Abstract. Presented in this paper is an empirical model for long-term rain attenuation prediction and statistical prediction of site diversity gain on a slant path. Rain attenuation prediction on a slant path is derived using data collected from tropical regions, and the formula proposed is based on Gaussian distribution. The proposed rain attenuation model shows a considerable reduction in prediction error in terms of standard deviation and root-mean-square (rms) error. The site diversity prediction model is derived as a function of site separation distance, frequency of operation, elevation angle and baseline orientation angle. The novelty of the model is the inclusion of low elevation angles and a high link frequency up to 70 GHz in the model derivation. The results of comparison with Hodge, Panagopoulos and Nagaraja empirical predictions show that the proposed model provides a better performance for site separation distance and elevation angle. The overall performance of the proposed site diversity model is good, and the percentage error is within the allowable error limit approved by International Telecommunication UnionRegion (ITU-R).
In content-based image retrieval (CBIR) system, one approach of image representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on principal component analysis (PCA) is implemented. Each image in a database is indexed using 174-dimensional feature vector comprising of 54-dimensional colour moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional gabor wavelet (GW48) and 40-dimensional wavelet moments (MW40). The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing. The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100.
Wireless communication encountered lots of issues in the channel as the signal is being propagated from transmitter to the receiver. The mobile users continue increasing and the quality of service is very poor due to unreliable nature of the channel. There has been signaling fading, attenuation, calls drop, interference, network capacity and signal loss which may be traced to path propagation problems. The data was collected from MTN base station at four different locations in and . The optimized model is recommended for better deployment and would be more accurate to be applied for path loss prediction in the suburban area.
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