreview.net/pdf?id=0no1Wp2R2j
    Code: https://github.com/ndhg1213/CMIDD
  5.  E.-H. Yang and L. Ye, “Markov knowledge distillation: Make nasty teachers trained by self-undermining knowledge distillation fully distillable,” In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15147. Springer, Cham. https://doi.org/10.1007/978-3-031-73024-5 10 (18 pages).
  6.  E.-H. Yang, S. M. Hamidi, L. Ye, R. Tan, and B. Yang, “Conditional mutual infor-mation constrained deep learning: Framework and preliminary results,” Proc. of the 2024 IEEE Int. Symp. Information Theory (ISIT 2024), Athens, Greece, July 7 - 12, 2024 (6 pages).
  7. S. M. Hamidi, L. Ye, R. Tan, and E.-H. Yang, “Fed-IT: Addressing class imbalance in federated learning through an information-theoretic lens,” Proc. of the 2024 IEEE Int. Symp. Information Theory (ISIT 2024), Athens, Greece, July 7 - 12, 2024 (6 pages).
  8. L. Ye, S. M. Hamidi, C. Tan, and E.-H. Yang, “Bayes conditional distribution estima-tion for knowledge distillation based on conditional mutual information,” Proc. of the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna Austria, May 7 - 11, 2024 (33 pages): https://openreview.net/pdf?id=yV6wwEbtkR
    Code: https://github.com/iclr2024mcmi/ICLRMCMI
  9. K. Zheng and E.-H. Yang, “Knowledge distillation based on transformed teacher matching,” Proc.of the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna Austria, May 7 - 11, , 2024 (17 pages): https://openreview.net/pdf?id=MJ3K7uDGGl
    Code: https://github.com/zkxufo/TTM
  10. A. H. Salamah, S. M. Hamidi, and E.-H. Yang, “A coded knowledge distillation frame-work for image classification based on adaptive JPEG encoding,” Pattern Recognition, August 2024 (11 pages),
  11. S. M. Hamidi and E.-H. Yang, “AdaFed: Fair federated learning via adaptive common descent direction,” TransactionsonMachineLearningResearch, pp. 1 – 33, Jan. 2024, https://openreview.net/pdf?id=rFecyFpFUp.
  12. E.-H. Yang, H. Amer, and Y. Jiang, “Compression helps deep learning in image clas-sification,” Entropy, 2021, 23, 881 (19 pages). https://doi.org/10.3390/e23070881.

Artificial Intelligence for Information Theory and Coding
  1.  A. H. Salamah, K. Zheng, L. Ye, and E.-H. Yang, “JPEG compliant compression for DNN vision,” IEEE Journal on Selected Areas in Information Theory, July 2024, https://doi.org/10.1109/JSAIT.2024.3422011.
  2. K. Zheng, A. H. Salamah, L. Ye, and E.-H. Yang, “JPEG compliant compression for DNN vision,” Proc. of the 2023 IEEE Int. Conf. Image Processing (ICIP), pp. 1875-1879, 2023. Code: https://github.com/zkxufo/OptS
  1. L. Ye, E.-H. Yang, A.H. Salamah, “Modeling and energy analysis of adversarial pertur-bations in deep image classification security,” Proc. of the 2022 Canadian Workshop on Information Theory, Ottawa, Canada, June 5 - 8, 2022 (6 pages).
  2. C. Sun and E-H. Yang, “A watermarking-based framework for protecting deep image classifiers against adversarial attacks,” Proc. of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Virtual, June 19 - 25, pp. 3329-3338, 2021.
  3. J. Bai, B. Chen, Y. Li, D. Wu, W. Guo, S.-T. Xia, and E.-H. Yang, “Targeted attack for deep hashing based retrieval,” In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision—ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, Vol 12346, pp. 618-634. (17 pages, top-notch conference in machine learning, accepted as oral (2% acceptance out of 5025 submissions)).

Classical Information Theory and Coding

Books and Proceedings:

  1. E.-H. Yang and B. J. Frey, Eds. Proceedings of the Eighth Canadian Workshop on Information Theory. The Canadian Society for Information Theory, Ottawa, Ontario, Canada, 2003.

Refereed Journal Papers:

