Abstract-Experimental pain has extensively been used as a tool for investigating neural mechanisms and the psychological factors involved in pain processing. The detection of existence and/or level of pain is vital when verbal information is not present e.g. for infants, disabled persons, anesthetized patients and animals also. This study shows that there is a firm relation between Electroencephalogram (EEG) and chronic pain levels and EEG can be used as a reliable tool for detecting, measuring and diagnosing pain levels in humans.This paper proposed a use of wavelet coherency in order to estimate the three pain levels and its usage as an index for pain measurement. Besides, wavelet coefficients are studied to show consistencies with EEG dynamic were extracted to provide the feature vector. A Hidden Markov Model (HMM) and a support vector machine (SVM) scheme was used for pain levels classification. This study confirms the hypothesis that brain pattern under the chronic pain mental task is mapped on EEG and the dependency of brain patterns to EEG is possible and detectable.Index Terms-Chronic pain index, electroencephalogram, SVM, HMM.

No abstract

No abstract

Abstract-The authors examine the impact of residential photovoltaic arrays and electric vehicles on distribution transformers by using 3-D surface and 2-D filled contour plots. These visualizations, somewhat unorthodox to power distribution analysis, elucidate the impact of hundreds of assets on distribution transformers on a single view. The visualizations are created with a smart grid computer model that accepts residential electrical recordings in one minute intervals. An analysis of simulation results shows that the electrical footprint experienced by a residential community and its distribution transformers stems from photovoltaic arrays rather than from electric vehicles. Additionally, the results indicate the existing distribution assets may be ready to support the proliferation of photovoltaic arrays and electric vehicles, a common concern across utilities in the United States.

No abstract