Professor  Subhrakanti Dey's Personal Page

 

Contents

EMAIL ME


Personal Details

Name :

SUBHRAKANTI DEY

Address :

Dept. of Electrical & Electronic Engineering
The University of Melbourne
Parkville VIC 3010

 

 

Phone :

(03) 8344 6299 

Fax: 

(03) 8344 6678 

Room :

5.8 

Email :

sdey@unimelb.edu.au  (PLEASE NOTE: E-MAIL ADDRESS HAS BEEN UPDATED!!)


Curriculum Vitae

Qualifications

  • PhD (1996), Department of Systems Engineering, RSISE, The Australian National University, Canberra, Australia.
  • 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

  • September 2007 - present: Professor, Department of Electrical and Electronic Engineering, The University of Melbourne, Australia
  • January 2004 –September 2007: Associate Professor & Reader, 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.

  • Cross layer resource allocation in wireless networks : The performance of the various layers of a wireless networks (both cellular and ad hoc or sensor networks) depend heavily on the physical layer due to the channel variations of the wireless medium. Effects of the channel fading effects on link layer and network layer algorithms such as call admission control, power control and rate control are thus important phenomena to be studied. My current research work involves optimal adaptive power and rate control for multiuser CDMA wireless communications over fading channels with outage probability constraints, cross layer optimization in ad hoc and multimedia over sensor networks etc. This work is also part of my involvement in ARC Special Research Centre for Ultra-Broadband Information Networks (CUBIN) .
  •  Distributed Sensor Networks:  estimation and control : Distributed sensor networks, specially wireless sensor networks are becoming increasingly popular in many applications such as defense, nanotechnology and many other biological applications. Since these sensors are small and work over bandwidth limited channels (e.g, a wireless medium), there are severe constraints on communication rate, computational resources and power. Distributed estimation and detection problems and networked control design in such a constrained environment are therefore highly nontrivial problems to look at. Current research interest includes design of optimal quantizers in decentralized estimation of states and parameters for various classed of complex dynamical systems, minimum data rate controller design for networked control systems with communication constraints, asymptotic performance analysis of distributed detection and estimation algorithms for sources with complex dynamics (such as that of a target in a hostile environment). The links with Information Theory are also being explored in the context of such estimation and control design.
  • Capacity optimization for fading wireless and fading optical channels:  In this project, ergodic and outage capacity for wireless radio and optical channels are optimized using nonlinear optimization theory, optimal power allocation laws are obtained for single as well as multiuser communication systems.
  • Statistical signal processing for large scale systems : In specific applications to queuing networks and communication networks in general and complex biological systems, special kinds of Markov chains known as nearly completely decomposable Markov chains are used. These models are also useful in operations research, management systems and any large scale models where there are strong interactions within groups and weak interaction amongst the groups. The aim of the project is to obtain reduced-complexity estimation and control algorithms for such models with partial information. Asymptotic error bounds are to be obtained for such models as well, which will define the performance bounds attainable.
  • Systems Biology: I am also interested in applications of systems theory to modeling biological and  neurological processes and systems. Tools from statistical signal processing and stochastic control are tailored for use for these applications, e.g., early deafness detection, modeling human sensory functions, gene expressions etc.

Publications

    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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.

Resource Allocation in Wireless Networks:

