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Haris Vikalo – Chronological list of publications

Journal Papers

  1. Y. Chen, A. Hashemi and H. Vikalo, "Accelerated distributed stochastic non-convex optimization over time-varying directed networks,” IEEE Transaction on Automatic Control, April 2025 (to appear).

  2. A. G. Aydin and H. Vikalo, "Viewport prediction via adaptive edge offloading," IEEE Networking Letters, 2024 (to appear).

  3. M. Ribero, H. Vikalo and G. de Veciana, "Federated learning at scale: Addressing client intermittency and resource constraints,” IEEE Journal of Selected Topics in Signal Processing, July 2024, pp: 1-14.

  4. M. Ribero and H. Vikalo, "Reducing communication in federated learning via efficient client sampling,” Pattern Recognition, vol. 148, 2024, pp. 110122.

  5. S. Consul, Z. Ke and H. Vikalo, "XHap: Haplotype assembly using long-distance read correlations learned by transformers," Bioinformatics Advances, vol. 3, no. 1, 2023, pp. 1-11.

  6. M. Sakthi, M. Arvinte and H. Vikalo, "Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information," IEEE Open Journal of Intelligent Transportation Systems, vol. 4, pp. 858-869, 2023, doi: 10.1109/OJITS.2023.3332043.

  7. Z. Ke and H. Vikalo, "Graph-based reconstruction and analysis of disease transmission networks using viral genomic data, Journal of Computational Biology, 30(7):796-813, Jun 2023.

  8. M. Ribeiro, H. Vikalo and G. de Veciana, "Federated learning under intermittent client availability and time-varying communication constraints," IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 1, January 2023, pp. 98-111.

  9. Y. Chen, A. Hashemi and H. Vikalo, "Communication-efficient variance-reduced decentralized stochastic optimization over time-varying directed graphs," IEEE Transactions on Automatic Control, vol. 67, no. 12, December 2022, pp. 6583-6594.

  10. A. Hashemi, A. Acharya, R. Das, H. Vikalo, S. Sanghavi, and I. Dhillon, "On the benefits of multiple gossip steps in communication-constrained decentralized federated learning," IEEE Transactions on Parallel and Distributed Systems, Special Section on Parallel and Distributed Computing Techniques for AI, ML, and DL, vol. 33, no. 11, November 2022, pp. 2727-2739.

  11. M. Ribero, J. Henderson, S. Williamson, and H. Vikalo, "Federated recommendations using differentially private prototypes," Pattern Recognition, volume 129, September 2022, pp. 108746.

  12. A. Hashemi, H. Vikalo, and G. de Veciana, "On the benefits of progressively increasing sampling sizes in stochastic greedy weak submodular maximization," IEEE Transactions on Signal Processing, vol. 70, no. 6, July 2022, pp. 3978-3992.

  13. A. Hashemi, R. Shafipour, H. Vikalo, and G. Mateos, "Towards accelerated greedy sampling and reconstruction of bandlimited graph signals," Signal Processing, vol. 195, June 2022.

  14. Z. Ke and H. Vikalo, "Real-time radio technology and modulation classification via an LSTM auto-encoder," IEEE Transactions on Wireless Communications, vol. 21, no. 1, January 2022, pp. 370-382.

  15. N. M. Arzeno and H. Vikalo, "Evolutionary clustering via message passing," IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 6, June 2021, pp. 2452-2466.

  16. A. Hashemi, M. Ghasemi, H. Vikalo and U. Topcu, "Randomized greedy sensor selection: Leveraging weak submodularity," IEEE Transactions on Automatic Control, vol. 66, no. 1, January 2021, pp. 199-212.

  17. A. Sankararaman, H. Vikalo, and F. Baccelli, "ComHapDet: A spatial community detection algorithm for haplotype assembly," BMC Genomics, vol. 21 (Suppl 9), September 2020, pp. 586:1-14.

  18. S. Barik and H. Vikalo, "Matrix completion and performance guarantees for single individual haplotyping," IEEE Transactions on Signal Processing, vol. 67, no. 18, September 2019, pp: 4782-4794.

  19. A. Hashemi and H. Vikalo, "Evolutionary self-expressive models for subspace clustering,” IEEE Journal of Selected Topics in Signal Processing, Special Issue on Data Science: Robust Subspace Learning and Tracking, vol. 12, no. 6, December 2018, pp. 1534-1546.

  20. S. Barik, S. Das, and H. Vikalo, "Viral quasispecies reconstruction via correlation clustering," Genomics, vol. 110, no. 6, November 2018, pp. 375-381.

  21. A. Hashemi and H. Vikalo, "Accelerated orthogonal least-squares for large-scale sparse reconstruction,” Digital Signal Processing, vol. 82, no. 11, November 2018, pp. 91-105.

  22. S. Ahn, Z. Ke and H. Vikalo, "Viral quasispecies reconstruction via tensor factorization with successive read removal," Bioinformatics, vol. 34, no. 13, July 2018, pp. i23–i31.

  23. A. Hassibi, A. Manickam, R. Singh, S. Bolouki, R. Sinha, K. Jirage, M. McDermott, B. Hassibi, H. Vikalo, G. Mazarei, L. Pei, L. Bousse, M. Miller, M. Heshami, M. Savage, M. Taylor, N. Gamini, N. Wood, P. Mantina, P. Grogan, P. Kuimelis, P. Savalia, S. Conradson, Y. Li, R. Meyer, E. Ku, J. Ebert, B. Pinsky, G. Dolganov, T. Van, K. Johnson, P. Naraghi-Arani, R. Kuimelis, G. Schoolnik, "Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip," Nature Biotechnology, 36, July 2018, pp. 738–745.

