On approximately universal schemes for two-hop networks

Full-duplex and half-duplex two-hop networks are considered. Explicit coding schemes which are approximately universal over a class of fading distributions are identified, for the case when the network has either one or two relays.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

An approximately H-1-optimal Petrov-Galerkin meshfree method: application to computation of scattered light for optical tomography

Nearly pollution-free solutions of the Helmholtz equation for k-values corresponding to visible light are demonstrated and verified through experimentally measured forward scattered intensity from an optical fiber. Numerically accurate solutions are, in particular, obtained through a novel reformulation of the H-1 optimal Petrov-Galerkin weak form of the Helmholtz equation. Specifically, within a globally smooth polynomial reproducing framework, the compact and smooth test functions are so designed that their normal derivatives are zero everywhere on the local boundaries of their compact supports. This circumvents the need for a priori knowledge of the true solution on the support boundary and relieves the weak form of any jump boundary terms. For numerical demonstration of the above formulation, we used a multimode optical fiber in an index matching liquid as the object. The scattered intensity and its normal derivative are computed from the scattered field obtained by solving the Helmholtz equation, using the new formulation and the conventional finite element method. By comparing the results with the experimentally measured scattered intensity, the stability of the solution through the new formulation is demonstrated and its closeness to the experimental measurements verified.

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.

Nanostructuring of p- and n-type skutterudites reaching figures of merit of approximately 1.3 and 1.6, respectively

Thermoelectric (TE) conversion of waste heat into useful electricity demands optimized thermal and electrical transport in the leg material over a wide temperature range. In order to gain a reasonably high figure of merit (ZT) as well as high thermal electric conversion efficiency, various conditions of the starting material were studied: industrially produced skutterudite powders of p-type DDy(Fe1-xCox)(4)Sb-12 (DD: didymium) and n-type (Mm, Sm)(y)Co4Sb12 (Mm: mischmetal) were used. After a rather fast reaction-melting technique, the bulk was crushed and sieved with various strainers in order to obtain particles below the respective mesh sizes, followed by ball-milling under three different conditions. The dependence of the TE properties (after hot pressing) on the micro/nanosized particles, grains and crystallites was investigated. Optimized conditions resulted in an increase of ZT for bulk material to current record-high values: from ZT similar to 1.1 to ZT similar to 1.3 at 775 K for p-type and from ZT similar to 1.0 to ZT similar to 1.6 at 800 K for n-type, resulting in respective efficiencies (300-850 K) of eta > 13% and eta similar to 16%. (C) 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.