Computer Engineering

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Computer Engineering

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Development of feedback mechanism for microcontroller based SMS electronic strolling message display board
(2014) Dogo, E. M.; Akogbe, A. M.; Folorunso, T. A.; Maliki, D.; Akindele, A. A.
Performance evaluation of mobile intelligent poultry liquid feed dispensing system using two-way controller technique
(The AIMS Publications, 2015) Olaniyi, O. M.; Folorunso, T. A.; Dogo, E. M.; Bima, M. E.; Adejumo, A.
Development and Implementation of Microcontroller-based Improved Digital Timer and Alarm System
(2016) Ajao, L. A.; Adegboye, M. A,; Dogo, E. M.; Aliyu, S. O.; Maliki, D.
Time plays an important role in our daily activities, more particularly in sectional events or conference arena where there is need for accurate time management. This paper focuses on the development and implementation of an improved digital timer with audio-visual unit using (PIC16F887) microcontroller chip and other electronics component such as LCD, 7-segment display, LED and buzzer as an I/O device. Thus, the need for this device in our daily activities is to monitor the time scheduled for events, updating and alert the audience using an audio-visual approach. The proposed system allows apt time management and avoids time wastage during seminar presentations and the likes. It particularly helps presenters to be time conscious, thus, making them to naturally adjust such that the allotted time is enough to cover up their presentation. The digital timer and alarm system presented herewith is also of advantage to the physically challenged like the deaf and blind in monitoring their sectional activities and to be fully involved about the event situation. The system was designed in different modules, and all were interfaced together with firmware chip to simplify the mechanism’s fault diagnoses and fault corrections.
A Model for an Enterprise Automated RFID-Based Pay and Park System
(ceur-ws.org, 2016) Dogo, E. M.; Ahmed, A.; Adelakun, M. O.
Traffic management is one of the challenging problems in urban cities as vehicle owners look for where to park and queue to pay for rented car parks usually on an hourly basis. Therefore, the choice of a suitable, reliable and flexible architecture for Radio Frequency Identification (RFID) based pay and park system readily comes to mind. It is assumed in this work that the parking lot is already known and secured by the vehicle owner in a closed car park; this paper therefore seeks to address and automate the billing system for enterprise car parks. To achieve this, a reliable and accurate enterprise star topology networked RFID based system, that computes the amount to be paid by a user which is calculated based on the time the user enters and exits the park, and the amount the park owner is charging at a particular point in time is proposed for the automated pay and park system. The system comprises of both software and hardware components integrated together. The developed prototype system is able to grant authorized users access to the park within 30ms after verification and open the barrier in 30ms whenever the emergency button is pressed for safety consideration.
Web-Based Decision Support System for Diagnosis of Ebola Virus Disease Using Bayesian Networks
(2016) Dogo, E. M.; Kolo, J. G.; Aror, O.; Rahman, A. T.
The recent epidemic of the Ebola Virus Disease (EVD) left many dead in West Africa and in other parts of the world. A major problem faced was late diagnosis or diagnostic error of the disease; this was due to largely unavailability of medical professionals familiar with the disease and low doctor to patient ratio. An accessible method for reliable diagnosis is required to offset the low ratio of doctors to population. This paper presents the application of Bayesian networks for diagnosis of EVD. A general procedure for implementing a Bayesian network model is proposed; thereafter we demonstrate how the resulting Bayesian network can be applied in a medical diagnostic decision support system. The system uses the questionnaire method to elicit symptoms and is accessible through web browsers over the internet and mobile phones to potential patients and medical practitioners. The system developed is able to provide diagnosis in the form of probabilities, for the presence or absence of EVD in an individual. The probability of an individual infected by the disease depends on present or absent of particular symptoms according to the gathered disease pathology. The system was successfully developed, and had a diagnostic accuracy of 77% when compared to the World Health Organization (WHO) algorithm, but refinements of the conditional probability distribution would provide the most accurate sensitivity to symptoms and also improve the accuracy of diagnosis. Finally, web functionality, performance and usability test on the developed web application is carried out by simulating various load patterns and the result was generally acceptable.
Intelligent Railway Cross Level Gates and Signaling System using Fuzzy Logic Control Technique
(Convenant University, 2016-05-09) Olaniyi, O. M., Abdullahi, I. M., Maliki, D., & Lasore, T. M.
