Media use and changing identities : the case of Cameroonians in Oslo

This empirical study explores the ways in which minority ethnic communities consume media and how they construct their different identities through their negotiation with media texts. This work focuses on four main types of media; television, newspapers, radio, and Internet. It draws on research among Cameroonians living in Oslo, examining their attitudes evaluations and uses of the media available to them. It uses qualitative interviews with 14 people as its main source of empirical data. This dissertation seeks to discuss the ways in which these individuals use the diverse media they have access to with particular reference to the process of the construction of diasporic and ethnic minority identities

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

Objective: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain–computer interface (BCI) applications. Approach: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results: We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance: The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI

Arc Flash Mitigation: Overview of Codependent System Studies Relevant to IEEE Standard 1584

Accidents due to arc flash events are currently of special interest in the electrical power industry. These events often result in serious injuries, deaths, equipment damage, facility shutdowns, lawsuits, and penalties. Risk assessments are usually performed by the power systems engineer during the design phase to mitigate the effects of potential arc flash occurrences. The objective of this thesis is to demonstrate the significance of arc flash hazard risk assessments implemented during the installation of electrical power equipment. This thesis presents a synopsis of the main industry design standards and codes that govern the design of electrical distribution systems in commercial and industrial facilities. Simulations were performed for a case study using SKM Power Tools to demonstrate the interpretation and practical application of these standards and codes. Electrical studies and analyses were performed on the model, and recommendations were provided to address the mitigation of potential arc flash incidents throughout the electrical network of the case study.Electrical and Computer Engineering, Department o