Zinc oxide nanowire networks for macroelectronic devices

Highly transparent zinc oxide (ZnO) nanowire networks have been used as the active material in thin film transistors (TFTs) and complementary inverter devices. A systematic study on a range of networks of variable density and TFT channel length was performed. ZnO nanowire networks provide a less lithographically intense alternative to individual nanowire devices, are always semiconducting, and yield significantly higher mobilites than those achieved from currently used amorphous Si and organic TFTs. These results suggest that ZnO nanowire networks could be ideal for inexpensive large area electronics. © 2009 American Institute of Physics.

Frequency diversity measurements at 2.4 GHz for wireless sensor networks deployed in tunnels

Wireless Sensor Networks (WSNs) which utilise IEEE 802.15.4 technology operate primarily in the 2.4 GHz globally compatible ISM band. However, the wireless propagation channel in this crowded band is notoriously variable and unpredictable, and it has a significant impact on the coverage range and quality of the radio links between the wireless nodes. Therefore, the use of Frequency Diversity (FD) has potential to ameliorate this situation. In this paper, the possible benefits of using FD in a tunnel environment have been quantified by performing accurate propagation measurements using modified and calibrated off-the-shelf 802.15.4 based sensor motes in the disused Aldwych underground railway tunnel. The objective of this investigation is to characterise the performance of FD in this confined environment. Cross correlation coefficients are calculated from samples of the received power on a number of frequency channels gathered during the field measurements. The low measured values of the cross correlation coefficients indicate that applying FD at 2.4 GHz will improve link performance in a WSN deployed in a tunnel. This finding closely matches results obtained by running a computational simulation of the tunnel radio propagation using a 2D Finite-Difference Time-Domain (FDTD) method. ©2009 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.