LAUTECH Journal of Engineering and Technology (LAUJET)
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Development of an Internet Of Things-Based Fingerprint Biometric Attendance System
Get PDFThis research explores the design and implementation of an Internet of Things (IoT) based fingerprint biometric attendance system. The traditional methods of attendance tracking often suffer from inaccuracies, time fraud, and significant administrative burdens. In response to these challenges, biometric systems have gained popularity for their ability to uniquely identify individuals through their physical or behavioural characteristics, offering a more reliable and secure approach to attendance management. The system proposed in this research utilizes fingerprint recognition, one of the most widely adopted biometric modalities, due to its high accuracy, ease of use, and cost-effectiveness. Integrating this system with the Internet of Things (IoT) expands its capabilities. The system comprises an ESP32 microcontroller, a fingerprint module, an OLED display, and a locally hosted Hypertext preprocessor (PHP)-based web interface. The OLED display serves as an immediate feedback mechanism for users, confirming whether their attendance has been successfully recorded by displaying the appropriate message. The web interface is designed for administrative use, allowing for the management of attendance records, user enrollment, and data exportation for further analysis. The results of this research demonstrate that the proposed IoT-based fingerprint biometric attendance system is a feasible and efficient solution. It offers a user-friendly interface for both students and administrators, significantly improving the accuracy and security of attendance tracking, verifying identities quickly under 1-2 seconds, with high accuracy. The system’s modular design and scalability also allow for future enhancements and adaptations to meet specific needs
Production of biofertiliser from soybean cake and soypod
Get PDFSoil fertility can be improved by the use of fertilizer, which can either be of chemical or biological sources. However, the excessive use of chemical fertilizers has caused a large number of environmental pollutions in water, air and soil. This research aimed to produce organic fertilizer from soybean cake and pod using Solid State Fermentation (SSF). Soybean cake and pod samples from WASIL Oil Company, Sagamu, Nigeria underwent NaOH pretreatment for delignification. Physico-chemical parameters including temperature, TDS, moisture content, pH, phosphate, potassium, calcium, nitrate, and sulphate were analyzed using standard methods. Microbial populations were determined through agar plate and broth culturing, while SEM, FTIR, and EDX analyses were conducted on raw and pretreated samples. Results showed that for soybean cake, temperature was 28.50°C, TDS 34.9mg/l, moisture content 58.4%, pH 7.56, phosphate 2.81mg/l, potassium 2.50 mg/l, calcium 38mg/l, nitrate 2.69 mg/l, and sulphate 32mg/l. For soybean pod, values were 28.40°C, 33.1mg/l, 51.2%, 7.06, 2.90mg/l, 2.8mg/l, 41mg/l, 2.84mg/l, and 36mg/l respectively. Microbial counts for soybean cake included Salmonella (2.4 x 10?), E. coli (2.1 x 10?), Bacillus (2.7 x 10²), and Aspergillus (3.7 x 10²), while soybean pod showed Salmonella (1.9 x 10?), E. coli (2.1 x 10?), Bacillus (2.4 x 10²), and Aspergillus (3.4 x 10²). SEM revealed irregular morphological shapes at surface layers. FTIR confirmed presence of amine and carboxyl groups indicative of fertilizers, while EDX detected nitrogen, phosphorous, and other nutrients. The study concluded that SSF is suitable for producing effective and economical organic fertilizer from soybean cake
A comparative analysis of zebra optimization algorithm and chaotic sinusoidal zebra optimization algorithm for video forgery detection system
Get PDFThis study presents a comparative evaluation of two metaheuristic optimization strategies: Zebra Optimization Algorithm (ZOA) and Chaotic Sinusoidal Zebra Optimization Algorithm (CSZOA) for enhancing the performance of Convolutional Neural Networks (CNNs) in video forgery detection. A dataset comprising 270 videos with deletion, duplication, and insertion forgeries was used to train and evaluate CNN models optimized with ZOA and CSZOA. The experimental results indicate that the CSZOA-CNN model consistently outperforms both the baseline CNN and ZOA-CNN models across all evaluation metrics, achieving an accuracy of 99.51%, a false positive rate of 0.32%, and a detection time of 39.86 seconds. These findings highlight the effectiveness of integrating chaotic sinusoidal dynamics into optimization processes to enhance CNN training efficiency and detection robustness in video forgery applications