  24. S. Ahn and H. Vikalo, "aBayesQR: A Bayesian method for reconstruction of viral populations characterized by low diversity," Journal of Computational Biology, vol. 25, no. 7, July 2018, pp: 637-648.

  25. H. Yang, J. Chun, and H. Vikalo, "Cyclic block coordinate minimization algorithms for DOA estimation in co-prime arrays," Signal Processing, vol. 145, no. 4, April 2018, pp. 272-284.

  26. A. Hashemi, B. Zhu and H. Vikalo, "Sparse tensor decomposition for haplotype assembly of diploids and polyploids," BMC Genomics, 19(Suppl 4):191, March 2018.

  27. H. Si, H. Vikalo, and S. Vishwanath, "Information-theoretic analysis of haplotype assembly," IEEE Transactions on Information Theory, vol. 63, no. 7, July 2017, pp. 3468-3479.

  28. S. Das and H. Vikalo, "Optimal haplotype assembly via a branch-and-bound algorithm," IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, vol. 3, no. 1, March 2017, pp. 1-12.

  29. E. O'Reilly, F. Baccelli, G. de Veciana, and H. Vikalo, "End-to-end optimization of high-throughput DNA sequencing" Journal of Computational Biology, 23(10): 789-800, October 2016.

  30. C. Cai, S. Sanghavi, and H. Vikalo, "Structured low-rank matrix factorization for haplotype assembly," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Structured Matrices in Signal and Data Processing, vol. 10, no. 4, August 2016, pp. 647-657.

  31. Z. Puljiz and H. Vikalo, "Decoding genetic variations: Communications-inspired haplotype assembly," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 3, June 2016, pp. 518-530.

  32. N. M. Arzeno, K. A. Lawson, S. V. Duzinski, and H. Vikalo, "Designing optimal mortality risk prediction scores that preserve clinical knowledge," Journal of Biomedical Informatics, vol. 56, August 2015, pp. 145-156.

  33. S. Ahn and H. Vikalo, "Joint haplotype assembly and genotype calling via sequential Monte Carlo algorithm," BMC Bioinformatics, 16:223, July 2015, doi:10.1186/s12859-015-0651-8.

  34. N. M. Arzeno and H. Vikalo, "Semi-supervised affinity propagation with soft instance-level constraints," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, May 2015, pp. 1041-1052.

  35. S. Das and H. Vikalo, "SDhaP: haplotype assembly for diploids and polyploids via semi-definite programming," BMC Genomics, 16:260, April 2015, doi:10.1186/s12864-015-1408-5.

  36. X. Shen, M. Shamaiah, and H. Vikalo, "Iterative learning of a DNA consensus sequence from high-throughput short reads: Algorithms and limits of performance," IEEE Transactions on Signal Processing, vol. 62, no. 17, September 2014, pp. 4425-4435.

  37. S. Barik and H. Vikalo, "Sparsity-aware sphere decoding: Algorithms and complexity analysis," IEEE Transactions on Signal Processing, vol. 62, no. 9, May 2014, pp. 2212-2225.

  38. M. Park, M. Nassar, and H. Vikalo, "Bayesian active learning for drug combinations," IEEE Transactions on Biomedical Engineering, 60(11):3248-55, November 2013 (doi: 10.1109/TBME.2013.2272322).

  39. S. Das and H. Vikalo, "Base calling for high-throughput short-read sequencing: Dynamic programming solutions," BMC Bioinformatics, 14:129, April 2013 (doi:10.1186/1471-2105-14-129).

  40. S.-H. Lee, M. Shamaiah, H. Vikalo, and S. Vishwanath, "Message-passing algorithms for coordinated spectrum sensing in cognitive radio networks," IEEE Communication Letters, vol. 17, no. 4, April 2013, pp. 812-815.

  41. M. Shamaiah, S.-H. Lee, S. Vishwanath, and H. Vikalo, "Distributed algorithms for spectrum access in cognitive radio relay networks," IEEE Journal on Sel. Areas in Communications - Cognitive Radio Series, vol. 30, no. 10, November 2012, pp. 1947-1957.

  42. T. Wu and H. Vikalo, "Joint parameter estimation and base-calling for pyrosequencing systems," IEEE Transactions on Signal Processing, vol. 60, no. 8, August 2012, pp. 4376-4386.

  43. M. Shamaiah, S. Banerjee, and H. Vikalo, "Greedy sensor selection under channel uncertainty," IEEE Wireless Communications Letters, vol. 1, no. 4, August 2012, pp. 376-379.

  44. S. Das and H. Vikalo, "OnlineCall: Fast online parameter estimation and base calling for Illumina's next generation sequencing," Bioinformatics, vol. 28, no. 13, July 2012, pp. 1677-1683.

  45. X. Shen and H. Vikalo, "ParticleCall: A particle filter for base calling in next-generation sequencing systems," BMC Bioinformatics, vol. 13, no. 160, July 2012.

  46. M. Shamaiah, X. Shen, and H. Vikalo, "Estimating parameters of sampled diffusion processes in affinity biosensors," IEEE Transactions on Signal Processing, vol. 60, no. 6, June 2012, pp. 3228-3239.

  47. M. Shamaiah, S.-H. Lee, and H. Vikalo, "Graphical models and inference on graphs in genomics," IEEE Signal Processing Magazine, Special Issue on Genomic and Proteomic Signal Processing in Biomolecular Pathways, vol. 29, no. 1, January 2012, pp. 51-65.

  48. M. Shamaiah and H. Vikalo, "Estimating time-varying sparse signals under communication constraints," IEEE Transactions on Signal Processing, vol. 59, no. 6, June 2011, pp. 2961 - 2964.

  49. X. Shen and H. Vikalo, "Inferring parameters of gene regulatory networks via particle filtering," EURASIP Journal on Advances in Signal Processing, Special Issue on Genomic Signal Processing, 2010, doi:10.1155/2010/204612.

