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| 3. |
- Adiels, Lars, 1952-, et al.
(författare)
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Test of CP violation with K0 and K‾0 at LEAR
- 1985
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Ingår i: Physics with antiprotons at LEAR in the ACOL era. - Gif sur Yvette : Editions Frontières. - 2863320351 ; , s. 467-482
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 7. |
- Adiels, L., et al.
(författare)
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Pi0 And Eta Spectroscopy At Lear
- 1986
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Ingår i: Tignes 1985, Proceedings, Physics With Antiprotons At Lear In The Acol Era. ; , s. 359-359
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Konferensbidrag (refereegranskat)
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| 8. |
- Stavrou, Photios A., et al.
(författare)
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Asymptotic Reverse-Waterfilling Characterization of Nonanticipative Rate Distortion Function of Vector-Valued Gauss-Markov Processes with MSE Distortion
- 2018
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Ingår i: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 ; , s. 14-20
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Konferensbidrag (refereegranskat)abstract
- We analyze the asymptotic nonanticipative rate distortion function (NRDF) of vector-valued Gauss-Markov processes subject to a mean-squared error (MSE) distortion function. We derive a parametric characterization in terms of a reverse-waterfilling algorithm, that requires the solution of a matrix Riccati algebraic equation (RAE). Further, we develop an algorithm reminiscent of the classical reverse-waterfilling algorithm that provides an upper bound to the optimal solution of the reverse-waterfilling optimization problem, and under certain cases, it operates at the NRDF. Moreover, using the characterization of the reverse-waterfilling algorithm, we derive the analytical solution of the NRDF, for a simple two-dimensional parallel Gauss-Markov process. The efficacy of our proposed algorithm is demonstrated via an example.
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| 9. |
- Stavrou, P. A., et al.
(författare)
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Filtering with fidelity for time-varying Gauss-Markov processes
- 2016
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Ingår i: Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016). - 0743-1546. - 9781509018376 ; , s. 5465-5470
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Konferensbidrag (refereegranskat)abstract
- In this paper, we revisit the relation between Nonanticipative Rate Distortion (NRD) theory and real-time realizable filtering theory. Specifically, we give the closed form expression for the optimal nonstationary (time-varying) reproduction distribution of the Finite Time Horizon (FTH) Nonanticipative Rate Distortion Function (NRDF) and we establish its connection to real-time realizable filtering theory via a realization scheme utilizing time-varying fully observable multidimensional Gauss-Markov processes. As an application we provide the optimal filter with respect to a mean square error constraint. Unlike classical filtering theory, our filtering approach based on FTH NRDF is performed with waterfilling. We also derive a universal lower bound to the mean square error of any causal estimator to Gaussian processes based on the closed form expression of FTH NRDF. Our theoretical results are demonstrated via an illustrative example.
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| 10. |
- Stavrou, Photios A., et al.
(författare)
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OPTIMAL ESTIMATION VIA NONANTICIPATIVE RATE DISTORTION FUNCTION AND APPLICATIONS TO TIME-VARYING GAUSS-MARKOV PROCESSES
- 2018
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Ingår i: SIAM Journal of Control and Optimization. - : SIAM PUBLICATIONS. - 0363-0129 .- 1095-7138. ; 56:5, s. 3731-3765
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we develop finite-time horizon causal filters for general processes taking values in Polish spaces using the nonanticipative rate distortion function (NRDF). Subsequently, we apply the NRDF to design optimal filters for time-varying vector-valued Gauss-Markov processes, subject to a mean-squared error (MSE) distortion. Unlike the classical Kalman filter design, the developed filters based on the NRDF are characterized parametrically by a dynamic reverse-waterfilling optimization problem obtained via Karush-Kuhn-Tucker conditions. We develop algorithms that provide, in general, tight upper bounds to the optimal solution to the dynamic reverse-waterfilling optimization problem subject to a total and per-letter MSE distortion constraint. Under certain conditions, these algorithms produce the optimal solutions. Further, we establish a universal lower bound on the total and per-letter MSE of any estimator of a Gaussian random process. Our theoretical framework is demonstrated via simple examples.
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