Influence of penetration angles on global-thermo-hydraulic-performance of shell and tube heat exchangers with multi-cross sectional tube configurations
Get PDFShell-and-Tube Heat Exchanger (STHE), a vital component for efficient energy management when used with Straight-Tube Geometries (STG) is associated with low Global-Thermo-Hydraulic-Performance (GTHP). These contribute to the high energy demand of processing plants. The recently developed STHEs with modified tube configurations have not adequately addressed these limitations and necessitated a continuous search for tubes with improved performance. Multi-cross sectional tube geometrical (MSTG) configurations are known to improve GTHP along flow lines. This process has not been thoroughly investigated. Therefore, this study was designed to investigate the influence of penetration angles (PAs) on the STHEs’ performances using MSTG configurations. ; The numerical analysis was evaluated in terms of GTHP indices on STHE with Convergent-Divergent-Tube-Geometry (CDTG) and Divergent-Convergent-Tube-Geometry (DCTG) configurations of varying penetration angles (PAs), 5,10,15,…,90°. Numerical GTHP for STHE with STG was 1.0, while that obtained for STHE with CDTG configurations for all PAs fall between 1.50 to 1.625 with the highest at PA indicating a 50% minimum improvement in GTHP over STHE-STG. For DCTG, GTHP were between 1.43 and 1.585 for all PAs with the highest at PA indicating a 43% minimum improvement in GTHP over STHE-STG. Replacing STHE-STG with STHE-MSTG can improved their GTHPs in processing plant
Renewable energy potential of bio-Oil from pyrolysis of gmelina arborea seeds cultivated in Nigeria
Get PDFThe increasing demand for renewable energy sources has intensified the search for sustainable alternatives to fossil fuels. Gmelina arborea seeds, an underutilized biomass abundant in Nigeria, hold potential for bio-oil production. This study aims to explore the feasibility of utilizing Gmelina arborea seeds for energy generation through pyrolysis, contributing to cleaner energy production and environmental conservation. Seeds were collected from LAUTECH, Ogbomoso, and subjected to air and oven drying to reduce moisture content before being ground into powder. Pyrolysis was performed in a bench-scale screw reactor at temperatures ranging from 485 to 596 °C. The bio-oil produced was characterized using standard ASTM methods, including proximate and ultimate analyses, Fourier Transform Infrared Spectroscopy (FTIR), and Gas Chromatography-Mass Spectrometry (GC-MS), to determine its chemical composition and energy potential. The bio-oil yield ranged from 21.3 to 25.3 wt.%, with the highest yield of 25.3 wt.% achieved at 596 °C. Characterization revealed favorable energy properties, including a Higher Heating Value (HHV) of 40.13 MJ/kg, kinematic viscosity, density, and flash point within practical application ranges. FTIR analysis identified functional groups such as alkenes, carboxylic acids, alcohols, ethers, and ketones, while GC-MS detected hydrocarbons like alkanes, alkenes, phenols, and naphthalene. The low nitrogen content (2.58-2.80 wt.%) indicates minimal environmental impact. This study highlights the viability of Gmelina arborea seeds as a renewable bioenergy feedstock, offering a cleaner, sustainable alternative to conventional fuels
Development of speech emotion recognition system using optimized convolutional neural network
Get PDFSpeech Emotion Recognition (SER) allows systems to interpret emotions in human speech, creating more natural and responsive interactions between people and machines. Due to the complex nature of emotion detection, several deep learning techniques have been utilized, yet limited research have focused on optimizing key hyperparameters of Convolutional Neural Network (CNN) for a more efficient system. Hence, this research optimized CNN with Mantis Search Algorithm (MSA) due to its ease of implementation, ability to preserve population diversity during the optimization process, ability to escape from the local optima and balance between exploration and exploitation operators. Audio data for four emotions: anger, fear, happiness and neutrality were acquired from Toronto Emotional Speech Set (TESS) available on Kaggle.com. The audio data were then converted into text using speech-to-text code and preprocessed using Natural Language Processing (NLP) techniques: tokenization, removal of stop words, lemmatization, removal of punctuations and lowercase conversion. Mantis Search Algorithm was then applied to optimize CNN for optimal selection of filter size and learning rate. The optimized CNN (MSA-CNN) was implemented using MATLAB R2023a software. The performance of the system was evaluated and compared with CNN classifier using False Positive Rate (FPR), Specificity (Spec), Sensitivity (Sen), Precision (Prec), Accuracy (Acc), and Recognition Time (RT). The optimized speech emotion recognition system showed improved values over CNN on all the metrics considered
