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CA : A Cancer Journal for Clinicians

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Table Of Integrals Series And Products

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Data Reduction And Error Analysis For The Physical Sciences

The paper describes the modelling of the average rainfall rate distribution measured at difierent locations in South Africa. There are three major aspects this paper addresses: to develop a rainfall rate model based on the maximum likelihood method (ML); to develop contour maps based on rainfall rate at 0:01% percentage of exceedence; and re-classiflcation of the ITU-R and Crane rain zones for the Southern Africa region. The work presented is based on flve- minute rainfall data converted to one-minute equivalent using a newly proposed hybrid method. The results are mapped and compared with conventional models such as the ITU-R model, Rice-Holmberg, Moupfouma and Crane models. The proposed rainfall rate models are compared and evaluated using root mean square and chi-square (´ 2 ) statistics. Then re-classiflcation of the rain zone using ITU-R and Crane designations is suggested for easy integration with existing radio planning tools. The rainfall rate contour maps at 0:01% percentage of exceedence are then developed for South Africa and its surrounding islands.

/pdf/rainfall-rate-probability-density-evaluation-and-mapping-for-1whprcsbnm.pdf

Rainfall Rate Probability Density Evaluation and Mapping for the Estimation of Rain Attenuation in South Africa and Surrounding Islands

Predicting the behavior of the stock market has been an area that has attracted the interest of many researchers particularly in the field of Machine Learning and time series analysis. The ability to analyse an entity such as the stock market that appears to lack synchronicity yet seemingly influenced by historic events remains an open issue in research. This study adopts a stacked Long Short Term Memory network model for predicting stock market behabiour. The data used is composed of historic stock market data from the American Stock Exchange, NASDAQ Composite (IXIC). Results obtained show that by making use of a stacked Long Stort Term Memory network model, future stock market behavior can be predicted.

Stock Market Behaviour Prediction using Stacked LSTM Networks

The cumulative distributions of rain intensity for 12 locations in South Africa are presented in this paper based on 5-year rainfall data. The rainfall rate having an integration time of 60 minutes is converted to the ITU-R recommended integration time of 1 minute for Durban in South Africa. Consequently, values of coefficients a and b so determined are used to convert 60-minute into 1- minute rain rates for other regions of South Africa. The resulting cumulative rain intensities are compared with the relevant ITU-R Recommendation P837. Based on this, an additional three rain zones are determined alongside the five ITU-R rain zones for South Africa.

/pdf/rainfall-rate-modeling-for-los-radio-systems-in-south-africa-4dxyioc8uu.pdf

Rainfall rate modeling for los radio systems in south africa

Global food security has become a very important research focus. This is due to the fact that food is a basic need of human beings and its adequate supply to meet the need of humans must be ensured. Plant diseases have, however, been one of the major problems threatening the adequate supply of food to humans. The early detection of these diseases can assist in their efficient management, thus making huge differences between survival and destruction of crops in farmlands affected by these plant diseases. Deep neural networks have been successfully applied in the field of artificial intelligence. This has inspired an increased research into the use of deep learning in the domains of image processing and computer vision. This paper present a study on the use of deep learning-based approach to identify diseased plants using leaf images by transfer learning. The study uses NASNet architeure for the convolutionary neural networks (CNN). The model is then trained and tested using a publicly available PlantVillage project dataset that contains varied images of plant leaves with multiple variations in infection status and location in the plants. Using the model, an accuracy rate of 93.82% was achieved.

Deep Learning Based on NASNet for Plant Disease Recognition Using Leave Images

The paper is focused on the modeling of raindrop size distribution in Durban, South Africa. The maximum likelihood estimator approach is employed with lognormal distribution to model the dropsize distribution for Durban. The raindrop size is classifled into two regimes based on the available data. The goodness-of-flt method (Kolmogrov-Smirnov test (K-test) is then employed to optimize the selected model for difierent rain rate regime. The results are compared with the existing models such as the work done in West-Africa, Europe and Asia. The proposed South Africa model seems appropriate for the region to estimate rain attenuation both in terrestrial and satellite links. The development of complex radio access networks has resulted in the increase of spectrum occu- pancy and demand for higher bandwidths; hence it is imperative to employ the advantages of higher frequencies, which are capable of supporting these demands. The obvious candidates are microwave and millimeter bands. The advantages ofiered by microwave and millimeter waves have attracted immense interest from academia and the communications industry. The main characteristics of both waves include short wavelength (hence small components), large bandwidth, and frequency re-use. An electromagnetic wave propagating through a region containing raindrops sufiers two attenuating efiects: one is absorption through which part of its energy is absorbed by the raindrops and transformed into heat; and the other is scattering where part of energy is scattered in all directions. The calculation of these two attenuating mechanisms is based on the understanding of the characteristics of raindrops. The best known raindrop size distribution model is Laws and Parsons (1), which was adopted by the International Telecommunication Union (ITU-R) for the calculation of speciflc attenuation due to rain. Marshall and Palmer (2) determined raindrop density by analytical method and further extended to other classes of rain type by Joss etal. (3). Most of the contributors used temperate data to model the DSD which may lead to overestimation of rain attenuation as conflrmed by several authors who had carried out measurement in some part of the world (4{6) and compared their results with the temperate counterparts. In southern part of Africa, less work has been done in this area and need proper attention because of its mixed climatic characteristics that describe most of the southern African regions. Furthermore, in the Southern Africa the advancement in the area of telecommunication at higher frequencies especially satellite communication which operates at bands that are impaired by hy- drometeor efiects. This paper aims to solve this problem by presenting the results of raindrop size distribution model measured in Durban, South Africa. The results will be useful for telecommuni- cation systems designers in estimating rain attenuation in the region. 2. STATISTICAL THEORY OF DSD MODELLING FOR DURBAN Earlier techniques used in modelling raindrop size distribution seem to have begun with flts to the average drop distributions measured at single rain rates. Most of these flts were applied directly to the drop size distribution data as by Marshall and Palmer (2). In order to improve the modelling approach and results, there is need to employ a systematic method such as moment and maximum likelihood techniques to estimate modelling parameters. Ajayi and Olsen (4) used the method of moments to estimate modelling parameters for the tropical region of West-Africa while Barclay etal. (7) considered the maximum likelihood method for the Australia. In this paper, the maximum likelihood approach is considered for Durban raindrop size distribution model. The estimator method is then applied to the three-parameter lognormal distribution.

Pius A. Owolawi

Papers

Rainfall Rate Probability Density Evaluation and Mapping for the Estimation of Rain Attenuation in South Africa and Surrounding Islands

Stock Market Behaviour Prediction using Stacked LSTM Networks

Rainfall rate modeling for los radio systems in south africa

Deep Learning Based on NASNet for Plant Disease Recognition Using Leave Images

Raindrop Size Distribution Model for the Prediction of Rain Attenuation in Durban