Garza Villarreal S.E.,Autonomous University of Nuevo Leon |
Brena R.F.,TEC de Monterrey
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011
This paper introduces an approach for discovering thematically related document groups (a topic mining task) in massive document collections with the aid of graph local clustering. This can be achieved by viewing a document collection as a directed graph where vertices represent documents and arcs represent connections among these (e.g. hyperlinks). Because a document is likely to have more connections to documents of the same theme, we have assumed that topics have the structure of a graph cluster, i.e. a group of vertices with more arcs to the inside of the group and fewer arcs to the outside of it. So, topics could be discovered by clustering the document graph; we use a local approach to cope with scalability. We also extract properties (keywords and most representative documents) from clusters to provide a summary of the topic. This approach was tested over the Wikipedia collection and we observed that the resulting clusters in fact correspond to topics, which shows that topic mining can be treated as a graph clustering problem. © 2011 Springer-Verlag.
Pang J.,Tufts University |
Driban J.B.,Tufts Medical Center |
McAlindon T.E.,Tufts Medical Center |
Tamez-Pena J.G.,TEC de Monterrey |
And 3 more authors.
IEEE Journal of Biomedical and Health Informatics | Year: 2015
Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually, the training data consist of whole shapes of certain organs or structures obtained manually by clinical experts. The resulting prior models enforce segmentation accuracy uniformly over the entire structure or structures to be identified. In this paper, we consider a new coupled prior shape model which is demonstrated to provide high accuracy, specifically in the region of the interest where precision is most needed for the application of the segmentation of the femur and tibia in magnetic resonance (MR) images. Experimental results for the segmentation of MR images of human knees demonstrate that the combination of the new coupled prior shape and a directional edge force provides the improved segmentation performance. Moreover, the new approach allows for equivalent accurate identification of bone marrow lesions, a promising biomarker related to osteoarthritis, to the current state of the art but requires significantly less manual interaction. © 2013 IEEE.
Minchala-Avila L.I.,Concordia University at Montreal |
Vargas-Martinez A.,Concordia University at Montreal |
Zhang Y.,Concordia University at Montreal |
Garza-Castanon L.E.,TEC de Monterrey
Conference on Control and Fault-Tolerant Systems, SysTol | Year: 2013
This paper presents a model-based fault-tolerant approach for designing a control strategy in order to integrate a diesel engine generator (DEG) as master generation unit, voltage and frequency leader, in an islanded microgrid configuration. The microgrid design is mainly composed of a hybrid wind-diesel-photovoltaic power system with a battery storage system (BSS). A model predictive control (MPC) scheme has been selected for this task, due to its flexibility and capability for handling constraints. Fault-tolerance is achieved in the DEG control system with the addition of a fault detection and diagnosis (FDD) module to the MPC structure, in order to reconfigure the control strategy when actuator faults in the DEG are present. Different operating conditions of the microgrid were simulated in order to test control robustness. Improved performance over a baseline controller, IEEE type 1 exciter, is achieved. Dynamic models of the microgrids components are presented and simulation results of the microgrid behavior in Matlab/Simulink®. © 2013 IEEE.
Tamez-Pena J.G.,TEC de Monterrey |
Tamez-Pena J.G.,Qmetrics Technologies LLC |
Farber J.,Qmetrics Technologies LLC |
Gonzalez P.C.,Qmetrics Technologies LLC |
And 3 more authors.
IEEE Transactions on Biomedical Engineering | Year: 2012
This paper presents a fully automated method for segmenting articular knee cartilage and bone from in vivo 3-D dual echo steady state images. The magnetic resonance imaging (MRI) datasets were obtained from the Osteoarthritis Initiative (OAI) pilot study and include longitudinal images from controls and subjects with knee osteoarthritis (OA) scanned twice at each visit (baseline, 24 month). Initially, human experts segmented six MRI series. Five of the six resultant sets served as reference atlases for a multiatlas segmentation algorithm. The methodology created precise knee segmentations that were used to extract articular cartilage volume, surface area, and thickness as well as subchondral bone plate curvature. Comparison to manual segmentation showed Dice similarity coefficient (DSC) of 0.88 and 0.84 for the femoral and tibial cartilage. In OA subjects, thickness measurements showed test-retest precision ranging from 0.014 mm (0.6%) at the femur to 0.038 mm (1.6%) at the femoral trochlea. In the same population, the curvature test-retest precision ranged from 0.0005 mm -1 (3.6%) at the femur to 0.0026 mm -1 (11.7%) at the medial tibia. Thickness longitudinal changes showed OA Pearson correlation coefficient of 0.94 for the femur. In conclusion, the fully automated segmentation methodology produces reproducible cartilage volume, thickness, and shape measurements valuable for the study of OA progression. © 2006 IEEE.
Bernal J.L.,Polytechnic University of the Valley of Mexico |
Castillo F.,TEC de Monterrey |
Oseguera J.,TEC de Monterrey |
Fraguela A.,Postgrado de Matematicas |
And 4 more authors.
European Conference of Chemical Engineering, ECCE'10, European Conference of Civil Engineering, ECCIE'10, European Conference of Mechanical Engineering, ECME'10, European Conference of Control, ECC'10 | Year: 2010
This work presents the computing of diffusion coefficients through an inverse problem of coefficient identification in a diffusion model, which considers several layers and Stefan type conditions. An approximate solution is obtained for the model, using qualitative and quantitative information from experimental results. To study the inverse problem we assume that we have information about the nitrogen concentration at different depths in the solid at the same time during the post-discharge nitriding process. To develop a stable numerical algorithm for the identification of the diffusion coefficients, a functional is built. This functional measures the deviation between the data theoretically obtained from the approximate solution of the model and the experimental data.