Abstract: Increase in computer processing speed and power results in an increase in heat flux dissipation, this necessitates higher transistor densities to reduce the path that a signal needs to travel, which in turn lead to the use of multichip modules, (arrays of chips placed on one substrate). In this study MATLAB, programming language was used to model the effect of fin geometry on cooling process of computer microchips. The fin geometries used in the study were pin fin, rectangular fin and triangular fin for Aluminium, Copper, Beryllium and Zinc as material of construction. From the results obtained at Multi Chip Module (MCM) power (which ranges from 500 to 900 watt) and the maximum chips surface temperature maintained at 90°C, triangular spine fin geometry exhibited higher heat dissipation per unit volume, higher heat dissipation efficiency and higher maximum heat loss per number of fins as compared to the pin and rectangular spine fin geometry. The results of the study will help heat sink designer in taking decision on the best fin geometry to be used for computer microchips application for a specific MCM power.Abstract: Increase in computer processing speed and power results in an increase in heat flux dissipation, this necessitates higher transistor densities to reduce the path that a signal needs to travel, which in turn lead to the use of multichip modules, (arrays of chips placed on one substrate). In this study MATLAB, programming language was used to model t...Show More
Abstract: Brain is the most complex organ amongst all the systems in human body. The study of the electrical signals produced by neural activities of human brain is called Electroencephalogram. Electroencephalogram (EEG) is a technique which is used to identify the neurological disorder of brain. Epilepsy is one of the most common neurological disorders of brain. Epilepsy needs to be detected efficiently using required EEG feature extraction such as: mean, standard deviation, median, entropy, kurtosis and skewness etc. The framework of proposed technique is an efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. Extraction of the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM), neural network analysis (NNA) and k-nearest neighbour (K-NN). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and k-NN. It has been found that the computation time of NNA classifier is lesser than SVM and k-NN to provide 100% accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NNA classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.Abstract: Brain is the most complex organ amongst all the systems in human body. The study of the electrical signals produced by neural activities of human brain is called Electroencephalogram. Electroencephalogram (EEG) is a technique which is used to identify the neurological disorder of brain. Epilepsy is one of the most common neurological disorders of b...Show More
