Fabrication of high-Q film bulk acoustic resonator (FBAR) filters withcarbon nanotube (CNT) electrodes

FBAR devices with carbon nanotube (CNT) electrodes have been developed withthe aim of taking advantage of the low density and high acoustic impedance ofthe CNTs compared to other known materials. The influence of the CNTs on thefrequency response of the FBAR devices was studied by comparing two identicalsets of devices, one set comprised FBARs fabricated with chromium/gold bilayerelectrodes, and the second set comprised FBARs fabricated with CNT electrodes.It was found that the CNTs had a significant effect on attenuating travellingwaves at the surface of the FBARs membranes due to their high elastic stiffness.Finite element analysis of the devices fabricated was carried out using COMSOLMultiphysics, and the numerical results confirmed the experimental resultsobtained. © 2010 IEEE.

Gated three-terminal device architecture to eliminate persistent photoconductivity in oxide semiconductor photosensor arrays

The composition of amorphous oxide semiconductors, which are well known for their optical transparency, can be tailored to enhance their absorption and induce photoconductivity for irradiation with green, and shorter wavelength light. In principle, amorphous oxide semiconductor-based thin-film photoconductors could hence be applied as photosensors. However, their photoconductivity persists for hours after illumination has been removed, which severely degrades the response time and the frame rate of oxide-based sensor arrays. We have solved the problem of persistent photoconductivity (PPC) by developing a gated amorphous oxide semiconductor photo thin-film transistor (photo-TFT) that can provide direct control over the position of the Fermi level in the active layer. Applying a short-duration (10 ns) voltage pulse to these devices induces electron accumulation and accelerates their recombination with ionized oxygen vacancy sites, which are thought to cause PPC. We have integrated these photo-TFTs in a transparent active-matrix photosensor array that can be operated at high frame rates and that has potential applications in contact-free interactive displays. © 2012 Macmillan Publishers Limited. All rights reserved.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

Air quality and climate impacts of alternative bus technologies in Greater London.

The environmental impact of diesel-fueled buses can potentially be reduced by the adoption of alternative propulsion technologies such as lean-burn compressed natural gas (LB-CNG) or hybrid electric buses (HEB), and emissions control strategies such as a continuously regenerating trap (CRT), exhaust gas recirculation (EGR), or selective catalytic reduction with trap (SCRT). This study assessed the environmental costs and benefits of these bus technologies in Greater London relative to the existing fleet and characterized emissions changes due to alternative technologies. We found a >30% increase in CO2 equivalent (CO2e) emissions for CNG buses, a <5% change for exhaust treatment scenarios, and a 13% (90% confidence interval 3.8-20.9%) reduction for HEB relative to baseline CO2e emissions. A multiscale regional chemistry-transport model quantified the impact of alternative bus technologies on air quality, which was then related to premature mortality risk. We found the largest decrease in population exposure (about 83%) to particulate matter (PM2.5) occurred with LB-CNG buses. Monetized environmental and investment costs relative to the baseline gave estimated net present cost of LB-CNG or HEB conversion to be $187 million ($73 million to $301 million) or $36 million ($-25 million to $102 million), respectively, while EGR or SCRT estimated net present costs were $19 million ($7 million to $32 million) or $15 million ($8 million to $23 million), respectively.