Browsing by Author "Lukman, Olawoyin"
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Item Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis(Mobile Information Systems, 2021-01-21) Quadri, Ramon; Nasir, Faruk; Kayode, Adewole; Abubakar, Abdulkarim; Lukman, Olawoyin; Abdulkarim, Oloyede; Haruna, Chiroma; Aliyu, Usman; Carlos, CalafateThe importance of wireless path loss prediction and interference minimization studies in various environments cannot be over- emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.Item DESIGN OF A GPS-BASED ACADEMIC PERSONNEL CLOCKING SYSTEM(Nigerian Journal of Technology (NIJOTECH), 2019-04-20) Temitope, Adeniran; Anyaegbu, Alfred; Lukman, Olawoyin; Ajibola, AjagbeEnsuring optimal productivity in the workplace is a major concern for employers of labour; hence the proposition of clocking solutions for employees in a bid to track their presence and punctuality at their duty posts. However, the peculiarity of the academic environment makes general methods of clocking unsuitable for lecturers. This paper presents design and implementation of a GPS-based clocking solution for academic personnel using a web App and an Android client. The web App; written in NodeJS and hosted on Heroku, in conjunction with the database (Mongo DB), registers and holds the schedule details of the lecturers. The android client which is installed on lecturers’ mobiles, works in synergy with the web App to accomplish clocking. This concept is reliant on GPS, thus, the lecturers’ mobiles require a clear view of the sky for successful clocking. The aspect of security can be improved upon with a few tweaks in the applications’ program codes. In all, the system utilizes existing and readily available resources to achieve clocking of academic personnel.Item Energy Consumption in Perpetual Wireless Sensor Networks(Zaria Journal of Electrical Engineering Technology, Department of Electrical Engineering, Ahmadu Bello University, Zaria, 2020-09-30) Abdulkarim, Oloyede; Lukman, Olawoyin; Faruk, Nasir; Abdulkarim, Abubakar; ABdulrahman, OtuozeAbstract: This paper investigates the amount of energy consumed in different configuration for perpetual wireless sensor networks. Wireless Sensor Networks (WSNs) represent an area of networking that become pivotal in many applications. The use of WSNs for the monitoring of environments, habitats as well as systems within industry and healthcare has made WSNs a crucial area of research within recent years. The principles behind WSNs involve the deployment of remote sensing and relay nodes, able to collect and transmit raw data for processing. Applications such as remote environmental monitoring present new challenges such as the prospect of developing networks that can operate perpetually to collect data for as long as possible. Simulation and theoretical analysis were done using the networking simulator DENSE. DENSE is presented to provide insight into what protocols and energy saving techniques can be employed to establish the possible feasibility of constructing PWSN. The results show that mesh is the most realistic due to its energy distribution and optimization over a large area for a great number of nodes; however, a small single hop based network can provide good result for energy consumption and packet success rates.Item A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram.(Journal of Ambient Intelligence and Humanized Computing., 2022-01-15) Musa, Nehemiah; Gital, Abdulsalam Yau; Aljojo, Nahla; Haruna, Chiroma; Kayode, Adewole; Majeed, Hammed; Faruk, Nasir; Abdulkarim, Abubakar; Ifada, Emmanuel; Yusuf, Folawiyo; Abdulkarim, Oloyede; Lukman, Olawoyin; Sikiru, Ismaeel; Ibrahim, KatibiThe success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applica- tions of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on bio- metric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.