Browsing by Author "Onidare, Samuel O"
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Item Enhancing WCDMA Power Control Mechanism with Channel Prediction(ABACUS, The Journal of the Mathematical Association of Nigeria, 2016) Ayeni, Adeseko A.; Onidare, Samuel O; Babatola, Adekunle; Adeniran, Temitayo C.A good power control algorithm is very essential in a Wideband Code Division Multiple Access (WCDMA) system. This is to prevent Near - Far effect or Multi - Access Interference (MAI) and its associated effects, which include degradation of channel capacity and signal quality, as well as significant drain of the user equipment (UE) battery power. To solve these problems, there is the need for an effective and efficient power control mechanism to compensate for the fading fluctuations in the transmit power level of the mobile stations (MS) such that the signal power from multiple UE's (Near or far) is made equal at the Base Station. The conventional power control algorithm, based on SIR, may lead to positive feedback or power escalation when the far away MS increases its transmit power to compensate for the interference from the near MS. This paper proposes a method, based on predicting the state of the transmission channels to develop an improved power control mechanism in WCDMA systems. The proposed method uses Kalman filtering and Linear Quadratic Gaussian (LQG) control for channel prediction. Some conventional methods of power control in WCDMA such as Fixed Step size method, Quantized Step size method and Ideal Method are examined. The proposed method is then compared with the conventional methods using MATLAB. Analysis of the simulations show that the proposed method recovers faster from deep fades and provides a more steady performance than the conventional methods.Item Experimental Determination of Path Loss Exponent for GSM 900 and 1800 Bands, in Ilorin Metropolis.(ABACUS, The Journal of the Mathematical Association of Nigeria, 2016) Ayeni, Adeseko A.; Onidare, Samuel O; Adebowale, Quadri R.; Adeniran, Temitayo C.The path loss exponent of an environment describes the propagation behaviour of the environment. This paper determines the path loss exponent of the GSM 900 MHz and 1800 MHz bands in Ilorin, Kwara state of Nigeria. A comprehensive signal strength measurement campaign, using an Agilent spectrum analyzer, was carried out in 12 different routes representing, virtually, the entire metropolis of Ilorin. In computing the path loss exponent, a different approach from the more frequently used linear regression approach, was used. The experimental data reveal a lot of findings, chief amongst which is the strong influence of the terrain profile on the path loss of the environment. Consequently, the path loss exponent obtained, especially for the GSM 900, is lower than the expected value as reported in the literatures.Item A Smart System For Monitoring Oil Pipeline Installations Using Fiber Optic Sensors(Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomosho., 2015) Akande, Tajudeen A.; Onidare, Samuel O; Ayeni, Olumuyiwa B.; Ayeni, Adeseko A.Oil Pipeline installations are national infrastructures of high economic value. This makes monitoring and protection of such installations against the threat of economic saboteurs a national issue for any government. In this paper, a system for smart monitoring of oil pipeline using optical fiber cable is presented as a solution to the inadequacy of the traditional human and/or animal physical monitoring. The designed system employs fiber optic as sensor laid along the oil pipeline installation and a software that analyses the optical signal generated to determine the occurrence of threat to the installations. The smart system takes advantage of the effect of environmental phenomenon on optical signal traversing the optical fiber sensor in the automated monitoring of oil pipeline installations. On the basis of the mathematical relationship between light intensity and applied pressure, the system is able to determine (against a pre-set threshold), an attempt or the actual vandalization of oil pipeline installation.Item Towards Statistical Machine Learning for Edge Analytics in Large Scale Networks: Real-Time Gaussian Function Generation with Generic DSP(Institute of Electrical and Electronics Engineers (IEEE), 2018-05-31) Oyekanlu, Emmanuel A; Onidare, Samuel O; Oladele, Paul OThe smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytics in large-scale IoT networks, reliable solutions are being designed such that many real-time decisions will be taken at the edge of the network close to where data is being generated. Gaussian functions are extensively applied in the field of statistical machine learning, pattern recognition, adaptive algorithms for function approximation, etc. It is envisaged that soon, some of these machine learning solutions and other Gaussian function based applications that have low computation and low-memory footprint will be deployed for edge analytics in large-scale IoT networks. Hence, it will be of immense benefit if an adaptive, low-cost, method of designing gaussian functions becomes available. In this paper, Gaussian distribution functions are designed using C28x real-time digital signal processor (DSP) that is embedded in the TMS320C2000 modem designed for powerline communication (PLC) at the low voltage distribution end of the smart grid, where numerous devices that generate massive amount of data exist. Open-source embedded C programming language is used to program the C28x for real-time gaussian function generation. The designed gaussian waveforms are stored in lookup tables (LUTs) in the C28x embedded DSP, and could be deployed for a variety of applications at the edge of the SG and IoT network. The novelty of the design is that the Gaussian functions are designed with a generic, low-cost, fixed-point DSP, different from state of the art in which Gaussian functions are designed using expensive arbitrary waveform generators and other specialized circuits. C28x DSP is selected for this design since it is already existing as an embedded DSP in many smart grid applications and in other numerous industrial systems that are part of the large scale IoT network, hence it is envisaged that integration of any gaussian function based solution using this DSP in the smart grid and other IoT systems may not be too challenging.