  1. J. C. Kieffer and E.-H. Yang, “Survey of grammar-based data structure compression,” IEEE BITS the Information Theory Magazine, pp. 1 – 12, Sept. 2022, https://doi.org/10.1109/MBITS.2022.3210891.
  2. X. Wang, E.-H. Yang, D.-K. He, L. Song, and X. Yu, “Rate distortion optimization: A joint framework and algorithms for random access hierarchical video coding,” IEEE Trans. on Image Processing, Vol. 29, pp. 9458 – 9469, Oct. 2020.
  3. H. Amer and E.-H. Yang, “Adaptive quantization parameter selection for low-delay HEVC via temporal propagation length estimation,” Signal Processing: Image Com-munication, Vol. 84, Mar. 2020 (12 pages).
  4. J. He, E.-H. Yang, F. Yang, and K. Yang, “Adaptive quantization parameter selection algorithm for H.265/HEVC based on inter-frame dependency,” IEEE Trans. Circuits Syst. Video Technol., Vol. 28, No. 12, pp. 3424 – 3436, Dec. 2018.
  5. H. Yin, H. Cai, E.-H. Yang, Y. Zhou, and J. Wu, “An efficient all-zero block detection algorithm for high efficiency video coding with RDOQ,” Signal Processing: Image Communication, Vol. 60, pp. 79 – 90, Feb. 2018.
  6. Y. Fang, V. Stankovic, S. Cheng, and E.-H. Yang, “Analysis on tailed distributed arithmetic codes for uniform binary sources,” IEEE Trans Commun., Vol. 64, No. 10, pp. 4305 – 4319, Oct. 2016 (15 double column pages).
  7. N. Hu and E.-H. Yang, Erratum to “Fast Mode Selection for HEVC Intra-Frame Coding With Entropy Coding Refinement Based on a Transparent Composite Model”, IEEE Trans. Circuits Syst. Video Technol., Vol. 26, DOI: 10.1109/TCSVT.2016.2555242, April 25, 2016 (3 double column pages).
  8. Y. Fang, V. Stankovic, S. Cheng, and E.-H. Yang, “Hamming distance spectrum of DAC codes for equiprobable binary sources,” IEEETransCommun., Vol. 64, No. 3, pp. 1232 – 1245, Mar. 2016
  9. E.-H. Yang and J. Meng, “New non-asymptotic random channel coding theorems for structured codes,” IEEE Trans. on Information Theory, Vol. 61, No. 9, pp. 4534 – 4553, Sept. 2015.
  10. N. Hu and E.-H. Yang, “Fast mode selection for HEVC intra frame coding with entropy coding refinement based on transparent composite model,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 25, No.9, pp. 1521 – 1532, Sept. 2015.
  11. H. Yin, E.-H. Yang, and X. Yu, “Fast soft decision quantization with adaptive pres-election and dynamic trellis graph,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 25, No. 8, pp. 1362 – 1375, Aug. 2015.
  1. C. Sun and E.-H. Yang, “An efficient DCT-based image compression system based on Laplacian transparent composite model,” IEEE Trans. on Image Processing, Vol. 24, No. 3, pp. 886 – 900, Mar. 2015.
  2. E.-H. Yang, C. Sun, and J. Meng, “Quantization table design revisited for image/video coding,” IEEE Trans. on Image Processing, Vol. 23, No. 11, pp. 4799 – 4811, Nov. 2014.
  3. J. Meng. and E.-H. Yang, “Constellation and rate selection in adaptive modulation and coding based on finite blocklength analysis and its application to LTE,” IEEE Trans. on Wireless Communications, Vol. 13 , No. 10 , pp. 5496 – 5508, Oct. 2014.
  4. S. Yang, S. Ho, J. Meng, and E.-H. Yang, “Capacity analysis of linear operator channels over finite fields,” IEEE Trans. on Information Theory, Vol. 60, No. 8, pp. 4880 – 4901, Aug. 2014.
  5. N. Hu and E.-H. Yang “Fast motion estimation based on confidence interval,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 24, No. 8, pp. 1310 – 1322, Aug. 2014.
  6. E.-H. Yang, X. Yu, J. Meng, and C. Sun, “Transparent composite model for DCT coefficients: Design and analysis,” IEEE Transactions on Image Processing, Vol. 23, No. 3, pp. 1303 – 1316, Mar. 2014.
  7. J. Zhang, E.-H. Yang, and J. C. Kieffer, “A universal grammar-based code for lossless compression of binary trees,” IEEE Trans. on Information Theory, Vol. 60, No. 3, pp. 1373 – 1386, Mar. 2014.
  8. E.-H. Yang, L. Zheng, and D.-K. He, “On the information theoretic performance com-parison of causal video coding and predictive video coding,” IEEE Trans. on Infor-mation Theory, Vol. 60, No. 3, pp. 1428 – 1446, Mar. 2014.
  9. J. Ho and E.-H. Yang, “Designing optimal multiresolution quantizers with error de-tecting codes,” IEEE Trans. on Wireless Communications, Vol. 12, No. 7, pp. 3588 – 3599, July 2013 (12 double-column pages)
  10. J. Meng and E.-H. Yang, “Interactive encoding and decoding based on binary LDPC codes with syndrome accumulation,” IEEE Trans. on Information Theory, Vol. 59, No. 9, pp. 3068–3103, May 2013 (36 double-column pages).
  11. J. Meng, E.-H. Yang, and D.-K. He, “Linear interactive encoding and decoding schemes for lossless source coding with decoder only side information,” IEEE Trans. on Infor-mation Theory, Vol. 57, No. 8, pp. 5281–5297, August 2011.
  12. E.-H. Yang, L. Zheng, D.-K. He, and Z. Zhang, “Rate distortion theory for causal video coding: Characterization, computation algorithm, and comparison,” IEEE Trans. on Information Theory, Vol. 57, No. 8, pp. 5258–5280, August 2011.
  13. James Ho and E.-H. Yang, “Cross layer coding optimization for mobile IPTV delivery,” IEEE COMSOC MMTC E-Letter, Vol. 6, No. 1, pp.52–54, January 2011.
  1. X. Yu, H. Wang, and E.-H. Yang, “Design and analysis of optimal noisy channel quantization with random index assignment,” IEEE Trans. Information Theory, Vol. 56, No. 11, pp. 5796–5804, Nov. 2010.
  2. E.-H. Yang and D.-K. He, “Interactive encoding and decoding for one way learning: Near lossless recovery with side information at the decoder,” IEEE Trans. Information Theory, Vol. 56, No. 4, pp. 1808–1824, April 2010.
  3. H. Wang, E.-H. Yang, Z. Zhao, and W. Zhang, “Spectrum sensing in cognitive radio using goodness of fit testing,” IEEETransactionsonWirelessCommunications, Vol.8, No. 11, pp. 5427–5430, Nov. 2009.
  4. D.-K. He, L. A. Lastras-Montano, E.-H. Yang, A. Jagmohan, and J. Chen, “On the redundancy of Slepian-Wolf coding,” IEEE Trans. Information Theory, Vol. 55, No. 12, pp. 5607–5627, Dec. 2009.
  5. J. Chen, D.-K. He, A. Jagmohan, L. A. Lastras-Montano, and E.-H. Yang, “On the linear codebook-Level duality between Slepian-Wolf coding and channel coding,” IEEE Trans. Information Theory, Vol. 55, No. 12, pp. 5575–5590, Dec. 2009.
  6. J. She, X. Yu, P.-H. Ho, and E.-H. Yang, “A cross-layer design framework for ro-bust IPTV services over IEEE 802.16 networks,” IEEE Journal on Selected Areas in Communications, Vol. 27, No. 2, pp. 235–245, Feb. 2009.
  7. E.-H. Yang and Longji Wang, “Joint optimization of run-length coding, Huffman cod-ing and quantization table with complete baseline JPEG decoder compatibility,” IEEE Trans. Image Processing, Vol. 18, No. 1, pp. 63–74, January 2009.
  8. X. Yu, E.-H. Yang, and H. Wang, “Down-sampling design in DCT domain with arbi-trary ratio for image/video transcoding,” IEEE Trans. Image Processing, Vol. 18, No. 1, 75–89, January 2009.
  9. E.-H. Yang and X. Yu, “Soft decision quantization for H.264 with main profile com-patibility,” IEEETransactionsonCircuitsandSystemsforVideoTechnology, Vol. 19, No. 1, pp. 122–127, January 2009.
  10. E.-H. Yang, D.-K. He, T. Uyetmasu, and R. W. Yeung, “Universal multiterminal source coding algorithms with asymptotically zero feedback: Fixed database case,” IEEE Trans. Information Theory,Vol. 54, No. 12, pp. 5575–5590, December 2008.
  11. E.-H. Yang and W. Sun, “On information embedding when watermarks and covertexts are correlated,” IEEE Trans. Information Theory, Vol. 54, No. 7, pp. 3340–3345, July 2008.
  12. Y. Jia, E.-H. Yang, D.-K. He, and S. Chan, “A greedy re-normalization method for arithmetic coding,” IEEE Trans. Communications, Vol. 55, No. 8, pp. 1494–1503, August 2007.
  1. E.-H. Yang and X. Yu, “Rate distortion optimization for H.264 inter-frame video cod-ing: A general framework and algorithms,” IEEE Trans. on Image Processing, Vol.16, No.7, pp. 1774–1784, July 2007.
  2. Z. Wang, G. Wu, H. R. Sheikh, E. P. Simoncelli, E.-H. Yang, and A. C. Bovik, “Quality aware images,” IEEE Trans. Image Processing, Vol. 15, No. 6, pp. 1680–1689, 2006.
