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In the present computational fluid dynamics (CFD) community, post-processing is regarded as a procedure to view parameter distribution, detect characteristic structure and reveal physical mechanism of fluid flow based on computational or experimental results. Field plots by contours, iso-surfaces, streamlines, vectors and others are traditional post-processing techniques. While the shock wave, as one important and critical flow structure in many aerodynamic problems, can hardly be detected or distinguished in a direct way using these traditional methods, due to possible confusions with other similar discontinuous flow structures like slip line, contact discontinuity, etc. Therefore, method for automatic detection of shock wave in post-processing is of great importance for both academic research and engineering applications. In this paper, the current status of methodologies developed for shock wave detection and implementations in post-processing platform are reviewed, as well as discussions on advantages and limitations of the existing methods and proposals for further studies of shock wave detection method. We also develop an advanced post-processing software, with improved shock detection.

Diabetic retinopathy (DR) is a leading cause of vision loss, caused by damage to the retina from complications of diabetes. Analysis of the retinal photographs for key characteristics of DR can result in early diagnosis and better management of DR. This paper presents a method for automated analysis and classification of the retina as DR or non-DR using two-field mydriatic fundus photography. The optic disc region is located by multi-level wavelet decomposition and recursive region growing from an automatically identified seed point. Blood vessels are extracted by applying histogram analysis on the two median filtered images. Red lesions are detected using three stage intensity transformation and white lesions from multi-level histogram analysis. The final classification of the retina as DR or non-DR is based on an aggregate of the lesions extracted from each image. The proposed method has been validated against diagnosis by a panel of expert ophthalmologists on images from 368 patients. The observed sensitivity and specificity were 80% and 50% respectively. The results show that automated screening based on two-field photography can be applied in routine screening.