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The 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 applications 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 biometric 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.

This paper examines the spatial variability of duty cycle in the GSM 900 and 1800 MHz bands within Kwara State, Nigeria. The results show spatial variance in the duty cycle with average occupancies of 1.67%, 17.76%, 10.55% and 0.39%, 11.00% and 5.11 in the rural, urban and all locations for 900 and 1800 MHz bands. Findings also show that there is very high positive correlation between rural 900/1800 MHz and urban 900/1800 MHz. But very high negative correlations exits between urban 900 and rural 1800, and urban 1800 and rural 1800. There is a weak and negative correlation between rural and urban 900 MHz, rural-urban 1800. These results clearly show the abundance of unutilised spectrum within the GSM bands. Therefore, regulatory commissions should adopt flexible spectrum reuse strategy to relax the regulatory bottlenecks to maximize the scarce radio resources in the licensed bands, especially for rural network deployments

The broadcast nature of radio propagation in wireless communication has been suspected as the loopholes of passive or active attacks by unauthorized users (eavesdroppers). The physical layer security techniques operate at the lowest stack of OSI layer against conventional cryptographic approaches, operating at the upper layer. However, techniques such as channel coding, power (directional antenna and artificial noise), and spread spectrum have been (and continuously) deployed to safeguard against sophisticated attacks. Most of these deployments are theoretical, and a few are enhanced for efficient security against an intruder.In this article, a boundary technique approach is proposed and applied to the physical layer to improve its secrecy-capacity and subdue adversary effects at the legitimate receiver. Hybrid performance metrics were adopted, and a Monte Carlo simulation was performed. The result obtained using secrecy outage probability, secrecy-capacity, and intercept-probability show that our proposed techniques enhance the secret transmission between the main transmitter and legitimate receiver. The simulation results were compared with the analytical methods. It was found that the channel between the transmitter and the main receiver has a better signal-to-noise ratio than the corresponding eavesdropper's channel. Conclusively, performance of the proposed technique is validated for applications in emerging wireless communication systems.

Rising from the outbreak of COVID-19 pandemic, all schools in Nigeria were put under lock and key, like other countries. This was to curtail the rapid spread of the virus, while the medical practitioners were in the laboratories. But due to longer period of shutdown to formal education, most higher institutions instantaneously keyed-in to any available social media platform (SMP) without understudying its efficiency and effectiveness for knowledge transfer. The consequences of this include non-satisfactory knowledge transfer, most especially, by the learners; hence, aim of knowledge impact is jeopardized. This study therefore is aimed to fill the gap to correct the existing damage, and prevent the likelihood of not-afore-put-to-test syndrome in the subsequent choice of SMP. To achieve this, University of Ilorin is chosen as the territory survey for being the most sought-after University for admission in Nigeria. Quantitative research methods were adopted, being the most appropriate for this study in the literature. A total of 200 respondents participated voluntarily with the adoption of Slovin’s formula for the sampling techniques; 156 students and 44 lecturers. The data collection process was performed through a survey comprising demographic, dichotomous, multiple choice, and open-ended questionnaire to capture detail information for the research work. Simple descriptive statistics was employed for the analysis. Our findings reveal that out of the seventeen SMPs put to test, zoom platform ranks the most effective and efficient for the knowledge delivery and acquisition by the lecturers and students of the University respectively, based on the designed principles. Therefore, making Zoom platform the most preferred for e-learning. This study recommends that choice of satisfactory SMP for knowledge delivery should be utmost concern of all institutions before embarking on virtual learning system

Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.