  1. J. Papandriopoulos, J.S. Evans and S. Dey, ``Optimal power control for Rayleigh-faded multiuser systems with outage constraints,'' IEEE Transactions on Wireless Communications ,  vol. 4, no. 6, pp. 2705-2715, November 2005.
  2. 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.
  3. 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.
  4. 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.
  5. 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 & Networking, article ID 24695, 17 pages, volume  2007.
  6. S. Dey and J.S. Evans, ``Outage Capacity and Optimal Power Allocation for Multiple Time-Scale Parallel Fading Channels,''  IEEE Transactions on Wireless Communications , vol. 6, no. 7, pp. 2369-273, July 2007.
  7. J.C.F. Li, S. Dey and J.S. Evans, ``Maximal lifetime rate and power allocation for wireless sensor networks with data distortion constraints,'' IEEE Transactions on Signal Processing, volume 56, no. 5, pp. 2076-2090, May 2008.
  8.  J. Papandriopoulos, S. Dey and J.S. Evans, ``Optimal and Distributed Protocols for Cross-Layer Design of Physical & Transport Layers in MANETs,”  IEEE/ACM Transactions on Networking, in press, August 2007.
  9. K. Chakrabarty, S. Dey and M. Franceschetti, ``Outage capacity for MIMO Poisson Fading Channels,’’ IEEE Transactions on Information Theory, to appear, November 2008.
  10. James C-F. Li and S. Dey, ``Near-optimal power allocation in Wireless Video Sensor Systems with Power-Rate-Distortion Constraints,’’ submitted to EURASIP Journal on Advances in Signal Processing, July 2008.
  11.  K. Chakrabarty, S. Dey and M. Franceschetti, `` Service outage based power and rate control for Poisson fading channels,'' submitted to IEEE Transactions on Information Theory, January 2008.
  12.  James C-F. Li and S. Dey, ``Delay-constrained Power Allocation for Outage Minimization in Wireless Relay Networks with Causal Feedback,” submitted to IEEE Transactions on Wireless Communications, July 2008.

Signal Processing for Sensor Networks:

  1. 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.
  2. M. Huang and S. Dey, `` Stability of Kalman Filtering with Markovian Packet Losses '',   Automatica, vol. 43, no. 4, pp. 598-607, March 2007.
  3. 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.
  4. A.S. Leong, S. Dey and J.S. Evans, `` Probability of Error Analysis for Hidden Markov Model Filtering with Random Packet Loss '', IEEE Transactions on Signal Processing, vol. 55, no. 3, pp. 809-821,  March 2007.
  5. A.S. 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.
  6. A.S. Leong, S. Dey and J.S. Evans, ``On Kalman Smoothing with Random Packet Loss,’’ IEEE Transactions on Signal Processings, vol. 56, No. 7,  pp. 3346-3351, July 2008.
  7. A.S. Leong, S. Dey and J.S. Evans, ``Power Allocation for State Estimation over Wireless Channels using Multiple Sensors,’’ IEEE Transactions on Aerospace and Electronic Systems, submitted, April 2008.
  8. N. Ghasemi and S. Dey, `` A Constrained MDP Approach to Dynamic Quantizer Design for HMM State Estimation,” IEEE Transactions on Signal Processing, in press, October 2008.
  9. S. Dey, A.S.C. Leong and J.S. Evans, ``Kalman Filtering with Faded Measuremenets,’’ Automatica, submitted, October 2008.

Signal Processing for Communications:

  1. R. Jana and S. Dey, ``Change Detection in Teletraffic Models'', IEEE Trans. on Signal Processing , vol. 48, no. 3, pp. 846-853, March 2000.
  2. R. Jana and S. Dey, ``3Gwireless Capacity Optimization for Widely Spaced Antenna Arrays,'' IEEE Personal Communications, vol. 7, no. 6, pp. 32-35, December 2000.

Control and Communication

    

       42. P. Minero,  M. Franceschetti, S. Dey and G. Nair, ``Data Rate Theorem for Stabilization over Time-Varying Feedback Channels,’’ IEEE Transactions on Automatic Control, in press, July  2008.

 


Probabilistic Pattern Recognition:

  1.  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.

Selected Conference Publications (Note: This is a partial list only, no longer updated regularly):


 

