Uppsala universitet
Subhrakanti Dey

Professor in
Wireless Sensor Networks


Phone:   +46 - 18 - 4717059 

Fax:   +46 - 18 - 471 7244 

Mail:   Subhrakanti.Dey AT signal.uu.se
In-house: 

Office:   72128

Address:   Signals and Systems 
Uppsala University 
P O Box 534
SE-751 21 Uppsala, Sweden

Research Interests:   Control Theory
Communication Theory
Wireless Sensor Networks (WISENET)
Signal Processing.

Courses:   Signal Processing

Curriculum Vitae:   Qualifications
PhD (1996), Department of Systems Engineering, RSISE, The Australian National University
Master of Technology (1993), Dept. of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India
Bachelor of Technology (1991), Dept. of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India

Professional Experience
2013 -present: Professor, Signals and Systems, Dept of Engineering Science, Uppsala University
September 2007 - 2013: Full Professor, Department of Electrical and Electronic Engineering, The University of Melbourne, Australia
January 2004 - September 2007: Associate Professor, Department of Electrical and Electronic Engineering, The University of Melbourne, Australia
April 2001 - December 2003: Senior Lecturer, Department of Electrical and Electronic Engineering, The University of Melbourne, Australia
February 2000 - April 2001: Lecturer, Department of Electrical and Electronic Engineering, The University of Melbourne, Australia
September 1998 - February 2000: Research Fellow, Department of Systems Engineering, RSISE, Australian National University, Canberra, Australia
September 1997 - September 1998: Research Associate, Institute for Systems Research, University of Maryland, College Park, Maryland, USA
September 1995 - September 1997: Research Fellow, Department of Systems Engineering, RSISE, Australian National University, Canberra, Australia

Research Interests:   The following project descriptions relate to some of my current interests. They are described briefly for the benefit of prospective graduate students who would like to work in these or related areas.

Information Processing for/via Wireless Sensor Networks:Networked sensor systems have been identified as a key technology for the 21st century as a result of tremendous advances in micro-electromechanical systems technology and fast wireless local area networks (LAN) infrastructures. Today's sensors can be realized in sizes that can be as small as a matchbox or even smaller. They can have on board processing power, sensing ability and communication capability through wireless links or the Internet to neighbouring sensors or a central processing unit and location and positioning knowledge. They can operate in the acoustic, seismic, infrared, magnetic modes as well as imagers and microradars. They can be deployed on the ground, in the air, under water, on human bodies, on vehicles and in buildings. Thus, in addition to early military applications, today's sensor networks can be applied for infrastructure security, environment and habitat monitoring, quality control and safety assurance in manufacturing industries, intelligent highway control, biological sensing and human health monitoring, to name a few. However, these sensor networks operate under resource-constrained environments due to limited energy, computation and communication capability which is often subject to randomly time-varying nature of the wireless channel, the harsh operating environments and sensor node failure etc. Classical information processing (such as detection/estimation) algorithms which rely on availability of exact analog measurements from the sensor need to be re-analysed and re-designed before they can be applied to wireless sensor networks(WSN). One of my most active research interests lie in design, development and analysis of statistical detection/estimation algorithms for WSN under the constraints of limited energy, communication and computational capability. The core of the research involving these estimation algorithms for WSN encompasses the quest for fundamental information theoretic limits as well as more practical design-based implementation and performance optimization under resource constraints.

Networked Control systems: Recent years have seen a tremendous growth in research in networked control systems, or systems with non-classical information structure, where a large number of sensors and actuators can be connected via wireless or a mixture of wired/wireless links. These types of wireless sensor and actuator networks (WSAN) arise in many industrial applications, manufacturing systems and remotely operated control systems. These networks are fundamentally different from traditional information networks in that the detrimental effects of networks (such as delay, loss of packets etc.) can have a far more devastating effect on the network performance, such as a nuclear plant becoming unstable or a target trajectory estimate going completely out of bound. My research interests are focused on stochastic estimation and control over bandwidth limited and randomly fading noisy communication channels, stochastic stability analysis of networked complex systems and practical controller design with robust performance guarantees for such systems.