Current manually operated gates at the railway cross levels of developing countries are stressful and time wasting. This has exposed pedestrians to high rate of accident resulting to loss of lives and drastic reduction of the country’s economy. Different systems have been developed to prevent rail accidents at the level crossing but they are not effective and in most cases are too expensive to implement. This study presents a prototype model of an intelligent railway cross level gates and signaling system using Mamdani fuzzy logic control technique. The intelligent system has the ability to detect the arrival/departure of a train and close/open the cross level gates respectively. The system response was evaluated with respect to time. The results after the evaluation of the developed system showed that the system with fuzzy intelligent control technique has a high response with respect to time compared to a system without an intelligent technique. The large scale implementation of the developed intelligent railway cross-level gate and signaling system can be used to prevent avoidable accident occurrence at the level crossings and thus, reduces loss of lives as well as improvement of the nation’s economy through efficient delivery of goods and services in Africa.
Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I. (2017). Development of an Artificial Neural Network Model for Daily Electrical Energy Management. Proceedings of the 2nd International Engineering Conference, (IEC 2017), Federal University of Technology Minna, Nigeria, pp 120-125.
(Federal University of Technology Minna, 2017-05-07) Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I
Efficient monitoring and control of electrical energy do not only prevent fire out-breaks caused by electrical appliances, but can also reduce excessive billings and prevent electrical installations. Most Energy Management Systems (EMS) for remote controlling of electrical appliances rely mostly on sensors, data and GSM networks which are un-reliable or even un-available in most part of developing world, this makes them less reliable. Therefore, there is need for an intelligent system that can manage electrical consumption intelligently using user-appliance interactive pattern over a period of time for intelligent control of users’ appliances in his/her absence. The model parameters (number of neurons and training algorithms) that affects its performance were first investigated and adopted. The performance of the developed model was evaluated using Regression analysis (R) and Mean Square Error (MSE) using ANN and Simulink tool boxes in Matlab R2015b. A good model can be used for real time control when deployed. Also, Scale Conjugate Gradient (SCG) training algorithm should also be used because of its high performance for pattern recognition problems. This work will go a long way in efficiently controlling household electrical appliances in the absence of the users thereby preventing fire disasters caused by electrical appliances, reducing the tariffs of consumers while increasing lifespan of electrical installations.
Development of Time Controlled Based Solar Radiation Tracking System
(Journal of Science Technology Mathematics and Education (JOSTMED), 2017-06-17) Maliki, D., Ibrahim I., Nuhu B. K., Abdullahi I. M., & Ajao, L. A
The increase in daily use of electricity with limited in the amount of fossil fuel necessitated researches to explore other methods of producing energy. Many renewable sources of electricity are in existence, one of the cheapest, free and the most abundant renewable source of energy is the electricity generated from the sun. Electricity from solar radiation is environmentally friendly as it poses no harmful hazard to the surrounding. Today, radiation from the sun can be harnessed with the use of the photovoltaic material like the solar panel. It was observed that the sun direction keeps changing during the day as a result of the rotation of the earth and obtaining maximum amount of solar energy from a fixed solar panel cannot be totally achieved throughout the day. To this extend, a time controlled based solar radiation tracking system was developed. The developed system is capable of continuously changing the direction of sun module as the sun transverse the sky with the use of an intelligent fuzzy rules base on input variations. The performance of the tracking system when compared with the fixed solar device gave an output of 19.54% increase in voltage output.
Nature-inspired Optimal Tuning of Scaling Factors of Mamdani Fuzzy Model for Intelligent Feed Dispensing System
(MEC Press, 2018) Ameh, C. A.; Olaniyi, O. M.; Dogo, E. M.; Aliyu, S.; Arulogun, O. T.
The increasing trends in intelligent control systems design has provide means for engineers to evolve robust and flexible means of adapting them to diverse applications. This tendency would reduce the challenges and complexity in bringing about the appropriate controllers to effect stability and efficient operations of industrial systems. This paper investigates the effect of two nature inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on PID controller for optimum tuning of a Fuzzy Logic Controller for Poultry Feed Dispensing Systems (PFDS). The Fuzzy Logic Controller was used to obtain a desired control speed for the conceptualized intelligent PFDS model. Both GA and PSO were compared to investigate which of the two algorithms could permit dynamic PFDS model to minimize feed wastage and reduce the alarming human involvement in dispensing poultry feeds majorly in the tropics. The modelling and simulation results obtained from the study using discrete event simulator and computational programming environment showed that PSO gave a much desired results for the optimally tuned FLC-PID, for stable intelligent PFDS with fast system response, rise time, and settling time compared to GA.
