Although the Discrete Cosine Transform (DCT) is wide{v used for feature extraction in pattern recognition, it is shown that it converges slowly for most theoretically smooth functions. A modification of the DCT is described, based on a change of variable, which changes it to a new transform, called the Discrete Chebyshev Transform DCh T, which converges vel)' rapidly for the same smooth functions. Although this rapid convergence is largely destroyed by the noise in real experimental data, the Discrete Chebyshev Transform is still generally better than the DCT when the sampling of the data can be selected at non-equidistant points. The improvement over the DCT gives a theoretical explanation for improved speech recognition obtained using Mel Feature Cepstral Coefficients. These choose the sampling frequencies of a DCT to correspond to the human perception of pitch. It is shown that this sam p ling is similar to the .�ampIing used in the Discrete Chebyshev Transform.

On the basis of concept, characteristics and related work of collaborative learning, this paper proposes a practical and efficient way based on multi-agent technology. At first, the paper introduces a collaborative learning framework which supporting group learning and illustrated the whole learning process. Secondly, the paper presents some key issues to implementation of the virtual learning environment, such as intelligent grouping, intelligent question & answer system, knowledge management, evaluation of group-learning effectiveness. Learning practice verifies the validity of the system.

No abstract

With the study of human visual system (HVS)