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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies
Olabode Idowu-Bismark
Covenant University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Large-scale parameter modelling for millimeter-wave multiple-input multiple-output channel in 5G ultra-dense network Olabode Idowu-Bismark; Francis Idachaba; Aderemi A. Atayero
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp794-807

Abstract

Network densification (ND) in 5G has been suggested as a solution to improve network capacity. ND has small cell backhaul as its bottleneck in the ensuing ultra-dense network (UDN). Due to the new deployment scenarios of small cells, it becomes necessary to thoroughly investigate the radio-propagation characteristics of the new transmission path between the base station and the small cells. The problem of the impact of small cell height on the backhaul large-scale parameters under typical outdoor-to-indoor (high-rise) and outdoor-to-outdoor (street canyon) scenarios was first investigated. Next, the probability distribution functions of the various parameters were investigated and modeled. Novel use of 5G NR air interface using a deterministic ray-tracing engine to characterize the backhaul at 28 GHz center frequency and 100 MHz bandwidth using 4x4 cross-polarized uniform planar array (UPA) at the base station and 2x2 multiple input, multiple output (MIMO) antennas at the small cells was proposed. New sets of models for root mean square (RMS) delay spread and RMS angular spread suitable for predicting network deployment in the two scenarios and similar environments were presented.
Design of a 175 GHz SiGe-based voltage-controlled oscillator with greater than 7.6 dBm power Oluseun Damilola Oyeleke; Aliyu Danjuma Usman; Kabir Ahmad Abubilal; Habeeb Bello; Olabode Idowu-Bismark
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v12i2.pp103-114

Abstract

In this research, we present a low phase noise (PN) and wide tuning range 175 GHz inductors and capacitors (LC) voltage-controlled oscillator (VCO) based on a differential Colpitts oscillator that was designed using a 0.13 μm bipolar complementary metal oxide semiconductor (BiCMOS) and simulated. The square of the tank Q-factor and the square of the oscillation amplitude were both maximized to reduce PN. With an extensive examination of parasitic, mathematical analysis of load impedances, and implementation of differential design, the PN was reduced, and the output power was enhanced. Using a supply voltage of 1.6 V, the VCO consumes 41.9 mA, resulting in a total power usage of 67 mW to prevent undesirable PN deterioration, an inter-stage LC filter at the VCO-buffer interface increases the swing at the buffer input. To make a better output, a buffer is used to isolate the load from the VCO core. In addition, the VCO has a high linearity and the overall, the VCO presented in this study demonstrates excellent performance and has the potential to be used in a wide range of applications that require a high-performance, low-power VCO.
Circularly polarized metamaterial Antenna in energy harvesting wearable communication systems Bala Inuwa; Olabode Idowu-Bismark; Oluseun Oyeleke; Jemitola Paul Olugbeji
Computer Science and Information Technologies Vol 4, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i2.p106-118

Abstract

When battery powered sensors are spread out in places that are sometimes hard to reach, sustaining them become difficult. Therefore, to develop this technology on a large scale such as in the internet of things (IoT) scenario, it is necessary to figure out how to power them. The proffered solution in this work, is to get energy from the environment using energy harvesting Antennas. This work presents a wearable circular polarized efficient receiving and transmitting sensors for medical, IoT, and communication systems at the frequency range of WLAN, and GSM from 900 MHz up to 6 GHz. Using a cascaded system block of a circularly polarized Antenna, a rectifier and t-matching network, the design was successfully simulated. A DC charging voltage of 2.8V was achieved to power-up batteries of the wearable and IoT sensors. The major contribution of this work is the tri-band Antenna system which is able to harvest reflected Wi-Fi frequencies and also GSM frequencies combined in a miniaturized manner. This innovative configuration is a step forward in building devices with over 80% duty cycle. 
Fifth-generation small cell backhaul capacity enhancement and large-scale parameter effect Olabode Idowu-Bismark; Francis Idachaba; Aderemi A. Atayero
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5198-5208

