Vol. 5 No. 1 (2011)

This research employs free model that uses only sentential features without paragraph context to extract topic sentences of a paragraph. For finding optimal combination of features, corpus-based classification is used for constructing a sentence classifier as the model. The sentence classifier is trained by using Support Vector Machine (SVM). The experiment shows that position and meta-discourse features are more important than syntactic features to extract topic sentence, and the best performer (80.68%) is SVM classifier with all features.

Individualized head-related impulse responses (HRIRs) to perfectly suit a particular listener remains an open problem in the area of HRIRs modeling. We have modeled the whole range of magnitude of head-related transfer functions (HRTFs) in frequency domain via principal components analysis (PCA), where 37 persons were subjected to sound sources on median plane. We found that a linear combination of only 10 orthonormal basis functions was sufficient to satisfactorily model individual magnitude HRTFs. It was our goal to form multiple linear regressions (MLR) between weights of basis functions…

In this study we propose a digital dermatoscopy method to measure the human skin roughness. By using this method we eliminate the use of silicon replica. Digital dermatoscopy consists of handheld digital microscope, image processing and information extraction of skin roughness level. To reduce the noise due to the variation of reflection factor on the skin we use median filter. Hence, by Fourier transform the skin texture is imaged in terms of 2D frequencyspatial distribution. Skin roughness is determined from its entropy, where the roughness level is proportional to the entropy. Three types…