Ali M. Baniyounes

EEG Mouse:A Machine Learning-Based Brain Computer Interface

The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time. Keywords—EEG; BCI; Data Mining; Machine Learning; SVMs; NNs; DWT; Feature Extraction

Review on Renewable Energy Potential in Australian Subtropical Region (Central and North Queensland)

In Australia the future demand for energy is predicted to increase rapidly. Conventional energy resources soaring prices and environmental impact have increased the interest in renewable energy technology. As a result of that the Australian government is promoting renewable energy; such as wind, geothermal, solar and hydropower. These types of energy are believed to be cost-effective and environmentally friendly. Renewable energy availability is controlled by climatic conditions such as solar radiation, wind speed and temperature. This paper aims to assess the potential of renewable energy resources, in particular wind and solar energy in an Australian subtropical region (Central and North Queensland) namely, Gladstone, Emerald, Rockhampton, Yeppoon, Townsville, and Cairns. Analysis is done by using the latest statistical state of Queensland energy information, along with measured data history of wind speed, solar irradiations, air temperature, relative humidity, and atmospheric pressure for those sites. This study has also shown that national assessments of solar and wind energy potential can be improved by improving local climatic data assessments using spatial databases of Central and North Queensland areas.