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This paper presents a predictive model for the Negative Bias Temperature Instability (NBTI) of PMOS under both short term and long term operation. Based on the reactiondiffusion (R-D) mechanism, this model accurately captures the dependence of NBTI on the oxide thickness (tox), the diffusing species (H or H2) and other key transistor and design parameters. In addition, we derive a closed form expression for the threshold voltage change (∆V th ) under multiple cycle dynamic operation. Model accuracy and efficiency were verified with 90nm experimental and simulation data. We further investigated the impact of NBTI on representative digital circuits.

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Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in the general case, an unobserved subsequence may occur at any time by yielding a temporal gap in the video. In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case. Specifically, we formulate the problem into a probabilistic framework: 1) dividing each activity into multiple ordered temporal segments, 2) using spatiotemporal features of the training video samples in each segment as bases and applying sparse coding (SC) to derive the activity likelihood of the test video sample at each segment, and 3) finally combining the likelihood at each segment to achieve a global posterior for the activities. We further extend the proposed method to include more bases that correspond to a mixture of segments with different temporal lengths (MSSC), which can better represent the activities with large intra-class variations. We evaluate the proposed methods (SC and MSSC) on various real videos. We also evaluate the proposed methods on two special cases: 1) activity prediction where the unobserved subsequence is at the end of the video, and 2) human activity recognition on fully observed videos. Experimental results show that the proposed methods outperform existing state-of-the-art comparison methods.