  50. H. Vikalo and M. Gokdemir, "An MCMC algorithm for estimation in real-time biosensor arrays," EURASIP Journal on Advances in Signal Processing, Special Issue on Genomic Signal Processing, 2010, doi:10.1155/2010/736301.

  51. H. Vikalo, B. Hassibi, and A. Hassibi, "Limits of performance of quantitative polymerase chain reaction systems," IEEE Transactions on Information Theory, Special Issue on Molecular Biology and Neuroscience, vol. 56, no. 2, February 2010, pp. 1-8.

  52. A. Hassibi, H. Vikalo, J. L. Riechmann, and B. Hassibi, "Real-time DNA microarray analysis," Nucleic Acids Research, vol. 37, no. 20, 2009, e132:1-12.

  53. M. El-Khamy, H. Vikalo, B. Hassibi, and R. J. McEliece, "Performance of sphere decoding of block codes," IEEE Transactions on Communications, vol. 57, no. 10, October 2009, pp. 2940-2950.

  54. S. Das, H. Vikalo, and A. Hassibi, "On scaling laws of biosensors: a stochastic approach," Journal of Applied Physics, vol. 105, no. 10, May 2009, pp. 102021-102021-7.

  55. H. Vikalo, B. Hassibi, and A. Hassibi, "Modeling and estimation for real-time microarrays," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Genomic and Proteomic Signal Processing, vol. 2, no. 3, June 2008, pp. 286-296.

  56. F. Parvaresh, H. Vikalo, S. Misra, and B. Hassibi, "Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Genomic and Proteomic Signal Processing, vol. 2, no. 3, June 2008, pp. 275-285.

  57. M. Stojnic, H. Vikalo, and B. Hassibi, "Speeding up the sphere decoder with H-infinity and SDP inspired lower bounds," IEEE Transactions on Signal Processing, vol. 56, no. 2, February 2008, pp. 712-726.

  58. A. Hassibi, H. Vikalo, and A. Hajimiri, "On noise processes and limits of performance in biosensors,"Journal of Applied Physics, vol. 102, no. 1, July 2007, pp. 014909-014909-12.

  59. H. Vikalo, B. Hassibi, and A. Hassibi, "A statistical model for microarrays, optimal estimation algorithms, and limits of performance," IEEE Transactions on Signal Processing, Special Issue on Genomics Signal Processing, vol. 54, no. 6, June 2006, pp. 2444-2455.

  60. H. Vikalo and B. Hassibi, "On joint detection and decoding of linear block codes on Gaussian vector channels," IEEE Transactions on Signal Processing, vol. 54, no. 9, September 2006, pp. 3330-3342.

  61. M. Stojnic, H. Vikalo, and B. Hassibi, "Maximizing the sum-rate of multi-antenna broadcast channels using linear preprocessing," IEEE Transactions on Wireless Communications, vol. 5, no. 9, September 2006, pp. 2338-2342.

  62. H. Vikalo, B. Hassibi, and P. Stoica, "Joint ML channel estimation and signal detection," IEEE Transactions on Wireless Communications, vol. 5, no. 7, July 2006, pp. 1838-1845.

  63. H. Vikalo, B. Hassibi, and U. Mitra, "Sphere-constrained ML detection for frequency-selective channels," IEEE Transactions on Communications, vol. 54, no. 7, July 2006, pp. 1179 - 1183.

  64. H. Vikalo and B. Hassibi, "On sphere decoding algorithm. II. Generalizations, second-order statistics, and applications to communications," IEEE Transactions on Signal Processing, vol. 53, no. 8, August 2005, pp. 2819 - 2834.

  65. B. Hassibi and H. Vikalo, "On sphere decoding algorithm. I. Expected complexity," IEEE Transactions on Signal Processing, vol. 53, no. 8, August 2005, pp. 2806 - 2818.

  66. H. Vikalo, B. Hassibi, A. Erdogan, and T. Kailath, "On H-infinity design techniques for robust signal reconstruction in noisy filter banks," EURASIP Signal Processing, vol. 85, no. 1, January 2005, pp. 1-14.

  67. H. Vikalo, B. Hassibi, and T. Kailath, "Iterative decoding for MIMO channels via modified sphere decoder," IEEE Transactions on Wireless Communications, vol.3, no. 6, November 2004, pp. 2299-2311.

  68. H. Vikalo, B. Hassibi, B. Hochwald, and T. Kailath, "On the capacity of frequency-selective channels in training-based transmission schemes," IEEE Transactions on Signal Processing, vol. 52, no. 9, September 2004, pp. 2572-2583.

  69. H. Vikalo and B. Hassibi, "On maximum-likelihood sequence detection for multiple antenna systems over dispersive channels," EURASIP Journal on Applied Signal Processing, Special Issue on Space-Time Coding, May 2002, pp. 525-531.

Conference Papers

  1. H. Chen and H. Vikalo, "Recovering labels from local updates in federated learning," Proceedings of the Forty-first International Conference on Machine Learning (ICML), Vienna, Austria, July 22-26, 2024.

  2. H. Chen and H. Vikalo, "Mixed-precision quantization for federated learning on resource-constrained heterogeneous devices," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, June 17-21, 2024.

  3. Y. Chen, C. Wang and H. Vikalo, "Fed-QSSL: A framework for personalized federated learning under bitwidth and data heterogeneity," 38th AAAI Conference on Artificial Intelligence (AAAI ’24), Vancouver, BC, Canada, February 20-27, 2024.

  4. H. B. Beytur, A. Aydin, G. de Veciana and H. Vikalo, "Optimization of offloading policies for accuracy-delay tradeoffs in hierarchical inference," IEEE International Conference on Computer Communications (INFOCOM ’24), May 20-23, Vancouver, BC, Canada.

