Chihuahua, Mexico

The Chihuahua Institute of Technology is a public university located in the city of Chihuahua, capital of the state of Chihuahua, in Mexico. Wikipedia.


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Ramirez-Alonso G.,Autonomous University of Chihuahua | Ramirez-Quintana J.A.,Chihuahua Institute of Technology | Chacon-Murguia M.I.,Chihuahua Institute of Technology
Pattern Recognition Letters | Year: 2017

Background initialization and background update are two important stages considered in the design of most background modeling algorithms. Commonly, these algorithms implement strategies in which their parameters have a very high adaptability in the background initialization stage in order to learn all the variations of the background. Contrary, in the background update phase, these parameters adapt slowly, in most cases with an exponential decay. This paper presents the BE-AAPSA method which automatically determines if the background initialization and the background update need to be re-initialized. Re-initialization is triggered if the video scene presents high variations, allowing the background to be defined more accurately. BE-AAPSA is based on a previously developed system, where two adaptive background models based on weight arrays with temporal learning mechanism identify dynamic objects within a video scene. The system implements four independent modules to treat the different factors that affect a correct definition of the dynamic object. In BE-AAPSA, the objective is to create a robust background estimation model where the learning rates for each pixel are calculated according to the results of the two adaptive weight arrays and the module where the video is classified. This approach allows handling different strategies to update learning rates at a pixel resolution. BE-AAPSA is validated with the SBI and SBMnet video databases and with a video created by concatenating scenes of the video categories presented in the CDnet 2014 database. According to the findings, BE-AAPSA produced highly accurate results with SBI and SBMnet and surpassed state-of-the-art methods with the CDnet video. These results demonstrate the importance of using an automatic re-initialization scheme in the background initialization and background update stages when the video scene presents a major change or involves jittering. Furthermore, it shows the benefits of handling in separate modules the analysis of the background estimation results. © 2017 Elsevier B.V.


Chacon-Murguia M.I.,Chihuahua Institute of Technology | Ramirez-Alonso G.,Chihuahua Institute of Technology
Applied Soft Computing Journal | Year: 2015

In this paper we propose a system that involves a Background Subtraction, BS, model implemented in a neural Self Organized Map with a Fuzzy Automatic Threshold Update that is robust to illumination changes and slight shadow problems. The system incorporates a scene analysis scheme to automatically update the Learning Rates values of the BS model considering three possible scene situations. In order to improve the identification of dynamic objects, an Optical Flow algorithm analyzes the dynamic regions detected by the BS model, whose identification was not complete because of camouflage issues, and it defines the complete object based on similar velocities and direction probabilities. These regions are then used as the input needed by a Matte algorithm that will improve the definition of the dynamic object by minimizing a cost function. Among the original contributions of this work are; an adapting fuzzy-neural segmentation model whose thresholds and learning rates are adapted automatically according to the changes in the video sequence and the automatic improvement on the segmentation results based on the Matte algorithm and Optical flow analysis. Findings demonstrate that the proposed system produces a competitive performance compared with state-of-the-art reported models by using BMC and Li databases. © 2015 Elsevier B.V.


Acosta-Cano J.,Chihuahua Institute of Technology | Sastron-Baguena F.,Technical University of Madrid
Journal of Applied Research and Technology | Year: 2013

Coupling shop floor software system (SFS) with the set of production equipment (SPE) becomes a complex task. It involves open and proprietary standards, information and communication technologies among other tools and techniques. Due to market turbulence, either custom solutions or standards based solutions eventually require a considerable effort of adaptation. Loose coupling concept has been identified in the organizational design community as a compensator for organization survival. Its presence reduces organization reaction to environment changes. In this paper the results obtained by the organizational design community are identified, translated and organized to support the SFS-SPE integration problem solution. A classical loose coupling model developed by organizational studies community is abstracted and translated to the area of interest. Key aspects are identified to be used as promoters of SFS-SPE loose coupling and presented in a form of a reference scheme. Furthermore, this reference scheme is proposed here as a basis for the design and implementation of a generic coupling solution or coupling framework, that is included as a loose coupling stage between SFS and SPE. A validation example with various sets of manufacturing equipment, using different physical communication media, controller commands, programming languages and wire protocols is presented, showing an acceptable level of autonomy gained by the SFS.


