School of Electrical Engineering and Technology (SEET)
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School of Electrical Engineering and Technology (SEET)
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Item A GSM-Based Remote Controlled Poultry Feed Dispensing System Using DTMF(2016) Ahmed, A.; Olaniyi, O. M.; Dogo, E. M.; James, E.Poultry rearing for small scale and commercial farmers has made large contribution in food production. With the recent decline in contribution of livestock subsectors to the national economy, there is a need to device a means of making poultry farming convenient, attractive and maximize yield. Automated Feeding is considered very important in poultry production; however, many famers in the tropical regions practice subsistence farming and mostly employ manual poultry feeding. This paper presents the development of a GSM-based remote-control poultry feed dispensing system via Dual Tone Multi-frequency (DTMF) for intermittent control of poultry feed dispensing. Proper knowledge in this area by farmer will help in running effective production and increase yield in meat and egg to maximize profit. The system is multi state fully input dependent system whose change of state can be controlled by a remote control. The developed mechatronic system reduces manpower, saves time and operates efficiently with minimal human involvement in poultry feeding. The system demonstrated practical effort regarding the improvement in performance of existing solid feed dispensing for high yield with minimal human interventionItem Wireless sensor networks for remote healthcare monitoring in Nigeria: Challenges and way forward(IEEE, 2013) Hassan, N. M.; Olaniyi, O. M.; Ahmed, A.; Dogo, E. M.Wireless sensor networks have gained a lot interest in the field of medicine with a wide range of capabilities. In most developed countries wireless sensor networks are being used in monitoring critical illnesses such as Cancer detection, cardiovascular diseases, monitoring asthmatic patient and in the treatment of Diabetes. Wireless sensor networks have enabled medical doctors to monitor patients remotely and give them timely feedback and support; potentially increasing the reach of health care by making it available anywhere at any time The application of Wireless Sensor Networks for remote medical monitoring is relatively new in Nigeria. Recently the Nigerian government embarked on the use of e-health to meet the health requirements of its remote and rural dwellers. In this paper, we discuss some of the core challenges facing the application of wireless sensor networks for remote medical care monitoring in Nigeria, and how these challenges can hinder the application of Wireless Sensor Networks for remote healthcare in Nigeria.Item Design of a Simple and Low-Cost Microcontroller Based Medicare Device for Heart Beat Monitoring(IEEE, 2013) Dogo, E. M.; Sado, F.; Adah, S. M.Heart beat monitoring is vital to ensuring healthiness of the human cardiovascular system, but availability of a simple and low-cost heart beat monitoring device that does not require expert medical personnel to handle still remains a challenge especially in rural and semi urban areas of developing countries like Nigeria. This paper describes the design and implementation of a simple, reliable, accurate and cost effective microcontroller based heart beat monitoring device with Liquid Crystal Display (LCD) and voice outputs. The heart rate of the subject is measured from the fingertip using optical sensors and the rate is then displayed on a text based LCD and voice outputs in English language and two Nigerian indigenous languages (Hausa and Yoruba).Item Cross-Layer Integration Approach for Improving QoS for IPv6 Based VOIP(iJET, 2014) Dogo, E. M.; Ahmed, A.; Olaniyi, O. M.Voice over IP (VOIP) is today one of the most innovative IP based Communication Technologies in the Telecommunications industry. This has made it to enjoy a high degree of success in its application in small, medium and large scale enterprises, primarily to save cost as well as leveraging on its enhance functionalities such as mobility and scalability. Despite all its successes, VOIP still faces challenges with Quality of Service (QoS) degradation. This paper proposes a cross-layer model to effectively manage interactions in the data, network and transport layers guided by tradeoff between three performance metrics that affect QoS of VOIP for an improved QoS for Voice over IPv6 (VOIPv6). The parameters taken into consideration in this proposed model are: packet loss, delay and throughput observe by the end user.Item Dataset for a wireless sensor network based drinking-water quality monitoring and notification system(Elsevier, 2019) Sithole, M. P. P.; Nwulu, N. I.; Dogo, E. M.This paper presents the collected experimental data for water quality monitoring which was conducted in ten experiments by using five different common sources of water contaminants namely soil, salt, washing powder, chlorine and vinegar and their combination. The data were collected indoors at room temperature during the day for several days using sensors that measure pH, turbidity, flow rate, and conductivity in water. The water consumption risk (CR) was calculated as deviation based on the water quality parameters standards proposed by the World Health Organisation (WHO) and the South African Department of Water Affairs (DWA), with respect to the sensor measurement readings obtained. While the error measurements were calculated based on the expected parameter measurement per conducted experiment and repeated for 26 measurements. Pure tap water was the benchmark of water safe for human consumption. The first five experiments were performed by introducing each contaminant into the water and thereafter, two contaminants in the sixth experiment and their additions until all different contaminants were experimented at once in the last experiment.Item IoT Based Security Management Framework for Heterogeneous Network Environment(2020) Ajibo, C. A.; Chinaeke-Ogbuka, I. M.; Dogo, E. M.; Ogbuka C. U.In an effort to curb the potential losses associated with the event of security bridge, admitting the uneven bandwidth support that characterizes most developing smart cities, we propose a neural inspired Multimodal Security Management System (MSMS) that is bandwidth-tolerant. The proposed system leverages on a Next-Generation Network (NGN) architecture in catering for the challenges associated with the provisioning of ubiquitous broadband access for IoT support in a heterogeneous morphological network environment. In order to evaluate the MSMS, we simulated the proposed cloud-based system on a Next Generation Network (NGN) architecture which utilizes Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) as transport technique in a Long Term Evolution (LTE) backbone infrastructure. We then compare its performance over a competitive alternative transport