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Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Buildings are key mediators between human activity and the environment around them, but details of energy usage and activity in buildings is often poorly communicated and understood. ECOS is an Eco-Visualization project that aims to contextualize the energy generation and consumption of a green building in a variety of different climates. The ECOS project is being developed for a large public interactive space installed in the new Science and Engineering Centre of the Queensland University of Technology that is dedicated to delivering interactive science education content to the public. This paper focuses on how design can develop ICT solutions from large data sets to create meaningful engagement with environmental data.

This paper presents the progress and recent history of Grid computing in Europe, aimed at the development of a sustainable European Grid infrastructure. This is achieved by examining the Enabling Grids for E-sciencE (EGEE) project, funded by the European Commission (EC) to build a multi-science Grid infrastructure for the European Research Area, and recently connected to the African continent through other EC funded projects. By considering these developments, this paper extracts lessons for development of Grid computing in Africa. Among these are the need to connect to the broader international Grid community to ensure the success of Grid computing, and the proposal of a new incremental model for Grid development that involves leveraging existing Grid infrastructures to support small Grid projects in less developed areas. These lessons are then applied to the African continent, with suggestions of the most effective avenues for development of an African Grid infrastructure., QC 20120227