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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Ilesanmi B. Oluwafemi
Ekiti State University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Radio frequency peak and average power density from mobile base stations in Ekiti State, Nigeria Ilesanmi B. Oluwafemi; Adedeji M. Faluru; Tayo D. Obasanyo
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.1879

Abstract

The ever-increasing number of mobile telecommunication base station as a result of increasing demand for broadband applications has raised a growing concern and worry over the health implications and safety of the radiations from these base stations by the resident of Ekiti State and Nigeria in Nigeria. Measurement of radio frequency was conducted in this research in order to study the electromagnetic field radiation level in Ekiti State Nigeria. Investigation was conducted with the four available mobile operators with the three sub-frequency band viz 900 MHz, 1800 MHz and 2100 MHz. The power density of radio frequency radiation was estimated through measurement with the aid of A 3-Axis RF Radiation Strength Meter TM-196 and Handheld Spectrum Analyzer Model NA-773, 144/430. The peak and average power density was computed using the method of theoretical calculation and the safety distance from the antennas were estimated. The measured and the calculated values were compared with the international commission on non-ionizing radiation protection (ICNIRP) standard for public and occupational exposure level. Results show that the radiations from the base stations adheres to the standard provided by ICNIRP
Predicting trapped victims in debris using signal analysis ensemble classification Enoch Adama Jiya; Ilesanmi B. Oluwafemi; Olayinka O. Ogundile; Oluwaseyi P. Babalola
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp493-505

Abstract

One major difficulty in pervasive computing is trapped human detection in search and rescue (SAR) scenarios. Accurately identifying trapped individuals is challenging due to noisy data and the curse of dimensionality. When non-line-of-sight (NLOS) conditions are present during catastrophic occurrences, the curse of dimensionality can result in blind spots in detections because of noise and uncorrelated data. Because machine learning algorithms are incredibly accurate, this work focuses on using ultra wideband (UWB) radar waves to detect individuals in NLOS scenarios and leveraging wireless communication to harmonize information. The paper uses ensemble methods to extract features using independent component analysis (ICA) and evaluate classification performance on both static and dynamic datasets. The testing results confirm the effectiveness of the proposed strategy, with classification accuracies of 87.20% for dynamic data and 88.00% for static data. Lastly, during SAR operations, our approach can assist engineers and scientists in making quick decisions.
Improved no-line-of-sight static and dynamic sensor data classification using KNN algorithm with PLS model Enoch Adama Jiya; Ilesanmi B. Oluwafemi; Francis Ayoleke Ibikunle; Nik Syahrim Nik Anwar
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9437

Abstract

The rising cases of structural collapses across the world have aggravated the problem of finding people under rubble as part of search and rescue (SAR) effort. The conventional search techniques, namely drill operation and the use of dog searching, are usually slow, labour-intensive and unsuccessful in difficult debris setting. Radar systems, though non-invasive, are limited by attenuation and multipath interference in non-line-of-sight (NLOS) environments. The proposed research will contribute to the improvement of victim recognition by creating an advanced machine learning (ML) model that will operate in the most challenging environmental settings. It suggests a modular prediction model combining both the K-nearest neighbor (KNN) and partial least squares (PLS) to extract features and reduce the dimensions. The procedure includes the derivation of essential signal characteristics, dataset validation, PLS application to get limited and discriminative feature amounts, and KNN classification under conditions of both fixed and dynamic conditions. Experimental findings indicate classification scores of 87.87 and 75.70 respectively in case of static and dynamic data. These results validate the practicability of the suggested solution in enhancing the forecasting accuracy during NLOS circumstances and emphasize its possible use in enhancing quicker, more dependable, and evidence-based official choices during the actual SAR operations.
Co-Authors Adedeji M. Faluru Enoch Adama Jiya Francis Ayoleke Ibikunle Nik Syahrim Nik Anwar Olayinka O. Ogundile Oluwaseyi P. Babalola Tayo D. Obasanyo