technique: "Internet Protocol Asynchronous Transfer Mode (IP/ATM)". Thus, we further evaluated the MEMS on the latter architecture. While, our proposed system is able to capture both textual, aural, and visual information of individuals in security vulnerable environments via installed smart microphones and cameras, it is also able to integrate this information's in predicting security threats. When compared with the popular Security Management System (SMS) "ShotSpotter", results show that our proposed system outperforms the ShotSpotter system by 0.87 and 0.45 in terms of efficiency and response time respectively. Finally, simulation of our proposed system on an IP/MPLS transport schemes shows that the former outperforms the latter with respect to overall network bandwidth utilization and average traffic loss in the ratio of 0.098 and 0.087 respectively.Item Artificial intelligence model for prediction of cardiovascular disease: An empirical study(AccScience Publishing, 2024) Umar, B. U.; Ajao, L. A.; Dogo, E. M.; Ajao, F. J.; Atama, M.Cardiovascular disease (CVD) is a disease related to the heart and blood vessels. Prediction of CVD is essential for early detection and diagnosis, which is however compounded by the complex interplay between medical history, physical examination outcomes, and imaging results. While the existing automated systems are fraught with the usage of irrelevant and redundant attributes, artificial intelligence (AI) helps in the identification of potential CVD populations by prediction models. This work aims at developing an AI model for predicting CVD using different classifications of machine learning techniques. The CVD dataset was obtained from the UCI repository containing about 76 cardiac attributes for training in various machine learning models, which include a hybrid of artificial neural network genetic algorithm (ANN-GA), artificial neural network, support vector machine (SVM), K-means, K-nearest neighbor (KNN), and decision tree (DT). The performance of the models was measured in terms of accuracy, means square error, sensitivity, specificity, and precision. The results showed that the hybrid model of ANN-GA performs better with an accuracy of 86.4%, compared to the SVM, K-means, KNN, and DT measured at 84.0%, 59.6%, 79.0%, and 77.8%, respectively. It was observed that the system performs better as the number of datasets increases in the database, with a fewer selection of attributes using genetic algorithm for selection. Thus, the ANN-GA model is recommended for CVD prediction and diagnosis.Item Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching(2024) Garuba, O. R.; Abdullahi, I. M.; Dogo, E. M.; Maliki, D.This research presents a cross-correlation similarity matching method to study the fingerprint transformation and thresholding impact. This work directly compares the impact of various transformations (rotation, translation, elastic deformation, and scaling) on the fingerprint matching performance at different threshold values, in contrast to the standard minutiae-based systems. In order to compare the template positions of the two fingerprints using plots, the cross-correlation similarity matching of fingerprints first selects suitable templates in the primary fingerprint and then uses template matching to assess the impact of each transformation on matching accuracy, FRR, and FAR in the secondary print. The findings highlight the potential of thresholding in developing reliable and practical fingerprint recognition systems.Item A decade bibliometric analysis of underwater sensor network research on the Internet of Underwater Things: An African perspective(Springer, Cham, 2020) Salami, A. F.; Dogo, E. M.; Makaba, T; Adedokun, E. A.; Muazu, M. B.; Sadiq, B. O.; Salawudeen, A. T.Recent advancements in cloud computing (CC) and the rapid growth of the Internet of Things (IoT) have tremendously revolutionized terrestrial wireless sensor networks (TWSN) communication. These have resultantly paved the way for the practical realization of underwater wireless sensor networks (UWSN) and the emergence of the Internet of Underwater Things (IoUT). The need for better environmental monitoring within the context of smart cities and the recent spate of global natural disasters has further aroused research interest in IoUT which has motivated a number of UWSN innovations, such as the development of tethered remotely operated underwater vehicles (ROUVs), untethered autonomous underwater vehicles (AUVs), unmanned/autonomous surface vehicles (USVs/ASVs) and other smart underwater technologies. While these inventions hold promising prospects for technologically advanced countries, the same assertion cannot be made for most African countries due to challenges inherent in research and development activities into critical IoUT/UWSN projects in the region. This chapter conducts a systematic bibliometric analysis that highlights the knowledge base for core research works in UWSN globally and within the African region. This research discovered 1025 peer-reviewed articles in 5 Scopus-indexed document sources published between 2008 and July 2019. Microsoft Excel and VOSviewer science mapping software tool was used to analyse the retrieved data from Scopus repository. The bibliometric analysis was used to evaluate specific criteria, namely, major subject area, document sources, most cited and productive authors, countries, institutions, funding institutions and most used keywords. The findings of this research indicated that UWSN/IoUT research is still in its infancy in the African region. This chapter concludes by highlighting vital missing links, essential research directions and unique technical recommendations that will be of relevance in helping the successful actualization of IoUT/UWSN research projects in Africa.Item A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks(IEEE, 2018) Dogo, E. M.; Afolabi, O. J.; Nwulu, N. I.; Twala, B.; Aigbavboa, C. O.In this paper, we perform a comparative evaluation of seven most commonly used first-order stochastic gradient-based optimization techniques in a simple Convolutional Neural Network (ConvNet) architectural setup. The investigated techniques are the Stochastic Gradient Descent (SGD), with vanilla (vSGD), with momentum (SGDm), with momentum and nesterov (SGDm+n)), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), Adaptive Gradient (AdaGrad), Adaptive Delta (AdaDelta), Adaptive moment estimation Extension based on infinity norm (Adamax) and Nesterov-accelerated Adaptive Moment Estimation (Nadam). We trained the model and evaluated the optimization techniques in terms of convergence speed, accuracy and loss function using three randomly selected publicly available image classification datasets. The overall experimental results obtained show Nadam achieved better performance across the three datasets in comparison to the other optimization techniques, while AdaDelta performed the worst.