  5. S. Consul, J. Robertson and H. Vikalo, "XVir: A transformer-based architecture for identifying viral reads from cancer samples," The Eighth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC), Houston, TX, September 2023.

  6. H. Chen and H. Vikalo, “Federated learning in non-IID settings aided by differentially private synthetic data," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Vancouver, Canada, June 18-22, 2023, pp. 5026-5035.

  7. H. Chen, C. Wang and H. Vikalo, "The best of both worlds: Accurate global and personalized models through federated learning with data-free hyper-knowledge distillation," International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 1-5, 2023.

  8. Y. Chen, A. Hashemi and H. Vikalo, "Accelerated distributed stochastic non-convex optimization over time-varying directed networks," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes Island, Greece, June 4-10, 2023, pp. 1-5.

  9. S. Consul, H. B. Beytur, G. de Veciana, H. Vikalo, Z. Shamsi, A. Liu and J. Boksiner, "RF-based Network Inference: Learning from Channel Usage and Activity Dynamics Data," IEEE MILCOM, November 2022.

  10. M. Sakthi, A. Tewfik, M. Arvinte, and H. Vikalo, "End-to-end system for object detection from sub-sampled radar data," 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, Aug. 29 - Sept. 2, 2022, pp. 1-5.

  11. Z. Ke and H. Vikalo, "Deep learning for assembly of haplotypes and viral quasispecies from short and long sequencing reads,” The 13th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), Chicago, IL, August 7-10, 2022, pp. 1-8.

  12. Z. Ke and H. Vikalo, "Graph-based reconstruction and analysis of disease transmission networks using viral genomic data," The Seventh International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2022), Chicago, IL, August 7, 2022, pp. 1-8.

  13. S. Bibikar, H. Vikalo, Z. Wang, and Xiaohan Chen, "Federated dynamic sparse training: Computing less, communicating less, yet learning better," The 36th AAAI Conference on Artificial Intelligence (AAAI-22), 36(6), Vancouver, BC, Canada, February 21-28, 2022, pp. 6080-6088.

  14. S. Consul, H. Beytur, G. de Veciana, and H. Vikalo, "RF-based network inference: Theoretical foundations," Proceedings IEEE MILCOM, December 2021, pp: 1-6.

  15. M. Ghasemi, A. Hashemi, H. Vikalo, and U. Topcu, "No-regret learning with high-probability in adversarial Markov decision processes," 37th Conference on Uncertainty in Artificial Intelligence (UAI), July 27th - July 29th, 2021.

  16. M. Stecklein, H. Beytur, H. Vikalo and G. de Veciana, "Optimizing resource constrained distributed collaborative sensing,” WS-16: Workshop on Spectrum Sharing Technology for Next Generation Communications, IEEE International Conference on Communications, Montreal, Canada, June 14-23, 2021.

  17. Y. Chen, A. Hashemi, and H. Vikalo, "Decentralized optimization on time-varying directed graphs under communication constraints," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, June 6-11, 2021.

  18. A. Hashemi, H. Vikalo, and G. de Veciana, "On the performance-complexity tradeoff in stochastic greedy weak submodular optimization," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, June 6-11, 2021.

  19. Z. Ke and H. Vikalo, "Real-time radio modulation classification with an LSTM auto-encoder" IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, June 6-11, 2021.

  20. M. Ghasemi, A. Hashemi, U. Topcu and H. Vikalo, "Online learning with implicit exploration in episodic Markov decision processes," The 2021 American Control Conference (ACC), New Orleans, LA, May 26-28, 2021.

  21. S. Lee, X. Zheng, J. Hua, H. Vikalo, and C. Julien, “Opportunistic federated learning: An exploration of egocentric collaboration for pervasive computing applications,” IEEE International Conference on Pervasive Computing and Communications (PerCom), Kassel, Germany, March 22-26, 2021.

  22. Z. Ke and H. Vikalo, "A convolutional auto-encoder for haplotype assembly and viral quasispecies reconstruction," Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), December 6-12, 2020.

  23. M. Ghasemi, A. Hashemi, H. Vikalo, and U. Topcu, "Identifying low-dimensional structures in Markov Chains: A nonnegative matrix factorization approach," The 2020 American Control Conference (ACC), Denver, CO, July 1-3, 2020.

  24. M. Usman, W. Wang, M. Vasic, K. Wang, H. Vikalo, and S. Khurshid, "A study of the learnability of relational properties," The 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020), London, UK, June 15-20, 2020.

  25. Z. Ke and H. Vikalo, "A graph auto-encoder for haplotype assembly and viral quasispecies reconstruction," The 34th AAAI Conference on Artificial Intelligence (AAAI-20), New York, NY, February 7-12, 2020.

  26. S. Consul and H. Vikalo, "Reconstructing intra-tumor heterogeneity via convex optimization and branch-and-bound search," The 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), Niagara Falls, NY, September 7-10, 2019.

  27. A. Sankararaman, H. Vikalo, and F. Baccelli, "ComHapDet: A spatial community detection algorithm for haplotype assembly," The 6th International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC), Niagara Falls, NY, September 7-10, 2019.

  28. S. Mourad, A. Tewfik and H. Vikalo, "Weighted subset selection for fast SVM training," The 27th European Signal Processing Conference (EUSIPCO), Coruña, Spain, September 2-6, 2019.

  29. M. Ghasemi, A. Hashemi, U. Topcu and H. Vikalo, "On submodularity of quadratic observation selection in constrained networked sensing systems," The 2019 American Control Conference (ACC), Philadelphia, PA, July 10-12, 2019.

  30. A. Hashemi, M. Ghasemi, H. Vikalo and U. Topcu, "Submodular observation selection and information gathering for quadratic models," 2019 International Conference on Machine Learning (ICML), Long Beach, CA, June 10-15, 2019.

