Direct Link-Aware Relay Selection for Average Interference-Constrained Underlay Cognitive Radio

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio network. We present a novel and optimal relay selection (RS) rule that minimizes the symbol error probability (SEP) of an average interference-constrained underlay secondary system that uses amplify-and-forward relays. A key point that the rule highlights for the first time is that, for the average interference constraint, the signal-to-interference-plus-noise-ratio (SINR) of the direct source-to-destination (SI)) link affects the choice of the optimal relay. Furthermore, as the SINR increases, the odds that no relay transmits increase. We also propose a simpler, more practical, and near-optimal variant of the optimal rule that requires just one bit of feedback about the state of the SD link to the relays. Compared to the SD-unaware ad hoc RS rules proposed in the literature, the proposed rules markedly reduce the SEP by up to two orders of magnitude.

Nitrogen-assisted electroless assembling of 3D nanodendrites consisting of Pd and N-doped carbon nanoparticles as bifunctional catalysts

We report a facile synthesis of three-dimensional (3D) nanodendrites of Pd nanoparticles (NPs) and nitrogen-doped carbon NPs (N-CNPs) by electroless deposition of Pd2+ ions. N-CNPs being an electron-enriched material act as a reducing agent. Moreover, the availability of a variety of nitrogen species in N-CNPs promotes the open arm structure as well as stabilizes the oriented 3D assembly of primary Pd NPs. The dendrites exhibit superior catalytic activity for methanol (0.5 M) oxidation in alkaline media (1 M NaOH) which is ascribed to the large electrochemical active surface area and the enhanced mass activity with repeated use. Further mass activity improvement has been realized after acid-treatment of dendrites which is attributed to the increment in the -OH group. The dendrites show higher mass activity (J(f) similar to 653 A g(-1)) in comparison with a commercial Pt-carbon/Pd-carbon (Pt-C/Pd-C) catalyst (J(f) similar to 46 and 163 A g(-1), respectively), better operational stability, superior CO tolerance with I-f/I-b (similar to 3.7) over a commercial Pt-C/Pd-C catalyst (I-f/I-b similar to 1.6 and 1.75, respectively) and may serve as a promising alternative to commercial Pt-C catalysts for anode application in alkaline fuel cells. To ensure the adaptability of our 3D-nanodendrites for other catalytic activities, we studied 4-nitrophenol reduction at room temperature. The 3D-nanodendrites show excellent catalytic activity toward 4-nitrophenol reduction, as well.

Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.