A quantum dot sensitized solar cell based on vertically aligned carbon nanotube templated ZnO arrays

We report on a quantum dot sensitized solar cell (QDSSC) based on ZnO nanorod coated vertically aligned carbon nanotubes (VACNTs). Electrochemical impedance spectroscopy shows that the electron lifetime for the device based on VACNT/ZnO/CdSe is longer than that for a device based on ZnO/CdSe, indicating that the charge recombination at the interface is reduced by the presence of the VACNTs. Due to the increased surface area and longer electron lifetime, a power conversion efficiency of 1.46% is achieved for the VACNT/ZnO/CdSe devices under an illumination of one Sun (AM 1.5G, 100 mW/cm2). © 2010 Elsevier B.V.

Partial soi power LDMOS with a variable low-kappa; Dielectric buried layer and a buried P layer

A power LDMOS on partial silicon on insulator (PSOI) with a variable low-κ dielectric (VLKD) buried layer and a buried p (BP) layer is proposed (VLKD BPSOI). At a low κ value, the electric field strength in the buried dielectric (EI) is enhanced, and a Si window makes the substrate share the vertical voltage drop, leading to a high vertical breakdown voltage (BV). Moreover, three interface field peaks are introduced by the BP, the Si window, and the VLKD, which modulate the fields in the SOI layer, the VLKD layer, and the substrate; consequently, a high BV is obtained. Furthermore, the BP reduces the specific on-resistance (Ron), and the Si window alleviates the self-heating effect (SHE). The BV for VLKD BPSOI is enhanced by 34.5%, and Ron is decreased by 26.6%, compared with those for the conventional PSOI, and VLKD BPSOI also maintains a low SHE. © 2006 IEEE.

Quasi-static three-point bending of carbon fiber sandwich beams with square honeycomb cores

Sandwich beams comprising identical face sheets and a square honeycomb core were manufactured from carbon fiber composite sheets. Analytical expressions were derived for four competing collapse mechanisms of simply supported and clamped sandwich beams in three-point bending: core shear, face microbuckling, face wrinkling, and indentation. Selected geometries of sandwich beams were tested to illustrate these collapse modes, with good agreement between analytic predictions and measurements of the failure load. Finite element (FE) simulations of the three-point bending responses of these beams were also conducted by constructing a FE model by laying up unidirectional plies in appropriate orientations. The initiation and growth of damage in the laminates were included in the FE calculations. With this embellishment, the FE model was able to predict the measured load versus displacement response and the failure sequence in each of the composite beams. © 2011 American Society of Mechanical Engineers.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.