Comparative analysis of the DC performance of DG MOSFETs on highly-doped and near-intrinsic silicon layers

A comparison of dc characteristics of fully depleted double-gate (DG) MOSFETs with respect to low-power circuit applications and device scaling has been performed by two-dimensional device simulation. Three different DG MOSFET structures including a conventional N+ polysilicon gate device with highly doped Si layer, an asymmetrical P+/N+ polysilicon gate device with low doped Si layer and a midgap metal gate device with low doped Si layer have been analysed. It was found that DG MOSFET with mid-gap metal, gates yields the best dc parameters for given off-state drain leakage current and highest immunity to the variation of technology parameters (gate length, gate oxide thickness and Si layer thickness). It is also found that an asymmetrical P+/N+ polysilicon gate DG MOSFET design offers comparable dc characteristics, but better parameter immunity to technology tolerances than a conventional DG MOSFET. (C) 2004 Elsevier Ltd. All rights reserved.

Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.