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Disthbution IJnli~nited CONTRACT TITLE: THEORETICAL STUDIES OF HIGH-POWER ULTRAVIOLET AND INFRARED MATERIALS
, 1978
"... ~f d1400 Wilson Boulevard nc assi ie ..."
Scenario Generation and Reduction for Long-term and Short-term Power System Generation Planning under Uncertainties
"... ii ..."
AGR 05
"... Responses of soil biological processes to elevated atmospheric [CO2] and nitrogen addition in a poplar plantation ..."
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Responses of soil biological processes to elevated atmospheric [CO2] and nitrogen addition in a poplar plantation
1Generalized Signal Alignment: On the Achievable DoF for Multi-User MIMO Two-Way Relay Channels
"... Abstract—This paper studies the achievable degrees of freedom (DoF) for multi-user multiple-input multiple-output (MIMO) two-way relay channels, where there are K source nodes, each equipped with M antennas, one relay node, equipped with N antennas, and each source node exchanges independent message ..."
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messages with an arbitrary set of other source nodes via the relay. By allowing an arbitrary information exchange pattern, the consid-ered channel model is a unified one. It includes several existing channel models as special cases:K-user MIMO Y channel, multi-pair MIMO two-way relay channel, generalized
TO CODE OR NOT TO CODE
, 2002
"... de nationalité suisse et originaire de Zurich (ZH) et Lucerne (LU) acceptée sur proposition du jury: ..."
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de nationalité suisse et originaire de Zurich (ZH) et Lucerne (LU) acceptée sur proposition du jury:
Minimum Variance Estimation of a Sparse Vector Within the Linear Gaussian Model: An
"... Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterizat ..."
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Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterization of the RKHS associated with the SLGM, we derive a lower bound on the minimum variance achievable by estimators with a prescribed bias function, including the important special case of unbiased estimation. This bound is obtained via an orthogonal projection of the prescribed mean function onto a subspace of the RKHS associated with the SLGM. It provides an approximation to the minimum achievable variance (Barankin bound) that is tighter than any known bound. Our bound holds for an arbitrary system matrix, including the overdetermined and underdetermined cases. We specialize
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