Browsing by Author "Sani Saminu"

Now showing 1 - 3 of 3
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    Design and Performance Analysis of a Tissue-Load Stable H-Slotted Patch Antenna for 2.45 GHz Breast Phantom Sensing Applications
    (Centrepoint Journal (Science Edition), 2026) Salaudeen K. Olawale; Sani Saminu; Yahya A. Suleiman; Muniru O. Idris; Adisa Y. Adebayo
    The detection of solid tumors, especially in the breast, is crucial to improving patient outcomes and minimizing the side effects of cancer treatment. This study presents a highly miniaturized rectangular H-slotted PIFA operating in the 2.45 GHz ISM band; the design incorporates an H-shaped slot on top of the radiating patch to reduce the antenna size and improve current-path meandering. Simulation results from CST Studio Suite indicate that the proposed design resonates at 2.45 GHz with a -10 dB impedance bandwidth of approximately 277 MHz (2.33 – 2.61 GHz), a record Return Loss (S11) of -51.65 dB, and a VSWR of 1.005 in free-space conditions, demonstrating a near-perfect impedance match. The antenna exhibits a realized gain of 3.5 dBi at 70% efficiency. and a directional radiation pattern with a directivity of 4.16 dBi, enabling effective electromagnetic energy penetration into breast tissues. The antenna was also shown to be stable in its resonant frequency while modeled within a three-layered breast phantom model (skin-fat-gland). The presence of a 5 mm malignant tumor produces a noticeable shift in the reflection coefficient from -51.65 dB to -39.20 dB, demonstrating the antenna’s sensitivity to dielectric-property variations between healthy and cancerous tissues. Finally, a maximum 1-g averaged specific absorption rate (SAR) of 0.103 W/kg at 10 mW input was achieved, which is substantially below the FCC safety limit of 1.6 W/kg. The results demonstrate that the proposed H-slotted antenna is a compact and stable sensor with the capability to detect early-stage breast tumors effectively
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    Design and Performance Analysis of a Tissue-Load Stable H-Slotted Patch Antenna for 2.45 GHz Breast Phantom Sensing Applications
    (University of Ilorin Library and Publications Committee, 2026) Salaudeen K. Olawale; Sani Saminu; Yahya A. Suleiman; Muniru O. Idris; Adisa Y. Adebayo
    The detection of solid tumors, especially in the breast, is crucial to improving patient outcomes and minimizing the side effects of cancer treatment. This study presents a highly miniaturized rectangular H-slotted PIFA operating in the 2.45 GHz ISM band; the design incorporates an H-shaped slot on top of the radiating patch to reduce the antenna size and improve current-path meandering. Simulation results from CST Studio Suite indicate that the proposed design resonates at 2.45 GHz with a -10 dB impedance bandwidth of approximately 277 MHz (2.33 – 2.61 GHz), a record Return Loss (S11) of -51.65 dB, and a VSWR of 1.005 in free-space conditions, demonstrating a near-perfect impedance match. The antenna exhibits a realized gain of 3.5 dBi at 70% efficiency. and a directional radiation pattern with a directivity of 4.16 dBi, enabling effective electromagnetic energy penetration into breast tissues. The antenna was also shown to be stable in its resonant frequency while modeled within a three-layered breast phantom model (skin-fat-gland). The presence of a 5 mm malignant tumor produces a noticeable shift in the reflection coefficient from -51.65 dB to -39.20 dB, demonstrating the antenna’s sensitivity to dielectric-property variations between healthy and cancerous tissues. Finally, a maximum 1-g averaged specific absorption rate (SAR) of 0.103 W/kg at 10 mW input was achieved, which is substantially below the FCC safety limit of 1.6 W/kg. The results demonstrate that the proposed H-slotted antenna is a compact and stable sensor with the capability to detect early-stage breast tumors effectively
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    Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events
    (Bima Journal of Science and Technology, 2024) Sani Saminu; Adamu Halilu Jabire; Hajara Abdulkarim Aliyu; Adamu Ya’u Iliyasu; Suleiman Abimbola Yahaya; Morufu Olusola Ibitoye; Guizhi Xu
    Prediction of Epileptic seizures is highly imperative to improve the epileptic patient’s life. Epileptic seizures occur due to brain cells excessive abnormal activity that leads to unprovoked seizures and may occur without prior notice. Therefore, preventive measure that monitor and alert the possible occurrence of the seizures is paramount. Commercial and clinical available epileptic seizure computer aided detection system that utilized deep learning algorithms suffers from many challenges. These challenges ranges from low accuracy and precision, sensitive to artifacts and noise, among others. To enhance and increase the accuracy and optimal performance of these networks, this paper endeavor to investigate various optimization algorithm to optimized the network components and parameters in the developed incremental Principal Components Analysis based Long Short-Term Memory (Inc-PCA-LSTM) network for the detection and classification of Electroencephalograph (EEG) epileptic seizure signals based on the big data scenario. The model proved to be effective in the characterization of seven seizure events. The Adam, Elu, Orthogonal, and L1/L2 performed better than their counterparts in optimization functions, activation functions, initialization functions, and regularisation techniques respectively. The accuracy values of 97.5%, 97.5%, 98.4%, and 98.5% was obtained for each of the mentioned core components receptively.