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Tien Giang Province, Vietnam

Nguyen H.,Nanjing Southeast University | Nguyen H.,Tien Giang University | Yang W.,Nanjing Southeast University | Yang W.,Nanjing University of Science and Technology | And 2 more authors.
Neurocomputing | Year: 2015

Face recognition is one of the fundamental problems of computer vision and pattern recognition. Based on the recent success of Low-Rank Representation (LRR), we propose a novel image classification method for robust face recognition, named Low-Rank Representation-based Classification (LRRC). Based on seeking the lowest-rank representation of a set of test samples with respect to a set of training samples, the algorithm has the natural discrimination to perform classification. We also propose a Kernel Low-Rank Representation-based Classification (KLRRC), which is a nonlinear extension of LRRC. KLRRC is firstly utilized to face recognition. By using the kernel tricks, we implicitly map the input data into the kernel feature space associated with a kernel function. We construct a transformation matrix to reduce the dimensionality of the kernel feature space, where LRRC is performed. Experimental results on several face data sets demonstrate the effectiveness and robustness of our methods. © 2015 Elsevier B.V. Source


Le M.T.,Tien Giang University | Lee J.,Kangwon National University
Geosystem Engineering | Year: 2013

Homogeneous ZnS-Co-doped nanoparticles (ZnS:Co) were accomplished through wet chemical method with the aid of a buffer solution at room temperature. Based on theoretical calculations, the pH of the reaction and the type of buffer solution were determined. The results of X-ray diffraction analysis showed that the change of lattice constant in ZnS:Co can be detected with a change of Co concentration. The average size of ZnS nanocrystallites, estimated by the Debye-Sherrer formula, was about 3 nm. Also, optical and magnetic properties were tested with the ZnS:Co nanoparticles in various Co concentrations. © 2013 Taylor & Francis. Source


Nguyen H.,Tien Giang University | Yang W.,Nanjing Southeast University | Sun C.,Nanjing Southeast University
Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 | Year: 2015

In this paper, based on Low-rank Representation (LRR) we present a new method, Transposed Discriminative Low-Rank Representation (TDLRR), for face recognition in which both training and testing images are corrupted. By adding a discriminative term into LRR function, we obtained a low-rank matrix recovery with the increase the discriminative ability between different classes. LRR of transposed data is also applied to extract the salient features of these recovered data so as to produce effective features for classification. In addition, the test samples are also corrected by using a low-rank projection matrix between the recovery results and the original training samples. Experimental results on three popular face databases demonstrate the effectiveness and robustness of our method. © 2015 IEEE. Source


Cao T.H.,Ho Chi Minh City University of Technology | Noi N.V.,Tien Giang University
International Journal of Intelligent Systems | Year: 2010

Lawry's label semantics for modeling and computing with linguistic information in natural language provides a clear interpretation of linguistic expressions and thus a transparent model for real-world applications. Meanwhile, annotated logic programs (ALPs) and its fuzzy extension AFLPs have been developed as an extension of classical logic programs offering a powerful computational framework for handling uncertain and imprecise data within logic programs. This paper proposes annotated linguistic logic programs (ALLPs) that embed Lawry's label semantics into the ALP/AFLP syntax, providing a linguistic logic programming formalism for development of automated reasoning systems involving soft data as vague and imprecise concepts occurring frequently in natural language. The syntax of ALLPs is introduced, and their declarative semantics is studied. The ALLP SLD-style proof procedure is then defined and proved to be sound and complete with respect to the declarative semantics of ALLPs. © 2010 Wiley Periodicals, Inc. Source


Trung T.B.,University of Medicine and Pharmacy | Hung V.P.,University of Medicine and Pharmacy | Hoang H.T.,Hochiminh City Institute of Physics | Tung L.M.,Tien Giang University | Lee J.,Kangwon National University
Geosystem Engineering | Year: 2015

A method for detecting glypican 3 (GPC3) liver cancer cells by coupling of anti-glypican 3 antibody (anti-GPC3) and magnetite nanoparticles (NPs) was investigated to detect GPC3 by enzyme-linked immunosorbent assay (ELISA) in this study. Magnetite NPs with the average size of 11 nm were synthesized by using co-precipitation method of Fe2+ and Fe3+ in NH3·H2O solution. First, silica was coated on the magnetite NPs using Stöber method to obtain Fe3O4/SiO2 core-shell structures and then 3-aminopropyltriethoxysilane (APTES) was treated on the Fe3O4/SiO2 by silanization reaction to achieve Fe3O4/SiO2/APTES nanostructures. After modified by APTES, the nanostructures were activated by glutaraldehyde (GA) to obtain functional groups on the nanostructures surface to bind with anti-GPC3 by covalent immobilization. The UV–vis spectroscopy was carried out to investigate the binding of anti-GPC3 to the NPs and binding efficiency (88.35%) was estimated by the Bradford method. The NPs bound anti-GPC3 (NPs/anti-GPC3) can detect GPC3 by using ELISA at low concentration (0.16 ng/ml). © 2015 The Korean Society of Mineral and Energy Resources Engineers (KSMER). Source

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