Acosta P.,Chihuahua Institute of Technology
Journal of the Franklin Institute | Year: 2014

It is well known that sliding mode control is based on the definition of an invariant manifold, where the system dynamics are forced to in a finite time. Such a manifold is somewhat arbitrarily defined, as long as the system dynamics are stable on it. Computational and control effort may vary depending on selected manifold. Obviously, if a system has naturally acceptable stable dynamics around a desired equilibrium point, no control is needed unless uncertainties or disturbances are present. It would be desirable that if such a system had uncertainties or disturbances, the control effort be designed only to overcome the effect of such factors. For a system with first order dynamics and affine control input, designing a sliding mode control overcoming only such uncertainties or disturbances is a trivial task. When a higher order dynamics system is involved, unit control may be used, where the input control signals are not discontinuous, but when only discontinuous control inputs are available, a design approach is not readily available. In this paper, taking advantage of the natural stable dynamics of a system, a sliding mode control approach is introduced for designing multiple discontinuous control inputs, where the control effort overcomes only uncertainties, disturbances or unstable dynamics. Two illustrative examples are given in order to show the feasibility of the method. © 2014 The Franklin Institute.


Maldonado A.,Autonomous University of Ciudad Juárez | Maldonado A.,Chihuahua Institute of Technology | Garcia J.L.,Autonomous University of Ciudad Juárez | Alvarado A.,Autonomous University of Ciudad Juárez | Balderrama C.O.,Autonomous University of Ciudad Juárez
International Journal of Advanced Manufacturing Technology | Year: 2013

Advanced manufacturing technology (AMT) is a relevant resource that has been extensively used in modern industries around the world with the aim of being competitive and maintaining high levels of quality and performance. There is a wide variety of tools and models available in the literature to support AMT selection and evaluation processes. Usually, they consist of analyses of tangible aspects, such as cost, time, speed, precision, among others; however, some other important aspects are commonly neglected, that is, the case of human factors and ergonomic characteristics. This paper presents a new methodology for the evaluation of ergonomic compatibility of AMT. This methodology may be considered as a decision aid; thus, decision makers might perform their duties in a more complete manner taking into account ergonomic attributes. Fuzzy axiomatic design applications are state of the art methods for decision making, and this paper contributes with a unique application for ergonomic compatibility evaluation for AMT. The first part of the paper presents the findings of an extensive literature review about important ergonomic attributes of AMT. Then, those attributes were originally structured following a multi-attribute axiomatic design approach for AMT ergonomic evaluation under a fuzzy environment. Also, a unique ergonomic compatibility survey was proposed for data collection and an original procedure was developed for AMT evaluation, a numerical example is provided. The ergonomic compatibility concept was tested and validated using the Cronbach's alpha test (α ≥ 0.7), finding that the instrument is suitable for the measurement of the proposed construct. © 2012 Springer-Verlag London Limited.


Acosta P.R.,Chihuahua Institute of Technology
Mathematical Problems in Engineering | Year: 2013

This paper deals with a class of second order sliding mode systems. Based on the derivative of the sliding surface, sufficient conditions are given for stability. However, the discontinuous control signal depend neither on the derivative of sliding surface nor on its estimate. Time delay in control input is also an important issue in sliding mode control for engineering applications. Therefore, also sufficient conditions are given for the time delay size on the discontinuous input signal, so that this class of second order sliding mode systems might have amplitude bounded oscillations. Moreover, amplitude of such oscillations may be estimated. Some numerical examples are given to validate the results. At the end, some conclusions are given on the possibilities of the results as well as their limitations. © 2013 Pedro R. Acosta.