  3. D.-K. He and E.-H. Yang, “The universality of grammar-based codes for sources with countably infinite alphabets,” IEEE Trans. Inform. Theory, Vol. 51, No. 11, pp. 3753–3765, November 2005.
  4. G. Wu and E.-H. Yang, “Joint watermarking and compression using scalar quantization for maximizing robustness in the presence of additive Gaussian attacks,” IEEE Trans. Signal Processing, Vol. 53, No. 2, pp. 834–844, February 2005.
  5. W. Sun and E. H. Yang, “Closed-form formulas for private watermarking capacities of Laplacian sources with the magnitude-error distortion measure and under Additive attacks,” Lecture Notes in Computer Science (LNCS) 3710, pp. 361–371, 2005.
  6. H.-F. Lu, P. V. Kumar, and E.-H. Yang, “On the input-output weight enumerators of product accumulate codes,” IEEE Communications Letters, Vol. 8, No. 8, pp. 520–522, August 2004.
  7. J. C. Kieffer, W. Szpankowski, and E.-H. Yang, “Problems on sequences: Information theory and computer science interface,” IEEE Trans. Inform. Theory, Vol. 50, No. 7, pp. 1385–1392, July 2004.
  8. W. Sun and E.-H. Yang, “On the Capacity Regions of Public Multiple-Access Gaussian Watermarking Systems”, Lecture Notes in Computer Science 3200, pp.38–51, 2004.
  9. J. C. Kieffer and E.-H. Yang, “Grammar-based lossless universal refinement source coding,” IEEE Trans. Inform. Theory, Vol. 50, No. 7, pp. 1415–1424, July 2004.
  10. D.-K. He and E.-H. Yang, “Performance analysis of grammar-based codes revisited,” IEEE Trans. Inform. Theory, Vol. 50, No. 7, pp. 1524–1535, July 2004.
  11. D.-W. Yue and E.-H. Yang, “Asymptotically Gaussian weight distribution and per-formance of multi-dimensional Turbo block codes and product codes,” IEEE Trans. Communications, Vol. 52, No.5, pp. 728–736, May 2004.
  12. A. Kaltchenko and E.-H. Yang, “Universal compression of ergodic quantum sources,” QuantumInformationandComputation, Vol.3, No.4, pp. 359–375, July, 2003.
  13. Y. Jia and E.-H. Yang, “Context-dependent multilevel pattern matching for lossless image compression,” IEEE Trans. Inform. Theory, Vol. 49, No. 12, pp. 3169–3184, December 2003.
  14. E.-H. Yang and Da-ke He, “Efficient universal lossless compression algorithms based on a greedy sequential grammar transform–Part two: With context models,” IEEE Trans. Inform. Theory, Vol. 49, No. 11, pp. 2874–2894, November 2003.
  1. E.-H. Yang and D.-K. He, “Huffman coding,” The Wiley Encyclopedia of Telecommu-nications, 2002.
  2. J. C. Kieffer and E.-H. Yang, “Structured grammar-based codes for universal lossless data compression,” Communications in Information and Systems, Vol. 2, No. 1, pp. 29-52, June 2002 (Invited Paper).
  3. E.-H. Yang and Z. Zhang, “The redundancy of source coding with a fidelity criterion–Part II: Coding at a fixed rate level with unknown statistics,” IEEE Trans. Inform. Theory, Vol.IT-47, No. 1, pp. 126–145, January 2001.
  4. E.-H. Yang, A. Kaltchenko, and J. C. Kieffer, “Universal lossless data compression with side information by using a conditional MPM grammar transform,” IEEE Trans. Inform. Theory, Vol.IT-47, no.6, pp. 2130–2150, September 2001.
  5. Q. Liu, E.-H. Yang, and Z. Zhang, “Throughput analysis of CDMA systems using multiuser receivers”, IEEE Transactions on Communications, Vol. 49, No. 7, pp. 1192–1202, July 2001.
  6. E.-H. Yang and Y. Jia, “Universal lossless coding of sources with large or unbounded alphabets,” Numbers,InformationandComplexity(Ingo Althofer, etal, eds.), Kluwer Academic Publishers, pp. 421-442, February 2000.
  7. J. C. Kieffer, E.-H. Yang, G. Nelson, and P. Cosman, “Universal lossless compression via multilevel pattern matching,” IEEE Trans. Inform. Theory, Vol.IT-46, No. 4, pp. 1227–1245, July 2000.
  8. J. C. Kieffer and E.-H. Yang, “Grammar based codes: A new class of universal lossless source codes,” IEEE Trans. Inform. Theory, Vol.IT-46, No. 3, pp. 737–754, May 2000.
  9. E.-H. Yang and J. C. Kieffer, “Efficient universal lossless compression algorithms based on a greedy sequential grammar transform–Part one: Without context models,” IEEE Trans. Inform. Theory, Vol.IT-46, No. 3, pp. 755–777, May 2000.
  10. B. Zhu, E.-H. Yang, and A. H. Tewfik, “Arithmetic coding with dual symbol sets and its performance analysis,” IEEE Trans. Image Processing, Vol. 8, No. 12, pp. 1667–1676, December 1999.
  11. E.-H. Yang and Z. Zhang, “The shortest common superstring problem: Average case analysis for both exact matching and approximate matching,” IEEE Trans. Inform. Theory, Vol. IT-45, No. 6, pp. 1867–1886, Sept. 1999.
  12. E.-H. Yang and Z. Zhang, “On the redundancy of lossy source coding with abstract alphabets,” IEEE Trans. Inform. Theory, Vol. IT-45, No. 4, pp. 1092–1110, May 1999.
  13. E.-H. Yang and Z. Zhang, “Variable rate trellis source encoding,” IEEE Trans. Inform. Theory, Vol. IT-45, No. 2, pp. 586–608, March 1999.
  1. E.-H. Yang and Z. Zhang, “An on-line universal lossy data compression algorithm via continuous codebook refinement–Part III: Redundancy analysis,” IEEE Trans. Inform. Theory, Vol. IT-44, No. 5, pp. 1782-1801, Sept. 1998.
  2. E.-H. Yang and J. C. Kieffer, “On the performance of data compression algorithms based upon string matching,” IEEE Trans. Inform. Theory, Vol. IT-44, No. 1, pp. 47–65, Jan. 1998.
  3. E.-H. Yang, Z. Zhang, and T. Berger, “Fixed slope universal lossy data compression,” IEEE Trans. Inform. Theory, Vol. IT-43, No. 5, pp. 1465–1476, Sept. 1997.
  4. E.-H. Yang and J. C. Kieffer, “On the redundancy of the fixed database Lempel-Ziv algorithm for ϕ-mixing sources,” IEEE Trans. Inform. Theory, Vol. IT-43, No. 4, pp. 1101–1111, July 1997.
  5. W.-S. Chen, E.-H. Yang, and Z. Zhang, “A new efficient image compression technique with index-matching vector quantization,” IEEE Trans. Consumer Electronics, Vol. 43, No.2, pp. 173–182, May 1997.
  6. John C. Kieffer and E.-H. Yang, “Ergodic behavior of graph entropy,” ERA Amer. Math. Society, Vol. 3, No. 1, pp. 11-16, 1997.
  7. R. Ahlswede, E.-H. Yang, and Z. Zhang, “Identification via compressed data,” IEEE Trans. Inform. Theory, Vol. IT-43, No.1, pp. 48–70, Jan. 1997.
  8. Z. Zhang, E.-H. Yang, and V. K. Wei, “The redundancy of source coding with a fidelity criterion–Part I: Known statistics,” IEEE Trans. Inform. Theory, Vol. IT-43, No.1, pp. 71–91, Jan. 1997.
  9. Z. Zhang and E.-H. Yang, “An on-line universal lossy data compression algorithm via continuous codebook refinement–Part II: Optimality for phi-mixing source Models,” IEEE Trans. Inform. Theory, Vol. IT-42, No.3, pp. 822–836, March 1996.
  10. E.-H. Yang and J. C. Kieffer, “Simple universal lossy data compression schemes derived from the Lempel–Ziv algorithm,” IEEE Trans. Inform. Theory, Vol. IT-42, No. 1, pp. 239–245, January 1996.
  11. J. C. Kieffer and En-hui Yang, “Sequential codes, lossless compression of individual sequences, and Kolmogorov complexity,” IEEE Trans. Inform. Theory, Vol. IT-42, No. 1, pp. 29–39, January 1996.
  12. En-hui Yang and Shi-yi Shen, “Distortion program-size complexity with respect to a fidelity criterion and rate distortion function,” IEEE Trans. Inform. Theory, Vol. IT-39, No. 1, pp. 288–292, 1993.
  13. En-hui Yang, “Limit theorems for Chaitin complexity,” Chinese Science Bulletin, Vol. 37, No. 21, 1992.
  14. En-hui Yang, “Universal almost sure data compression for abstract alphabets and arbitrary fidelity criterions,” Problems of Control and Information theory, Vol.20(6), pp. 397–408, 1991.
  1. En-hui Yang and Shi-yi Shen, “Chaitin complexity, Shannon information content of a single event, and infinite random sequences—Part one,”, Science in China Series A, Vol. 34, No.10, pp. 1183–1193, 1991.
  2. En-hui Yang and Shi-yi Shen, “Chaitin complexity, Shannon information content of a single event, and infinite random sequences—part two,” Science in China Series A , Vol. 34, No. 11, pp.1307–1319, 1991.
  3. En-hui Yang, “The proof of Levin’s conjecture,” Chinese Science Bulletin, Vol. 34, No.21, pp.1761–1765, Nov. 1989.

