School of Electrical Engineering and Technology (SEET)

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School of Electrical Engineering and Technology (SEET)

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Start date: 2024

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Now showing 1 - 10 of 13
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    Comparative Analysis of Machine Learning Algorithms for Eccentricity Fault Classification in Salient Pole Synchronous Machine
    (IEEE, 2024-03-22) Shejwalkar, Ashwin; Yusuf, Latifa; Ilamparithi, Thirumarai Chelvan
    The paper performs a comparative study of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for the classification of Static Eccentricity (SE) and Dynamic Eccentricity (DE) faults in a Salient Pole Synchronous Machine (SPSM). The SPSM was subjected to varying SE and DE severities, unbalanced source voltages, and load conditions. Stator and field current data were measured, and various time-domain and frequency-domain features were extracted from the above-mentioned data. Both networks were fed these features and compared based on classification accuracy, robustness, and computational complexity.
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    Deployment of an Electronic-based Approach for Fruits Juice Ingredient Analysis
    (International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) Landmark University, 2024-04-04) Kufre Esenowo Jack; Lanre Joseph Olatomiwa; Yahaya Asizehi Enesi; Grace Idowu Olaleru; Nnaemeka Emmanuel Ugwuogor; Babawuya Alkali
    This paper considers the deployment of an electronic-based fruits juice ingredient analysis. Most of the fruits juice products available in the market now contain water in large quantities than the active ingredients. This design attempts to respond to the end user's complaints. By putting on this design, it is expected to serve as a quality check and control for our teeming enterprising fruit substance producers. The system was designed and simulated using proteus and implemented using hardware electronic components. The system uses an infrared transmitter, fruit sample handler, and infrared receiver to realize its design. The instrument was calibrated with natural pineapple juice with 60% of water content. The outputs of this device were displayed using a cathode ray oscilloscope and voltmeter respectively. Five different samples of fruit juice were analyzed namely: A, B, C, D, and E. Results showed that all fruit juice contains a reasonable quantity of water which is not regarded as an adulteration since it is the natural content of the fruit. However, water content above 60% may be considered as much. It is recommended that fruit juice producers employ this system for their quality and control checks. Moreover, further research should take into consideration, the colour and viscosity of different fruit juices with a view to seeing how the system can analyze them, while the output should incorporate a microcontroller for an intelligent analysis and digital display.
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    Deploying A Standalone Facial and Emotion Recognition Classroom Management System on Resource-Constrained.
    (Izmir Turkiye, 2024-12-20) Abdullahi, I.M., Maliki, D., Abdulqudus, A., Abraham, S.A, & Ibrahim, M.
    In recent times, it has been proven in most industries that deep learning can play a huge part in the development and automation of processes otherwise performed manually by humans alone. The trend however has encountered more of a shift and tend towards transfer learning where standalone systems can be built on weights that have been extensively trained to be use-case agnostic. This project seeks to address the problem of student truancy. The methodology applied is a combination of a deep learning use-case agnostic weight embedding obtained from a popular network called Face net. Recognition is performed by computing facial distances using the weight embedding. Also addressed is the common reliance on internet for functionality present in most modern-day systems by deploying all the resources necessary on a resource-constrained development board. Emotions during class are also analyzed to improve classroom experience which will be displayed on a web application dashboard powered by artificial intelligence back-end. The results obtained show an above average recognition rate of 0.63 with emotional recognition accuracy of 0.72. The implications of these results are that accurate attendance can be taken in an organization with minor increments to the system such as increased computational capabilities.
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    Towards The Development of An Intelligent Evaporative Cooling System for Post-Harvest Storage of Selected Fruits
    (Federal University of Technology, Minna, 2024-12-03) Isah, O.R., Adebayo S.E., Nuhu, B.K., Umar, B.U., Maliki, D
    Poor management of post-harvest storage of fruits and vegetables has led to enormous food wastage and economic loss globally. Refrigerating systems have been adopted over the years to avert these losses; however, installing them is expensive and can cause chilling injury and moisture loss to the fruits and vegetables when they go below 20℃ temperature. An evaporative cooling system has recently been widely used to preserve fruits and vegetables because it’s cheap to implement, especially for small-scale farmers. This system reduces the temperature and increases the air humidity in their chamber by removing latent heat from the evaporated water when exposed to sunlight. The existing evaporative system has been efficient in preserving the quality of fruits and vegetables as well as extending their shelf-life; however, they lacked automated operation and control mechanisms, intelligent mechanisms capable of identifying the physical state of the fruits, adaptive control techniques for the storage and remote monitoring, feedback scheme of the system for use by the farmers. The abovementioned limitations have prevented the system from achieving optimal performance in preserving fruits. Hence, this research aims to develop a multi-chamber evaporative cooling preservative system for post-harvest storage of fruits. In the first step, Tomato images were collected and trained with the MobileNetV2 model, achieving accuracy, precision and recall of 88%, 89% and 88% respectively. Overall, the model performs well, however, fine- tuning the model or using more training data could help improve its performance further
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    A Joint Optimization Scheme for Enhanced Breast Cancer Diagnosis using Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO)
    (International Conference of the Faculty of Science, 2025-01-14) Ahmed, Y.E., Abdullahi, I.M., Maliki, D., & Akogbe, M. A
