A Process for Co-Designing Educational Technology Systems for Refugee Children

There is a growing interest in the potential for technology to facilitate emergency education of refugee children. However, designing in this space requires knowledge of the displaced population and the contextual dynamics surrounding it. Design should therefore be informed by both existing research across relevant disciplines, and from the practical experience of those who are on the ground facing the problem in real life. This paper describes a process for designing appropriate technology for these settings. The process draws on literature from emergency education, student engagement and motivation, educational technology, and participatory design. We emphasise a thorough understanding of the problem definition, the nature of the emergency, and of socio-cultural aspects that can inform the design process. We describe how this process was implemented leading to the design of a digital learning space for children living in a refugee camp in Greece. This drew on involving different groups of participants such as social-workers, parents, and children

Design of optimal subband filter banks for image discrimination.

Performance of the proposed methods is demonstrated in extensive experiments, which justify the new approaches.The primary objective of this work is to improve texture classification system performance. The work is extended to improve the accuracy with which the faulty components of a printed circuit board are detected from a video sequence of infrared images generated by warming the board at power up. The direct motivation of this research is to enhance the FAULT DETECTION and IDENTIFICATION (FDI) system performance based on classification of the components in the circuit boards. The classification problem may be divided into the stages of feature extraction, dimensionality reduction and pattern recognition. Central to this work is that the signal representation plays a crucial role in the classification performance. Specifically, it is proposed that designing an optimal sub-band filterbank for fault detection and identification or texture classification improves the classification performance when the filterbank is used for that purpose.Optimal filters designed with image compression in mind do not guarantee optimality with respect to discrimination. Therefore, approaches for the design of optimal filterbanks with optimal discrimination are proposed. A simulated annealing algorithm is used to find the optimal filter coefficients by maximizing class separability. Algorithms are developed to find the optimal filterbank for a given dataset and to classify an unknown texture or to find if the given component is faulty or not.The focus of this dissertation is on the design of subband filterbanks for feature extraction and classification of images. One of the major conclusions of the experiments is that the wavelet used for decomposing the images for classification plays a crucial role in the classification task. Furthermore, the commonly used octave band decomposition is evaluated against alternative decompositions. It is concluded that non-octave decompositions are generally superior. Also, the classification performance using various feature extraction techniques along with dimensionality reduction methods are compared. A quadrature mirror filterbank designed is tested in texture classification and fault detection, and results in superior classification performance compared to other filterbanks