The on-line measurement of chemical composition under different operating conditions is an important problem in many industries. An approach based on hybrid signal preprocessing and artificial neural network paradigms for estimating composition from chemometric data has been developed. The performance of this methodology was tested with the use of near-infrared (NIR) and Raman spectra from both laboratory and industrial samples. The sensitivity-of-composition estimation as a function of spectral errors, spectral preprocessing, and choice of parameter vector was studied. The optimal architecture of multilayer neural networks and the guidelines for achieving them were also studied. The results of applications to FT-Raman data and NIR data demonstrate that this methodology is highly effective in establishing a generalized mapping between spectral information and sample composition, and that the parameters can be estimated with high accuracy.

His academic background includes a B.S. in Mechanical Engineering with minor in instrumentation and control, an M.S. in Metallurgical Engineering, and M.S. and Ph.D. in Nuclear Engineering. Dr. Naghedolfeizi's research interests include instrumentation and measurement systems, applied articial intelligence, information processing, and engineering education. He is the author of numerous research and pedagogical articles in his areas of expertise.

Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high classification performance, even with a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is an important and challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature learning (DFL) method, which combines spectral and spatial information into a hypergraph Laplacian. First, a subset of discriminative features is selected, which preserve the spectral structure of data and the inter- and intra-class constraints on labeled training samples. A feature evaluator is obtained by semi-supervised learning with the hypergraph Laplacian. Secondly, the selected features are mapped into a further lower-dimensional eigenspace through a generalized eigendecomposition of the Laplacian matrix. The finally extracted discriminative features are used in a joint sparsity-model algorithm. Experiments conducted with benchmark data sets and different experimental settings show that our proposed method increases classification accuracy and outperforms the state-of-the-art HSI classification methods.

Random walk (RW) method has been widely used to segment the organ in the volumetric medical image. However, it leads to a very large-scale graph due to a number of nodes equal to a voxel number and inaccurate segmentation because of the unavailability of appropriate initial seed point setting. In addition, the classical RW algorithm was designed for a user to mark a few pixels with an arbitrary number of labels, regardless of the intensity and shape information of the organ. Hence, we propose a prior knowledge-based Bayes random walk framework to segment the volumetric medical image in a slice-by-slice manner. Our strategy is to employ the previous segmented slice to obtain the shape and intensity knowledge of the target organ for the adjacent slice. According to the prior knowledge, the object/background seed points can be dynamically updated for the adjacent slice by combining the narrow band threshold (NBT) method and the organ model with a Gaussian process. Finally, a high-quality image segmentation result can be automatically achieved using Bayes RW algorithm. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for liver segmentation (p < 0.001).

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