Surface electromyography (SEMG) signals are widely used in fatigue identification. Fatigue after high intensity exercise and sports training needs to be balanced with rest to allow biochemical reactions during sports ...
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Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and i...
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Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. controlling the false positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this paper, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space.
We consider the class of multiscale stochastic models developed by Chou, Willsky and Benveniste (see IEEE Trans. on Automatic control, vol.39, no.3, 1994) and by Luettgen, Karl, Willsky and Tenney (see IEEE Trans. Sig...
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We consider the class of multiscale stochastic models developed by Chou, Willsky and Benveniste (see IEEE Trans. on Automatic control, vol.39, no.3, 1994) and by Luettgen, Karl, Willsky and Tenney (see IEEE Trans. signalprocessing, vol.41, no.12, 1993) for signal and image modeling. These are Markov random field models on trees that describe signals in a scale-recursive way. In particular, they are state-space models with dynamics with respect to scale and have available fast algorithms for smoothing data. We present a maximum likelihood (ML) procedure for estimating the state-space parameters of these models from data. The procedure uses the expectation-maximization (EM) algorithm to iteratively solve for the ML estimates. Each iteration consists of (1) an expectation step that takes advantage of the fast smoother available for these multiscale models and (2) a maximization step that is also fast. We present an example of using this procedure to identify parameters based on imagery data and, subsequently, to perform multiscale target detection.
Key issues regarding the operation of the broadband integrated services digital network (BISDN) via satellite are presented herein. The specific issues, challenges, and their resolutions are detailed. In particular, t...
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Key issues regarding the operation of the broadband integrated services digital network (BISDN) via satellite are presented herein. The specific issues, challenges, and their resolutions are detailed. In particular, the impact of error characteristics and propagation delay on the operation of BISDN via satellite is discussed. Solutions are presented for removing adverse effects and providing high-quality service to users of BISDN via satellite.
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