  31. S. Mourad, H. Vikalo and A. Tewfik, "Online selective training for faster neural network learning," IEEE Data Science Workshop (DSW), Minneapolis, MN, June 2-5, 2019.

  32. R. Shafipour, A. Hashemi, G. Mateos and H. Vikalo, "Online topology inference from streaming stationary graph signals," IEEE Data Science Workshop (DSW), Minneapolis, MN, June 2-5, 2019.

  33. S. Consul, A. Hashemi and H. Vikalo, "A MAP framework for support recovery of sparse signals using orthogonal least squares," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019.

  34. A. Hashemi and H. Vikalo, "Evolutionary subspace clustering: Discovering structure in self-expressive time-series data," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019.

  35. M. Ribero, D. Chizhik, R. A. Valenzuela, R. W. Heath Jr. and H. Vikalo, "Deep learning propagation models over irregular terrain," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019.

  36. A. Hashemi, O. F. Kilic, and H. Vikalo, “Near-optimal distributed estimation for a network of sensing units operating under communication constraints,” 57th IEEE Conference on Decision and Control (CDC), Miami Beach, FL, December 17-19, 2018.

  37. A. Hashemi, R. Shafipour, H. Vikalo and G. Mateos, "A novel scheme for support identification and iterative sampling of bandlimited graph signals," 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, November 26-29, 2018.

  38. S. Ahn, Z. Ke and H. Vikalo, "Viral quasispecies reconstruction via tensor factorization with successive removal," 26th Conference on Intelligent Systems for Molecular Biology (ISMB), Chicago, IL, July 6-10, 2018.

  39. A. Hashemi, M. Ghasemi, H. Vikalo and U. Topcu, "A randomized greedy algorithm for near-optimal sensor scheduling in large-scale sensor networks," The 2018 American Control Conference (ACC), Milwaukee, WI, June 27-29, 2018. (Best student paper award finalist).

  40. A. Hashemi, R. Shafipour, H. Vikalo and G. Mateos, "Sampling and reconstruction of graph signal via weak submodularity and semidefinite relaxation," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Alberta, Canada, April 15-20, 2018.

  41. S. Ahn, Z. Ke and H. Vikalo, "Viral quasispecies reconstruction via tensor factorization," 55th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, October 3-6, 2017.

  42. S. Mourad, A. Tewfik and H. Vikalo, "Data subset selection for efficient SVM training," The 25th European Signal Processing Conference (EUSIPCO), Kos island, Greece, August 28 - September 2, 2017.

  43. A. Hashemi, B. Zhu and H. Vikalo, "Sparse tensor decomposition for haplotype assembly of diploids and polyploids," The 4th International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC), Boston, MA, August 20-23, 2017.

  44. S. Ahn and H. Vikalo, "aBayesQR: A Bayesian method for reconstruction of viral populations characterized by low diversity," The 21st Annual International Conference on Research in Computational Molecular Biology (RECOMB), Hong Kong, May 3-7, 2017.

  45. X. Zheng, H. Vikalo, S. Song, L. K. John and A. Gerstlauer, "Sampling-based binary-level cross-platform performance estimation," Design, Estimation and Test in Europe (DATE), Lausanne, Switzerland, March 27-31, 2017.

  46. A. Hashemi and H. Vikalo, "Recovery of sparse signals via branch-and-bound least squares," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, March 5-9, 2017.

  47. N. M. Arzeno and H. Vikalo, "Evolutionary affinity propagation," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, March 5-9, 2017.

  48. S. Barik and H. Vikalo, "Binary matrix completion with performance guarantees for single individual haplotyping," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, March 5-9, 2017.

  49. A. Hashemi and H. Vikalo, "Sparse linear regression via generalized orthogonal least-squares," 2016 IEEE GlobalSIP Symposium on Signal Processing of Big Data, Washington DC, December 2016.

  50. H. Yang, H. Vikalo, and J. Chun, "Nonnegative gridless compressive sensing for co-prime arrays," 2016 IEEE GlobalSIP Symposium on Sparse Signal Processing for Communications, Washington DC, December 2016.

  51. V. Va, H. Vikalo, and R. Heath, "Beam tracking for mobile millimeter wave communication systems," 2016 IEEE GlobalSIP Symposium on Transceivers and Signal Processing for 5G Wireless and mm-Wave Systems, Washington DC, December 2016.

  52. C. Cai, S. Sanghavi, and H. Vikalo, "Structurally-constrained gradient descent for matrix factorization in haplotype assembly problems," IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), Shanghai, March 2016.

  53. T. Goodall, A. C. Bovik, H. Vikalo, and N. G. Paulter Jr., "Non-uniformity correction of IR images using natural scene statistics," IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, December 2015.

  54. A. Gupta, S. Barik, and H Vikalo, "Distributed self localization of sensors with Poisson deployment using extended Kalman filter," IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, March 2015, pp. 1500-1505.

  55. S. Das and H. Vikalo, "Optimal haplotype assembly with statistical pruning," IEEE GlobalSIP14 - Workshop on Genomic Signal Proc. and Statistics, Atlanta, GA, Dec. 2014.

  56. S. Das and H. Vikalo, "Single individual haplotyping with low rank semidefinite programming," NIPS 2014 Workshop on Machine Learning in Computational Biology (MLCB), Montreal, Canada, December 2014.

  57. H. Si, H. Vikalo and S. Vishwanath, "Haplotype assembly: An information-theoretic view," IEEE Information Theory Workshop, Tasmania, Australia, November 2-5, 2014.

  58. Z. Puljiz and H. Vikalo, "Iterative learning of single individual haplotypes from high-throughput DNA sequencing data," 8th International Symposium on Turbo Codes and Iterative Information Processing (ISTC), Bremen, Germany, August 2014.