Cross layer optimization for resource allocation in wireless networks: In almost all types of wireless (cellular, ad hoc and sensor networks), it has now been established (arguably) that cross-layer design is the most effective way to optimize their performance. My research interests lie in design and analysis of optimum resource allocation algorithms for wireless cellular, ad hoc and sensor networks through power control, data rate control, optimal scheduling and media access algorithms especially when only partial channel information is available, and lifetime maximization of sensor networks. These algorithms are based on applications of nonlinear convex optimization or convex approximations to problems that are inherently non-convex in nature. Some of my more recent interests also include wireless optical communication systems and their capacity analysis problems as well as emerging areas such cognitive radio networks.

Fundamentals of statistical signal processing for complex systems: A small percentage of my research efforts is spent pursuing fundamental results in statistical signal processing in applications to large-scale complex systems aiming for reduced-complexity algorithms with rigorous performance guarantees. Although these algorithms are mostly generic, they have potential applications in biological systems, manufacturing, telecommunications and defence.

Journal Editorial Responsibilities:   2015- present: Associate Editor, IEEE Transactions on Control of Network Systems

2014- present: Associate Editor, IEEE Transactions on Signal Processin

2004-2007: Associate Editor, IEEE Transactions on Automatic Control

2007-2010: Associate Editor, IEEE Transactions on Signal Processing

2003- 2013: Associate Editor, Systems and Control Letters (Elsevier)

Publications:  

Here only journal publications are shown. A full list of publications can be found here.

Journal Publications:

Hidden Markov Model Signal Processing

1. V. Krishnamurthy, S. Dey and J. P. Leblanc, ``Blind Equalization of IIR Channels using Hidden Markov Models and Extended Least Squares'', IEEE Transactions on Signal Processing, vol.43, No. 12, pp. 2994-3006, December 1995.

2. S. Dey, V. Krishnamurthy and T. Salmon-Legagneur, ``Estimation of Markov Modulated Time-series via EM Algorithm'', IEEE Signal Processing Letters, vol. 1, pp. 153-155, October 1994.

3. L. Shue, B. D. O. Anderson and S. Dey, ``Exponential stability of filters and smoothers for hidden Markov models'', IEEE Transactions on Signal Processing, vol. 46, no. 8, pp. 2180-2194, August 1998.

4. L. Shue, S. Dey, B.D.O. Anderson and F. De Bruyne, ``On State-estimation of a 2-state hidden Markov model with quantisation,'' IEEE Transactions on Signal Processing, vol 49, no. 1, pp. 202-208, January 2001.

5. S. Dey, ``Reduced-complexity filtering for partially observed nearly completely decomposable Markov chains", IEEE Transactions on Signal Processing, vol. 48, no. 12, pp. 3334-3344, December 2000.

6. L. Shue and S. Dey, ``Complexity reduction in fixed lag smoothing for hidden Markov models,'' IEEE Transactions on Signal Processing, , vol. 50, no. 5, pp. 1124-1132, May 2002.

7. V. Krishnamurthy and S. Dey, ``Reduced-complexity spatio-temporal image-based tracking filters for maneuvering targets'', IEEE Transactions on Aerospace and Electronic Systems , vol.39, no: 4, pp. 1277-1291, October 2003.

8. S. Dey and I. M. Mareels, ``Reduced complexity estimation for large-scale hidden Markov models,'' IEEE Transactions on Signal Processing , vol. 52, no. 5, pp. 1242-1249, May 2004.

Stochastic Estimation and Control

9. S. Dey and J. B. Moore, ``Risk-sensitive filtering and smoothing for Hidden Markov Models'', Systems and Control Letters, Vol. 25, No. 5, pp. 361-366, August 1995.

10. J. B. Moore, R. J. Elliott and S. Dey, ``Risk-sensitive Generalizations of Minimum Variance Estimation and Control'', Journal of Mathematical Systems, Estimation and Control (summary), Vol. 7, no. 1, pp. 123-126, 1997.

11. S. Dey and J. B. Moore, ``Risk-sensitive dual control'', Int. Journal of Robust and Nonlinear Control, volume 7, no: 12, pp. 1047-1056, December 1997.

12. R. J. Elliott, J. B. Moore and S. Dey, ``Risk-sensitive maximum likelihood sequence estimation'', IEEE Transactions on Circuits and Systems Part I, Vol. 43, no. 9, pp. 805-810, September 1996.

