DEPARTMENT OF COMPUTER ENGINEERING

This project presents the design and implementation of an automated waste management system utilizing an Arduino Uno microcontroller, ultrasonic sensors, and a servo motor to enhance efficiency and hygiene in waste disposal. The system continuously monitors the fill level of a waste bin using an ultrasonic sensor, which provides real-time data to the Arduino. When the sensor detects that the bin is nearing capacity or a user is present, the Arduino activates a servo motor to automatically open andclose the bin lid, enabling touchless operation and reducing the risk of contamination. Powered by a 9V replaceable battery, the system is portable and well-suited for environments with unreliable electricity supply. Rapid lid response, with positive user feedback regarding convenience and hygiene. The project highlights the potential for scalable, low-cost smart waste solutions in both urban and rural settings, and lays the groundwork for future enhancements such as IoT connectivity, renewable energy integration,and automated waste sorting for improved sustainability and resource management.

Wireless Sensor Networks (WSNs) play a crucial role in modern communication systems, particularly in environmental monitoring, industrial automation, and smart cities. However, a major challenge in WSNs is optimizing energy consumption due to the limited power resourcesof sensor nodes. One of the most effective ways to enhance energy efficiency is through data compression, which reduces the amount of transmitted data while preserving essential information.
This project explores the integration of machine learning-based data compression techniques to improve energy-efficient transmission in WSNs. A hybrid approach is proposed, combining Run-Length Encoding (RLE) as a traditional lossless compression method with Principal Component Analysis (PCA) as a machine learning algorithm to reduce data redundancy while maintaining accuracy. The study focuses on temperature sensor datasets collected over a specified period, ensuring real-world applicability.
The methodology involves preprocessing raw temperature data, applying Run-Length Encoding (RLE) for initial redundancy reduction, and then leveraging PCA to extract principal components, further reducing data dimensions before transmission. The efficiency of the proposed model is evaluated based on key metrics such as compression ratio, reconstruction accuracy, and energy savings. Performance comparisons are made with conventional lossless compression algorithms like Huffman Coding and Arithmetic Coding to assess improvements.
Preliminary results indicate that the combined approach achieves a higher compression ratio while preserving critical temperature variations, leading to significant energy savings in wireless
transmissions. This work contributes to advancing energy-efficient data handling in WSNs, making it highly relevant for resource-constrained environments. Future research directions include expanding the model to handle multi-sensor data streams and implementing real-time adaptive compression strategies

This project aims to design and execute a Printed Circuit Board (PCB) of a Vertical Farm Irrigation System that will utilize Internet of Things (IoT) and Controlled Environment Agriculture (CEA) to bring greater automation, water control, and overall crop yield. The project aims at optimization of irrigation in vertical farming, by offering a convenient, compact, and durable PCB-based solution that can monitor and manage the soil moisture, temperature, and humidity in real-time. It solves the problems of traditional manual irrigation techniques such as inefficiency by incorporating sensor feedback with microcontroller decision-making to guarantee accuracy in water dispersion and energy conservation in a regulated agricultural setting. The Methodology involved the design, development and the implementation of a twolayer PCB was done with the KiCad Electronic Design Automation (EDA) software. The system architecture is a platform that combines sensing, processing, actuation, interface, and power management into a single platform. It has notable features such as the ESP32 microcontroller as the means of data processing and control, a capacitive soil moisture sensor, a DHT11 temperature and humidity sensor, a PIR motion sensor, a SIM800L GSM to monitor remotely and also connect to the internet, and a 16x2 LCD to display data locally. The buck converter LM2596 makes sure of power supply and a 12V relay powers the pump to allow automated irrigation. The whole circuit was modelled, experimented and developed on a custom PCB to guarantee signal quality, power saving and environmental sustainability. The results demonstrated that the system was able to automate irrigation in one of the small-scale vertical farm prototypes and ensure the optimal soil moisture and environmental conditions with minimal human interventions. The experimental findings indicated proper sensor measurements, pump activation, and stability of operation in different environmental conditions. PCB-based design was found to be small, dependable and economical and greatly minimized wastages of water and manpower. This project illustrates how PCB-based IoT systems can be used to develop smart and sustainable agriculture to offer a scalable and cost-effective precision irrigation system in developing countries such as Nigeria.

The real estate sector in Nigeria continues to face significant challenges, including the prevalence of fraudulent property listings, unverified agents, lack of transparency, and inefficient processes for property transactions. This research proposes the development of a web-based property listing platform designed to address these issues by leveraging technology to enhance the efficiency, security, and reliability of real estate dealings. The platform aims to provide a secure environment where property seekers, verified agents, and legitimate sellers can interact, access accurate property information, and complete transactions with greater confidence. The study follows a structured methodology, beginning with requirement gathering through stakeholder consultations, literature review, and analysis of existing platforms. The system design translates these requirements into an architecture that integrates agent verification, property document authentication, advanced search and filtering options, interactive mapping, and secure communication features. The implementation phase involves the development of a user-friendly frontend and a robust backend, followed by rigorous testing to ensure system reliability, security, and performance. Deployment and hosting will make the platform accessible to the target audience, while evaluation will measure its impact in terms of usability, trust enhancement, and fraud reduction. The expected outcome of this research is a functional, scalable, and user-centered platform that contributes to transforming real estate transactions in Nigeria. The project is anticipated to provide a reference model for similar solutions in developing economies, demonstrating how digital technologies can promote transparency, accountability, and efficiency in informal markets. In addition to delivering a practical solution, the research aims to enrich the body of knowledge on secure web-based systems for property management, offering insights for future academic inquiry and professional practice.

