Uppsala universitet
Subhrakanti Dey

Professor in
Signal Processing

Phone:   +46 - 18 - 4717059 
Fax:   +46 - 18 - 471 7244 
Mail:   subhrakanti.dey AT angstrom.uu.se
In-house: 
Office:   73116
Address:   Signals and Systems 
Department of Electrical Engineering
Uppsala University 
Box 65
SE-751 03 Uppsala, Sweden
Research Interests:   Networked (including wireless) Control Systems
Detection and Estimation with Energy Harvesting Sensor Networks
Security and Privacy in Cyber-Physical Systems
Distributed Optimisation and Machine Learning
Courses:   Signals and Systems (1TE661) VT23
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
2024- IEEE Fellow (for contributions to networked control systems and performance optimization over wireless and sensor networks)
2022 - present: Professor of Signal Processing and Head of Signals and Systems Division, Dept of Electrical Engineering, UPPSALA UNIVERSITY
2018-2022: Professor, National University of Ireland, Maynooth, Ireland (on leave of absence from UPPSALA UNIVERSITY)
2017-2018: Professor of Telecommunications, Institute of Telecommunications Research, University of South Australia, Australia (on leave of absence from UPPSALA UNIVERSITY)
2013 - 2017: Professor of Wireless Sensor Networks, 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/post-docs who would like to work in these or related areas.

Disrtributed Estimation, Control and Optimization for Networked Cyber-Physical Systems: Networked systems are ubiquitous. Examples include human engineered infrastructure and communication networks, social and economic networks, and naturally occurring biological networks such as bio-cellular networks or biological swarms. Most advanced engineered networked systems include both physical components as well as cyber components. The burgeoning research field of cyber physical systems (CPS) refers broadly to the next generation of engineered systems that requires efficient integration of computing, communication, and control technologies achieving stability, robustness, reliability and optimized performance in many important application domains. Wireless sensor and actuator networks (WSAN) will form an integral part of the future CPS and will play critical roles in grand endeavours such as building future intelligent highway systems with zero automotive related fatality and significantly reduced congestion and delays, providing location-independent access to world-class health care, and delivering blackout-free electricity generation and distribution through the future smart grid. An essential task in the ``sense-process-communicate-decide-actuate" cycle in WSANs involves design and analysis of stochastic estimation and control algorithms that will provide efficient, secure, robust, reliable, and often safety-critical performances under such networking and resource related constraints and uncertainties. The convergence of feedback control, information processing and communication technologies has thus raised new challenging fundamental questions in the design and development of such estimation and control algorithms for networked systems. My research aims to address both fundamental limits and robust, energy-efficient design principles for such systems.