13. S. Dey and J. B. Moore, ``Risk-sensitive filtering and smoothing via Reference Probability Methods'', IEEE Trans. Automatic Control, vol. 42, no. 11, pp. 1587-1591, November 1997.

14. S. Dey and J. B. Moore, ``Finite-dimensional risk-sensitive filters and smoothers for nonlinear discrete-time systems'', IEEE Transactions on Automatic Control, vol. 44, no. 6, pp. 1234-1239, June 1999.

15. C. D. Charalambous, S. Dey and R. J. Elliott, ``New finite-dimensional risk-sensitive filters: small noise limits'', IEEE Trans. Automatic Control, vol. 43, no. 10, pp. 1424-1429, October 1998.

16. S. Dey and C. D. Charalambous, ``On asymptotic stability of continuous -time risk-sensitive filters with respect to initial conditions,'' Systems and Control Letters , vol. 41, no. 1, pp. 9-18, 2000.

17. S. Dey and C. D. Charalambous, ``Discrete-time risk-sensitive filters with non-Gaussian initial conditions and their ergodic properties,'' Asian Journal of Control, , vol. 3, no.4, pp. 262-271, December 2001.

18. G. Yin and S. Dey, ``Weak convergence of hybrid filtering problems involving nearly completely decomposable hidden Markov chains,'' SIAM Journal on Control & Optimization , vol. 41, no. 8, pp. 1820-1842, 2003.

Optimal Resource Allocation in Wireless (cellular/ad hoc/sensor) Networks and Free-Space Optical Channels:

19. S. Dey and J.S. Evans, ``Optimal power control over multiple time-scale fading channels with service outage constraints,'' IEEE Transactions on Communications, vol. 53, no.4, pp. 708-717, April 2005.

20. J. Papandriopoulos, J.S. Evans and S. Dey, ``Optimal power control for Rayleigh-faded multiuser systems with outage constraints,'' IEEE Transactions on Wireless Communications, volume 4, no. 6, pp. 2705-2715, November 2005.

21. T. Alpcan, T. Basar and S. Dey, "A Power Control Game Based on Outage Probabilities for Multicell Wireless Data Networks,'' IEEE Transactions on Wireless Communications, vol. 5, no. 4, pp. 890-899, April 2006.

22. J. Papandriopoulos, J.S. Evans and S. Dey, "Outage-based Optimal Power Control for Generalized Multiuser Fading Channels,''IEEE Transactions on Communications, Vol. 54, no. 4, pp. 693-703, April 2006.

23. S. Dey and J.S. Evans, ``Outage capacity and optimal power allocation for multiple time-scale parallel fading channels'', IEEE Transactions on Wireless Communications, volume 6, no. 7, pp. 2369-2373, July 2007.

24. M. Huang and S. Dey, ``Combined Rate and Power Allocation with Link Scheduling in Wireless Data Packet Relay Networks with Fading Channels,'' EURASIP Journal on Wireless Communications and Networking, volume 2007, article ID 24695, 17 pages.

25. J.C.F. Li, S. Dey and J.S. Evans, ``Maximal lifetime rate and power allocation for wireless sensor systems with data distortion constraints,'' IEEE Transactions on Signal Processing, volume 56, no. 5, pp. 2076-2090, May 2008.

26. K. Chakraborty, S. Dey and M. Franceschetti, ``Outage Capacity of MIMO Poisson Fading Channels,'' IEEE Transactions on Information Theory, vol. 54, no. 11, pp. 4887-4907, November 2008.

27. J. Papandriopoulos, S. Dey and J.S. Evans, ``Optimal and Distributed Protocols for Cross-Layer Design of Physical and Transport Layers in MANETs,'' IEEE/ACM Transactions of Networking, vol. 16, no. 6, pp. 1392-1405, December 2008.

28. K. Chakraborty, S. Dey and M. Franceschetti, ``Service outage based power and rate control for Poisson fading channels,'' IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2304-2318, May 2009.

29. J.C-F. Li and S. Dey, ``Outage Minimization in Wireless Relay Networks with Delay Constraints and Causal Channel Feedback,'' European Transactions on Telecommunications, vol. 21, pp. 251-265, 2010.

