Browsing by Author "Hussein U. Suleiman"

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    Development of energy demand and carbon emission dataset for Nile University of Nigeria
    (Elsevier, 2023-06-28) Tahir A. Zarma; Paul O. Micheal; Ahmadu A. Galadima; Tologon Karataev; Adekunle Akanni Adeleke; Oghenewvogaga Oghorada; Hussein U. Suleiman
    The global energy crisis and ozone layer depletion as a result of carbon emissions have increased the awareness and acceptance of renewable energy sources as an alternative form of electric power, resulting in the sizing of renewable energy sources. However, in order to properly size an energy power system, the information being addressed, such as the load demand, is critical. The Load demand data of Nile University campus is obtained from one of its power stations (PS-1) for a period of eight month. The data was measured from the bus bar of the power station using smart meters on a weekly basis. To power the university campus, the diesel generators are synchronized using Genset controllers with suitable communications interfaces and a SMA hybrid controller, which continually checks the power output of the power sources as well as the working condition of all loads in the busbar. The diesel generators are synchronized using SMA hybrid controllers and combined with the other source of the energy at a common bus bar and used to power the university campus. Additionally, carbon emission data were obtained from the PV solar system reading.
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    Human Detection For Crowd Count Estimation Using CSI of WiFi Signals
    (International Conference on Electronics Computer and Computation (ICECCO), 2019-12-01) Omotayo Oshiga; Hussein U. Suleiman; Sadiq Thomas; Petrus Nzerem; Labaran Farouk; Steve Adeshina
    We address the problem of crowd estimation in situations such as indoor events using anonymous and non-participatory CSI of WiFi Signals. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on statistical mechanics, and relying only on sets of machine learning techniques.In this paper, a framework for crowd count estimation is presented which utilizes Chebyshev filter and SVD to remove background noise in the CSI data, PCA to reduce the dimensionality of the CSI data and spectral descriptors for feature extraction. From the extracted feature, a set of classiffying algorithms are then utilised for training and testing the accuracy of our crowd estimation framework The aim of this framework to effectively and efficiently extract the channel information in WiFi signals across OFDM carriers reflected by the presence of human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed framework. Our result depict that our estimation becomes more–rather than less–accurate when the crowd count increases.