Studies on the Effect of an External Flaw on the Tensile Strength of Short Kevlar Fiber-Thermoplastic Polyurethane Composites

The effect of an external flaw on the tensile strength of short kevlar fiber-thermoplastic composites has been studied with respect to fiber content, fiber orientation, location of the external flaw, and the temperature of test. The composites showed a three-step reduction in tensile strength with increasing flaw size. The critical flaw-length region was shifted to higher flaw-size levels with increasing fiber content. With increasing temperature, the critical flaw length was increased in the case of unfilled TPU, whereas it remained more or less constant in the case of short kevlar fiber-filled-TPU composite.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions