University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Fabrication and gas sensitivity of polyaniline–titanium dioxide nanocomposite thin film

Abstract: A polyaniline–titanium dioxide (PANI/TiO2) nanocomposite was prepared by an in-situ chemical oxidation polymerization approach in the presence of colloidal TiO2 at room temperature, and the PANI/TiO2 nanocomposite thin film was formed on a silicon substrate covered with interdigital electrodes to fabricate a gas sensor via the self-assembly method. The gas-responses of the PANI/TiO2 thin film to NH3 and CO toxic gases were examined. The results showed that the response, reproducibility and stability of the PANI/TiO2 thin film to NH3 were superior to CO gas. Compared with NH3 and CO gases, humidity had less effect on the resistance of the PANI/TiO2 thin film. It was also found that the difference between pure PANI and PANI/TiO2 thin films was not only in gas-sensing property but also in surface morphology.

A multilayer perceptron-based medical decision support system for heart disease diagnosis

Abstract: The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have shown great potential to be applied in the development of medical decision support systems (MDSS). In this paper, a multiplayer perceptron-based decision support system is developed to support the diagnosis of heart diseases. The input layer of the system includes 40 input variables, categorized into four groups and then encoded using the proposed coding schemes. The number of nodes in the hidden layer is determined through a cascade learning process. Each of the 5 nodes in the output layer corresponds to one heart disease of interest. In the system, the missing data of a patient are handled using the substituting mean method. Furthermore, an improved back propagation algorithm is used to train the system. A total of 352 medical records collected from the patients suffering from five heart diseases have been used to train and test the system. In particular, three assessment methods, cross validation, holdout and bootstrapping, are applied to assess the generalization of the system. The results show that the proposed MLP-based decision support system can achieve very high diagnosis accuracy (>90%) and comparably small intervals (<5%), proving its usefulness in support of clinic decision process of heart diseases.