30. Y.Y. He and S. Dey, ``Outage Minimization for Parallel Fading Channels with Limited Feedback," EURASIP Journal of Wireless Communications and Networking, 2012:352 doi:10.1186/1687-1499-2012-352.

31. Y.Y. He and S.Dey, ``Throughput Maximization in Poisson Fading Channels with Limited Feedback," IEEE Transactions on Communications, vol. 61, no. 10, pp. 4343-4356, October 2013.

32. Y.Y He and S. Dey, "Sum Rate Maximization For Cognitive MISO Broadcast Channels: Beamforming Design and Large Systems Analysis", IEEE Transactions on Wireless Communications, vol. 13, no. 5, pp. 2383-2401, May 2014.

Cognitive Radio Networks:

33. Y.Y. He and S. Dey, ``Power Allocation in Spectrum Sharing Cognitive Radio Networks with Quantized Channel Information,''IEEE Transactions on Communications, vol. 59, no. 6, pp. 1644-1656, June 2011.

34. E. Nekouei, H. Inaltekin and S. Dey, ``Throughput scaling in cognitive multiple-access with average power and interference constraints," IEEE Transactions on Signal Processing, vol. 60, no. 2, pp. 927-946, February 2012.

35. Y. Y. He and S. Dey, ``Throughput maximization in cognitive radio under peak interference constraints with limited feedback," IEEE Transactions on Vehicular Technology, vol. 61, no. 3, pp. 1287-1305, March 2012.

36. A. Limmanee and S. Dey and J.S. Evans, Service-outage Capacity Maximization in Cognitive Radio for Parallel Fading Channels,'' IEEE Transactions on Communications, vol.~61, no.~2, pp.~507-520, February 2013.

37. Y. Y. He and S. Dey, ``Power Allocation for Outage Minimization in Cognitive Radio Networks with Limited Feedback," IEEE Transactions on Communications, vol. 61, no.7, pp. 2648 - 2663, July 2013.

38. E. Nekouei, H. Inatekin and S. Dey, ``Throughput Analysis for the Cognitive Uplink Under Limited Primary Cooperation," vol. 64, no. 7, pp. 2780-2796, July 2016.

39. E. Nekouei, H.~Inatekin and S.~Dey‚ “Power Control and Multiuser Diversity for the Distributed Cognitive Uplink," IEEE Transactions on Communications, vol. 62, no. 1, pp. 41-58, January 2014 .

40. A. Limmanee, S. Dey and E. Nekouei, ``Optimal Power Policies and Throughput Scaling Analyses in Fading Cognitive Broadcast Channels with Primary Outage Probability Constraint," EURASIP Journal on Wireless Communications and Networking, 2014, 2014:35, doi:10.1186/1687-1499-2014-35.

Signal Processing for Sensor Networks:

41. M. Huang and S. Dey, ``Dynamic Quantizer Design for Hidden Markov State Estimation via Multiple Sensors with Fusion Centre Feedback'', IEEE Transactions on Signal Processing, vol. 54, no. 8, pp. 2887-2896, August 2006.

42. A.S. Leong, S. Dey and J.S. Evans, "Probability of error analysis for Hidden Markov Model filtering with random packet losses,'' IEEE Transactions on Signal Processing, Vol. 55, no.3, pp. 809-821, March 2007.

43. M. Huang and S. Dey, ``Dynamic quantization for multi-sensor estimation over bandlimited fading channels,'' IEEE Transactions on Signal Processing, volume 55, no. 9, pp. 4696-4702, September 2007.

44. A.S.C. Leong, S. Dey and J.S. Evans, ``Error exponents for Neyman-Pearson detection of Markov chains in noise,'' IEEE Transactions on Signal Processing, vol. 55, no. 10, pp. 5097-5103, October 2007.

45. A.S.C. Leong, S. Dey and J.S. Evans, ``On Kalman Smoothing with Random Packet Loss,'' IEEE Transactions on Signal Processing, Vol. 56, No. 7, pp. 3346-3351, July 2008.

46. N. Ghasemi and S. Dey, ``A Constrained MDP Approach to Dynamic Quantizer Design for HMM State Estimation'', IEEE Transactions on Signal Processing, vol. 57, no. 3, pp. 1203-1209, March 2009.