Sensed Outlier Detection for Water Monitoring Data and a Comparative Analysis of Quantization Error Using Kohonen Self-Organizing Maps
(IEEE, 2018) Dogo, E. M.; Nwulu, N. I.; Twala, B. and Aigbavboa, C. O.
Measurement values obtained from sensors deployed in the field are sometimes prone to deviation from known patterns of the sensed data which is referred to as outlier or anomalous readings. The reasons for this outlier may include noise, faulty sensor errors, environmental events and cyber-attack on the sensor network, resulting in faulty and missing data that greatly affects quality of the raw data and its subsequent analysis. This paper employs the Self-Organizing Maps (SOM) algorithm to visualise and interpret clusters of sensed data obtained from fresh water monitoring sites, with patterns of similar expressions in a graphical form. With the aim of detecting potential anomalous sensed data, so that they could be investigated and possibly removed to guarantee the quality of the overall dataset. Furthermore, a comparative study of the effects of four different well known neighborhood functions (gaussian, bubble, triangle and mexican hat) with varying neighborhood radius (σ) and learning rate (η) values on Quantization Error (QE) metric was conducted. From the experiment conducted a 3.45% potentially anomalous sensed data were discovered from the entire dataset, in addition, our initial finding suggests a very insignificant variation of the QE based on our dataset and the experiments conducted.
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
(i-Manager, 2018) DOGO, E. M.; NWULU, N. I.; NKONYANA, T.; OLANIYI, O. M.; AIGBAVBOA, C. O.
This paper reviews scholarly articles on the application of blockchain technology for secure electronic voting (e-voting). Furthermore, the feasibility of using blockchain technology to replace the existing manual or semi-digitized voting system in developing countries with Nigeria as a case study is performed. To analyse the current state and preparedness of adopting Blockchain Enabled E-voting (BEEV) system in Nigeria, this paper employs the qualitative SWOT (Strengths, Weaknesses, Opportunities, and Threats) and PEST (Political, Economic, Social, and Technological) analysis approach. This evaluation leads us to identify internal and external factors and the strategic direction in adopting BEEV in Nigeria. It is the authors' opinion that this approach could also be tailored to evaluate situations of other developing countries.
Fingerprint Based Driver’s Identification System.
(Federal Univesity of Technology Minna, 2018-09-06) Inalegwu, O. C., Maliki, D., Agajo J., Ajawo, L. A., & Abu, A. D
This design work presents a proposed replacement to the current system used by the Federal Road Safety Commission (FRSC) for checking licensed/unlicensed drivers. It gives a faster and less tedious way of identifying registered and licensed road users using biometric captures. The system employs the use of an Arduino board to control and process the functioning of other peripherals: the fingerprint scanner and the Organic Light Emitting Diode (OLED) screen connected to it to achieve its purpose. The prototype system developed was able to displays driver’s information on the OLED screen (Age, Name, Sex and License ID); the average response time of the system was also calculated to be 1.41 seconds, which is a good response time considering the system in question. The false Accept rate and false reject rate were relatively low (after a sample test with 25 individuals); at 4% and 8% respectively. Also, for its implementation, the components are readily available, relatively cheap and the system is on that can be easily adopted by the FRSC if access to their already existing database is granted. Consequently, it is safe to say that the developed system measured up to the design expectations; it meets the aim of a proposed replacement for the present analogue and easy to beat system employed by the FRSC.
A Comparison of Strategies for Missing Values in Data on Machine Learning Classification Algorithms
(IEEE, 2019) Makaba, T.; Dogo, E.
Dealing with missing values in data is an important feature engineering task in data science to prevent negative impacts on machine learning classification models in terms of accurate prediction. However, it is often unclear what the underlying cause of the missing values in real-life data is or rather the missing data mechanism that is causing the missingness. Thus, it becomes necessary to evaluate several missing data approaches for a given dataset. In this paper, we perform a comparative study of several approaches for handling missing values in data, namely listwise deletion, mean, mode, k-nearest neighbors, expectation-maximization, and multiple imputations by chained equations. The comparison is performed on two real-world datasets, using the following evaluation metrics: Accuracy, root mean squared error, receiver operating characteristics, and the F1 score. Most classifiers performed well across the missing data strategies. However, based on the result obtained, the support vector classifier method overall performed marginally better for the numerical data and naïve Bayes classifier for the categorical data when compared to the other evaluated missing value methods.