  59. S. Ahn and H. Vikalo, "Deterministic sequential Monte Carlo for haplotype inference," IEEE Global Conference on Signal and Information Processing, Austin, TX, December 2-4, 2013.

  60. Z. Puljiz and H. Vikalo, "Message-passing algorithms for haplotype assembly," Asilomar Conference on Systems, Signals & Computers, Asilomar, November 3-6, 2013 (invited).

  61. N. Arzeno-Gonzales and H. Vikalo, "Exploiting time series properties for mortality prediction in pediatric brain injury," Workshop on Role of Machine Learning in Transforming Healthcare, International Conference on Machine Learning, Atlanta, June 16-21, 2013.

  62. X. Shen, M. Shamaiah, and H. Vikalo, "Message passing algorithm for inferring consensus sequence from next-generation sequencing data," IEEE International Symposium on Information Theory, Istanbul, Turkey, July 7-12, 2013.

  63. S. Barik and H. Vikalo, "Expected complexity of sphere decoding for sparse integer least-square problems," IEEE Intern'l Conf. on Acoustic, Signal, and Speech Processing, Vancouver, Canada, May 26-31, 2013.

  64. X. Shen and H. Vikalo, "A message-passing algorithm for reference-guided sequence assembly from high-throughput sequencing data," 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Washington, D.C., TX, December 2012.

  65. C. H. Lee, N. M. Arzeno, J. C. Ho, H. Vikalo, and J. Ghosh, "An imputation-enhanced algorithm for ICU mortality prediction," Computing in Cardiology (CinC), Krakow, Poland, September 2012.

  66. M. Shamaiah and H. Vikalo, "Base calling error rates in next-generation DNA sequencing," IEEE Workshop on Statistical Signal Processing, Ann Arbor, MI, August 2012.

  67. M. Park, M. Nassar, B. Evans, and H. Vikalo, "Adaptive experimental design for drug combinations," IEEE Workshop on Statistical Signal Processing, Ann Arbor, MI, August 2012.

  68. X. Shen and H. Vikalo, "A sequential Monte Carlo base-calling method for next-generation DNA sequencing," 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), San Antonio, TX, December 2011, pp. 121-122.

  69. T. Wu, H. Vikalo, and M. Shamaiah, "An MCMC Algorithm for base calling in sequencing-by-synthesis," Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 2011 (invited).

  70. S.-H. Lee, M. Shamaiah, S. Vishwanath, and H. Vikalo, "A message-passing algorithm for spectrum access in cognitive radio relay networks," Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 2011.

  71. S. Das and H. Vikalo, "Base-calling for Illumina's next-generation sequencing via Viterbi algorithm," 49th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2011, pp. 1733-1736 (invited).

  72. M. Shamaiah and H. Vikalo, "A dual Kalman filter for parameter-state estimation in real-time DNA microarrays," IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Boston, MA, August 30 - September 3, 2011, pp. 7614-7617 (invited).

  73. S.-H. Lee, M. Shamaiah, and H. Vikalo, "Message-passing for base-calling in sequencing-by-synthesis systems," IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 2011, pp. 777-780.

  74. M. Shamaiah, S.-H. Lee, S. Vishwanath, and H. Vikalo, "Distributed routing in networks using affinity propagation," IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 2011, pp. 3036-3039.

  75. M. Shamaiah, X. Shen, and H. Vikalo, "Sequential Monte Carlo method for parameter estimation in diffusion models of affinity-based biosensors," IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 2011, pp. 525-528.

  76. M. Shamaiah, S. Banerjee, and H. Vikalo, "Greedy sensor selection: Leveraging submodularity," IEEE Conference on Decision and Control (CDC), Atlanta, GA, December 2010.

  77. S. H. Lee, M. Shamaiah, and H. Vikalo, "Inference of gene-regulatory networks using message-passing algorithms," IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Cold Spring Harbor, NY, November 2010.

  78. S. Das and H. Vikalo, "Optimal base-calling in Illumina's sequencing-by-synthesis system," IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Cold Spring Harbor, NY, November 2010.

  79. M. Shamaiah, X. Shen, and H. Vikalo, "On parameter estimation for diffusion processes in real-time biosensors," Proc. of IEEE Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, November 2010.

  80. S.-H. Lee, M. Shamaiah, and H. Vikalo, "Optimal Estimation in DNA Microarrays via Global Optimization," in Proc. of IEEE Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, November 2010.

  81. T. Wu and H. Vikalo, "Maximum likelihood DNA sequence detection via sphere decoding," in Proceedings of IEEE ICASSP, Dallas, TX, March 2010.

  82. X. Shen and H. Vikalo, "Inferring parameters of gene regulatory networks via particle filtering," in Proceedings of IEEE ICASSP, Dallas, TX, March 2010.

  83. S. H. Lee, H. Vikalo, and S. Vishwanath, "Further results on message-passing algorithms for motif finding," in Proceedings of IEEE ICASSP, Dallas, TX, March 2010.

  84. M. Shamaiah and H. Vikalo, "'Rao-Blackwellized unscented Kalman filter for nonlinear systems with bandwidth constrains," in Proceedings of IEEE ICASSP, Dallas, TX, March 2010.

  85. M. Shamaiah and H. Vikalo, "Compressed sensing for bandwidth constrained systems," in Proceedings of IEEE ICASSP, Dallas, TX, March 2010.

  86. H. Vikalo and B. Hassibi, "Limits of performance of real-time microarrays," in Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 30 - October 2, 2009 (invited).

  87. M. Gokdemir and H. Vikalo, "An MCMC algorithm for parameter estimation in stochastically modeled real-time biosensor arrays," in IEEE Workshop on Statistical Signal Processing, Cardiff, UK, August 2009, pp. 121-124.