Security and Privacy in Cyber-Physical Systems: CPS will form the core of many critical infrastructure facilities such as power grids, advanced healthcare systems etc. Ensuring their safety and reliability is crucial. It has been illustrated that traditional cryptographic {\em cyber security} measures are not sufficient for CPS, as these tools are ineffective against cyber-physical attacks or attacks launched by authenticated users. A recent example of such an attack was the ``Stuxnet Attack'' which resulted in a complex malware infecting uranium enrichment facilities and causing damage to approximately 1000 centrifuges in 2010. Current supervisory control systems used in power networks such as SCADA are also prone to such cyber-physical attacks, as demonstrated via various recent unauthorized intrusions into several electricity grids. Similarly, ``privacy preservation'' of information during distributed operation in CPS is also extremely important. A critical example is medical CPS where lack of such privacy can result in confidential patient data being compromised leading to misuse or abuse of such data. Implementation of distributed signal processing, control/optimization tasks in CPS requires guarantees of such privacy while still providing a near-optimal performance.
Joint design of resource allocation and signal processing/control algorithms in CPS leveraging energy harvesting and wireless power transfer: Wireless sensor and actuator networks (WSAN) consisting of hundreds or thousands of nodes are envisaged to support tether-free embedded sensing and control applications over a long period of time, typically 20-30 years. To date, traditional grid-powered and non-rechargeable battery powered devices have been primarily used to power such networks and the corresponding engineering design goal has been to either minimize power/energy consumption for a particular sensing/control task or to maximize the operational lifetime of the network for a fixed amount of energy resource. In this setting, optimal resource allocation for energy-limited wireless sensor networks (WSN) has been investigated largely in the context of information gathering. Energy-efficient NSPC algorithms have been also investigated primarily for WSANs powered by non-rechargeable batteries. In contrast to non-rechargeable battery powered devices, energy harvesting based rechargeable batteries or energy storage devices offer several significant advantages in deploying large scale WSANs. These devices, when integrated into the sensor/actuator nodes, offer the freedom from the cost-prohibitive task of periodically replacing batteries in hundreds/thousands of nodes, and the possibility for NCS to operate in a virtually everlasting manner, along with reducing their carbon footprint. Widespread adoption of such energy harvesting devices based on solar/wind/electromagnetic/vibrational energy are being considered for large scale WSANs in applications such as home and building automation, industrial manufacturing, and structural health monitoring. In addition, a recent breakthrough in the area of wireless power transfer technology based on coupled magnetic resonance, has also opened up a new energy management paradigm in WSNs. While wireless power transfer is still at its infancy, there have been some recent encouraging experimental results using various modes of wireless power transfer. Our research addresses design and analysis of distributed signal processing/control algorithms over WSAN powered by energy harvesting and wireless power transfer, co-designed with event-triggered sensor transmission protocols.
Distributed machine learning, optimization and control over networks and graphs: Modern complex cyber-physical systems consist of a large number of disparate sub-systems, often geographically dispersed and connected via an underlying communication infrastructure network. Each sub-system is required to perform a certain task in an optimized manner, while contributing to several overall system-wide performance objectives. Traditionally, optimal decision making for cyber physical systems relied on gathering all data at a central controller and computing and communicating control actions to all individual sub-systems over wired networks. The current proliferation of wireless sensor and actuator networks and internet of things has prioritized distributed decision making, often with local and incomplete information, under uncertainties inherent to the sub-systems (e.g. measurement errors) as well as to the environment they are operating in (e.g. communication link failure, hostile takeover by adversaries etc.). While most of the research efforts have focused on distributed static optimization problems, a bigger challenge lies in distributed control and optimization of stochastic dynamical systems, under the constraints of unreliable communication networks, incomplete knowledge of the often non-stationary underlying stochastic parameters, and restrictions on available on-board computational facilities at individual sub-systems or nodes. Similarly, in the age of big data , modern statistical machine learning tools deal with large-dimensional data sets and/or large sample sizes, thus requiring distributed machine learning methods over communication graphs. While recent advances have been made in distributed machine learning methods with perfect communication between participating nodes, much research needs to be done to address design and development of such algorithms over graphs that have randomly varying communication links along with communication constraints. Our research addresses design and analysis of distributed optimisation and statistical machine learning algorithms over networks, their performance analysis and privacy guarantees.
Current Journal Editorial Responsibilities:   2020- present: Senior Editor, IEEE Transactions on Control of Network Systems

2022- present: Senior Editor, IEEE Control Systems Letters

2022-present: Associate Editor, Automatica

2017-present: Editor, IEEE Transactions on Wireless Communications

Publications:  

Here only journal publications and some recent conference publications are shown. A full list of publications can be found here.

Journal Publications (and some recent conference publications):

Distributed Optimisation, Privacy and Machine Learning

1. L. Huang, J. Wu, D. Shi, S. Dey and L. Shi, ``Differential Privacy in Distributed Optimization with Gradient Tracking," IEEE Transactions on Automatic Control , in press, (accepted Dec. 28, 2023).

2. N. Dal Fabbro, S. Dey, M. Rossi and L. Schenato, ``SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing,'' Automatica , accepted for publication (October 2023), available here.

3. A. Maritan, S. Dey and L. Schenato, ``FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation," European Control Conference 2024, available here.

4. X. Chen, L. Huang, L. He, S. Dey and L. Shi, ``A Differentially Private Method for Distributed Optimization in Directed Networks via State Decomposition," IEEE Transactions on Control of Network Systems, vol. 10, no. 4, pp. 2165-2177, Dec. 2023..

5. W. Huo, L. Huang, S. Dey and L. Shi, ``Neural Network-based Distributed Generalized Nash Equilibrium Seeking for Uncertain Nonlinear Multi-agent Systems," IEEE Transactions on Control of Network Systems , in press, (accepted July 31, 2023).