Refereed Conference Papers:

  1. B. Shan, Y. Fang, V. Stankovic, S. Cheng, and E.-H. Yang, “ Depth-first decoding of distributed arithmetic codes for uniform binary sources,” Proc. of the 2020 Data Compression Conference, Snowbird, UT, USA, Mar. 24 - 27, pp. 393-393, 2020.
  2.  E.-H. Yang and X.-W. Wu, “On optimal information-theoretically secure key man-agement,” Proc.ofthe2019CanadianWorkshoponInformationTheory, Hamilton, Ontario, June 2 - 6, 2019 (6 pages).
  3. H. Amer and E.-H. Yang, “Low-delay HEVC adaptive quantization parameter selection through temporal propagation length estimation,” Proc. of the 2018 IEEE Int. Conf. Image Process., Athens, Greece, October 7-10, pp. 211 – 215, 2018.
  4. H. Amer, A. Rashwan, and E-H. Yang, “Fully connected network for HEVC CU split decision equipped with Laplacian transparent composite model,” Proc. of the 2018 Picture Coding Symposium, San Francisco, CA, USA, June 24-27, pp. 189 – 193, 2018.
  5.  E.-H. Yang and X.-W. Wu, “Information-theoretically secure key generation and man-agement,” Proc.ofthe2017IEEEInternationalSymposiumonInformationTheory (ISIT 2017), Aachen, Germany, June 25 - 30, pp. 1529-1533, 2017.
  6. X. -W. Wu, E.-H. Yang, and J. Wang, “Lightweight security protocols for Internet of Things,” Proc. of the 2017 IEEE Int. Symp. Personal, Indoor and Mobile Radio Communications, Montreal, Canada, Oct. 8-13, 2017 (7 double column pages).
  7. Y. Shan and E.-H. Yang, “Fast HEVC intra coding algorithm based on machine learn-ing and Laplacian transparent composite model,” Proc. of the 2017 Int. Conf. Acous-tics, Speech, and Signal Process., New Orleans, USA, March 5-9, pp. 2642 – 2646, 2017 (5 double column pages).
  8. J. Bian and E.-H. Yang, “’Bipartite grammar-based representations of large sparse binary matrices: Framework and transforms,” Proc. of the 2016 Int. Symp. Inf. Theory and Its Applications, Monterey, California, USA, Oct. 30 - Nov. 2, pp. 241 – 245, 2016.
  1. H. Amer and E.-H. Yang, ”Scene-based low delay HEVC encoding framework based on transparent composite modeling,” Proc. of the 2016 IEEE Int. Conf. Image Process., Phoenix, Arizona, USA, Sept. 25 - 28, pp. 809 – 813, 2016.
  2.  E.-H. Yang, “On coding for data analytics: New information distances,” Proc. of the 2016 Information Theory and Applications Workshop, San Diego, California, U.S.A., Jan. 31– Feb. 5, 2016 (invited paper) (6 double-column pages).
  3.  E.-H. Yang, X. Yu, and J. Meng, “Set mapping induced image perceptual similarity distance,” Proc. of the 2015 Information Theory and Applications Workshop, San Diego, California, U.S.A., Feb. 1–Feb. 6, 2015 (invited paper) (10 double-column pages).
  4. N. Hu and E.-H. Yang, “Fast inter mode decision for HEVC based on transparent com-posite model,” Proc.ofthe2015IEEEInternationalConferenceonImageProcessing, Quebec City, Canada, Sept. 27 - 30, pp. 1533 – 1537, 2015.
  5. Y. Gao, E.-H. Yang, and D. He, “Low-complexity rate control in video coding based on bi-geometric transparent composite models,” Proc. of the 2015 IEEE International Conference on Image Processing, Quebec City, Canada, Sept. 27 - 30, pp. 1419 – 1423, 2015.
  6. C. Sun and E.-H. Yang, “An efficient DCT-based image compression system based on transparent composite model,” Proc. of the 2014 IEEE International Conference on Image Processing, Paris, France, Oct. 27 - 30, pp. 5611 - 5615, 2014.
  7. N. Hu and E.-H. Yang, “Fast intra mode decision for HEVC based on transparent com-posite model,” Proc.ofthe2014IEEEInternationalConferenceonImageProcessing, Paris, France, Oct. 27 - 30, pp. 3710 – 3714, 2014.
  8.  E.-H. Yang and X. Yu, “Transparent composite model for large scale image/video processing,” Proc. of the 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, October 6 - 9, pp. 38–44, 2013.
  9. N. Hu and E.-H. Yang, “Confidence interval based motion estimation,” Proc.  of the 2013 IEEE International Conference on Image Processing, Melbourne, Australia, September 15 - 18, pp. 1588–1592, 2013.
  10.  E.-H. Yang, C. Sun, and J. Meng, “Quantization table design revisited for image/video coding,” Proc.ofthe2013IEEE InternationalConferenceonImageProcessing, Mel-bourne, Australia, September 15 - 18, pp. 1855–1859, 2013.
  11. J. Ho and E.-H. Yang, “Optimal multiresolution quantization with error detecting codes for broadcast channels,” Proc. of the 2013 IEEE Intern. Symp. Information Theory, Istanbul, Turkey, July 7 - 12, pp. 554–558, 2013.
  12. J. Zhang, E.-H. Yang, and J. C. Kieffer, “Redundancy analysis in lossless compression of a binary tree via its minimal DAG representation,” Proc. of the 2013 IEEE Intern. Symp. Information Theory, Istanbul, Turkey, July 7 - 12, pp. 1914–1918, 2013.
  1. J. Ho, E.-H. Yang, and J. Meng, “On separation of source and channel coding in the finite block length regime,” Proc. of the 2013 Canadian Workshop on Information Theory, Toronto, Ontario, June 18 - 21, pp. 97 – 100, 2013.
  2. K. Rapaka and E.-H. Yang, “A high throughput multi-symbol CABAC frame-work for hybrid video codecs,” Proc. of the 2013 Data Compression Conference, Snowbird, Utah, Mar. 20–22, 2013.
  3. J. Meng and E.-H. Yang, “Constellation and rate selection in adaptive modulation and coding based on finite blocklength analysis,” Proc. of 2013 IEEE Wireless Commu-nications and Networking Conference, Shanghai, China, April 7–10, pp. 4065–4070, 2013.
  4.  E.-H. Yang and J. Meng, “New non-asymptotic random channel coding theorems,” Proc.ofthe2013InformationTheoryandApplicationsWorkshop, San Diego, Cali-fornia, U.S.A., Feb. 10–Feb. 15, 2013 (invited paper) (8 double-column pages).
  5. M. Torbatian and E.-H. Yang, “Causal Coding of Multiple Jointly Gaussian Sources,” Proc.ofthe50thAllertonConferenceonCommunications,Control,andComputing, Urbana-Champaign, Illinois, Oct. 1–5, pp. 2060 - 2067, 2012 (8 double-column pages).
  6. D. Xu, J. Meng, and E.-H. Yang, “Non-asymptotic fixed-rate Slepian-Wolf coding theorem,” Proc.ofthe50thAllertonConferenceonCommunications,Control,and Computing, Urbana-Champaign, Illinois, Oct. 1–5, 2012 (8 double-column pages).
  7.  E.-H. Yang and J. Meng, “Channel capacity in the non-asymptotic regime: Taylor-type expansion and computable benchmarks,” Proc. of the 50th Allerton Conference on Communications, Control, and Computing, Urbana-Champaign, Illinois, Oct. 1–5, pp. 278 - 285, 2012 (invited paper; 8 double-column pages).
  8.  E.-H. Yang and J. Meng, “Jar decoding: LDPC coding theorems for binary input memoryless channels,” Proc. of the 2012 IEEE Intern. Symp. Information Theory, Cambridge, MA, U.S.A., 1-6 July, pp. 2861 - 2865, 2012.
  9.  E.-H. Yang and J. Meng, “Non-asymptotic equipartition properties for independent and identically distributed sources,” Proc. of the 2012 Information Theory and Appli-cations Workshop, San Diego, California, U.S.A., Feb. 5–Feb. 10, pp. 39 – 46 , 2012 (invited paper).
  10.  E.-H. Yang and C. Sun, “Dithered soft decision quantization for baseline JPEG encod-ing and its joint optimization with Huffman coding and quantization table selection,” Proceedings of Forty-Fifth Asilomar Conference on Signals, Systems and Computers, Monterey, California, USA, November 6 – 9, pp. 249 - 253, 2011.
  11. J. She, E.-H. Yang, and P.-H. Ho, “Coded wireless video broadcast/multicast: A cross-layer framework and end-to-end distortion analysis,” Proceedings of the 6th ACM Workshop on Wireless Multimedia Networking and Computing, Miami, FL, USA, Oc-tober 31 – November 04, pp. 7–24, 2011.
  1. J. Meng, E.-H. Yang, and Z. Zhang, “Tree interactive encoding and decoding: Con-ditionally Φ-mixing sources,” Proc. of the 2011 IEEE Intern. Symp. Information Theory, Saint-Petersburg, Russia, July 31–August 5, pp. 1871 – 1875, 2011
  2. J. Meng and E.-H. Yang, “Credit-based variable-to-variable length coding: key con-cepts and preliminary redundancy analysis,” Proc. of the 2011 Canadian Workshop on Information Theory, Kelowna, British Columbia, Canada, May 17–20, pp.74 – 77, 2011.
  3. L. Zheng and E.-H. Yang, “On optimum fixed-rate causal scalar quantization design for causal video coding,” Proc.ofthe2011CanadianWorkshoponInformationTheory, Kelowna, British Columbia, Canada, May 17–20, pp. 58–61, 2011.
  4. L. Zheng, D.-K. He, and E.-H. Yang “On the information theoretic performance com-parison of causal video coding and predictive video coding,” Proc. of the 2011 Infor-mation Theory and Applications Workshop, San Diego, California, U.S.A., Feb. 6–Feb. 11, pp. 1–7, 2011 (invited paper).
  5.  E.-H. Yang, C. Sun, and L. Zheng, “An improved iterative algorithm for calculating the rate distortion performance of causal video coding for continuous sources and its application to real video data,” Proc. of the 28th Picture Coding Symposium, Nagoya, Japan, December 7-10, pp. 98–101, 2010.
