The role of temporary accommodation buildings for post-disaster housing reconstruction

The number of houses damaged or destroyed after disasters is frequently large, and re-housing of homeless people is one of the most important tasks of reconstruction programmes. Reconstruction works often last long and during that time, it is essential to provide victims with the minimum conditions to live with dignity, privacy, and protection. This research intends to demonstrate the crucial role of temporary accommodation buildings to provide spaces where people can live and gradually resume their life until they have a permanent house. The study also aims to identify the main problems of temporary accommodation strategies and to discuss some principles and guidelines in order to reach better design solutions. It is found that temporary accommodation is an issue that goes beyond the simple provision of buildings, since the whole space for temporary settlement is important. Likewise, temporary accommodation is a process that should start before a disaster occurs, as a preventive pre-planning. In spite of being temporary constructions, these housing buildings are one of the most important elements to provide in emergency scenarios, contributing for better recovery and reconstruction actions.The first author gratefully acknowledges the financial support of Fundacao para a Ciencia e a Tecnologia, FCT, through grant SFRH/BD/73853/2010

Wearable device for Malaysian ringgit banknotes recognition based on embedded decision tree classifier

The purpose of this project is to develop a wearable Malaysian Ringgit banknotes recognition device for assisting the visually impaired people to recognize the value of Malaysian banknotes. In this project, the RGB values in six different classes of the banknotes (MYR 1, MYR 5, MYR 10, MYR 20, MYR 50 and MYR 100) were taken at 12 different points (6 upside, 6 downside) using colour sensor (TCS 34725) before three features called RB, RG, and GB were extracted from the RGB values. After that, these features are used to model the embedded Decision Tree Classifier (DTC) in Matlab for recognizing each classes of banknote. Cross validation with 10-fold was used to select the optimize DTC which is based on the smallest cross validation loss. The performance of optimize DTC model is presented in confusion matrix and compared with Naïve Bayesian and k-Nearest Neighbour classifier before this model is implemented in Lilypad Arduino. The performance of the device in term of accuracy is evaluated by asking 10 subjects to use the device. Result shows that the proposed embedded optimize DTC model managed to achieve 84.7% accuracy which outperforms other classifier. In conclusion, proposed device is successfully developed and it should be possible, therefore, to integrate other features (instead of colour) in recognizing the ringgit banknote