  1. R. K. Boel, J. B. Moore and S. Dey, ``Geometric Convergence of Filters for Hidden Markov Models,'' Proc. of 34th IEEE Conf. on Decision and Control, pp. 69-74, New Orleans, December 1995.
  2. S. Dey and J. B. Moore, ``On finite-dimensional risk-sensitive estimation,'' Proc. International Symposium on Signal Processing and its Applications, vol. 2, pp. 849-852, Gold Coast, Australia, August 1996.
  3. S. Dey, R. J. Elliott and J. B. Moore, ``Finite-dimensional risk-sensitive estimation for continuous-time nonlinear systems,'' Proc. European Control Conference, Brussels, July 1997.
  4. L. Shue, B. D. O. Anderson and S. Dey, ``On steady state properties of certain Max-plus products,'' Proc. of the ACC , Philadelphia, Pennsylvania, USA, June 1998.
  5. S. Dey and S. I. Marcus, ``A framework for mixed estimation of hidden Markov models,'' Proc. of IEEE CDC , pp. 3473-3478, December 1998, Tampa, Florida.
  6. S. Dey and C. D. Charalambous, ``Discrete-time risk-sensitive filters with non-Gaussian initial conditions and their ergodicity properties,'' Proc. of the ACC , pp. 4403-4407, San Diego, USA, June 1999.
  7. F. De Bruyne and S. Dey, ``Change Detection and its Application to Adaptive Control,'' Proc. of the ACC 2000 , Chicago, June 2000, to appear.
  8. R. Jana and S. Dey, ``Mobile Capacity Enhancement using Unequally Spaced Antenna Arrays,'' to appear, Proc. of IEEE VTC 2000 , Japan, May 2000.
  9. S. Dey, ``Reduced-complexity filtering for partially observed nearly completely decomposable Markov chains,'' to appear, Proc. of Mathematical Theory of Networks and Systems , Perpignan, France, June 2000.
  10. R. Jana and S. Dey, ``Mobile Capacity Optimization for Widely Spaced Antenna Arrays,'' Proc. IEEE International Conference on Third Generation Wireless Communications , Silicon Valley, USA, June 2000, to appear.
  11. L. Shue and S. Dey, ``Reduced-complexity smoothing for hidden Markov models,'' Proceedings of the IEEE CDC 2000 , Sydney, Australia, to appear.
  12. N. Bui and S. Dey, ``Stochastic power control in CDMA with Markov fading channels,'' Proc. IEEE International Conference on Third Generation Wireless and Beyond , to appear, June 2001.
  13. V. Krishnamurthy and S. Dey, ``Reduced-complexity estimation for Poisson processes modulated by nearly completely decomposable Markov chains, '' Proc. of IEEE International Symposium on Information Theory , pp. 207, June 2001, Washington D.C.
  14. N. Bui and S. Dey, ``Optimal power control in CDMA over Markov fading channels,'' Proc. of IEEE International Symposium on Information Theory , pp. 79, July 2002, Lausanne, Switzerland.
  15. G. Nair, S. Dey and R. J. Evans, ``Communication-limited stabilizability of jump Markov linear systems,'' CD-ROM Proc. Mathematical Theory of Networks and Systems (MTNS) , Southbend, USA, August 2002.
  16. V. Krishnamurthy and S. Dey, ``Reduced-complexity spatio-temporal image-based tracking of maneuvering targets,'' Proceedings of the 5th Intl. Conference on Information Fusion , vol. 1, pp. 743-750, Annapolis, Maryland, USA, June 2002.
  17. J. Papandriopoulos, J. Evans and S. Dey, ``Iterative power control and multiuser detection with outage probability constraints'', to appear, Proc. ICC 2003 , Anchorage, May 2003.
  18. J. Papandriopoulos, J. Evans and S. Dey, ``Optimal power control in CDMA networks with constraints on outage probability,'' Proc. of the Wi'Opt 2003: Modelling and optimization in mobile, ad-hoc and wireless networks , to appear, Sophia-Antipolis, France, March 2003.
  19. S. Dey and F.A. Galati, ``Information theoretic quantiser design in decentralized estimation for hidden Markov models'', Proc. of ICASSP 2003 , to appear, Hong Kong, April 2003.
  20. J. Papandriopoulos, J.S. Evans and S. Dey, ``A framework for joint power control and multiuser detection with outage probability specifications,'' GLOBECOM 2003 , submitted.
  21. S. Dey and J.S. Evans, ``Optimal power control in wireless data networks with outage-based utility guarantees,'' CDC 2003.
  22. G.N. Nair, S. Dey and R.J. Evans, ``Infimum data rates for stabilizing Markov jump linear systems,'' CDC 2003.

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Created : 15th March, 2000


Last Modified : 8th August, 2002
HTML by : Melissa Norfolk
Maintained by : Subhrakanti Dey