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Time series measured in real world is often nonlinear, even chaotic. To effectively extract desired information from measured time series, it is important to preprocess data to reduce noise. In this Letter, we propose an adaptive denoising algorithm. Using chaotic Lorenz data and calculating root-mean-square-error, Lyapunov exponent, and correlation dimension, we show that our adaptive algorithm more...
We revisit the classic problem of developing a correlation model for natural videos and studying their theoretical rate distortion bounds. We propose the correlation coefficient of two pixels in two nearby video frames as the product of the spatial correlation coefficient of these two pixels, as if they were in the same frame, and a variable to quantify the temporal correlation between these two video...
With the shrinking device size, process variations have a growing impact on circuit performance for today's integrated circuit (IC) technologies. In this paper, a hierarchical modeling for performance analysis is built. A novel parameter reduction method based on CH (Correlation-Hessian matrix) is presented. It accounts for all correlations, from manufacturing process dependence, to high-level analysis...
We revisit the classic problem of developing a spatial correlation model for natural images and video by proposing a conditional correlation model for relatively nearby pixels that is dependent upon four parameters. The conditioning is on local texture and the conditional correlation model is presented for each of the nine 4 by 4 intra-modes used in the AVC/H.264 video coding standard. We use this...
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