Effective water treatment for rural communities in Suriname : a comparison of point-of-use ceramic filters and centralized treatment with sand filters

For countless communities around the world, acquiring access to safe drinking water is a daily challenge which many organizations endeavor to meet. The villages in the interior of Suriname have been the focus of many improved drinking water projects as most communities are without year-round access. Unfortunately, as many as 75% of the systems in Suriname fail within several years of implementation. These communities, scattered along the rivers and throughout the jungle, lack many of the resources required to sustain a centralized water treatment system. However, the centralized system in the village of Bendekonde on the Upper Suriname River has been operational for over 10 years and is often touted by other communities. The Bendekonde system is praised even though the technology does not differ significantly from other failed systems. Many of the water systems that fail in the interior fail due to a lack of resources available to the community to maintain the system. Typically, the more complex a system becomes, so does the demand for additional resources. Alternatives to centralized systems include technologies such as point-of-use water filters, which can greatly reduce the necessity for outside resources. In particular, ceramic point-of-use water filters offer a technology that can be reasonably managed in a low resource setting such as that in the interior of Suriname. This report investigates the appropriateness and effectiveness of ceramic filters constructed with local Suriname clay and compares the treatment effectiveness to that of the Bendekonde system. Results of this study showed that functional filters could be produced from Surinamese clay and that they were more effective, in a controlled laboratory setting, than the field performance of the Bendekonde system for removing total coliform. However, the Bendekonde system was more successful at removing E. coli. In a life-cycle assessment, ceramic water filters manufactured in Suriname and used in homes for a lifespan of 2 years were shown to have lower cumulative energy demand, as well as lower global warming potential than a centralized system similar to that used in Bendekonde.

Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.