Electrical & Electronics Engineering

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Electrical & Electronics Engineering

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Author: search.filters.author.Ajiboye, Johnson Adegbenga

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Now showing 1 - 8 of 8
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    Performance Analysis of Data Normalization Methods
    (International Engineering Conference 2017, 2017-10-17) Ajiboye, Johnson Adegbenga; Aibinu M.A
    Statistical Data Normalization is a very important input preprocessing operation that should be done before data is fed into the training network. However, there is need for a suitable selection of normalization technique since normalization on the input has potential of varying the structure of the data and may impact on the outcome of the analysis. This paper investigates and evaluates some important statistical normalization techniques by studying thirty published papers that used wine dataset available in the UCI repository and their impact on performance accuracy. Results reveal that Min-Max normalization technique had the best performance accuracy of 95.91% on the average among all the other normalization types.
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    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 Aibinu
    This 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.
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    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 Adegbenga
    This 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.
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    Performance Analysis of Statistical Time Division Multiplexing Systems
    (Leonardo Electronic Journal of Practices and Technologies, 2010) Ajiboye, Johnson Adegbenga; Yinusa Ademola Adediran
    Multiplexing is a way of accommodating many input sources of a low capacity over a high capacity outgoing channel. Statistical Time Division Multiplexing (STDM) is a technique that allows the number of users to be multiplexed over the channel more than the channel can afford. The STDM normally exploits unused time slots by the non-active users and allocates those slots for the active users. Therefore STDM is appropriate for bursty sources. In this way STDM normally utilizes channel bandwidth better than traditional Time Division Multiplexing (TDM). In this work, the statistical multiplexer is viewed as M/M/1queuing system and the performance is measured by comparing analytical results to simulation results using Matlab. The index used to determine the performance of the statistical multiplexer is the number of packets both in the system and the queue. Comparison of analytical results was also done between M/M/1 and M/M/2 and also between M/M/1 and M/D/1 queue systems. At high utilizations, M/M/2 performs better than M/M/1. M/D/1 also outperforms M/M1.
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    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.U
    A 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).
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    Pattern and Feeder Design for the Production of Grey Cast Iron Brackets
    (IUP Journal of Mechanical Engineering, 2013-08-01) Mary Adebola Ajiboye; Matthew Sunday Abolarin; Ajiboye, Johnson Adegbenga
    In this paper, the design and production of sand casting for a gray cast iron bracket was carried out. The bracket was divided into different sections called Appendages A,B,C and D, and Ribs E and F. Efficient feeder design is important so as to minimize casting defects such as porosity and incomplete filling to the barest minimum. The feeder or riser is used to feed metal to the casting as it solidifies; therefore, they are designed and positioned such as to ensure filling the cavity during solidification. The implication of this is that the riser must be designed to be large enough so that it solidifies only after the casting and it should contain a sufficient volume of metal capable of supplying the shrinkage contraction which occurs on cooling from the casting temperature to the completion of solidification. Based on the feeder design in this work, two bracket castings were produced.
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    Design and Implementation of a 5 kVA Inverter
    (IUP Journal of Electrical and Electronic Engineering, 2016-10-01) Ajiboye, Johnson Adegbenga; Chukwuka Anene; Mary Adebola Ajiboye; Abraham U. Usman
    The paper describes the design and construction of a 5 kVA Pulse Width Modulated (PWM) Metal Oxide Semiconductor Field Effect Transistor (MOSFET)-based inverter, which works on the principle of PWM. The inverter uses IC SG3524 and a pair of Twelve MOSFETs to drive the load. The design and implementation starts with the power supply. Component selection was made with the aid of electronics data book, which made the design and calculations very easy. One main feature of this inverter is the monitoring section, and the battery-charging section connected to the inverter circuit. The inverter converts DC supply of the battery into AC power supply required by most electrical appliances/equipment when the AC main is not available; and when the AC main is available, the supply goes to the AC main sensor, the relays and battery charging section of the inverter. This inverter can be used for domestic purpose, and it is not recommended for industrial purpose where high current is required for application, such as starting a heavy-duty motor
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    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.A
    This 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.