Design and Implementation of a Wireless Patient Health Monitoring System
(IEEE, 2019) O. Manzombi, O.; Dogo, E. M.; Nwulu, N. I.
This paper presents the design and implementation of an IoT wireless patient's health monitoring system. The system can be used to continuously monitor the body temperature and pulse rate of a patient located in a hospital room using biomedical sensors. The temperature and pulse rate values are taken from the sensors and processed by an Arduino Uno. Furthermore, they are sent wirelessly via RF communication using a 433 MHz transmitter and receiver kit. The readings are encoded and sent to the receiver where they are decoded and displayed on an LCD screen. Finally, the temperature and pulse rate values are also displayed and stored online using an Arduino Ethernet Shield 2 for future analysis.
Development of a Small Scaled Microcontroller-Based Poultry Egg Incubation System
(IEEE, 2019) Kutsira, G. V., Nwulu, N. I. and Dogo, E. M.
Owing to an increase in the commercial production of chickens and demand for local consumption as a source of protein in both rural and urban areas in developing countries. This paper proposes a cost-effective incubator for hatching poultry eggs with minimal human involvement. The paper describes the design and implementation of a prototype microcontroller-based electrical incubator system. The developed incubator has optimized temperature and humidity that facilitates higher hatchability rate provided that the egg fertility is high. The prototype incubator was evaluated by loading it with 6 presumed fertile eggs. The percentage of hatchability obtained was 67% (4 out of 6 egg). The remaining two eggs were not hatched as they may not have been fully fertilized.
Development of a Wireless Sensor Network Based Water Quality Monitoring and Notification System
(2019) Sithole, M. P. P.; Nwulu, N. I.; and Dogo, E. M.
In this paper, we present a water quality monitoring and notification system. It is also integrated with a consumer alert system on the safety of the water in accordance with the WHO water quality standard. The consumer alert system is made up of a buzzer for notification, a red-Light Emitting Diode (LED) and green-LED as an indicator for unsafe water and safe water respectively. Five sources of contaminants in water namely, soil, chlorine, vinegar, salt, washing powder and their combination were used in this to validate the performance of the system. Wireless communication between the measuring subsystem and the analysis and notification subsystem was established for mobility using radio frequency modules. The error in measurements and the consumption risk per water parameter were calculated on MS Excel as part of the analysis and presented in this document. This paper also presents an added functionality by using a Light Dependent Resistor (LDR) for turbidity measurement and LEDs in the notification subsystem. The notification system accommodates people with disabilities as the buzzer can be heard by those who can't see, and the LEDs can be seen by those who can't hear. The developed system was compared for functionality and performance using the quality of the results measured in comparison with the expected results.
Parameter Investigation and Analysis for Elite Opposition Bacterial Foraging Optimization Algorithm
(Federal University of Technology Minna, 2019-04-22) Maliki, D., Muazu, M. B., Kolo, J.G., & Olaniyi, O. M
The investigation and analysis of algorithm parameters is an important task in most of the global optimization techniques. However, finding the best set of parameter value for the optimum performance of an algorithm still remain a challenging task in a modified Bacteria Foraging Optimization Algorithm (BFOA) since most toe the existing research focuses on the application o the algorithm and likewise it benchmarking with the global test function. The Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA) is a modified nature inspired optimization algorithm from BFOA which focuses on the generation of an elite solution from the opposition solution for an optimization process. This research is focuses on the investigation of such parameters population size, probability of elimination dispersal, step size and number of chemotaxis so as to determine the extent to which they affect the optimal solution from the EOBFOA with respect to global minimum or least minimum standard deviation. From the results obtained, it was observed that the global minimum in EOBFOA depend on the exploitation ability of the bacteria in the search space.
Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection
(IEEE, 2020) Dogo, E. M.; Nwulu, N. I.; Twala, B.; Aigbavboa, C. O.