Wireless Sensor Networks (WSNs) play a crucial role in modern communication systems, particularly in environmental monitoring, industrial automation, and smart cities. However, amajor challenge in WSNs is optimizing energy consumption due to the limited power resourcesof sensor nodes. One of the most effective ways to enhance energy efficiency is throughdatacompression, which reduces the amount of transmitted data while preserving essential
information. This project explores the integration of machine learning-based data compression techniquestoimprove energy-efficient transmission in WSNs. A hybrid approach is proposed, combiningRun-Length Encoding (RLE) as a traditional lossless compression method with Principal
Component Analysis (PCA) as a machine learning algorithm to reduce data redundancywhilemaintaining accuracy. The study focuses on temperature sensor datasets collectedover aspecified period, ensuring real-world applicability. The methodology involves preprocessing raw temperature data, applying Run-Length Encoding(RLE) for initial redundancy reduction, and then leveraging PCA to extract principal components, further reducing data dimensions before transmission. The efficiency of the proposed model isevaluated based on key metrics such as compression ratio, reconstruction accuracy, andenergysavings. Performance comparisons are made with conventional lossless compression algorithmslike Huffman Coding and Arithmetic Coding to assess improvements. Preliminary results indicate that the combined approach achieves a higher compressionratiowhile preserving critical temperature variations, leading to significant energy savings in wirelesstransmissions. This work contributes to advancing energy-efficient data handling inWSNs, making it highly relevant for resource-constrained environments. Future research directionsinclude expanding the model to handle multi-sensor data streams and implementing real-timeadaptive compression strategies.

In many institutions, business firms and organizations, the collection of attendance data is taken very seriously as the management at these organizations tend to dislike absenteeism, late coming and these organizations seem to sanction the individuals involved. These organizations also hope to encourage punctuality by rewarding people who are perpetually on time. Traditionally, the method of attendance taking used to be done manually using a physical register booklet. This method is prone to manipulation and impersonation. The attendance register could get damaged, stolen or lost. Therefore, several electronic techniques were developed to counter some notable flaws typical with the traditional method. These electronic techniques are not without flaws and drawbacks as well. This project implements a solution that makes use of an electronic technique (Biometric Fingerprint Recognition) for human identification as well as some other novel techniques to battle gaffes present in currently used technologies.

This project focuses on the implementation and analysis of a Virtual Private Server (VPS) for a Digital One Health Surveillance System. The aim of the work is to ensure a stable and reliable infrastructure that can support both the core system components and integrated AI health models without interruptions. The One Health approach requires a platform that can process data from human, animal, and environmental health sources in real time, and this can only be achieved with a server that offers improved performance, security, and scalability. In this project, a Hostinger KVM2 VPS was deployed and configured with EasyPanel, aPostgreSQL database, and both the frontend (React) and backend (Node.js) services. AI health
models were installed locally using Ollama, and a benchmarking script was developed to measure and compare the performance of different models. These tests helped verify that the upgraded VPS could run AI model inference alongside the application without system crashes or database shutdowns, which was a major limitation of the previous VPS setup.

In the contemporary landscape of business intelligence (BI), the choice of interactive dashboard tools plays a pivotal role in enabling data-driven decision-making processes within organizations. This study conducts a comprehensive comparative analysis of three leading BI tools: Looker, Tableau, and Power BI, with a specific focus on their interactive dashboard capabilities. The research aims to provide i sights into the strengths and weaknesses of each tool, aiding organizations in making informed decisions regarding their BI tool selection. Key aspects examined include user interface and ease of use, data connectivity, visualization capabilities, data modeling and transformation features, collaboration and sharing functionalities, integration with advanced analytics and machine learning, cost considerations, customization and extensibility options, as well as support and community resources

The efficiency and reliability of photovoltaic (PV) systems are largely determined by their ability to extract maximum power under varying environmental conditions. This project presents the implementation of an IoT-based investigative system for Maximum Power Point Tracking (MPPT) in photovoltaic arrays, focusing on the comparative performance of the MPPT and Pulse Width Modulation (PWM) charge controllers. The system integrates voltage and current sensors with an ESP32 microcontroller to measure and record PV parameters in real time. Through IoT connectivity, the collected data is transmitted to a cloud-based platform for remote monitoring, analysis, and visualization, enabling real-time tracking of PV performance. Experimental tests were conducted under different irradiance and temperature levels to evaluate the charging efficiency, dynamic response, and adaptability of both controllers. The MPPT controller dynamically adjusted the operating point of the PV module to maximize energy extraction, while the PWM controller maintained a simpler, fixed switching mechanism. Additionally, the system allowed for a detailed analysis of the relationship between light intensity, temperature, and PV output performance, with the readings interpreted from real-time graphical charts. These insights revealed how environmental variations affect energy generation and charge controller efficiency.

The growing incidence of zoonotic diseases and other health risks underscores the pressing necessity for integrated surveillance systems that connect the sectors of human, animal, and environmental health. This initiative centers on the creation and deployment of a Stakeholder Portal for a Digital One Health Surveillance System, which aims to improve real-time data exchange, collaboration across sectors, and informed decision-making. The portal is built using React for the frontend and Python for its backend functionalities, and it features an AI-driven chatbot powered by the Gemini API to enable automatic responses and stakeholder interaction. Notable attributes include secure user authentication with role-based access, engaging data visualization dashboards, and management of real-time surveillance data. Health personnel have unique access to enhanced dashboard features, while general users can interact with publicly available information. The system guarantees data privacy and security by adhering to global standards, offering a scalable, user-friendly platform that can be customized to fit various healthcare settings. This effort plays a crucial role in bolstering public health resilience by promoting effective communication and proactive measures against emerging health threats within the One Health approach