Vulnerability of Bluetooth receiver to impulsive noise has been assessed in this paper. Impulsive noise environment of an Electricity transmission substation environment is modelled as a Symmetric Alpha Stable process...
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ISBN:
(纸本)9781849192392
Vulnerability of Bluetooth receiver to impulsive noise has been assessed in this paper. Impulsive noise environment of an Electricity transmission substation environment is modelled as a Symmetric Alpha Stable process. Parameters of the model are estimated from the measurements carried out in control room of an electricity transmission substation.
Vulnerability of Bluetooth receiver to impulsive noise has been assessed in this *** noise environment of an Electricity transmission substation environment is modelled as a Symmetric Alpha Stable *** of the model are...
详细信息
Vulnerability of Bluetooth receiver to impulsive noise has been assessed in this *** noise environment of an Electricity transmission substation environment is modelled as a Symmetric Alpha Stable *** of the model are estimated from the measurements carried out in control room of an electricity transmission substation.
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.
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