  6. G. Wu and E.-H. Yang, “A new efficient method of computing MDCT in MP3 audio coding,” Proc.ofthe2010Inter.Conf.MultimediaInfo.NetworkingandSecurity, Nanjing, Jiangsu, China, November 4-6, pp. 63–67, 2010.
  7. J. Meng and E.-H. Yang, “On the error exponent to redundancy ratio of interactive encoding and decoding,” Proc. ofthe2010IEEEIntern. Symp. InformationTheory, Austin, Texas, U.S.A., June 13–18, pp. 121–125, 2010.
  8. F. Teng, E.-H. Yang, and X. Yu, “Optimal multiresolution quantization for broadcast channels with random index assignment,” Proc. of the 2010 IEEE Intern. Symp. Information Theory, Austin, Texas, U.S.A., June 13–18, pp. 181–185, 2010.
  9. S. Yang, J. Meng, and E.-H. Yang, “Coding for linear operator channels over finite fields,” Proc. of the 2010 IEEE Intern. Symp. Information Theory, Austin, Texas, U.S.A., June 13–18, pp. 2413–2417, 2010.
  10.  E.-H. Yang and J. Meng, “On interactive encoding and decoding for distributed lossless coding of individual sequences,” Proc. of the 2010 Information Theory and Applica-tions Workshop, San Diego, California, U.S.A., Jan. 31–Feb. 5, pp. 1–7, 2010 (invited paper).
  11. S. Yang, S.-W. Ho, J. Meng, and E.-H. Yang, “Optimality of subspace coding for linear operator channels over finite fields,” Proc. of 2010 IEEE Information Theory Workshop, Cairo, Egypt, Jan. 6-8, pp. 1–5, 2010.
  1. J. Meng and E.-H. Yang, “Interactive encoding and decoding based on syndrome ac-cumulation over binary LDPC ensembles: Universality and rate-adaptivity,” Proc. of the 47th Allerton Conference on Communications, Control, and Computing, Urbana-Champaign, Illinois, September 30–Oct. 2, pp. 1502–1509, 2009 (invited paper).
  2. X. Yu, D.-K. He,and E.-H. Yang, “Adaptive quantization with balanced distortion distribution and its application to H.264 intra coding,” Proc. of the 2009 IEEE Intern. Conf. Image Processing, Cairo, Egypt, November 7–11, pp. 1049–1052, 2009.
  3.  E.-H. Yang and L. Wang, “Full rate distortion optimization of MPEG 2 video coding,” Proc. of the 2009 IEEE Intern. Conf. Image Processing, Cairo, Egypt, November 7–11, pp. 605–608, 2009.
  4.  E.-H. Yang, L. Zheng, Z. Zhang, and D.-K. He, “A computation approach to the minimum total rate problem of causal video coding,” Proc. of the 2009 IEEE Intern. Symp. Information Theory, Seoul, Korea, June 28 to July 3, pp. 2141–2145, 2009.
  5. J. C. Kieffer, E.-H. Yang, and W. Szpankowski, “Structural complexity of random binary tree,” Proc. of the 2009 IEEE Intern. Symp. Information Theory, Seoul, Korea, June 28 to July 3, pp. 635–639, 2009.
  6.  E.-H. Yang, L. Zheng, D.-K. He, and Z. Zhang, “On the rate distortion theory for causal video coding,” Proc.ofthe2009InformationTheoryandApplicationsWorkshop, San Diego, California, U.S.A., Feb. 8–13, pp. 385 – 391, 2009 (invited paper).
  7. G. Wu, E.-H. Yang, and D.-K. He, “Joint watermarking and compression for Gaussian and Laplacian sources using uniform vector quantization,” Proc. of the 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, April 19-24, pp. 1493–1496, 2009.
  8. Y. Zhou and E.-H. Yang, “Joint robust watermarking and compression using variable-rate scalar quantization,” Proc.ofthe2009CanadianWorkshoponInformationThe-ory, Ottawa, Ontario, Canada, May 13–15, pp. 183–186, 2009.
  9. X. Yu, H. Wang, E.-H. Yang, and M. Uysal, “Distortion SNR exponent for vector quan-tization on MIMO channels,” Proc.ofthe2009CanadianWorkshoponInformation Theory, Ottawa, Ontario, Canada, May 13–15, pp. 79–82, 2009.
  10. J. Meng, E.-H. Yang, and D.-K. He, “Interactive encoding and decoding based on syndrome accumulation over a binary regular LDPC ensemble,” Proc. of the 2009 Canadian Workshop on Information Theory, Ottawa, Ontario, Canada, May 13–15, pp. 42–45, 2009.
  11.  E.-H. Yang and L. Wang, “Joint optimization of run-length coding, context-based arithmetic coding, and quantization step sizes,” Proc. of the 2009 IEEE Canadian Conference on Electrical and Computer Engineering, St. John’s, Newfoundland and Labrador, Canada, May 3-6, pp.678–681, 2009.
  1.  E.-H. Yang and L. Wang, “Entropy constrained color splitting for palette image,” Proc. of the 2009 IEEE Intern. Conf. Multimedia & Expo, Cancun, Mexico, June 28 - July 3, pp. 109–112, 2009.
  2. J. Meng, E.-H. Yang, and D.-K. He,“Low-density linear IED schemes for lossless source coding with decoder only side information,” Proc. of the 46th Allerton Confer-ence on Communications, Control, and Computing, Urbana-Champaign, Illinois, USA, September 23–26, pp. 661–668, 2008.
  3.  E.-H. Yang and D.-K. He, “On interactive encoding and decoding for lossless source coding with decoder only side information,” Proc. of the 2008 IEEE Intern. Symp. Inform. Theory, Toronto, Canada, July 6–11, pp. 419–423, 2008.
  4. W. Sun and E.-H. Yang, “On achievable distortion regions of analog Gaussian water-marking,” Proc. of the 2008 IEEE Intern. Symp. Inform. Theory, Toronto, Canada, July 6–11, pp. 1987–1991, 2008.
  5. X. Yu, H. Wang, and E.-H. Yang, “Optimal quantization for noisy channels with random index assignment,” Proc. of the 2008 IEEE Intern. Symp. Inform. Theory, Toronto, Canada, July 6–11, pp. 2727–2731, 2008.
  6. A. Kaltchenko, E-H. Yang, and N. Timofeeva, “Bias reduction of the nearest neighbor entropy estimator”, Procedeengsofthe2008IEEEInternationalConferenceonCom-putationalTechnologiesinElectricalandElectronicsEngineering, Novosibirsk, Russia, pp. 261–265, 2008.
  7.  E.-H. Yang and D.-K. He, “Two results on interactive lossless source encoding and decoding with side information at the decoder,” Proc. of the Third (2008) Intern. Conf. on Communications and Networking in China, Hangzhou, China, August 25-27, pp. 90–94, 2008 (invited paper).
  8.  E.-H. Yang and D.-K. He, “Near lossless source coding with side information at the decoder: Beyond conditional entropy,” Proc. of the 2008 Information Theory and Applications Workshop, San Diego, California, U.S.A., Jan. 27–Feb. 1, pp. 454–460, 2008 (invited paper).
  9. J. She, X. Yu, F. Hou, P. H. Ho, and E.-H. Yang, “A framework of cross-layer super-position coded multicast for robust IPTV services over WiMAX,” Proc. of the 2008 IEEE Wireless Communications and Networking Conference, Las Vegas, Nevada, USA, March 31–April 4, pp. 3139–3144, 2008.
  10. X. Yu, E.-H. Yang, and H. Wang, ‘’Down-sampling in DCT domain using linear trans-form with double-sided multiplication for image/video transcoding,” Proc. of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing, Las Vegas, Nevada, USA, March 30 - April 4, pp. 1377–1380, 2008.
  11. A. Kaltchenko, N. Timofeeva, and E.-H. Yang, “Exact analysis of the bias of the nearest neighbor entropy estimator for I.I.D. information sources,” Proc. of the 45th AllertonConferenceonCommunications,Control,andComputing, Urbana-Champaign, Illi-nois, USA, September 26–28, pp. 165–168, 2007.
  1.  E.-H. Yang and L. Wang, “Joint optimization of run-length coding, Huffman coding and quantization table with complete baseline JPEG decoder compatibility,” Proc. of the 2007 IEEE Intern. Conf. Image Processing, San Antonio, Texas, USA., Sept. 16–19, pp. III-181–III-184, 2007.
  2. A. Kaltchenko, E.-H. Yang, and N. Timofeeva, “Entropy estimators with almost sure convergence and an O(1/n) variance,” Proc. of the 2007 IEEE Information Theory Workshop, Lake Tahoe, California, USA, Sept. 2–6, pp. 644-649, 2007.
  3. A. Kaltchenko, I. Kotsireas, N. Timofeeva, En-hui Yang, “Entropy rate estimators with a near-optimal upper bound on variance,” Proc. of XI Intern. Symp. Problems of Redundancy in Information and Control Systems, Saint Petersburg, Russian, July 2–6, pp. 18–21, 2007.
  4. J. Wang, E.-H. Yang, and X. Yu, “An efficient motion estimation method for H.264-based video transcoding with spatial resolution conversion,” Proc. of the 2007 IEEE Intern. Conf. Multimedia & Expo, Beijing, China, July 2–5, pp. 444–447, 2007.
  5.  E.-H. Yang, and D.-K. He, “Universal data compression with side information at the decoder by using traditional universal lossless compression algorithms,” Proc. of the 2007 IEEE Intern. Symp. Inform. Theory, Nice, France, June 24–29, pp. 431–435, 2007.
  6. X. Ma and E.-H. Yang, “Constructing LDPC codes by 2-lifts,” Proc. of the 2007 IEEE Intern. Symp. Inform. Theory, Nice, France, June 24–29, pp. 1231–1235, 2007.
  7.  D.-K. He, L. A. Lastras-Montano, and E.-H. Yang, “Redundancy of variable rate Slepian-Wolf codes from the decoder’s perspective,” Proc. of the 2007 IEEE Intern. Symp. Inform. Theory, Nice, France, June 24–29, pp. 1321–1325, 2007.