Imbalanced class distribution and missing data are two common problems and occurrences in water quality anomaly detection domain. Learning algorithms in an imbalanced dataset can yield an overrated classification accuracy driven by a bias towards the majority class at the expense of the minority class. On the other hand, missing values in data can induce complexity in the learning classifiers during data analysis. These two problems pose substantial challenges to the performance of learning algorithms in real-life water quality anomaly detection problems. Hence, the need for them to be carefully considered and addressed to achieve better performance. In this paper, the performance of a range of several combinations of techniques to deal with imbalanced classes in the context of binary-imbalanced water quality anomaly detection problem and the presence of missing values is extensively compare. The methods considered include seven missing data and eight resampling methods, on ten different learning state-of-the-art classifiers taking into account diversity in their learning philosophies. The different classifiers are evaluated using stratified 5-fold cross-validation, based on three performance evaluation metrics namely accuracy, ROC-AUC and F1-measure. Further experiments are carried out on nineteen variants of homogeneous and heterogeneous ensemble techniques embedded with resampling and missing value strategies during their training phase as well as an optimized deep neural network model. The experimental results show an improvement in the performance of the learning classifiers, especially when dealing with the class imbalance problem (on the one hand) and the incomplete data problem (on the other hand). Furthermore, the neural network model exhibit superior performance when dealing with both problems.
Explorative analysis of AUV-aided cluster-based routing protocols for Internet of intelligent underwater sensors
(Elsevier, 2020) Salami, A. F., Adedokun, E. A., Al-Turjaman, F., Bello-Salau, H., Sadiq, B. O., Dogo, E. M.
Contemporary innovations in underwater acoustic technology (UAT), smart systems (SS), vehicular ad-hoc networks (VANET), micro-electromechanical systems (MEMS), and artificial intelligence (AI) coupled with recent advancements in the field of Internet of underwater things (IoUT) have led to the development of interesting engineering solutions for underwater sensor networks (UWSN). UWSN performs collaborative event observation for adaptive decision-making through a specialized network of submerged sensors, surface sinks, and coastal base station by relying on interactive communication, intelligent computing, and smart sensing. UWSN is obviously a critical and essential asset for smart cities (SC) and because of the explosive potential of UWSN technology; it has been garnering increasing attention from academic researchers and industrial experts in various fields. However, the performance of UWSN applications is limited due to issues closely tied to the underwater environment such as surface noise, narrow bandwidth, long propagation delays, high-temperature gradients, bio-fouling, corrosion, and erratic water current activities. These issues lead to high-energy consumption, high deployment costs, rapid route failures, frequent retransmissions, low reliability, and other challenges that have instigated UWSN researchers to proffer solutions in the form of different routing protocols. Cluster-based routing (CBR) is one of these proposed solutions where the network adopts a dynamic hierarchical process of logically grouping the nodes into cluster heads (CHs) and cluster members (CMs) with respect to well-defined performance indicators. Researchers have also established that CBR protocols are relatively more versatile and capable of yielding better performance in terms of fault tolerance, resource awareness, and route efficiency for large-scale UWSNs. This chapter, therefore, discusses the architecture, network model, and technical features of AUV-aided water quality monitoring (WQM) as a target application for the Internet of intelligent underwater sensors. This research furthermore conducts an explorative analysis of state-of-the-art CBR protocols for UWSNs. This work conducts simulation-based network and statistical analysis to provide useful technical insights on the performance analysis of selected CBR protocols.
Combating Road Traffic Congestion with Big Data: A Bibliometric Review and Analysis of Scientific Research
(Springer, 2021) Dogo, E.M.; Makaba, T.; Afolabi, O.J.; Ajibo, A.C.
Road traffic congestion is one of the challenging problems confronting city dwellers globally. It is majorly caused by either one or a combination of recurrent congestion, nonrecurrent congestion, and precongestion conditions in urban road networks. This chapter performs a bibliometric analysis and reviews the volume of literature linking big data with combating road traffic congestion between 2011 and 2020. The review employs a quantitative analysis of bibliometric science mapping tool to highlight features that affect knowledge accumulation. The chapter also reviews the intellectual structure of knowledge based on total publications and citations. The key scholars, documents, affiliations, regions, data, and algorithms that influenced the development of this research area are analyzed. The results of documents co-citation evaluation show that the key research clusters are salient elements linked with the development and deployment of connected and autonomous vehicles (CAVs) technology. These research clusters are traffic flow prediction, congestion and accidents alert systems, security and privacy mitigation, vehicle emission profiles, travel time estimation, optimization of vehicular routing, journey planning and congestion prediction, and travel and parking guidance. Finally, the chapter presents the way forward and future research direction for sustainable road traffic management in the context of smart city initiatives leveraging on big data.