47. A.S.C. Leong, S. Dey and J.S. Evans, ``Asymptotics and Power Allocation for State Estimation over Fading Channels", IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 1, pp. 611-633, January 2011.

48. A.S.C. Leong, S. Dey, G. Nair and P. Sharma, ``Power Allocation For Outage Minimization in State Estimation Over Fading Channels," IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3382--3397, July 2011.

49. A.S. Leong and S. Dey, ``On Scaling Laws of Diversity Schemes in Decentralized Estimation,'' IEEE Transactions on Information Theory, vol. 57, no. 7, pp. 4740-4759, July 2011.

50. C-H. Wang and S. Dey, ``Distortion Outage Minimization in Nakagami Fading Using Limited Feedback," EURAPSIP Journal on Advances in Signal Processing, 2011:92 doi:10.1186/1687-6180-2011-92.

51. F. Li, J.S. Evans and S. Dey, ``Design of Distributed Detection Schemes for Multiaccess Channels,'' IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 2, pp. 1552-1569, April 2012.

52. F. Li, J.S. Evans and S. Dey, "Decision Fusion over Noncoherent Fading Multiaccess Channels," IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4367-4380, September 2011.

53. C-H. Wang, A.S.C Leong and S. Dey, ``Distortion Outage Minimization and Diversity Order Analysis for Coherent Multi-access,'' IEEE Transactions on Signal Processing, vol. 59, no. 12, pp. 6144-6159, December 2011.

54. N. Ghasemi and S. Dey, ``Dynamic Quantization and Power Allocation for Multisensor Estimation of Hidden Markov Models,'' IEEE Transactions on Automatic Control, vol. 57, no. 7, pp. 1641-1656, July 2012.

55. D. Ciuonzo, P. Salvo-Rossi and S. Dey, ``Massive MIMO Channel-Aware Decision Fusion,'' IEEE Transactions on Signal Processing, vol. 63, no. 3, pp. 604-619, February 2015.

56. M. Nourian, S. Dey and A. Ahlen, ``Distortion Minimization in Multi-Sensor Estimation with Energy Harvesting,'' IEEE Journal on Selected Areas in Communications, vol. 33, no. 3, pp. 524-539, March 2015.

57. S. Knorn, S. Dey, A. Ahlen and D. Quevedo, ``Distortion Minimization in Multi-Sensor Estimation Using Energy Harvesting and Energy Sharing,'' IEEE Transactions on Signal Processing, vol. 63, no. 11, pp. 2848-2863, June 2015.

58. A. Shirazina, S. Dey, D. Ciuonzo and P. Salvo-Rossi, ``Massive MIMO for Decentralized Estimation of a Correlated Source, '' IEEE Transactions on Signal Processing, vol. 64, no. 10, pp. 2499 - 2512, 2016.

59. X. Guo, A. S. Leong and S. Dey, ``Estimation in Wireless Sensor Networks with Security Constraints ,'' IEEE Transactions on Aerospace and Electronic Systems, in press, (accepted July 2016).

60. X. Guo, A.S. Leong and S. Dey, ``Distortion Outage Minimization in Distributed Estimation with Estimation Secrecy Outage Constraints,'' IEEE Transactions on Signal and Information Processing on Networks, in press, September, 2016.

Control and Communications:

61. M. Huang and S. Dey, ``Stability of Kalman filtering with Markovian packet losses'', Automatica, vol. 43, no. 4, pp. 598-607, April 2007.

62. P. Minero, M. Franceschetti, S. Dey and G. Nair, ``Data Rate Theorem for Stabilization over Time-Varying Feedback Channels,'' IEEE Transactions on Automatic Control, vol. 54, no. 2, pp. 243-255, February 2009.

63. S. Dey, A.S.C. Leong and J.S. Evans, ``Kalman Filtering with Faded Measurements,'' Automatica, volume 45, no. 10, pp. 2223-2233, October 2009.

64. M. Huang, S. Dey, G. Nair and J.H. Manton, ``Stochastic Consensus over Noisy Networks with Markovian and Arbitrary Switches,'' Automatica, vol. 46, no. 10, pp. 1571-1583, October 2010.