  8. W. Sun and E.-H. Yang, “On reversible embedding when watermarks and covertexts are correlated,” Proc. of the 2007 IEEE Intern. Symp. Inform. Theory, Nice, France, June 24–29, pp. 2451–2455, 2007.
  9. L. Zheng, D.-K. He, and E.-H. Yang, “On optimum conventional quantization for source coding with side information at the decoder,” Proc. of the 2007 Canadian Workshop on Information Theory, Edmonton, Alberta, Canada, June 5–8, pp. 97–100, 2007.
  10. H. Wang and E.-H. Yang, “On space-time coding with finite bit feedback,” Proc. of the 2007 Canadian Workshop on Information Theory, Edmonton, Alberta, Canada, June 5-8, pp. 124–127, 2007.
  11. A. Kaltchenko and E.-H. Yang, “Cooling of spins via quantum data compression,” Proc.ofthe2007CanadianWorkshoponInformationTheory, Edmonton, Alberta, Canada, June 5-8, pp. 184–187, 2007.
  1. J. Chen, D.-K. He, and E.-H. Yang, “Slepian-Wolf coding for general sources and its duality with channel coding,” Proc.ofthe2007CanadianWorkshoponInformation Theory, Edmonton, Alberta, Canada, June 5-8, pp. 57–60, 2007.
  2. J. Chen, D.-K. He, and E.-H. Yang,“On the codebook-level duality between Slepian-Woif coding and channel coding,” Proc.ofthe2007InformationTheoryandApplica-tions Workshop, San Diego, California, U.S.A., Jan. 29–Feb. 2, pp. 84–93, 2007.
  3.  E.-H. Yang and W. Sun, “Combined source coding and watermarking,” Proc. of the 2006 IEEE Information Theory Workshop, Chengdu, China, Oct.22–26, pp. 322–326, 2006 (invited paper).
  4.  D.-k. He, L. Lastras-Montano, and E.-H. Yang, “On the relationship between re-dundancy and decoding error probability in Slepian-Wolf coding,” Proc. of the 2006 IEEE Information Theory Workshop, Chengdu, China, Oct.22–26, pp. 332–336, 2006 (invited paper).
  5.  D.-k. He, L. Lastras-Montano, and E.-H. Yang, “Improving the redundancy of Slepian-Wolf coding by feedback,” Proc.ofthe44thAllertonConferenceonCommunications, Control, and Computing, Urbana-Champaign, Illinois, September 27–29, pp. 861–867, 2006 (invited paper).
  6. W. Sun and E.-H. Yang, “On joint compression and information embedding when watermarks and covertexts are correlated,” Proc.ofthe2006IEEEInformationTheory Workshop, Chengdu, China, October 22-26, pp. 298–302, 2006.
  7. H. Wang and E.-H. Yang, “Space-time coding with feedback,” Proc. of the 2006 IEEE Information Theory Workshop, Chengdu, China, October 22-26, pp. 438–442, 2006.
  8.  E.-H. Yang and X. Yu, “Rate distortion optimization of H.264 with main profile com-patibility,” Proc. of the 2006 IEEE Intern. Symp. Inform. Theory, Seattle, Washing-ton, USA, July 9–14, pp. 282–286, 2006.
  9.  D.-k. He, L. Lastras-Montano, and E.-H. Yang, “A lower bound for variable rate Slepian-Wolf coding,” Proc.ofthe2006IEEEIntern.Symp. Inform. Theory, Seattle, Washington, USA, July 9–14, pp. 341–345, 2006.
  10.  D.-k. He and E.-H. Yang, “On the duality between Slepian-Wolf coding and channel coding,” Proc. of the 2006 IEEE Intern. Symp. Inform. Theory, Seattle, Washington, USA, July 9–14, pp. 2546–2550, 2006.
  11. W. Sun and E.-H. Yang, “Algorithms for computing joint compression and private watermarking rate regions,” Proc. of the 2006 IEEE Intern. Symp. Inform. Theory, Seattle, Washington, USA, July 9–14, pp. 178–182, 2006.
  12.  E.-H. Yang and W. Sun, “On information embedding when watermarks and covertexts are correlated,” Proc. of the 2006 IEEE Intern. Symp. Inform. Theory, Seattle, Washington, USA, July 9–14, pp. 346–350, 2006.
  1.  E.-H. Yang and X. Yu, “Rate distortion optimization in H.264,” Proc. of the Inaugural Workshop on Information Theory and Applications, San Diego, U.S.A., Feb. 6 to 10, 2006 (invited paper).
  2. N. Nguyen and E.-H. Yang, “End-to-end loss discrimination for improved throughput performance in heterogeneous networks,” Proc. of the 2006 IEEE Consumer Commu-nications and Networking Conference, Las Vegas, NV, U.S.A., January 8–10, Volume 1, pp. 538–542, 2006.
  3. J. Xu and E.-H. Yang, “Rate-distortion optimization for MP3 audio coding with com-plete decoder compatibility,” Proc.ofthe2005IEEEInternationalWorkshoponMul-timedia Signal Processing, Shanghai, China, Oct. 30 to Nov. 2, pp. 1–4, 2005.
  4.  E.-H. Yang and G. Wu, “Joint compression and blind watermarking: A case study in the JPEG-compatible scenario,” Proc. of the 43th Allerton Conference on Communi-cations, Control, and Computing, Urbana-Champaign, Illinois, September 28–30, pp. 1859–1868, 2005 (invited paper).
  5.  E.-H. Yang, D.-K. He, T. Uyematsu, and R. W. Yeung, “String matching-based uni-versal source codes for source networks with asymptotically zero feedback,” Proc. of the 2005 IEEE Intern. Symp. Inform. Theory, Adelaide, Australia, Sept. 4–9, pp. 2349–2353, 2005.
  6.  E.-H. Yang, L. Song, J. C. Kieffer, and G. I. Shamir, “On the pointwise redundancy of the LZ78 algorithm,” Proc. of the 2005 IEEE Intern. Symp. Inform. Theory, Adelaide, Australia, Sept. 4–9, pp. 495–499, 2005.
  7.  E.-H. Yang and X. Yu, “Optimal soft decision quantization design for H.264,” Proc. of the 2005 Canadian Workshop on Information Theory, Montreal, Quebec, Canada, June 5–8, pp.223-226, 2005.
  8. G. Wu and E.-H. Yang, “A joint image compression and blind watermarking system with JPEG decoder compatibility,” Proc. of the 2005 Canadian Workshop on Infor-mation Theory, Montreal, Quebec, Canada, June 5–8, pp.119-122, 2005.
  9.  E.-H. Yang and W. Sun, “On watermarking and compression rates of joint compres-sion and private watermarking systems with abstract alphabets,” Proc. of the 2005 Canadian Workshop on Information Theory, Montreal, Quebec, Canada, June 5–8, pp. 296-299, 2005.
  10.  E.-H. Yang and X. Yu, “On joint optimization of motion compensation, quantization and baseline entropy coding in H.264 with complete decoder compatibility” the Proc. of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA, March 18–23, pp. II325-328, 2005.
  11.  E.-H. Yang, D.-K. He, and J. C. Kieffer, “Grammar-based coding: New perspectives,” Proc. of the 2004 IEEE Information Theory Workshop, San Antonio, Texas, U.S.A., October 24-29, pp. 105–110, 2004. (Invited paper)
  1. X. Ma and E.-H. Yang, “Low density parity check codes with fast decoding convergence speed,” Proc.ofthe2004IEEEIntern. Symp. Inform. Theory, Chicago, U.S.A., June 27 – July 2, 2004.
  2.  E.-H. Yang and D.-K. He, “Context-dependent vs context-free: performance compari-son of grammar-based codes,” Proc.ofthe2003IEEEIntern. Symp. Inform. Theory, Yokohama, Japan, June 29 – July 4, pp. 51-51, 2003.
  3.  D.-K. He and E.-H. Yang, “On the universality of grammar-based codes for sources with countably infinite alphabets,” Proc. of the 2003 IEEE Intern. Symp. Inform. Theory, Yokohama, Japan, June 29 – July 4, pp. 50-50, 2003.
  4. Y. Jia and E.-H. Yang, “Lossless image compression using context-dependent multilevel 2D pattern matching,” Proc. of the 2003 IEEE Intern. Symp. Inform. Theory, Yokohama, Japan, June 29 – July 4, pp. 475-475, 2003.
  5. A. Kaltchenko and E.-H. Yang, “Invariance of stationary and ergodic properties of a quantum source under memoryless transformations,” Proc. of the 2003 IEEE Intern. Symp. Inform. Theory, Yokohama, Japan, June 29 – July 4, pp. 458-458, 2003.
  6. G. Wu, E.-H. Yang, and W. Sun, “Optimization strategies for quantization watermark-ing with applications to image authentication,” the Proc. of the 2003 IEEE Interna-tional Conference on Acoustics, Speech, and Signal Processing, Hong Kong, China, April 6–10, Vol. 5, pp. V 672-V 675, 2003.
  7. A. Kaltchenko and E.-H. Yang, “Universal compression of classically correlated sta-tionary quantum sources,” Proc. of SPIE Vol. 5105 Quantum Information and Com-putation, pp. 104–115, SPIE, Bellingham, WA, 2003.
  8. Y. Jia, E.-H. Yang, D.-K. He, and S. Chan, “Speeding up arithmetic coding using greedy re-normalization,“ Proc. DCC’2003(Snowbird, Utah), USA, March 25 - 27, pp. 432-432, 2003.
  9.  D.-K. He and E.-H. Yang, “The compression performance of grammar-based codes re-visited,” Proc. of the 2002 IEEE Intern. Symp. Inform. Theory, Lausanne, Switzer-land , June 30 - July 5, pp. 290.
  10.  E.-H. Yang, “On the joint optimization of model selection and coding,” Proc. of the 2002 IEEE Intern. Symp. Inform. Theory, Lausanne, Switzerland , June 30 - July 5, pp. 206.
  11.  E.-H. Yang and D.-K. He, “Yang-Kieffer algorithms and 1-D run-length encoding: Compression performance comparison,”Proc. of the 2001 Canadian Workshop on In-formation Theory, Vancouver, British Columbia, Canada, June 3-5, pp. 28–31.
  12. Y. Jia and E.-H. Yang, “Context-dependent multilevel pattern matching for lossless image compression,” Proc.ofthe2001CanadianWorkshoponInformationTheory, Vancouver, British Columbia, Canada, June 3-5, pp. 48–51.