Chacon-Murguia M.I.,Chihuahua Institute of Technology | Perez-Vargas F.J.,Chihuahua Institute of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

This paper presents a method to detect fire regions in thermal videos that can be used for both outdoor and indoor environments. The proposed method works with static and moving cameras. The detection is achieved through a linear weighted classifier which is based on two features. The features are extracted from candidate regions by the following process; contrast enhance by the Local Intensities Operation and candidate region selection by thermal blob analysis. The features computed from these candidate regions are; region shape regularity, determined by Wavelet decomposition analysis and region intensity saturation. The method was tested with several thermal videos showing a performance of 4.99% of false positives in non-fire videos and 75.06% of correct detection with 7.27% of false positives in fire regions. Findings indicate an acceptable performance compared with other methods because this method unlike other works with moving camera videos. © 2011 Springer-Verlag Berlin Heidelberg.


Ramirez-Quintana J.A.,Chihuahua Institute of Technology | Chacon-Murguia M.I.,Chihuahua Institute of Technology
Pattern Recognition | Year: 2015

This paper proposes a novel bio-inspired neural system based on Self-organizing Maps (SOMs) and Cellular Neural Networks (CNNs), called SOM-CNN, to detect dynamic objects in normal and complex scenarios. A contribution of our work is a Retinotopic SOM (RESOM) architecture feasible for video and motion analysis. It is inspired by the visual perception mechanism of the human visual cortex, and satisfactorily addresses the disadvantages encountered by other methods in the area. We also propose a new CNN scheme for image thresholding, called Neighbor Threshold CNN (NTCNN), and a self-adapting parameter scheme for the RESOM and the NTCNN models. The proposed system can deal with sudden and gradual illumination changes, dynamic backgrounds, camouflage, camera jitter, and stopped dynamic objects. Experimental results on complex scenarios, using the Precision (Pe), Recall (Rc), F measure, (F1) and Similarity (Si) metrics, yield acceptable average performances with Pe=0.875, Rc=0.8316, F1=0.843 and Si=0.741. Results also show that our proposed system performs better than other methods that have been suggested in the literature. The system can process information at 35 fps, rendering it suitable for real-time applications. © 2014 Elsevier Ltd. All rights reserved.


Chacon-Murguia M.I.,Chihuahua Institute of Technology | Gonzalez-Duarte S.,Chihuahua Institute of Technology
IEEE Transactions on Industrial Electronics | Year: 2012

Object detection is a fundamental aspect in surveillance systems. Although several works aimed at detecting objects in video sequences have been reported, many are not tolerant to dynamic background or require complex computation in addition to manual parameter adjustments. This paper proposes an adaptive object detection method to work in dynamic backgrounds without human intervention. The proposed method is based on a neural-fuzzy model. The neural stage, based on a one-to-one self-organizing map (SOM) architecture, deals with the dynamic background for object detection as well as shadow elimination. The fuzzy inference Sugeno system mimics human behavior to automatically adjust the main parameters involved in the SOM detection model, making the system independent of the scenario. Results of the model over real video scenes show its robustness. These findings are comparable to the results obtained with human intervention to define the parameters of the model. A quantitative comparison with methods reported in the literature is also provided to show the performance of the system. © 2012 IEEE.


Ramirez-Quintana J.A.,Chihuahua Institute of Technology | Chacon-Murguia M.I.,Chihuahua Institute of Technology | Chacon-Hinojos J.F.,Chihuahua Institute of Technology
Engineering Letters | Year: 2012

Artificial Neural Networks (ANNs) have been useful for decades to the development of Image Processing algorithms applied to several different fields, such as science, engineering, industry, security and medicine. This close relationship between ANNs and Image Processing has motivated a study of 160 papers that propose and deal with said algorithms. The information contained in these papers is analyzed, commented and then classified according to its contribution and applications. Then, some important aspects of recent visual cortex-based ANN models are described to finally discuss about the conclusions reached throughout the process.

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