Seyi E. Olukanni

Work place: Department of Physics, Confluence University of Science and Technology Osara, Nigeria



Research Interests: Models of Computation, Signal Processing, Analysis of Algorithms, Theory of Computation


Seyi E. Olukanni is a Ph.D student in the Department of Physics, Federal University Lokoja, Kogi State, Nigeria. He received his M.Sc degree in 2017 and B.Sc degree in the year 2008. Both degrees are in physics from University of Ilorin and University of Abuja respectively. His area of interest includes Electromagnetism, Signal Processing, Internet of Things and Radio communications. He can be contacted through

Author Articles
Radio Spectrum Measurement Modeling and Prediction based on Adaptive Hybrid Model for Optimal Network Planning

By Seyi E. Olukanni Joseph Isabona Ituabhor Odesanya

DOI:, Pub. Date: 8 Aug. 2023

Path loss model is fundamental to effective network planning. It provides adequate information on the extent of signal loss and help to improve the quality of service of cellular communication in an area. In this paper we used a hybrid wavelet and improved log-distance model for modeling and prediction of propagation path loss in an irregular terrain. The prediction accuracy of the proposed model was quantified using five statistical metrics. As seen presented in Table 2 and Table 3, the proposed model outperformed the standard log-distance model, the COST234 Hata and Okumura Hata models by an average of 20%. 

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Stability Analysis of COVID-19 Model with Quarantine

By Oladipupo S. Johnson Helen O. Edogbanya Jacob Emmanuel Seyi E. Olukanni

DOI:, Pub. Date: 8 Aug. 2023

In this paper, A 6 (six) compartmental (S, IU, IS, IA, Q, R) model was presented to examine the dynamical behavior of disease transmission in the system with quarantine effect on the symptomatic infected, asymptomatic infected and Reproduction number R0 within a given population. The parameters model was analyzed and estimated experimentally using the real data of COVID-19 confirmed cases for Ethiopia via MATLAB 2021a. Reproduction number R0 which is a key indicator to whether a disease outbreak spread force will persist or die out within population. R0 was found using the next generation matrix with Gaussian elimination method to obtain the inverse of the transitive matrix. The model also aims at reducing R0 owning to the fact that when the basic reproduction number is less than 1 infected person, disease dies out and when the reproduction number is greater than 1 infected person, the disease persists. The facts about R0 geared us to mathematically check for the Routh-Hurwitz stability criteria and Lyapunov Functions to concisely establish the necessary and sufficient conditions for the Local and Global stability of model. results show that, when R0 < 1 and R0 > 1 the diseases free equilibrium and endemic equilibrium points are locally and globally asymptotically stable respectively. In order to interpret results and recommend possible control measure of disease, The dynamics of the Quarantine compartment in model was tested via sensitivity analysis to experimentally investigate transition/ transmission pattern. The effect of quarantine analysis on the model shows that preventive measures such as increase in quarantine with treatments during disease outbreak will significantly decrease the Reproduction number. Hence, increase in Quarantine compartment will flatten the curve of (S, IU, IS, IA, Q, R) dynamic model correspondingly.

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Enhancing Lte Rss for a Robust Path Loss Analysis with Noise Removal

By Seyi E. Olukanni Joseph Isabona Ituabhor Odesanya

DOI:, Pub. Date: 8 Jun. 2023

Wavelet transform has become a popular tool for signal denoising due to its ability to analyze signals effectively in both time and frequency domains. This is important because the information that is not visible in the time domain can be seen in the frequency domain. However, there are many wavelet families and thresholding techniques (such as haar, Daubechies, symlets, coiflets, meyer Gaussian, morlet, etc) thatare available for the analysis of signals, and choosing the best out of them all is usually time-consuming, thus making it a difficult task for researchers. In this article, we proposed and applied a stepwise expository-based approach to identify the wavelet family and thresholding technique using real-time signal power data acquired from Long-Term Evolution (LTE). We found out from the results that Rigrsure thresholding with the Daubenchies family outperforms others when engaged in practical signal processing. The stepwise expository-based approach will be a relevant guide to effective signal processing over cellular networks, globally. For validation, different datasets were used for the analysis and Rigrsure outperforms the other thresholding techniques.

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A Gaussian Process Regression Model to Predict Path Loss for an Urban Environment

By Seyi E. Olukanni Ikechi Risi Salifu. F. U. Johnson Oladipupo S.

DOI:, Pub. Date: 8 May 2023

This research paper presents a Gaussian process regression (GPR) model for predicting path loss signal in an urban environment. The Gaussian process regression model was developed using a dataset of path loss signal measurements acquired in two urban environments in Nigeria. Three different kernel functions were selected and compared for their performance in the Gaussian process regression model, including the squared exponential kernel, the Matern kernel, and the rotational quadratic kernel. The GPR model was validated and evaluated using various performance metrics and compared with different regression models. The results show that the Gaussian process regression model with the Matern kernel outperforms the linear regression and the support vector regression, but the decision tree and the random forest regression did better than the GPR in both cities. In the city of Port Harcourt, the GPR has a RMSE value of 3.0776 dB, the DTR has 2.0005 dB, the SVR has 3.6047 dB, the RFR has 1.0459 dB, and the LR 3.5947dB. The proposed GPR model provides more accurate and efficient approach to predict path loss compared to traditional methods. The extensive data collection and analysis conducted has resulted in a well-developed and accurate model.

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