  1.  E.-H. Yang and D.-K. He, “Efficient universal lossless data compression algorithms based on a greedy sequential context-dependent grammar transform,” Proc. of the 2001 IEEE Intern. Symp. Inform. Theory, Washington D.C., USA, June 24–29, pp. 78.
  2.  E.-H. Yang and Y. Jia, “YK data compression algorithms: Complexity, implemen-tation, and experimental results,” Proc. of the 2001 IEEE Intern. Symp. Inform. Theory, Washington D.C., USA, June 24–29, pp. 77.
  3.  E.-H. Yang, “Grammar-based coding and its applications,” Proc. of the 22th Annual Meeting of the Canadian Applied & Industrial Mathematics Society, Victoria, British Columbia, Canada, June 7-9, 2001, pp. 39 (invited paper).
  4. J. Kieffer and E.-H. Yang, “Structurally equivalent grammars in lossless data com-pression,” Proc.ofthe38thAllertonConferenceonCommunications,Control,and Computing, Oct.4–6, 2000 (invited paper).
  5.  E.-H. Yang and J. Guo, “Lossless image coding via one-dimensional grammar based codes,” Proc.ofthe16thIFIPWorldComputerCongress—2000InternationalCon-ference on Communication Technology(Beijing, China), August 21–25, pp. 966–972, 2000.
  6.  E.-H. Yang, A. Kaltchenko, and J. C. Kieffer, “Universal lossless data compression with side information by using a conditional MPM grammar transform,” Proc. of the 2000 IEEE Intern. Symp. Inform. Theory, Sorrento, Italy, June 25–30, pp. 298.
  7.  E.-H. Yang and Y. Jia, “Universal lossless coding of sources with large and unbounded alphabets,” Proc. of the 2000 IEEE Intern. Symp. Inform. Theory, Sorrento, Italy, June 25–30, pp. 16.
  8. J. C. Kieffer, R. Stites, and E.-H. Yang, “A universal lossless resolution scalable pro-gressive image code,” Proc.ofthe2000IEEEIntern.Symp.Inform.Theory, Sorrento, Italy, June 25–30, pp.295.
  9. J. C. Kieffer, P. Flajolet, and E.-H. Yang, “Data compression via binary decision diagrams,” Proc. of the 2000 IEEE Intern. Symp. Inform. Theory, Sorrento, Italy, June 25–30, pp.296.
  10. A. Banerji and E.-H. Yang, “Applications of YK algorithms to the Internet transmis-sion of web-data: Implementation issues and modifications,” Proc. DCC’2000(Snowbird, Utah), USA, March 28–30, pp. 546, 2000.
  11. J. C. Kieffer and E.-H. Yang, “Lossless data compression via guided approximate bisections,” Proc.of the 34th Annual Conference on Information Sciences and Systems, Princeton, USA, March 15–17, pp. TP6-1–TP6-6, 2000(invited paper).
  12. J. K. Lanctot, Ming Li, and E.-H. Yang, “Estimating DNA sequence entropy,” Proceed-ings of the Eleventh Annual ACM-SIAM Symposium on Discrete Algorithms(SODA’2000), San Francisco, California, USA, Jan. 9–11, 2000, pp. 409–418.
  1.  E.-H. Yang and J. C. Kieffer, “Universal source coding theory based on grammar trans-forms,” Proc.ofthe1999IEEEInformationTheoryandCommunicationsWorkshop, Kruger National Park, South Africa, June 20-25, pp. 75–77(invited paper).
  2.  E.-H. Yang and Y. Jia, “Efficient universal compression of integer sequences by using multilevel arithmetic coding,” Proc.ofthe1999CanadianWorkshoponInformation Theory, Kingston, Ontario, Canada, June 15-18, pp. 40–43.
  3. J. C. Kieffer and E.-H. Yang, “A simple technique for bounding the pointwise redun-dancy of the 1978 Lempel-Ziv algorithm,” Proc.DCC’99(Snowbird,Utah), pp. 434-441.
  4.  E.-H. Yang and Z. Zhang, “Abstract alphabet source coding theorem revisited: Re-dundancy analysis,” Proc. of the 1998 IEEE Intern. Symp. Inform. Theory, MIT, Boston, USA, August 16-21, pp. 69.
  5.  E.-H. Yang and Z. Zhang, “The redundancy of universal fixed rate source coding,” Proc. of the 1998 IEEE Intern. Symp. Inform. Theory, MIT, Boston, USA, August 16-21, pp. 172.
  6.  E.-H. Yang and Z. Zhang, “Variable rate trellis source encoding,” Proc. of the 1998 IEEE Intern. Symp. Inform. Theory, MIT, Boston, USA, August 16-21, pp. 369.
  7. J. Kieffer and E.-H. Yang, “Redundancy of MPM data compression system,” Proc. of the 1998 IEEE Intern. Symp. Inform. Theory, MIT, Boston, USA, August 16-21, pp. 136.
  8. J. Kieffer and E.-H. Yang, “Design of context-free grammars for lossless data compres-sion,” Proc.ofthe1998IEEEInformationTheoryWorkshop, Killarney, Ireland, June 22–26, pp. 84–85.
  9. J. Kieffer and E.-H. Yang, “Lossless data compression algorithms based on substi-tution tables,” Proc.ofthe1998CanadianConferenceonElectricalandComputer Engineering(Waterloo, Ontario), Vol. 2, pp. 629–632, May 24–28, 1998.
  10.  E.-H. Yang and Z. Zhang, “On the analysis and design of variable rate trellis source codes,” Proc. of the 1998 Canadian Conference on Electrical and Computer Engineer-ing(Waterloo, Ontario), Vol. 2, pp. 541–544, May 24–28, 1998.
  11. J. Kieffer, E.-H. Yang, I. Park, and S. Yakowitz, “Complexity of preprocessor in MPM data compression systems,” Proc. DCC’98(Snowbird, Utah), pp. 554.
  12.  E.-H. Yang and Z. Zhang, “Abstract alphabet source coding theorem revisited,” 1997 Symposium on Information Theory at the International Conference on Statistical In-ference, Combinatorics, and Related Areas, Dec. 18–21, 1997, Varanasi, India, pp. 75 (invited paper).
  13. Bin Zhu, En-hui Yang, and Ahmed. H. Tewfik , “Dual set arithmetic coding and its applications to image coding,” EUSIPCO-96, VIII European Signal Proc. Conf., Trieste, Italy, Vol. 1, pp. 324-327, Sept. 1996.
  1.  E.-H. Yang and Z. Zhang, “The Gold-Washing data compression algorithm(III): Re-dundancy analysis,” Proc. of the 1996 IEEE Workshop on Inform. Theory, Haifa, Israel(invited paper).
  2. B. Zhu, E.-H. Yang, A. H. Tewfik, and J. C. Kieffer, “Efficient coding of wavelet trees and its applications to image coding,” Proc. 1996 SPIE Conf. on Visual Communica-tions and Image Processing(Orlando, FL, USA), Vol. 2727, pp. 512–523.
  3. R. Ahlswede, E.-H. Yang, and Z. Zhang, “Identification via compressed data(II),” Proc. of the 1995 IEEE Intern. Symp. Inform. Theory, Whistler, B.C., Canada, pp. 69(long talk).
  4. Z. Zhang, E.-H. Yang, and V. K. Wei, “On the redundancy of lossy source coding,” Proc. of the 1995 IEEE Workshop on Inform. Theory, Rydzyna, Poland, pp. 31-32 (invited paper).
  5.  E.-H. Yang and J. C. Kieffer, “String matching data compression algorithms: Perfor-mance analysis,” Proc.ofthe33thAllertonConferenceonCommunications,Control, and Computing, pp. 881–890, Oct., 1995 (invited paper).
  6. Q. Liu, E.-H. Yang, and Z. Zhang, “A fixed slope universal sequential algorithm for lossy source coding based on the Gold-washing mechanism,” Proc. of the 33th Allerton Conference on Communications, Control, and Computing, pp. 466–474, Oct., 1995.
  7. W.S. Chen, E.-H. Yang, and Z. Zhang, “A variant of address vector quantization for image compression using lossless conditional entropy coding,” Proc. ICASSP’95 (Detroit, USA), May, pp. 2483–2486.
  8. W.S. Chen, Z. Zhang, and E.-H. Yang, “A hybrid adaptive vector quantizer for image compression via the Gold-washing mechanism,” Proc. 1995 SPIE Visual Communica-tion and Image Processing(Taipei, Taiwan), Vol. 2501, pp. 635–646.
  9. W.S. Chen, Z. Zhang, E.-H. Yang, and S. S. Bor, “A modified stack search algorithm for VQ-based tree encoding,” Proceedingsofthe1995IEEEGlobalTelecommunications conference(Singapore), Vol. 1, pp. 462–467.
  10. Z. Zhang, E.-H. Yang, and V. K. Wei, “On the redundancy of lossy source coding(II),” Proc. of the 1995 IEEE Intern. Symp. Inform. Theory, Whistler, B.C., Canada, pp. 190(long talk).
  11.  E.-H. Yang, Z. Zhang, and T. Berger, “Fixed-slope universal algorithms for lossy source coding via lossless codeword length functions,” Proc. of the 1995 IEEE Intern. Symp. Inform. Theory, Whistler, B.C., Canada, pp. 79.
  12. Z. Zhang and E.-H. Yang, “The gold-washing algorithm(II): Optimality for ϕ-mixing sources,” Proc. of the 1995 IEEE Intern. Symp. Inform. Theory, Whistler, B.C., Canada, pp. 81.
  1. R. Ahlswede, E.-H. Yang, and Z. Zhang, “Identification via compressed data,” Proc. of the 1994 IEEE-IMS Workshop on Inform. Theory and Statistics, Virginia, USA, pp. 31 (invited paper).
  2. J. C. Kieffer and E.-H. Yang, “On the OPTA function for lossless compression of individual sequences via recursive sequential codes,” Proc. of the 1994 IEEE Intern. Symp. Inform. Theory, Trondheim, Norway, pp.7.
  3.  En-hui Yang, “Kieffer’s Sample Converses for source coding,” Proc. of the 1993 IEEE Int. Symposium on Inform. Theory, San Antonio, TX, USA, pp. 337.
  4.  En-hui Yang, “Generalized limit theorems for program-size complexity,” (in Chinese) Proc. of the First Young Scientists Conf. sponsored by Chinese Association of Science and Technology, pp. 124–128, Beijing, China, 1992.
  5.  En-hui Yang, “Kolmogorov complexity and universal coding,” (in Chinese) Proc. of China Conf. on Inform. Theory and Commun. Theory, Oct. 1989.

