Classification of frontal alpha asymmetry using k-Nearest Neighbor

Frontal alpha asymmetry is used as the EEG feature in this study. Total number of 43 students participated in EEG data collections of relax and non-relax conditions. The spectral power of the alpha band for both left and right brain are extracted using data segmentations and then the Asymmetry Score (AS) is computed. Subtractive clustering is used to predetermine the number of cluster center that are presented in the data. While Fuzzy C-Means (FCM), is used to discriminate the EEG data into an appropriate cluster after the total number of cluster had been determined. The classification rate obtained from the k-Nearest Neighbor (k-NN) classifier is 84.62% which gives the highest classification rate

Corrosion Classification, Rate And Corrosion Product Type: A Review

Corrosion is a slow process that occurs primarily on metal surfaces, but the corrosion related losses are of high order. When considering only the loss of metal, the damages cannot be measured. Except for the least active noble metals, corrosion occurs with all metals. The indirect losses are much higher. Consideration must also be given to the cost of fabrication and cost of preventing corrosion. Indirect losses are higher than the direct losses. When a structure such as building or bridge collapsed due to problems with corrosion the damage often involves the loss of human life and property and the cost of subsequent reconstruction and alternative solution. Corrosion is quite noticeable in some types of corrosion and is only seen when an accident occurs

A computer vision approach to classification of birds in flight from video sequences

Bird populations are an important bio-indicator; so collecting reliable data is useful for ecologists helping conserve and manage fragile ecosystems. However, existing manual monitoring methods are labour-intensive, time-consuming, and error-prone. The aim of our work is to develop a reliable system, capable of automatically classifying individual bird species in flight from videos. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than when stationary. We present our work in progress, which uses combined appearance and motion features to classify and present experimental results across seven species using Normal Bayes classifier with majority voting and achieving a classification rate of 86%