Optimization of H2SO4-modification of ITU bentonitic clay under box Behnken design
Get PDFBentonite clay from Itu, Akwa-Ibom State, Nigeria was modified using sulfuric acid (H2SO4). The chemical compositions of the raw (RI) and H2SO4 modified (HI) Itu clay was determined using X-ray fluorescence (XRF) technique. Box Behnken Design (BBD) was used to optimize the H2SO4 and clay modification process using wet acidification method. The process parameters considered for the optimization were H2SO4 concentration (0.1-6.0 M), activation temperature (60-100 oC) and activation time (5-10 minutes). Optimum catalyst yield of 6.12 g was obtained in 7.5 min and at 60 oC when 6 M H2SO4 concentration was used for clay modification. The predicted value of the catalyst yield was found to be in agreement with its observed values (R2 = 0.9681 and Adj R2 = 0.9271). These results revealed that the process parameters had significant influence on the clay modification process. The XRF analysis of the samples also revealed that the RI and HI are calcium montmorillonite with SiO2/Al2O3 ratio values of 3.20 and 5.48 respectively
Determination of Some Selected Mechanical Properties of Recycled Aluminium Alloy Modified with Micro- And Nano-Sized Additives
Get PDFThe growing demand for sustainable materials has intensified interest in recycled aluminium alloys, which present a more energy-efficient alternative to primary aluminium production. However, recycled aluminium typically suffers from diminished mechanical properties due to the presence of impurities and microstructural irregularities. This study explored the influence of nano- and micro-sized reinforcements (black powder, graphite, and waste glass) on the mechanical performance of recycled aluminium alloys. Aluminium was mixed with 6%, 8%, and 10% weight fractions of each reinforcement, and processed using the sand casting technique to produce composite samples. The composites were evaluated for their physical, metallurgical, and mechanical properties. Results indicate that nano-scale reinforcements enhanced hardness, impact strength, and tensile strength relative to their micro-scale counterparts. The highest hardness (84.36 HV) was achieved with 10% nano-glass (GL10), while 8% nano-black powder (BP8) yielded the highest impact strength (18.67 J). Additionally, 10% nano-graphite (G10) produced the highest tensile strength (103 MPa), surpassing the micro-reinforced equivalent (78 MPa). These findings confirm the potential of nano-reinforced recycled aluminium alloys for use in high-performance engineering applications
A systematic approach to the development of an iot-enabled cardiovascular monitoring device: A systematic approach to the development of an iot-enabled cardiovascular monitoring device
No full textCardiovascular 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
In-situ determination of terrestrial gamma dose rate within Ladoke Akintola University of Technology, Ogbomoso southwest Nigeria: The study of Terrestrial gamma radiation within Ladoke Akintola University of Technology
No full textThe understanding of terrestrial gamma radiation and its effect on human being is intertwined with advancements in radiation science, health physics, and environmental monitoring. It is in view of this that the study investigated the terrestrial radiation level within Ladoke Akintola University Technology Main Campus (MC), College of Health Sciences (CHS) and Teaching Hospital (TH) of the institution. A total of 137 sampling points were assessed for terrestrial gamma dose rate using a portable radiation dosimeter. The measured dose rate was subjected to statistical analysis using analysis of variance with a Tukey post-hoc test. The results of the gamma dose rates ranged from 0.10 - 0.22 µSvhr-1, with mean values of 0.150 µSvhr-1 for MC, 0.11 - 0.24 µSvhr-1 with a mean value of 0.152 µSvhr-1 for CH, and 0.10 - 0.22 µSvhr-1 with a mean value of 0.170 µSvhr-1 for TH. The statistical analysis and the post-hoc test revealed that the medical activities involving the use of radiation at the TH contributed significantly to the dose level of the environment at p = 0.05. The estimated annual effective dose equivalent ranged from 0.18 to 0.42 mSvyr-1 which is within the recommended limit of 1 mSvyr-1 for the public set by International Commission on Radiological Protection. The findings of this study provide valuable information on the radiation level of three studied environments and hereby recommended that the general public be radiation cautious by minimizing the amount of time spent within the environment of teaching hospital to mitigate the radiological hazard