6. X. Chen, L. Huang, K. Ding, S. Dey and L. Shi, ``Privacy-Preserving Push-sum Average Consensus via State Decomposition,'' IEEE Transactions on Automatic Control , vol. 68, no. 12, pp. 7974 - 7981, Dec. 2023.

7. A. Maritan, G. Sharma, L. Schenato and S. Dey, ``Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus,'' accepted for publication in IEEE GLOBECOM 2023 , available here.

8. G. Sharma and S. Dey, ``On Analog Distributed Approximate Newton with Determinantal Averaging,'' Proceedings of IEEE PIMRC 2022 available here.

9. A. Naha and S. Dey, ``Policy Gradient-based Model Free Optimal LQG Control with a Probabilistic Risk Constraint,'' submitted to IEEE CDC 2024 , available here.

10. A. Maritan, L. Schenato and S. Dey, ``Novel bounds for incremental Hessian estimation with application to zeroth-order federated learning,'' IEEE Open Journal of Control Systems , (early access) available here.

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, vol. 3, no. 1, pp. 12-24, 2017.

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, vol. 62, no. 1, pp. 277-290, Jan. 2017.

71. A.S. Leong, S. Dey and D. Quevedo, ``Sensor Scheduling in Event Triggered Estimation with Packet Drops,'' IEEE Transactions on Automatic Control, vol. 62, no. 4, pp. 1880-1895, April 2017.

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, accepted for publication, Feb 2017.

73. S. Knorn and S. Dey, ``Optimal energy allocation for linear control with packet loss under energy harvesting constraints,'' Automatica, vol. 77, pp. 259-267, March 2017.

CPS Security and privacy 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, vol. 62, no. 2, pp. 941-947, Feb. 2017.

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, vol. 3, no. 1, pp. 1-11, 2017.

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, vol. 78, pp. 194-201, April 2017.

78. X. Guo, A. S. Leong and S. Dey, ``Estimation in Wireless Sensor Networks with Security Constraints ,'' IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no.2, pp. 544-561, 2017.

78. K. Ding, S. Dey, D. E. Quevedo and L. Shi, ``Stochastic Game in Remote Estimation under DoS Attacks, " IEEE Control System Letters, vol. 1, no. 1, pp. 146-151, July 2017.

79. X. Ren, J. Wu, S. Dey and L. Shi, ``Attack Allocation on Remote State Estimation in Multi-Systems: Structural Results and Asymptotic Solution'', Automatica, vol. 87, pp. 184-194, Jan. 2018.

80. K. Ding, X. Ren, D. Quevedo, S. Dey and L. Shi, ``DoS Attacks on Remote State Estimation with Asymmetric Information,'' IEEE Transactions on Control of Network Systems, vol. 6, no. 2, pp. 653-666, 2019.

81. A.S. Leong, D. Quevedo, D. Dolz and S. Dey, ``Transmission Scheduling for Remote State Estimation over Packet Dropping Links in the Presence of an Eavesdropper, '' IEEE Transactions on Automatic Control, vol. 69, no. 9, pp. 3732-3739, Sep. 2019.

82. A.S. Leong, D. Quevedo, D. Dolz and S. Dey, ``Information Bounds for State Estimation in the Presence of an Eavesdropper,'' IEEE Control Systems Letters, vol. 3, no. 3, pp. 547-552, July 2019.

83. K. Ding, X. Ren, D. Quevedo, S. Dey and L. Shi, ``Defensive deception against reactive jamming attacks in remote state estimation,'' Automatica, vol. 113, March 2020.

84. Y. Ni, X. Ren, S. Dey and L. Shi, ``Remote State Estimation with a Strategic Sensor Using a Stackelberg Game Framework, IEEE Transactions on Control of Network Systems, vol. 8, no. 4, pp. 1613-1623, Dec. 2021.

85. J. Lu, D Quevedo, V. Gupta and S. Dey, ``Stealthy hacking and secrecy of controlled state estimation systems with random dropouts,'' IEEE Transactions on Automatic Control, vol. 68, no. 1, pp. 31-46, January 2023.

86. M. Huang, K. Ding, S. Dey, Y. Li and L. Shi, ``Learning-Based DoS Attack Power Allocation in Multi-Process Systems," IEEE Transactions on Neural Networks and Learning Systems, in press (accepted Jan 2022).



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.