LAUTECH Journal of Engineering and Technology

Inverter-based resources and grid stability: a comparative study of grid-forming and grid-following control

Vincent Mbey — 2025-12-30

The increasing penetration of renewable energy sources has accelerated the transition from synchronous generator–dominated power systems to grids heavily supported by Inverter-Based Resources (IBRs). Within this transformation, two distinct inverter control paradigms have emerged: Grid-Following Inverters (GFLs) and Grid-Forming Inverters (GFMs). GFLs synchronize to the existing grid voltage and supply controlled active and reactive power, while GFMs establish their own voltage and frequency references, thereby providing system-strengthening services traditionally delivered by synchronous machines. This paper presents an in-depth comparative analysis of GFL and GFM technologies, focusing on their control principles, dynamic performance, stability characteristics, and roles in renewable energy integration. A critical evaluation of their applications, limitations, and hybrid deployment strategies is also provided. The analysis highlights that GFMs are increasingly necessary to enable stable, resilient, and renewable-dominated future grids.

Physico-mechanical properties, tribological behaviour and metallurgical characteristics of aluminium metal matrix composites reinforced with agricultural residues: A Review

Bashir Adegoke AMUDA — 2025-12-30

Aluminium Metal Matrix Composites (AMMCs) that are reinforced with agricultural residues (such as rice husk ash, coconut shell ash, sugarcane bagasse ash, maize/corn-cob ash, palm kernel and shell ash) have garnered increasing attention as environmentally friendly, low-cost, and lightweight alternatives to traditional ceramic reinforcements. This review gathers recent findings on (1) physico-mechanical properties (tensile strength, hardness, ductility, density), (2) tribological behaviour (wear rate, friction coefficient, wear mechanisms), and (3) metallurgical characteristics (microstructure, interfacial bonding, phase formation, porosity) of agro-reinforced AMMCs. The review describes common synthesis methods (stir casting and powder metallurgy), emphasises critical microstructure–property correlations, pinpoints persistent challenges (particle agglomeration, inadequate wettability, porosity, variable pre-treatment) and optimisation process. Key fabrication techniques are briefly outlined, and future research directions encompassing hybridisation techniques and surface modifications are provided.

A systematic approach to the development of an iot-enabled cardiovascular monitoring device

Solomon Nwaneri — 2025-12-30

Cardiovascular diseases are a major health concern and a leading cause of global mortality. Conventional cardiovascular monitoring devices are expensive and cannot remotely monitor the cardiovascular system. This study aimed to apply a systematic approach for the development of an IoT-enabled cardiovascular monitoring device. The system is powered by two 3.7V lithium-ion batteries and consists of an electrocardiogram (ECG) module, a blood oxygen level and heart rate module, a 16×2 liquid crystal display screen, an I2C interface, a Wi-Fi microcontroller unit, a resistor, a transistor, a buzzer, and several connecting wires. The circuit was designed and simulated, followed by device construction. The microcontroller was programmed to collect and transmit patient data to a database over a Wi-Fi network. The data are accessed by a desktop application at an interval of thirty seconds, and displayed in tables and ECG graphs. Pilot testing was conducted to determine the efficacy of the device. The ECG waveforms obtained exhibited typical ECG features, with consistent and periodic peaks, indicating a regular heart rhythm. The recorded mean SpO₂ and heart rate values of 97.5 ± 1.01 and 81.27 ± 22.97 Beats per minute (BPM) align closely with values reported in previous studies involving healthy adults. Test results demonstrate the device’s feasibility and safety for real-time cardiovascular patient monitoring.

Development of an ai-driven secure safe box biometric authentication system using speech and face recognition technique

Nnamdi Okomba — 2025-12-30

The rapid advancement of Artificial Intelligence (AI) has enabled multimodal biometric systems that integrate speech and face recognition for more secure and reliable authentication. Unlike Unimodal systems that depend on a single trait and are prone to noise, lighting variations, spoofing, and user variability, multimodal systems combine complementary modalities to enhance robustness. In this study, Convolutional Neural Networks (CNN) were applied for face recognition and Recurrent Neural Networks (RNN) for speech recognition, with feature extraction using Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), and Perceptual Linear Prediction (PLP). A custom dataset was collected using ESP32-CAM for facial images and a CV-02 speech module for voice samples under varying environmental conditions. The CNN model achieved 93.5% accuracy, while the RNN gave 92.7% accuracy. When integrated to the app, the multimodal system significantly outperformed unimodal approaches with reduced False Acceptance Rate of 1.5% and the False Rejection Rate of 3.5%. These show that combining CNN and RNN models with advanced speech features provides robust, accurate, and secure real-time authentication, resilient to environmental and user-based variability.

Enhanced chicken swarm optimization-tuned convolutional neural network for fingerprint-based ethnicity identification

Olmuyiwa Bamikole Ige — 2025-12-30

Identification of human being based on fingerprints have proven to be highly reliable. Researches have been done on fingerprint ethnicity identification, which are characterized with high false positive rate and high recognition time. This research developed Fingerprint Identification System with an enhanced CSO combined with CNN for better fingerprint ethnicity identification. One thousand two hundred (1200) subjects’ fingerprint images were acquired from three major ethnic groups in Nigeria (Yoruba, Igbo and Hausa) with equal ratio of male to female between the ages of 17–50 years, using Secugen Hamster Plus Fingerprint Scanner; Six hundred (600) acquired subjects’ fingerprints were augmented and used for training while the remaining 50% were used for testing. The raw images were pre-processed; CNN hyperparameters were tuned using CSO and CSO enhanced with Chaotic theory. The implementation was done using MATLAB R2023a software. The performance of the ICSO-CNN was evaluated and compare with CNN and CSO-CNN at a benchmark of 0.75 threshold value using, False Positive Rate (FPR) and Recognition Time (RT). The FPR and RT using CNN for Yoruba, Igbo and Hausa were 3.5% and 60.54s; 3.75% and 59.31s and 4% and 58.04s, respectively. The FPR and RT using CSO-CNN for Yoruba, Igbo and Hausa were 2.25% and 49.87s; 2.5% and 48.82s and 2% and 49.79s, respectively, while the corresponding values for the enhanced CSO-CNN were 1.75% and 36.64s, 2% and 38.56s and 1.5% and 39.35s. The developed fingerprint-based ethnicity identification system gave an improved identification performance over CNN and CSO-CNN. The developed ICSO-CNN can be used by security agencies for proper identification of criminals based on ethnicity.