Technical Reports

Selected Technical Reports

  1. E.-H. Yang and W. Szpankowski, "Report of the National Science Foundation Workshop on Information Theory and Computer Science Interface," prepared for U.S. National Science Foundation, October 2004.
  2. E.-H. Yang, "A tutorial report on efficient universal lossless data compression algorithms based on a greedy sequential grammar transform," prepared for Hughes Network Systems under Contract 2814301, January 1999.
  3. E.-H. Yang, "Efficient universal lossless data compression algorithms: Implementation and experimental results," prepared for Hughes Network Systems under Contract 2814301, April 1999.
  4. E.-H. Yang, "Flow diagrams for the greedy sequential grammar transform and its corresponding data compression algorithms," Dept. of ECE, University of Waterloo, 1999.
  5. E.-H. Yang and Z. Zhang, "The redundancy of source coding with a fidelity criterion — Part III: Coding at a fixed distortion level with unknown statistics," Dept. of ECE, University of Waterloo, 1999.

A full list of over 30 technical reports is available upon request. Several reports were prepared for NSERC, Canada Research Chairs Program, CITO, and industry partners including Hughes Network Systems, Leitch Technology, and Research In Motion.

Patents

The lab holds over 250 patents and patent applications worldwide. The proposed methods, algorithms, and systems have been transferred into data processing, communications, and data security products benefiting people in over 170 countries. A selection of key patents is listed below.

Data Compression & Image/Video Coding

  • P1 E.-H. Yang, "Method for lossless data compression using greedy sequential grammar transform and sequential coding," U.S. Patent No. 6,762,699, July 13, 2004.
  • P2 E.-H. Yang and D.-K. He, "Method for lossless data compression using greedy sequential context-dependent grammar transform," Patent No. 6,801,141, October 5, 2004.
  • P4–P7 E.-H. Yang and J. Zeng, "Method, system, and software product for color image encoding," Patents Nos. 7,525,552 (2009); 8,022,963 (2011); 8,106,919 (2012); 8,253,757 (2012).
  • P8–P12 E.-H. Yang and L. Wang, "Method, system, and computer program product for optimization of data compression," Patents Nos. 7,570,827 (2009); 7,742,643 (2010); 7,978,923 (2011); 8,194,990 (2012).
  • P17–P18 E.-H. Yang and X. Yu, "Soft decision and iterative video coding for MPEG and H.264," Patent Nos. 8,005,140 (2011); 8,331,441 (2012).

Data Security & Cryptography

  • P50 E.-H. Yang, "Methods and computer program products for encryption key generation and management," Patent No. 9,703,979, July 11, 2017.
  • P53 E.-H. Yang, J. Meng, X. Yu, H. Zhang, and T. Szuchewycz, "Personalized and cryptographically secure access control in trusted execution environment," Patent No. 11,093,604, Aug. 17, 2021.
  • P55 Y. Xiang, J. Meng, and E.-H. Yang, "Personalized and cryptographically secure access control in operating systems," Patent No. 11,126,754, Sept. 21, 2021.
  • P59 E.-H. Yang, "Methods and devices for optimal information-theoretically secure encryption key management," Patent Application No. 16/880,010, Aug. 18, 2022.
  • P60 E.-H. Yang, X. Yu, and J. Meng, "Methods, systems and computer program products for data protection by policing processes accessing encrypted data," Patent Application No. 15/728,914, Aug. 31, 2022.

A selection of key patents is listed above. Full list available upon request.