    One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.
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    Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching
    (Faculty of Science Lafiya, 2025-01-12) Garuba O.R., Abdullahi, I.M., Dogo, E.M., & Maliki, D
    One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.
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    A Smart Real-time Attendance System using Smart Data Filtering and Selection Techniques
    (2024-04-09) Ibrahim M. A., Maliki, D., I. A. Dauda, A. Y. Ogaji, S. Yakubu
    Cooperate organizations, firms, companies, and educational institutions in Nigeria and the whole world are concerned about attendance of students and employees as the case may be, student overall performance is affected by it. In order to provide solutions for attendance management systems, a variety of techniques and technologies were used in the development of the attendance systems. However, most of these systems lack the flexibility of use and appropriate resource management. This paper presents the development of a smart real-time attendance system that uses smart data filtering and selection techniques to parse user-defined attendance instructions, optimize performance, and improve efficiency and flexibility. This system also employs a multi-factor approach in terms of security engaging the use of RFID technology and fingerprint biometrics to manage attendance records. Also, the system uses a wireless (Wi-Fi) communication approach for real-time communication. The performance of the system was mainly evaluated in terms of throughput, latency, and accuracy showing an average delay of 3 seconds per student, 21.95Mbps average throughput, and zero percent false acceptance.
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    Design and Implementation of Real Time Internet of Things (IoT) Enhanced Irrigation System
    (El-Amin University Journal of Computing (EAUJC), 2024-04-01) J. A. Ojo; Ajiboye, Johnson Adegbenga; M. A. Ajiboye; D. J. Ajiboye; H. O. Ohize; A. A. Isa
    Irrigation is a practice that has existed for a long time. Irrigation is the process of supplying water to the soil during drought or unfavourable weather conditions. Over the years, irrigation practices have evolved in order to eliminate the risk of manual irrigation. This risk includes over irrigation, under irrigation, erosion among others. Modern irrigation practices aim to reduce these problems by incorporating sensor technology, Internet of Things (IoT) and automations. The aim of this work is to design and a Real-Time IoT enhanced irrigation system which utilizes data about the condition of the environment to automate the irrigation process. This system makes use of soil moisture sensor, a rain sensor and a temperature and humidity sensor to capture real time environmental data and makes logic decisions based on the collected data. An ESP 32 microcontroller functions as the brain of the system by collecting data from the sensors and controlling the pump accordingly. The system also employs lot technology using Arduino Cloud loT platform in order to provide remote accessibility. The experimental evaluation involved subjecting the irrigation system to two distinct soil conditions; one dry and the other wet. The results demonstrate the functionality of the system: when rain sensor readings fall below the set threshold of 30% and soil moisture sensor readings drop below 15%, the irrigation pump is activated to compensate for the lack of rainfall and soil moisture. Furthermore, the system responds to environmental conditions, activating the pump for an extended period when relative humidity is below 60% and the temperature exceeds 25°C. Conversely, when the soil is already wet, indicated by high soil moisture sensor readings, the pump remains permanently turned off. This automated irrigation system showcases the potential to optimize water usage and enhance efficiency in response to dynamic environmental factors.
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    DSP in Communication Engineering - A Review
    (I3C 2024, 2024-04-22) Ajiboye, Johnson Adegbenga; Jiya Z.J; Paul M.; Ajiboye M.A; Ajiboye D.J; Majin R.N
    This paper provides a comprehensive review of Digital Signal Processing (DSP) in communication engineering, elucidating its fundamental principles, practical applications, and recent advancements. Beginning with an overview of DSP's distinguishing features and historical evolution, the paper delineates its pivotal role in processing real-world signals, including speech, image, and seismic data. Furthermore, the introduction of Software Defined Radio (SDR) is examined, underscoring its transformative impact on communication systems by enabling dynamic spectrum access and multi-standard operation through DSP algorithms. Additionally, the emergence of Quantum Signal Processing is explored, highlighting its significance in secure communication through Quantum Key Distribution (QKD) and Quantum Error Correction. Despite the benefits offered by DSP, challenges such as computational complexity and signal distortions are addressed, emphasizing the need for advanced techniques and algorithms to mitigate these issues. Ultimately, this paper elucidates DSP's enduring relevance and innovation in shaping the future of communication engineering.
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    To-Go;A secure ride-hailing App with advanced route optimization and VoIP for enhanced user experience for Nigeria transport system
    (Proceedings 2nd International Conference on Intelligent and Sustainable Power and Energy Systems (ICISPES 2024), 2024-12-14) Oyewobi S. Stephen; Abdulkadir Olayinka Abdulbaki; Lawal O. Lawal; Bala Abdulfattah Emi
    To-Go is an innovative ride-hailing platform that provides users with a secure, efficient, and seamless transportation experience. Leveraging advanced route optimization algorithms and integrated Voice over Internet Protocol (VoIP) technology, To-Go minimizes travel time and enhances user engagement. The app's robust security features ensure rider safety and driver accountability, utilizing real-time GPS tracking, in-app emergency response, and rigorous driver vetting processes. To-Go's intuitive interface and VoIP-enabled communication enable effortless ride scheduling, live support, and seamless driver-rider interactions. As an app intended to boost user satisfaction in Nigeria To-Go delivers precise routes and secure VoIP interactions. It resolves the safety and communication obstacles typical in this field. Through the application of sophisticated algorithms and VoIP tools. To-Go delivers a dependable platform for secure and efficient rides fostering smart transportation innovations in Nigeria.