In the past years, when wireless network improvement occurs from 1G/2G to third generation (3G), the rate in the use of real-time traffic oriented applications for voice, video and data increases. Consequently, the bandwidth to be backhauled from the cell site to the mobile switching center increases rapidly. 3G network is most prevalent in Nigeria with wide area of coverage. However, in recent times, poor subscribers’ mobile broadband experience is still the major challenge faced by many GSM operators. One of the major causes of this challenge is the use of wrong backhaul for radio access network (RAN). This lead to poor traffic throughput, high packet loss or frame loss at the cell edge. To overcome this challenge, the use of appropriate backhaul technology is crucial. Third Generation Partnership Program (3GPP) recommends the use of either asynchronous transfer mode (ATM) or internet protocol (IP) as the backhaul technologies for its RAN. This paper presents the performance analysis and the comparison of the ATM RAN and IP RAN backhaul technologies using six different 3G sites (with 3G base stations) located at Ado local government area of Ekiti State, Nigeria. The performance of each base station with different backhaul technology was evaluated in term of average maximum throughput per day. The effect of frame loss (for ATM RAN network) and packet loss (for IP RAN network) on traffic throughput were also analyzed. The comparison of the overall result analysis shows that the 3G base stations with IP-based RAN backhaul has better performance than the base station with ATM-based RAN backhaul.
In face recognition system, the accuracy of recognition is greatly affected by varying degree of illumination on both the probe and testing faces. Particularly, the changes in direction and intensity of illumination are two major contributors to varying illumination. In overcoming these challenges, different approaches had been proposed. However, the study presented in this paper proposes a novel approach that uses deep learning, in a MATLAB environment, for classification of face images under varying illumination conditions. One thousand one hundred (1100) face images employed were obtained from Yale B extended database. The obtained face images were divided into ten (10) folders. Each folder was further divided into seven (7) subsets based on different azimuthal angle of illumination used. The images obtained were filtered using a combination of linear filters and anisotropic diffusion filter. The filtered images were then segmented into light and dark zones with respect to the azimuthal and elevation angles of illumination. Eighty percent (80%) of the images in each subset which forms the training set, were used to train the deep learning network while the remaining twenty percent (20%), which forms the testing set, were used to test the accuracy of classification of the deep learning network generated. With three successive iterations, the performance evaluation results showed that the classification accuracy varies from 81.82% to 100.00%.
Renewable energy sources could be harnessed to provide intermittent power and their integration into the grid has improved power availability. Nonetheless, ensuring the stability of the output of such a system has been a major concern. The inability to control the output of renewable resources such as solar results in operational challenges in power systems. To compensate for the fluctuating and unpredictable features of solar photovoltaic power generation, electrical energy storage systems have been introduced that may be integrated into the grid. In this paper, a solar photovoltaic model for an on-grid energy storage device was developed using MATLAB/Simulink, and the model was optimized using a fuzzy logic algorithm. The overall simulation results show that the output of the PV model can be controlled using a fuzzy-based optimization algorithm. The result of the fuzzy logic controller gave a better performance with good voltage stability. Also, the fuzzy-based optimization helps boost the voltage profile of the system.
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