Abstract

The proliferation of handheld devices has continued to push the demand for higher data rates. Network providers will use small cells as an overlay to macrocell in fifth-generation (5G) for network capacity enhancement. The current cellular wireless backhauls suffer from the problem of insufficient backhaul capacity to cater to the new small cell deployment scenarios. Using the 3D digital map of Lagos Island in the Wireless InSite, small cells are deployed on a street canyon and in high-rise scenarios to simulate the backhaul links to the small cells at 28 GHz center frequency and 100 MHz bandwidth. Using a user-defined signal to interference plus noise ratio-throughput (SINR-throughput) table based on an adaptive modulation and coding scheme (MCS), the throughput values were generated based on the equation specified by 3GPP TS 38.306 V15.2.0 0, which estimates the peak data rate based on the modulation order and coding rate for each data stream calculated by the propagation model. Finding shows achieved channel capacity is comparable with gigabit passive optical networks (GPON) used in fiber to the ‘X’ (FTTX) for backhauling small cells. The effect of channel parameters such as root mean squared (RMS) delay spread and RMS angular spread on channel capacity are also investigated and explained.
Evolutionary trends in automatic speech recognition with artificial intelligence: a systematic literature review Gabriel Oluwatobi Sobola; Emmanuel Adetiba; Olabode Idowu-Bismark; Abdultaofeek Abayomi; Raymond Jules Kala; Surendra Colin Thakur; Sibusiso Moyo
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.pp20-43

Abstract

Human beings depend greatly on communication and continually seek ways to overcome language barriers. Automatic speech recognition (ASR) has emerged as a vital tool for enhancing human interaction. Early ASR research relied on probabilistic models, particularly the hidden Markov model (HMM) and Gaussian mixture model (GMM), with mel-frequency cepstral coefficients (MFCCs) for feature extraction, leading to the creation of Audrey at Bell Laboratories. Subsequently, artificial intelligence (AI) approaches, especially deep learning, have transformed ASR and produced systems such as Jasper, Whisper, Google Assistant, Microsoft Cortana, Apple Siri, and Amazon Alexa. This paper presents a systematic literature review that examines ASR’s evolution, the AI architectures employed, their features, strengths and weaknesses, and the performance gains achieved since AI was integrated into probabilistic modelling. A snowballing approach was used to identify relevant studies from Google Scholar and Scopus to address five research questions, iterating through backward and forward searches until no new information was found. Findings reveal that ASR dates back to the 1920s with the Radio Rex toy and has since advanced through architectures including deep learning, recurrent neural networks (RNN), support vector machines (SVM), and transformers, all contributing to improved performance measured by reduced word error rates (WER).
Ethylene gas-based fruit expiry predictor: a sustainable solution to fruit wastage Olabode Idowu-Bismark; Anyiam Chinemere Eberchukwu; Oluwadamilola Oshin; Ibiang Emmanuel; Tonwe Mary; Emmanuel Adetiba
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.v15i2.9809

Abstract

Globally, environmental and economic issues are very apparent regarding food wastage. Fruits play a significant role since they can be damaged easily. Ethylene gas is one of the significant gases produced by fruits when they begin to ripen; it tests whether the fruit is ripe or else spoilable. This work presents a sustainable solution for fruit wastage: an ethylene gas-based fruit expiry predictor (FEP). The system is designed and developed around advanced sensing and artificial intelligence (AI) models for real-time monitoring of ethylene levels and temperature in forecasting the spoilage of fruits. The system consists of non-invasive sensors for detecting ethylene and temperature, a data processing microcontroller, and an AI model trained on a large dataset to make accurate predictions regarding the expiry of fruit. The AI model processes the information collected by the sensors and then displays the grade (level of ripeness of the fruit) on a liquid crystal display (LCD) screen. This solution improves fruit management with reduced wastage in line with international sustainability targets. These would enable real-time and highly accurate predictions of fruit spoilage, allowing end-users informed choices that will eventually lead to reducing the carbon footprint from food waste and increasing food security.
Co-Authors Abdultaofeek Abayomi Aderemi A. Atayero Aliyu Danjuma Usman Anyiam Chinemere Eberchukwu Bala Inuwa Emmanuel Adetiba Emmanuel Adetiba Francis Idachaba Gabriel Oluwatobi Sobola Habeeb Bello Ibiang Emmanuel Jemitola Paul Olugbeji Kabir Ahmad Abubilal Oluseun Damilola Oyeleke Oluseun Oyeleke Oluwadamilola Oshin Raymond Jules Kala Sibusiso Moyo Surendra Colin Thakur Tonwe Mary