Waste paper-sawdust composites for the clean-up of oils with varied viscosities: an experimental study

Tolulope Kolajo — 2025-12-30

Over the years, oil spills have remained a predominant global occurrence. The improper disposal of dusts, flakes and shavings generated from the primary and secondary wood conversion processes, as well as waste paper and paper products has resulted in the release of greenhouse gases into the environment. In view of these environmental dangers, the use of waste papers and sawdust to remediate oil spills presents an ingenious way of utilizing their absorbent properties for environmental benefits. In this study, varying proportions (100:0, 80:20, 60:40, 40:60) of print waste papers and Gmelina arborea sawdust were made into pellets and evaluated for the clean-up of light, medium and heavy oils. The composite pellets were tested for their physical (Thickness Swelling (TS), Water Absorption (WA)) and their impact strength properties. The mean WA was 279.01%, showing excellent absorbent properties, while TS has a mean value of 11.79% and ranged from 8.13% to 16.67% across all samples. The mean low impact velocity strength was 16.64 N/m², which is considered adequate for normal handling conditions. The pellet composition of 100:0 had the highest remediation (21.43, 17.44 and 16.09%) while 40:60 had the lowest values (11.90, 12.79 and 9.2%) for light, medium and heavy oil viscosities, respectively. Composite pellets from waste papers and sawdust have potential in the clean-up of oil spills of varying viscosities, which can be applicable to other oil-based effluents from industries like paint and preservatives.

Development of a modified fuzzy logic-based system in decongesting traffic at road junctions

JOHN OLUWASEGUN OGUNYODE — 2025-12-30

Traffic congestion at road junctions is a growing concern in urban areas, significantly impacting travel time, fuel consumption, and environmental pollution. As cities expand and vehicle ownership increases, traditional traffic management systems struggle to handle the rising volume of vehicles, leading to frequent bottlenecks at key intersections. Existing traffic decongestion methods, such as fuzzy-based algorithms, suffer from design complexity and tuning inaccuracy. To address these limitations, this work proposes a modified fuzzy logic-based algorithm integrated with the Spider Wasp Optimization (SWO) algorithm for efficient traffic decongestion at road junctions. Traffic parameters including vehicle arrival rate, queue length, and waiting time were generated using a MATLAB R2023a-based stochastic traffic simulation model. These inputs fed into a fuzzy logic controller that determined adaptive green signal durations for each lane. The SWO algorithm, modeled on the predatory and resource allocation behavior of spider wasps, was employed to optimize the fuzzy rule weights and membership function parameters. System performance was evaluated using queue length, average vehicle delay, throughput, signal timing efficiency, green time utilization, and intersection delay index as performance metrics. Comparative simulation results demonstrated that the proposed hybrid SWO-fuzzy system outperformed the standalone fuzzy logic controller by reducing congestion, improving signal utilization efficiency, and enhancing traffic flow stability. The developed model exhibited adaptive capability to varying traffic scenarios without human intervention, thereby improving road safety, reducing fuel consumption, and enhancing commuter experience.

Secretary bird optimised support vector machine model for adire fabric defect detection

AFOLAKE OGUNLEYE — 2025-12-30

Adire fabric is a traditional hand-dyed fabric indigenous to the Yoruba people of Nigeria. It is produced using manual resist-dyeing techniques involving materials such as raffia, wax, starch, or stitching. These handcrafted processes while culturally significant, make the fabric highly susceptible to a variety of defects such as stains, tears, pattern misalignment, colour variation, colour crocking, sulphur-mark and, colour-smear. Traditional visual inspection methods are labour-intensive, time consuming and prone to error. Hence, this study developed an optimised Support Vector Machine (SVM) model using the Secretary Bird Optimisation Algorithm (SBOA) for automated detection of Adire fabric defects. 234 Adire fabric images were acquired using a Redmi 14C 50MP digital camera and augmented to 884. Among the data collected, 396 images represented various type of defects while 488 were non-defective (Normal). Preprocessing involved Gaussian filtering and Contrast Limited Adaptive Histogram Equalisation (CLAHE), while Gray-Level Co-occurrence Matrix (GLCM) was used for texture-based feature extraction. SBOA was applied to optimise SVM hyperparameters (penalty factor , kernel type, gamma ( ), and polynomial degree) yielding the SBOA-SVM model, implemented in MATLAB R2023a. The performance of the model was evaluated against standard SVM using accuracy, sensitivity, specificity, false positive rate as metrics. SBOA-SVM outperformed standard SVM across all defect categories. For colour crocking and colour smear, SBOA-SVM achieved accuracy of 95.81% and 96.04%, and sensitivity of 85.29% and 85.61%, respectively, while the corresponding values obtained for standard SVM were 93.55% and 94.23%. Challenging defects like colour variation, tear, and stain, SBOA-SVM improved sensitivity to 70.45%, 72.73%, and 72.50% at specificity above 98%. The developed SBOA-SVM model demonstrated superior accuracy and efficiency, establishing its potential for automated defect detection in Adire and other related textile applications.