LAUTECH Journal of Engineering and Technology (LAUJET)
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Comparative Analysis of Reactive and Proactive Spectrum Handoff Techniques in a Cognitive Radio System
Get PDFSpectrum Handoff (SH) is an important concept in Cognitive Radio Networks (CRN) in which a cognitive user vacates the spectrum and re-establishes new communication links through other idle spectrums to avoid interference. However,
reactive and proactive methods, which are the two major techniques used for SH, have their strength and weaknesses. Hence, in this paper, a performance analysis of the two techniques in a CRN was carried out to evaluate the performance of the SH technique. MATLAB was used for the analysis by generating random data using a random integer generator and a Primary User (PU) signal. An Energy Detector (ED) was used to detect the presence of idle spectrum. SH technique was
then carried out using reactive and proactive methods. Handoff Delay (HD), Collision Rates (CR) and Average Throughput (AT) were the metrics used to analyse the effectiveness of each SH technique in CRN. Simulation results demonstrated that the proactive spectrum handoff scheme exhibited lower latency, fewer collisions, and higher throughput compared to other schemes, especially in dynamic spectrum environments. It indicates its potential as an effective mechanism for cognitive radio user handoff management
Development of a smart plastic collection system with iot remote cloud payment platform
Get PDFPlastic bottle littering has undoubtedly become an alarming global crisis and a major source of concern that poses potential hazards to landfills, habitats, water bodies, and the ozone layer. A number of these plastic bottles significantly find their way into the drainage systems leading to total drainage blockage resulting in flooding which has rendered many lives dead and homeless. Given the severity of plastic bottle pollution, immediate action is required to lessen its negative consequences. Consequently, while the existing work deployed calibrated sensors that impaired sensing accuracy, and also no consideration for incentives to encourage depositors. This project employs Autodesk Inventor software for robust mechanical modeling, incorporates sensor-based components for precise sensing, implements a control mechanism, and utilizes IoT remote cloud for payment systems to foster local involvement and social accountability aimed at eradicating plastic bottle littering. This initiative outlines the development of a smart plastic collection system with a view to not only curtailing plastic bottle littering but also motivating people with a reward. The smart plastic collection system accepts plastic bottles and all other objects such as glass, metal, and can
Development of bandwidth allocation scheme in wireless communication networks using the Shapley game theory
Get PDFABSTRACT
Bandwidth allocation is essential for efficient wireless network performance and user satisfaction. Traditional methods like Minimum-Maximum, Proportional allocation, and Weighted Fair Queuing often fail under dynamic conditions, causing unfairness and inefficiency. This study introduces a new bandwidth allocation approach using non-competitive bankruptcy game theory. Users (nodes) request cache space and bandwidth based on their needs, treated as claimants in a system with limited bandwidth. When demand exceeds supply, the Shapley value allocates bandwidth fairly based on individual contributions. Network slicing was used to create virtual networks, each dedicated to specific services and allocated bandwidth using the bankruptcy model, guided by Quality of Service (QoS) parameters like delay, throughput, and reliability. Cache memory was allocated from the kernel to reduce latency and enhance performance. Simulated in MATLAB R2023a, the model’s performance was measured using QoS and bandwidth fairness and compared to Minimum-Maximum and Proportional methods. With bandwidth demands ranging from 500 to 6000 Mbps and 10 to 20 slices, the Shapley method showed 99.7% fairness, 97% QoS efficiency, and only 0.000315 variance. In contrast, Minimum-Maximum averaged 77% fairness with the highest variance of 0.075479. The Shapley method proved most effective, practical, and adaptable for modern bandwidth-limited wireless networks
Geotechnical properties of lateritic soil stabilized with rice husk ash and potassium carbonate for pavement construction
