Browsing by Author "Sikiru, Ismaeel"

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    Hybridization of OFDM and Physical Layer Techniques for Information Security in Wireless System.
    (by Sule Lamido University Journal of Science & Technology, 2023-03-31) Lukman .A, Olawoyin; Abdul -Rahman, Musrafah; Nasir, Faruk; Abdulkarim, Oloyede; Temitayo, Adeniran; Imam-Fulani, Yusuf; Olabamji, Lasisi; Sikiru, Ismaeel; Bashir, Abdullahi Baba
    Due to quest for high data rate, reliable and secure communication, this has motivated both wired and wireless network access service providers to deploy a next-generation network with ability to meet the required need. The use orthogonal frequency division multiplexing (OFDM) enables reliable transmission of various data traffic by optimizing subcarrier, power, and allocation of bits among different users. Traditionally, securing data in wireless system is always at the upper layer of open system interconnection (OSI) Model by using data encryption techniques. However, such techniques may not be acceptable for future decentralized networks due to their high complexity in implementation and computation. In this work, an OFDM IEEE 802.11a wireless system is used and physical layer encryption (PLE) schemes are implemented in securing the information transfer between two legitimate parties. In the simulation, the source data are encrypted by obfuscation with dummy data in between the encrypted data of which 52 subcarriers were considered of which 25 subcarriers are reserved for dummy data and 27 were for data. The simulation was conducted for four different modulation techniques i.e., BPSK, QPSK, 16 QAM, and 64 QAM. The result obtained shown that for all the modulation schemes, the key rate increases with an increase in the reserved subcarrier bits. Also, the security level increased when substantial percentages of the subcarriers are reserved for dummy data.
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    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, Katibi
    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 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.