  88. M. Gokdemir and H. Vikalo, "A particle filtering algorithms for parameter estimation in biosensor arrays," IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Minneapolis, MN, May 2009.

  89. S.-H. Lee, H. Vikalo, and S. Vishwanath, "Message-passing for motif finding," IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Minneapolis, MN, May 2009.

  90. S. Das, H. Vikalo, and A. Hassibi, "Stochastic modeling of reaction kinetics in biosensors using the Fokker-Planck equation," IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Minneapolis, MN, May 2009.

  91. A. Miduthuri, T. Wu, and H. Vikalo, "Target estimation in real-time polymerase chain reaction using sequential Monte Carlo," IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS), Minneapolis, MN, pp. 1-4, May 2009.

  92. H. Vikalo and A. Hassibi, "Estimation in real-time affinity-based biosensors," in Proc. of IEEE Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, November 2008.

  93. H. Vikalo, F. Parvaresh, S. Misra, and B. Hassibi, "Sparse measurements, compressed sampling, and DNA microarrays," in Proceedings of IEEE ICASSP, Las Vegas, NV, 2008.

  94. H. Vikalo, B. Hassibi, and A. Hassibi, "On estimation in real-time microarrays," in Proceedings of IEEE ICASSP, Las Vegas, NV, 2008.

  95. H. Vikalo, B. Hassibi, and A. Hassibi, "Signal processing aspects of real-time DNA microarrays," Computational Advances in Multi-Sensor Adaptive Processing, St. Thomas, U.S. Virgin Islands, 2007 (invited).

  96. H. Vikalo, A. Hassibi, and B. Hassibi, "Signal processing for real-time microarrays," in Proc. of IEEE Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, November 2007 (invited).

  97. H. Vikalo, F. Parvaresh, and B. Hassibi, "On recovery of sparse signals in compressed DNA microarrays," in Proceedings of IEEE Asilomar Conf. on Signals, Systems and Computers, November 2007.

  98. M. Stojnic, B. Hassibi, and H. Vikalo, "PEP analysis of SDP-based non-coherent signal detection, IEEE International Symposium on Information Theory (ISIT), Nice, France, pp. 2916-2920, June 2007.

  99. H. Vikalo, B. Hassibi, M. Stojnic, and A. Hassibi, "Modeling the kinetics of hybridization in microarrays," in IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Tuusula, Finland, June 2007.

  100. H. Vikalo, B. Hassibi, and A. Hassibi, "ML detection of DNA initial copy in polymerase chain reaction processes," in Proceedings of IEEE ICASSP, Honolulu, HI, April 2007.

  101. M. Stojnic, B. Hassibi, and H. Vikalo, "PEP analysis of the SDP based joint channel estimation and signal detection," in Proceedings of IEEE ICASSP, Honolulu, HI, April 2007.

  102. M. El-Khamy, H. Vikalo, B. Hassibi, and R. J. McEliece, "On the performance of sphere decoding of block codes," in Proceedings of IEEE International Symposium on Information Theory (ISIT), Seattle, WA, July 2006.

  103. H. Vikalo, B. Hassibi, and A. Hassibi, "On joint maximum-likelihood estimation of PCR efficiency and initial amount of target," in Proceedings of IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS), College Station, TX, May 28-30, 2006.

  104. M. Stojnic, H. Vikalo, and B. Hassibi, "Asymptotic analysis of the Gaussian broadcast channel with perturbation preprocessing," in Proceedings of IEEE ICASSP, Toulouse, France, May 2006.

  105. H. Vikalo, B. Hassibi, and A. Hassibi, "On limits of performance of DNA microarrays," in Proceedings of IEEE ICASSP, Toulouse, France, May 2006.

  106. M. Stojnic, H. Vikalo, and B. Hassibi, "Further results on speeding up the sphere decoder," in Proceedings of IEEE ICASSP, Toulouse, France, May 2006.

  107. H. Vikalo, A. Hassibi, and B. Hassibi, "Optimal estimation of gene expression levels in microarrays," in Proc. IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS), Newport, RI, May 22-25, 2005.

  108. A. Hassibi and H. Vikalo, "A probabilistic model for inherent noise and systematic errors of microarrays," in Proc. IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS), Newport, RI, May 22-25, 2005.

  109. M. El-Khamy, H. Vikalo, and B. Hassibi, "Bounds on the performance of sphere decoding of linear block codes," in Proc. IEEE ITSOC Information Theory Workshop on Coding and Complexity (ITW), New Zealand, 2005.

  110. M. Stojnic, H. Vikalo, and B. Hassibi, "An efficient H-infinity estimation approach to speed up the sphere decoder," in Proc. International Conference on Wireless Networks, Communications, and Mobile Computing (WirelessCom), Kaanapali Beach, Maui, Hawaii, June 13-16, 2005.

  111. M. Stojnic, H. Vikalo, and B. Hassibi, "An H-infinity based lower bound to speed up sphere decoder," in Proceedings of The Sixth IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), June 2005.

  112. M. Stojnic, H. Vikalo, and B. Hassibi, "A branch-and-bound approach to speed up the sphere decoder," in Proc. ICASSP 2005, Philadelphia, PA, 2005.

  113. H. Vikalo and B. Hassibi, "Statistical approach to ML decoding of linear block codes on symmetric channels," in Proceedings of IEEE International Symposium on Information Theory (ISIT), Chicago, IL, 2004.

  114. M. Stojnic, H. Vikalo, and B. Hassibi, "Rate maximization in multi-antenna broadcast channels with linear preprocessing," in Proceedings of IEEE Global Telecommunications Conference, Dallas, TX, November-December 2004, pp. 3957-3961.