Get PDFThe production of Ordinary Portland Cement (OPC) is a significant source of CO? emissions, contributing approximately 7% of global CO? emissions. The high cost of OPC and the need for sustainable alternatives necessitate the exploration of waste materials like rice husk ash (RHA). This study investigates the effect of potassium carbonate as a chemical activator for RHA in stabilizing lateritic soil. Lateritic soil samples were collected from the Aroje, Ogbomoso, Nigeria, and stabilized with varying proportions of RHA and potassium carbonate. Standard laboratory tests including Atterberg limits, compaction, California Bearing Ratio (CBR), and Unconfined Compressive Strength (UCS) were conducted in accordance with BS 1377.Chemical and mineralogical analyses were performed using XRF, XRD, and SEM. Results showed that the optimal mix of 16% RHA and 4% potassium carbonate produced the most significant improvements in soil performance. The plasticity index reduced to 0.00%, indicating non-plastic behavior. The unsoaked CBR values increased from 16.3% to 71.8%. Similarly, the UCS improved from 96.5 kPa to 367.9 kPa. Maximum Dry Density (MDD) decreased from 1.87 g/cm³ to 1.75 g/cm³, while Optimum Moisture Content (OMC) increased from 13.2% to 18.6%. XRF confirmed the high pozzolanic activity of RHA, with silica content exceeding 65%, and SEM images revealed a denser, cementitious matrix formation in the stabilized soil. In conclusion, the combination of rice husk ash and potassium carbonate significantly enhances the geotechnical properties of lateritic soil, making it an eco-friendly alternative to cement for road construction
Techno-Economic and Environmental Analysis of an Off-grid Hybrid Renewable Energy System for Rural Electrification
Get PDFRural electrification is key to socio-economic development in developing countries like Nigeria. However, extending the national grid to remote rural areas is expensive and time consuming, and the traditional power supply method also involves utilization of a standalone renewable energy source such as Solar which have their associated drawbacks due to their unreliability in nature. Hence, this research carried out a techno-economic and environmental analysis of an off-grid hybrid renewable energy system for rural electrification using Kepler Optimization Algorithm (KOA). Feasibility study on electricity and hourly load demand assessment of Alayin village was conducted and village load profile was estimated. Mathematical modeling of each hybrid RES was formulated. A KOA technique was employed to carry out optimal sizing and check the cost efficiency of BG/ PHES/ Battery, PV/PHES/Battery, and the hybrid RES (PV/BG/PHES/Battery). Simulation of the model was done using MATLAB R2021a. The value obtained was validated with Gravitational Search Algorithm (GSA) for performance evaluation using Levelized Cost of Energy (LCOE), Loss of Power Supply Probability (LPSP) and CO2 reduction in manure management as performance metrics. The results of the analysis showed an appreciable reduction on the LCOE and CO2 with high reliability using KOA-hybrid RES compared with GSA-hybrid RES
Utilization of local gum Arabic as an adhesive for the production of particle board from maize cobs and coconut shells
Get PDFThe increase in the cost of building and furniture materials like particle board, ceiling and roofing sheets in Nigeria worsens by the day. The development of an alternative to curb this menace using agricultural waste such as maize cobs and coconut shells are required. In this study, maize cob, coconut shell and gum Arabic as adhesive were used in the development of particle board. The particle board was produced by mixing maize cob and coconut shell in different proportion. The physical and mechanical properties of the particle board such as density, water absorption, impact strength. compressive and tensile tests were investigated. The density ranged from 518.6 -843.3 Kg/m3, water absorption was between 11.05 and 70.2% by varying time of immersion at 30min, 1hr and 2hr. The compressive strength, tensile strength and impact strength fell within the range of 15.9 - 20.15MPa, 18 - 62KPa and 175 - 274.06KJ/m2 respectively. The results showed that the maize cob, coconut shell and gum Arabic are good candidates for building and furniture applications
Waste paper-sawdust composites for the clean-up of oils with varied viscosities: an experimental study: Waste paper-sawdust composites for the clean-up of oils with varied viscosities: an experimental study
No full textOver 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
Explainable ensemble deep learning model for predicting diabetic retinopathy based on APTOS 2019 eye pack dataset
