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.

BIOMASS FUEL-BRIQUETTES AND IMPROVED STOVES IN DINSHO, ETHIOPIA

This report is a study of the development and implementation of a biomass fuel briquette and improved stove project in the highlands of Ethiopia. The primary goal of the project was to determine if the introduction of an improved stove would affect the acceptability of fuel briquettes. The secondary goal was to establish briquette and improved stove manufacturing associations in Dinsho and Rira towns. Two problems encountered during the project were cultural differences in material valuation, and difficulty working with local administrative frameworks and multi-organization communication difficulties. Both briquettes and improved stoves received positive feedback from respondents. Survey data indicated that a price of 0.75 Ethiopian birr per briquette would make them a competitive fuel source against fuelwood. Recommendations for feedstock sourcing and supply, capital investment, labor reduction, estimating cost effectiveness, appropriate technology design, development work setbacks, and valuation paradigms for fuel briquette, improved stove, and development work projects.