Comparison of rain attenuation prediction models with ground measurement data for Penang

2011 ◽  
Vol 5 (13) ◽  
pp. 1546 ◽  
Author(s):  
R. Nalinggam ◽  
W. Ismail ◽  
J.S. Mandeep
Author(s):  
A. I. O. Yussuff

The restrained use of millimeter bands is due to severe rain attenuation. Attenuation is caused when rain cells intersects radio wave’s propagation path; resulting in deep fades. The effect of rainfall is more severe in tropical regions characterized by heavy rainfall intensity and large raindrops; hence, rain attenuation analyses are essential to study rain fade characteristics for use in earth-space link budget analysis, for outage prediction resulting from rain attenuation. Tropical regions are particularly challenged with signal outage, necessitating the formulation and development of suitable prediction model(s) for the region. Therefore, extensive knowledge of the propagation phenomena mitigating system availability and signal quality in these bands are required. Daily rainfall data were collected from the Nigerian Meteorological Services for Lagos for spanning January to December 2010. Results showed that although, the ITU-R model out-performed the other prediction models under consideration, none of prediction models matched the measurement data.


2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Olufunke G. Darley ◽  
Abayomi I. Yussuff ◽  
Adetokunbo A. Adenowo

This paper investigated the performances of some rain attenuation prediction models at some GSM network locations in Lagos, Nigeria, using remote sensing at Ku band. Remote sensing is a collection and interpretation of information about an object without physical contact with the object being measured. Three popular terrestrial prediction models were considered in this work. These are ITU-R P.530-17, Lin and Silva Mello Models. Ten years (2010-2019) annual rainfall data with hourly integration time were sourced from the Nigerian Meteorological Agency (NIMET) and link budgets for three microwave links (Tarzan Yard, Kofo Abayomi and GLO Shop) in Victoria Island at 18 GHz were obtained from Global Communications Limited (GLO), Nigeria. Data analysis and comparison of the microwave links rainfall estimates were carried out to identify the most suitable of the three models at the selected locations of interest. Measurement data obtained from both NIMET and GLO were used to validate the predicted attenuation data from the three selected models. The ITU-R P.530-17 prediction model overestimated the measurement at Tarzan Yard; closely followed by Silva Mello, while Lin underestimated the measured data.  Again, at Kofo Abayomi station, the ITU-R model overestimated the measurement, while both Silva Mello and Lin models underestimated the measurement. At the GLO Shop, the Silva Mello overestimated the measured value, while ITU-R and Lin underestimated the measurement. At 0.01% of time exceeded, NIMET measurement was higher (at 48.2 dB) than that of Tarzan Yard, Kofo Abayomi and GLO shop (43.1, 46.3 and 37.0 dB respectively). These results will provide useful information in mitigating signal outages due to rain for mobile communication systems. Keywords- Path attenuation, Prediction models, Rainfall rate, Terrestrial microwave links, Tropical region


2014 ◽  
Vol 955-959 ◽  
pp. 3879-3882
Author(s):  
Yan Fang Ming ◽  
Li Yang

A hyper-spectral remote sensing instrument AVIRIS was used to map lithology. Ground measurement data of lithology from ASTER Spectral Library were used to analyze the characters of the spectrum and form the model to estimate the type of lithologies. we process the spectrum with the methods of spectral angle mapping, and spectral absorption index etc. To enhance the significance of the spectrum character, we used the methods of spectral angle mapping, and spectral absorption index etc to process the spectrum. An AVIRIS data covers the Cuprite mining district in western Nevada, USA was used to do the experiment, result shows that the lithologies got from the AVIRIS have a high consistence with groud measurement.


Author(s):  
Kehan Gao ◽  
Taghi M. Khoshgoftaar ◽  
Amri Napolitano

Software defect prediction models that use software metrics such as code-level measurements and defect data to build classification models are useful tools for identifying potentially-problematic program modules. Effectiveness of detecting such modules is affected by the software measurements used, making data preprocessing an important step during software quality prediction. Generally, there are two problems affecting software measurement data: high dimensionality (where a training dataset has an extremely large number of independent attributes, or features) and class imbalance (where a training dataset has one class with relatively many more members than the other class). In this paper, we present a novel form of ensemble learning based on boosting that incorporates data sampling to alleviate class imbalance and feature (software metric) selection to address high dimensionality. As we adopt two different sampling methods (Random Undersampling (RUS) and Synthetic Minority Oversampling (SMOTE)) in the technique, we have two forms of our new ensemble-based approach: selectRUSBoost and selectSMOTEBoost. To evaluate the effectiveness of these new techniques, we apply them to two groups of datasets from two real-world software systems. In the experiments, four learners and nine feature selection techniques are employed to build our models. We also consider versions of the technique which do not incorporate feature selection, and compare all four techniques (the two different ensemble-based approaches which utilize feature selection and the two versions which use sampling only). The experimental results demonstrate that selectRUSBoost is generally more effective in improving defect prediction performance than selectSMOTEBoost, and that the techniques with feature selection do help for getting better prediction than the techniques without feature selection.


Author(s):  
Andryan Suhendra

Ground measurement helps illustrate a situation of land map to ease a civil engineer determining the center point of a building. From the situation map can determined the further works such as determining the point of building, land leveling, determining the point of the foundation and the volume of work for hoarding the land. A tool used for measurement activities is theodolite. Theodolite serves as a tool to determine the angle formed between the two points at the time of measurement. Drawing a situation map requires the results of the point measurement data. Theodolite is divided into two types, digital and manual. This study compares measurements results using both digital and manual theodolite performed at Kijang Campus, Binus University. From the comparison of data processing generated large differences in the coordinate system on the situation map with ranging from 1.31% to 322.67% on the abscissa axis and 0.39% to 41.83% on the ordinate axis. 


Author(s):  
Islam Md Rafiqul ◽  
Md Moktarul Alam ◽  
Ali Kodhim Lwas ◽  
Sarah Yasmin Mohamad

Attenuation due to rain is an important constraint in microwave radio link design especially at frequencies above 10 GHz. It restricts the path length of radio communication systems and limits the use of higher frequencies for line-of-sight microwave links and satellite communications. In order to predict the attenuation due to rain accurately rainfall intensity is required with 1-minute integration time. Rainfall is a meteorological phenomenon with complex structure due to its variability in space, duration and occurrence frequency, particularly in tropical and equatorial regions. Since, the statistical distribution of rain attenuation is obtained from the rain rate distribution for the region considered, it should be noted that the accuracy of the rain rate measurement affects the accuracy of the attenuation estimation. This paper presents rain intensity with 1-minute integration time measured for 6 years in Malaysia, it’s distribution, comparison with other prediction models and impact on high frequency microwave links.


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