  115. H. Vikalo, A. Hassibi, and B. Hassibi, "Nucleic acid detection using bioluminescence regenerative cycle and statistical signal processing," in Proceedings of IEEE Workshop on Genomic Signal Processing and Statistics (GENSIPS) , Baltimore, MD, May 26-27, 2004.

  116. H. Vikalo, B. Hassibi, and U. Mitra, "Sphere-constrained ML detection for channels with memory," in Proceedings of 37th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 2003.

  117. H. Vikalo and B. Hassibi, "On joint ML detection and decoding," in Proceedings of IEEE International Symposium on Information Theory (ISIT), Yokohama, Japan, June 2003.

  118. H. Vikalo, B. Hassibi, and U. Mitra, "Sphere-constrained ML detection for frequency-selective channels," in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, April 2003.

  119. H. Vikalo, B. Hassibi, and P. Stoica, "On joint ML channel estimation and signal detection for SIMO channels," in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, April 2003.

  120. H. Vikalo and B. Hassibi, "Low-complexity iterative detection and decoding of multi-antenna systems employing channel and space-time codes," Asilomar 2002.

  121. H. Vikalo and B. Hassibi, "On the expected complexity of sphere decoding for frequency-selective channels," Allerton 2002.

  122. H. Vikalo and B. Hassibi, "Modified Fincke-Pohst algorithm for low-complexity iterative decoding over multiple antenna channels," IEEE ISIT, Lausanne, Switzerland, 2002.

  123. H. Vikalo and B. Hassibi, "Towards closing the capacity gap on multiple antenna channels," IEEE ICASSP, Orlando, FL, 2002.

  124. B. Hassibi and H. Vikalo, "On the expected complexity of integer least-squares problems," IEEE ICASSP, Orlando, FL, 2002.

  125. H. Vikalo and B. Hassibi, "Low-complexity iterative decoding over multiple antenna channels via a modified sphere decoder," Allerton 2001.

  126. B. Hassibi and H. Vikalo, "On the expected complexity of sphere decoding," Asilomar 2001.

  127. H. Vikalo, B. Hassibi, B. Hochwald, and T. Kailath, "Optimal training for frequency-selective fading channels," IEEE ICASSP, Salt Lake City, UT, 2001.

  128. H. Vikalo, B. Hassibi, and T. Kailath, "On robust multiuser detection," Proc. 34th Asilomar conference on signals, systems and computers, Asilomar, November 2000.

  129. S. Mudulodu, H. Vikalo, A. Paulraj, and T. Kailath, "CDMA multiser detection based on state-space estimation techniques," Proc. 34th Asilomar conference on signals, systems and computers, Asilomar, November 2000.

  130. H. Vikalo, A. T Erdogan, B. Hassibi, and T. Kailath, "Exponential-quadratic optimal signal reconstruction in noisy filter banks," Proc. SPIE 2000, San Diego, August 2000.

  131. T. Simunic, H.Vikalo, and G. De Micheli: "Energy efficient design of portable wireless systems", Proc. ISLPED 2000, Italy, June 2000.

  132. H. Vikalo, B. Hassibi, and T. Kailath, "Mixed H2/H-infinity optimal signal reconstruction in noisy filter banks," Proc. ICASSP 2000, Istanbul, Turkey, June 2000.

  133. H. Vikalo, B. Hassibi, and T. Kailath, "On H-infinity optimal signal reconstruction in noisy filter banks," Proc. ICASSP '99, Phoenix, AZ, March 1999.

  134. H. Vikalo and R. S. Blum, "Distributed detection in dependent nonGaussian noise," Proc. 1997 IEEE Int'l Symposium on Information Theory, Ulm, Germany, June 1997.

  135. H. Vikalo and R. S. Blum, "Distributed detection of known signals in Gaussian mixture noise which is dependent from sensor to sensor," Proc. International Conference on Telecommunications," Melbourne, Australia, April 1997.

  136. Z. Kovacic, S. Bogdan, and H. Vikalo, "Design and parameter adaptation of a fuzzy servo controller," Proc. VI International Fuzzy Systems Association World Congress, Sao Paulo, Brazil, July 1995.

Book chapters

  1. M. Shamaiah and H. Vikalo, "Estimation of Time-Varying Sparse Signals in Sensor Networks, in Filtering from Undersampled Data with Introduction to Compressed Sensing, (editors A. Carmi, L. Mihaylova, and S. Godsill), Springer, 2013.

  2. A. Hassibi, H. Vikalo, J. L. Riechmann, and B. Hassibi, "FRET-Based Real-Time DNA Microarrays," in Functional Genomics: Methods and Protocols, Springer, 2012.

  3. S. Das, H. Vikalo, and A. Hassibi, "Affinity-Based Biosensors: Stochastic Modeling and Figures of Merit," in Integrated Microsystems: Mechanical, Photonic, and Biological Interfaces, Taylor and Francis LLC 2011.

  4. T. Kailath, H. Vikalo, and B. Hassibi, "MIMO Receive Algorithms," in Space-Time Wireless Systems: From Array Processing to MIMO Communications, (editors H. Bolcskei, D. Gesbert, C. Papadias, and A. J. van der Veen), Cambridge University Press, 2005.

  5. B. Hassibi and H. Vikalo, "Maximum-Likelihood Decoding and Integer Least-Squares: The Expected Complexity," in Multiantenna Channels: Capacity, Coding and Signal Processing, (editors J. Foschini and S. Verdu), American Mathematical Society (AMS) 2003, pp. 161-191.

Theses

  1. H. Vikalo, Sphere Decoding Algorithms for Digital Communications, PhD Thesis, Stanford University, 2003.

  2. H. Vikalo, Distributed Detection in Impulsive Noise, Masters' Thesis, Lehigh University.

  3. H. Vikalo, Adaptive and Optimal Fuzzy Control of Electrical Drives, Bachelors' Thesis, University of Zagreb.