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).
The computer science program at Fort Valley State University (FVSU) , a unit of University System of Georgia, is presently undergoing a major revision to reflect the most current trends in the job market and the ABET computer science curriculum requirements. Additionally, the curriculum redesign is needed to increase the program's appeal to students and employers. The underlying principle for this redesign is to provide more flexibility for students to take major and free elective courses and lessen the emphasis on traditional mathematics requirements (such as Calculus II).Currently, the major area in curriculum of computer science at FVSU includes 60 credit hours of which 9 hours are major electives and 6 hours free electives. The revised program will include 33 credit hours in core curriculum of computer science, 12 credit hours in major electives, and 15 credit hours in free electives. The mathematics requirements will include 17 credit hours with Calculus II placed under restricted electives.The increased number of credit hours in both restricted and free electives will allow students to obtain academic concentrations or minors in fields of interest. It should be noted that most minor and concentration programs at FVSU require 15-18 credit hours.It is anticipated that this program revision along with other academic success measures such as building a meaningful student support system would help increase the retention, recruitment, and graduation of students while maintaining a quality undergraduate computer science program aligned with both the University System of Georgia and the ABET requirements. This paper presents curriculum revision and enhancement to the computer science program at FVSU. The details regarding the student support system will be presented in a future article.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.