School of Electrical Engineering and Technology (SEET)
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School of Electrical Engineering and Technology (SEET)
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Item ANALYSIS OF SPECTRUM OCCUPANCY PREDICTION RESULTS FOR MAITAMA ABUJA(International Conference on Communication and Information Science (ICCIS), 2024) Ajiboye, Johnson Adegbenga; Mary Adebola Ajiboye; Babatunde Araoye Adegboye; Daniel Jesupamilerin Ajiboye; Jonathan Gana Kolo; Abiodun Musa AibinuThis research investigates the efficacy of Artificial Neural Networks (ANN) in predicting spectrum occupancy in Maitama, Abuja, Nigeria, focusing on frequency bands ranging from 30 MHz to 300 MHz. The primary objective was to evaluate the accuracy of ANN-based predictions of spectrum usage and compare these predictions with actual measurements. The study employed ANN to forecast spectrum occupancy across various frequency bands, and the predicted data were then compared with empirical measurements to assess the performance of the model. The analysis revealed that prediction errors were generally low across all frequency bands, with most errors falling below 1.5%. Specifically, the 30-47 MHz sub-band demonstrated an average percentage difference between the actual and predicted value of 0.087%, with a maximum error of 1.12% occurring at frequency of 44.65 MHz. For the 47.05-68 MHz band, the average percentage difference was slightly higher at 0.106%, and the maximum error was 2.18% occurring at frequency of 50.2 MHz. In the 68.05-74.8 MHz band, the average percentage error was 0.040%, but with highest error of 0.232% at frequency of 73.95 MHz. The 74.85-87.45 MHz band showed the most accurate predictions with an average error of just 0.010%, and a maximum error of 0.174% at 75.1 MHz. Overall, the highest prediction error was 0.106% in the 47.05-68 MHz band, whereas the lowest was 0.010% in the 74.85-87.45 MHz band. These results highlight the high accuracy of ANN in predicting spectrum usage, demonstrating its potential for effective spectrum management and planning in Maitama, Abuja.Item STATE OF THE ART ON PATH LOSS MODEL DEVELOPMENT(Humminbird Publications and Research International, 2024-01-29) Ibukun Aderele Adeyemi; Jonathan Gana Kolo; Ajiboye, Johnson AdegbengaThis is a study of path loss prediction modelling. Path loss modelling is widely applied in determining mobile wireless signal propagation in a given environment. This helps radio network planners to have an accurate view of requirements to obtain good quality of service when deploying radio networks. The empirical models are exhaustively analysed and compared with the emerging machine learning models. Also, mention is made of RIS models which are beginning to gather some attention due to their focus on the programmable electromagnetic properties. The study was able to establish empirical models as the most simple and efficient method of path loss prediction models. Attention is paid to the application of these models in both 900MHz and 1800MHz in urban, suburban and rural areas. This is due to the wide application of these frequencies in mobile wireless communication. The machine learning models present better results and give a high level of accuracy for diverse environments. However, they require large volume of data and environmental features extraction at both 2D and 3D to get reliable model. This makes it imperative to carry out field measurement tasks that are basically synonymous with methodologies employed in empirical approach to modelling. The variation in vegetation determines the best fit model for each particular case as well as the derivation of path loss exponent. The RIS modelling approach gives positive views especially at higher frequencies. The tuneable properties of the surfaces give a wide berth in application across different frequency spectrum. Complex and large volume of computation required in use of RIS implies that machine learning models, especially deep learning models will be better off incorporated into the process. It is thus beneficial to the researcher to ensure that a good grasp of the different approaches highlighted is obtained such that the benefits available are merged to produce finer results.Item HYBRID AUTOREGRESSIVE NEURAL NETWORK (ARNN) MODEL FOR SPECTRUM OCCUPANCY PREDICTION(NJEAS, 2022) Ajiboye, Johnson Adegbenga; Adegboye B.A; Aibinu A.M; Kolo J.G; Ajiboye M.A; Usman A.UA secondary spectrum user cannot transmit in a channel before sensing and knowing the spectrum occupancy state as this may cause interference. This poses a major challenge because these operations ought to be performed in each time slot and thereby causing a substantial delay before the user gains access to the spectrum, leading to inefficient utilization. Therefore, a channel predictive system will mitigate this problem. In this work, an ensemble machine learning model for spectrum occupancy prediction was developed. The developed model was trained using a sample of Power Spectrum Density (PSD) data collected from the field for a period of twenty four hours within a frequency range of 30-300 MHz. The frequency range was grouped into sub bands. Based on the training data and the corresponding output data, the neural network model trains itself to come up with the best weights which can generally be used by the AR model for unseen data. After computing the weights, the performance was first tested on the entire training data, on the validation dataset and on the test dataset. Prediction results revealed an overall accuracy of 98.32% with band 4 (74.85-87.45 MHz) having the highest accuracy of 99.01% and the lowest accuracy of 89.39% in band 2 (47.05-68 MHz).Item Design and Development of An IoT – Based Multi – Health Vital Signs Monitoring System(I3C 2024, 2024-04-22) Adegboye N.J; Dauda U.S; Ajiboye, Johnson Adegbenga; Ohize H.O; Adegboye B.AThis study presents the design and development of an IoT-based multi-health vital signs monitoring system that can monitor a patient’s basic health physiological parameters in real-time. In this system, four (4) sensors were used to capture the data from the patient. These are body temperature sensor, electrocardiogram (ECG) sensor, accelerometer sensor and the eye blink sensor. The hardware modules were interfaced with the liquid crystal display (LCD) to display the required data. Memory modules stored the designated phone numbers. The GSM module retains connectivity with the cellular networks acting as SMS receiver, which sends data on the patient’s vital signs. The LCD displays the data, which can be seen through the IoT. The microcontroller was programmed using C++ programming language and connects all sensors. This enabled conveyance of data on the patient’s health condition via IoT to the doctor for further processing and analysis.