Non-linear feature extraction methods, neighborhood preserving embedding (NPE) and supervised NPE (SNPE), were employed to effectively represent the IR spectral features of stomach and colon biopsy tissues for classification, and improve the classification accuracy for diagnosis of malignancy. The motivation was to utilize the NPE and SNPE's capability of capturing non-linear spectral behaviors by simultaneously preserving local relationships in order that minute spectral differences among classes would be effectively recognized. NPE and SNPE derive an optimal embedding feature such that the local neighborhood structure can be preserved in reduced spaces (variables). The IR spectra collected from stomach and colon tissues were represented by several new variables through NPE and SNPE, and also by using the principal component analysis (PCA). Then, the feature-extracted variables were subsequently classified into normal, adenoma and cancer tissues by using both k-nearest neighbor (k-NN) and support vector machine (SVM), and the resulting accuracies were compared with each other. In both cases, the combination of SNPE-SVM provided the best classification performance, and the accuracy was substantially improved compared to when PCA-SVM was used. Overall results demonstrate that NPE and SNPE could be potential feature-representation strategies useful in biomedical diagnosis based on vibrational spectroscopy where effective recognition of minute spectral differences is critical.

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Two group acceptance sampling plans are considered for a two-parameter generalized exponential distribution when the life-test is truncated at a pre-specified time. It is assumed that the shape parameter of the generalized exponential distribution is known. The design parameters such as the number of groups and the acceptance number are obtained by satisfying the producer's and consumer's risks at the specified quality levels in terms of medians, under the assumption that the termination time and the number of items in each group are prefixed. Examples are provided for illustrative purposes.

Competitive activities always support improvement in quality. In this study, a repetitive group sampling plan is designed to compare the two process yield indices of respective suppliers. A ratio statistic of two yield indices is proposed for estimated linear profiles under normal assumptions. The operating characteristic function is derived by using the distribution of the ratio statistic. Designed parameters are optimized to minimize the sample size required.

Two approaches for si Jating the reliability function are considered -one using the total hazard estimator and the other using importance sampling. It is shown both for the Wheatstone Bridge system and also for a triangular system that the total hazard estimator has significantly smaller variance when compared both to the standard importance sampling estimator and also to an improved version of it.