This diploma work deals with design and implementation of the database neutral object-relational (OR) layer in C++ language over inherited database. The goal is to create the layer to encase the access to database from the application layer. Suggested layer will stem from the object-relation mapping technology, which is currently available for many object-programming language, such as C#, Java or Visual Basic. The work consists of three main parts. The forepart is focused on clearing object-relation mapping technology. It briefly overviews differences in capabilities and levels of implementation of various approaches. The next part describes significant properties of databases considered as back-ends for data storage in the project. The aim of this part is to present enough information to support database neutral design of the OR layer. The rest of the document deals with design and implementation of OR layer for the considered environment followed by the summarization of results and overall evaluational.

Life on Earth is strongly related to the Sun, which makes it a vital star to study and understand. To improve our knowledge of the way the Sun works, many satellites have been launched into space to monitor the Sun‟s activities where the one of main focus is the effect of these activities on the Earth‟s climate; PICARD is one such satellite. Due to the noise associated with SODISM images, the clarity of these images and the appearance of solar features are affected. Image denoising and enhancement are the main techniques to improve the visual appearance of SODISM images. Affective de-noising algorithm methods depend on a proper detecting of noise present in the image. The aim is to identify which type of noise is present in the image. To reach this point, supervised machine-learning (ML) classifier is used to classify the type of noise present in the image. Furthermore, this work introduces a novel technique developed to enhance the quality of SODISM images. In this thesis, the Modified Undecimated Discrete Wavelet Transform (M-UDWT) technique is used to de-noise and enhance the quality of SODISM images. The proposed method is robust and effectively improves the quality of SODISM images, and produces more precise information and clear feature are brought out. In addition, the non wavelet enhancement is developed as well in this thesis. The results of this algorithm is discussed. The new methods are also assessed using two different methods: subjective (by human observation) and objective (by calculation)

Information on deforestation and forest management is limited due to lack of understanding as to how socioeconomic factors affect deforestation. Without adequate data, it is difficult to manage deforestation; particularly in Chipuriro where deforestation is occurring at a rapid rate. This research was carried out to address the problem. Surveys and landsat images were used to collect data. To quantify deforestation, images were processed using Geographical Information System. Pearson’s Correlation Coefficient was used to deduce the relationship between socioeconomic factors and deforestation. Socioeconomic factors were obtained through household surveys and focus group discussions. Results indicated high rates of deforestation in Ward 18 (4.7% per annum) while in Ward 16 woodland cover increased throughout the study period (7.7% per annum). Population growth with its demand on fuel wood, settlement and agricultural land caused a significant decrease in woodland cover in Ward 18. Plantations increased the area under woodlands in Ward 16. / Environmental Sciences / M. Sc. (Environmental Management)

Information on deforestation and forest management is limited due to lack of understanding as to how socioeconomic factors affect deforestation. Without adequate data, it is difficult to manage deforestation; particularly in Chipuriro where deforestation is occurring at a rapid rate. This research was carried out to address the problem. Surveys and landsat images were used to collect data. To quantify deforestation, images were processed using Geographical Information System. Pearson’s Correlation Coefficient was used to deduce the relationship between socioeconomic factors and deforestation. Socioeconomic factors were obtained through household surveys and focus group discussions. Results indicated high rates of deforestation in Ward 18 (4.7% per annum) while in Ward 16 woodland cover increased throughout the study period (7.7% per annum). Population growth with its demand on fuel wood, settlement and agricultural land caused a significant decrease in woodland cover in Ward 18. Plantations increased the area under woodlands in Ward 16. / Environmental Sciences / M. Sc. (Environmental Management)