This paper is concerned with the implementation of EKF-SLAM (Extended Kalman Filter-Simultaneous Localization and Mapping) algorithm using a cloud computing architecture based on ROS (Robot Operating System). The localization and mapping is essential step to navigate a mobile robot in unknown environment. The implemented EKF-SLAM has used a landmark that sensed using IR Emitter sensor provided by the Kinect camera to update a map of the environment and simultaneously estimate the robot's position and orientation within the map. The implementation was done using three parts. The first one was the TurleBot Mobile robot with the Kinect camera, which was simulated in Gazebo environment. The second part was the EKF-SLAM running under the MATLAB to generate the Map and Location data. The third part was the ROS Master node, which runs on the cloud to enable part one and two to communicate using topics. The scan data from Kinect camera and the location data from the odometer is transferred from the first part to the second part through ROS Master node after impaired with zero mean Gaussian noise . Then the second part performs EKF-SLAM and transmit the corrections to the first part through the ROS Master node as well.

Refractive error is one of optical defect in the human visual system. Refractive error is a very common disease these days in all populations and in all age groups. Uncorrected and undetected refractive error contributes to visual impairment, blindness and places a considerable burden on a person in the world. The long use of technological devices such as smart phones also poses a new burden on the human eye. The intensity and brightness of these digital devices open a new door for high prevalence of eye refractive errors. Early medical diagnosis of the disease may help in avoiding complications and blindness. Data mining algorithms can be applied to help in ophthalmology and detection of an eye disease at an early stage. So mining the ophthalmology data in efficient manner is a critical issue. This research work deals with development of an integrated knowledge-based system that helps to detect eye refractive error early and provides appropriate advice for the patients. In this study, the hybrid knowledge discovery process model of data mining that was developed for academic research is used. About 9000 ophthalmology data from selected eye health centers are used to build the model. The sample data was preprocessed for missing values, outliers, and noise. Then the model is built using decision tree (J48 and REPTree) and rule induction (JRip and part) algorithms. The part algorithm has registered better predictive performance with accuracy of 60% and 96.45% for subjective and objective based model evaluation, respectively as compared to J48, REPTree, and JRip. Finally, the knowledge discovered with this algorithm is further used to build the knowledge-based systems. The Java programing language is used to integrate data mining results to knowledge-based system. The performance of the proposed system is evaluated by preparing test cases. Overall, the knowledge based system resulted in 89.2% accuracy.  Finally the study concludes that discovering knowledge using data mining techniques could be used as a functional eye refractive error detection system.

Groundnuts (Arachis hypogaea), also known as peanuts are the edible seeds of a legume plant that grow to maturity in the ground. Diseases and its diagnosis methodology are the main challenges to groundnut production. Manual means of identifying plant disease is the most difficult activity with a high rate of mistake and time taking procedures. Therefore, the main aim of this research was to design an image processing system to identify groundnut plant disease. In general, the major significance of the study is to provide effective and simple groundnut plant disease diagnosis system that supports disease controlling mechanism and experts in the domain area. While the process of developing the system, researchers used purposive sampling techniques for acquiring 320 sample leaves images from four classes of groundnuts plant. Those are Cercospora personatum, Cercospora arachidicola, Puccinia arachidis, and healthy leaf from Bishan-Babile and Gemechu peasant associations in Babile district, and Awdal peasant association in Gursum districts in East Hararghe zone of Oromiya Regional State. Also, the researchers conducted a survey study which helps to identify experts view and deep understanding of the domain area. The design, phase used; adaptive median filtering, K-means clustering, SVM and ANN techniques with MATLAB programming tool. In general, the overall result shows that the developed system achieved a better result with an accuracy of 90.6%.

In this work, we have developed a research grant management system in Haramaya University. The main aim of this project was to automate the manual working system in order to facilitate grant application process, improve time efficiency, save manual cost, improve the flow of information among researchers, and eliminate work delay. In the process of developing the system, researchers conducted a survey research which helps to identify the stakeholders and experts view. Delphi technique was used to identify the view of stakeholders. Whereas a questionnaire was used to collect data from purposely selected researchers to undertake user acceptance test. Finally, we adopted an iterative and incremental method. For the design, we used the UML Modeling language and PHP, JavaScript, Jquery, Json, Bootstrap and CSS used for the implementation. The user acceptance test found the system is acceptable with an average of 94.45%. Through all this process the system was successfully developed, tested and deployed.