Browsing by Author "Zakariyya, Rabiu Sale"

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    Epilepsy Detection and Classification for Smart IoT Devices Using hybrid Technique
    (IEEE, 2019-12-10) Saminu, Sani; Guizhi, Xu; Zhang, Shuai; Isselmou, Abd El Kader; Zakariyya, Rabiu Sale; Jabire, Adamu Halilu
    Epilepsy is a type of neurological disorder which can happen without serious warning and affects people almost at any age. It is a brain disorder caused by sudden and unprovoked seizures as a result of excitation of a lot of brain cells simultaneously which may lead to physical symptoms abnormalities and deformation such as failure in concentration, memory, attention etc. therefore, proper and efficient method of continues monitoring and detection of these epileptic seizures is paramount. This work presents an effective and efficient technique suitable for smart, low cost, power and real time devices that can be easily integrated with recent 5G network IoT devices for mobile applications, home and health care centers for monitoring and alert the doctors and patients about its occurrence to prevent a sudden collapse and consciousness which may cause injury and death. We proposed a low computational cost features extraction method by utilizing the efficacy of time-frequency, statistical and non-linear features known as hybrid techniques. The efficiency and accuracy of these smart devices is highly depends on quality of feature extraction methods and classifier performance. Therefore, this work employed two machine learning classifiers, support vector machine (SVM) and feedforward neural network (FFNN) to detect and classify interictal (normal) and ictal (seizure) signals. Discrete wavelet transform (DWT) is employed to decomposes the signals into decomposition levels as sub-bands of the signals to capture the non-stationarity of the EEG signals. Mean, median, maximum, minimum etc. were calculated for each sub-band as statistical parameters, non-linear features such as sample entropy, approximate entropy and wavelet energy were also calculated. The combination of features is then fed to two classifiers for the classification. Based on the performance measures such as accuracy, sensitivity and specificity, our proposed approach reveals a promising result with highest accuracy of 99.6%.
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    OSTBC-MIMO Performance Evaluation Using Different Modulation Schemes
    (Faculty of Technology Education, Abubakar Tafawa Balewa University Bauchi, 2020-09) Saminu, Sani; Jabire, Adamu Halilu; Jajere, Adamu Muhammed; Sadiq, Abubakar Muhammad; Zakariyya, Rabiu Sale
    This paper presents the performance evaluation of Orthogonal Space Time Block Codes on Multiple Input Multiple Output (OSTBC-MIMO) system using our proposed extended Alamouti’s STBC scheme based on Orthogonal design. The model was evaluated with different modulation schemes such as BPSK, QPSK, 8-PSK, and 16-PSK for different antenna configurations. From our results, BPSK outperforms other modulations scheme. The system model was developed in Matlab environment and Bit error rate against signal to noise ratio was used to evaluate the performance under Rayleigh faded channel. Our proposed scheme improves the channel capacity gains, data throughput, and mitigate fading and interference.