Get PDFDetection of diabetic retinopathy (DR) as early as possible is vital in mitigating the complicated issues associated with the disease. Recent advances in artificial intelligence (AI), particularly deep learning (DL) techniques, have led to appreciable increase in the accuracy of predicting various disease classes. However, the challenge of AI models is the difficulty in providing insights into how and why a model arrives in attaining decision-making to facilitate trust and adoption in clinical settings. Therefore, this study aimed to enhance the detection rate of DR and explain the significant regions on the image for the model's overall performance. This study utilised Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Simple Recurrent Neural Networks (SRNN), and XGBoost in an ensemble model (EM). Specifically, Shapley Additive exPlanations (SHAP), a popular Explainable Artificial Intelligence (XAI) technique was utilised to identify and provide insights to which parts of the images features that contribute to the model's overall performance. After a series of experiments using the APTOS 2019 eye pack dataset collected from the Kaggle repository to evaluate the performance of CNN, LSTM, SRNN, and XGBoost. The EM outperformed all the other models with 95.63% accuracy, 97.79% precision, 93.64% recall rate, 98.79% F1-score and 97.75% AUC score. Also, SHAP analysis revealed significant regions on the image that influenced predictions, thus showing how important interpretability was for the model. The results imply that the ensemble DL, particularly with XGBoost, enhances the detection of DR, thereby improving the efficiency of screening tests and supporting personalised treatment plans in clinical practice through integrating these advanced models with XAI tools, creating trust towards automated diagnostic systems
Performance analysis of deep learning-based automatic modulation recognition over wireless communication
Get PDFAutomatic Modulation Recognition (AMR) based on Deep Learning (DL) is an efficient technique to improve spectrum utilization by replacing the old way of detecting modulation type through the allocation of modulation information in the signal frame. However, DL models have the problem of low recognition accuracy when dealing with a dataset containing in-phase and quadrature channel data. Hence, in this work, the enhancement of DL models that automatically recognize different types of modulation techniques with an increase in recognition accuracy was carried out. The two utilized dataset were RadioML2016.10a and RadioML.2016.10b. Convolutional Neural Network with RadioML2016.10a (ECNN-1) and RadioML2016.10b (ECNN-2) and Long Short-Term Memory with RadioML2016.10a (ELSTM-1) and RadioML2016.10b (ELSTM-2) were implemented in Python 3 using Google Colab. Adam optimizer was applied to optimize the hyperparameters of DL models. ECNN-1 and ECNN-2 have recognition accuracy values of 81% and 88%. The accuracy values obtained for ELSTM-1 and ELSTM-2 were 79% and 85%. The ROC AUC score for the ECNN-1, ECNN-2, ELSTM-1, and ELSTM-2 were 89.63%, 92.90%, 90.92%, and 92.81%, respectively. The experimental results showed an improvement in modulation recognition accuracy for both enhanced CNN and LSTM models
Optimal location and sizing of thyristor controlled series compensation on Nigerian longitudinal transmission system using dragonfly algorithm: Optimal location and sizing of thyristor controlled series compensation on Nigerian longitudinal transmission system using dragonfly algorithm
No full textEnhancement of longitudinal transmission system through voltage profile and line flow control is achievable through Thyristor Controlled Series Compensator (TCSC) incorporation in power systems. The use of an existing method such as arbitrary placement of TCSC was found to be ineffective for these purposes compared to the optimal placement approach. Power flow equations of the power system were linearized with the use of the Newton-Raphson (NR) iterative technique at the steady state. Dragonfly Algorithm (DA) was adopted for optimal placement of the TCSC and simulated in MATLAB R2018b environment. The DA was implemented on the Nigerian 28-bus power system for normal loading and at 25% overload. The voltage profile deviations of buses 9, 16, and 22 that were more than ±5% were controlled to fall within the acceptable ranges and the heavily loaded transmission lines were redirected. The optimized placement of TCSC gave a better result when compared with the conventional TCSC placement