65. D. Quevedo, A. Ahlen, A.S.C. Leong and S. Dey, ``Control of Transmission Powers for State Estimation over Multiple Fading Wireless Channels," Automatica, vol. 48, no. 7, pp. 1306-1316, July 2012.

66. A.S.Leong, S.Dey and G.N.Nair, "Quantized Filtering Schemes for Multi-Sensor Linear State Estimation: Stability and Performance Under High Rate Quantization," IEEE Transactions on Signal Processing, vol. 61, no.15, pp. 3852 - 3865, August 2013.

67. M. Nourian, A.S.C. Leong and S. Dey, “Optimal Energy Allocation for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgments and Energy Harvesting Constraints," IEEE Transactions on Automatic Control, Volume 59, no. 8, pp. 2128-2143, August 2014.

68. M. Nourian, A.S.C. Leong, S. Dey and D. Quevedo, "An Optimal Transmission Strategy for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgements," IEEE Transactions on Control of Network Systems, vol. 1, no. 3, pp. 259-271, 2014.

69. S. Dey, A. Chiuso and L. Schenato, "Remote estimation with noisy measurements subject to packet loss and quantization noise," IEEE Transactions on Control of Network Systems, vol. 1, no. 3, pp. 204-217, 2014.

70. Y. Li, F. Zhang, D.E. Quevedo, V.K. Lau, S. Dey and L. Shi, ``Power Control of an Energy Harvesting Sensor for Remote State Estimation," IEEE Transactions on Automatic Control, accepted for publication, March 2016.

71. A.S. Leong, S. Dey and D. Quevedo, ``Sensor Scheduling in Event Triggered Estimation with Packet Drops,'' IEEE Transactions on Automatic Control, accepted for publication, August 2016.

72. S. Dey, A. Chiuso and L. Schenato, ``Feedback Control over lossy SNR-limited channels: linear encoder-decoder-controller design,'' IEEE Transactions on Automatic Control, provisionally accepted, August 2016.

73. S. Knorn and S. Dey, ``Optimal energy allocation for linear control with packet loss under energy harvesting constraints,'' Automatica, in press, (accepted October 19, 2016).

CPS Security in Networked Control Systems :

74. Y. Li, D. Quevedo, S. Dey and L. Shi, ``SINR-based DoS Attack on Remote State Estimation: A Game-theoretic Approach," IEEE Transactions on Control of Network Systems, accepted for publication, March 2016.

75. E. Kung, S. Dey and L. Shi, ``The Performance and Limitations of epsilon-Stealthy Attacks on Higher Order Systems,'' IEEE Transactions on Automatic Control, in press, (accepted April 29, 2016).

76. Y. Li, D.E. Quevedo, S. Dey and L. Shi, ``A Game-theoretic Approach to Fake-Acknowledgment Attack on Cyber-physical Systems,'' IEEE Transactions on Signal and Information Processing on Networks, in press, September 2016.

77. K. Ding, Y. Li, D. Quevedo, S. Dey and L. Shi, "A Multi-channel Transmission Schedule for Remote State Estimation under DoS Attacks, " Automatica, in press, (accepted November 28, 2016).

Compressive Sensing:

78. J.M. Scarlett, J.S. Evans and S. Dey, ``Compressed Sensing with Prior Information: Information-Theoretic Limits and Practical Decoders," IEEE Transactions on Signal Processing, vol. 61, no. 2, pp. 427-439, February 2013.

79. A. Shirazina and S. Dey, ``Power Constrained Sparse Gaussian Linear Dimensionality Reduction over Noisy Channels," IEEE Transactions on Signal Processing, vol. 63, no. 21, pp. 5837-5852, Nov. 2015.

Signal Processing for Communications:

80. R. Jana and S. Dey, ``Change Detection in Teletraffic Models'', IEEE Trans. on Signal Processing , vol. 48, no. 3, pp. 846-853, March 2000.

81. R. Jana and S. Dey, ``3Gwireless Capacity Optimization for Widely Spaced Antenna Arrays,'' IEEE Personal Communications Magazine, vol. 7, no. 6, pp. 32-35, December 2000.

Probabilistic Pattern Recognition:

82. J. S. Baras and S. Dey, ``Combined compression and classification with learning vector quantization'', IEEE Transactions on Information Theory, vol. 45, no. 6, pp. 1911-1920, September 1999.