Dr. Iván E Villalón Turrubiates | ITESO (original) (raw)
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Papers by Dr. Iván E Villalón Turrubiates
... Outstanding Citizen - City of Salamanca Mexico, 2008; MS Scholarship 165660 - CONACYT Mexico ... more ... Outstanding Citizen - City of Salamanca Mexico, 2008; MS Scholarship 165660 - CONACYT Mexico 2001-2003; Ph.D. Scholarship 165660 - CONACYT Mexico 2005-2007; Marquis Who's Who in the World - 2007; Best Evaluated Professor - TECMilenio University 2010. ...
In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the... more In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the deaf population in the age group between 3 and 29 years access education with the support of a hearing aid. Additionally, those who have been incorporated frequently face inappropriate educational strategies which poorly develop the use of Mexican Sign Language (MSL) and therefore academical success and opportunities for insertion in the workplace are difficult. This research explores a novel mexican sign language lexicon video dataset containing the dynamical gestures most frequently used by MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. MX-ITESO-100 data set is composed of a lexicon of 100 gestures and 5,000 videos from three participants with different grammatical elements. Additionally, the data set is evaluated in a two-step neural network model with an accuracy greater than 99%. and thus serves as a benchmark for future training ...
Mathematics
The capability analysis of a process against requirements is often an instrument of change. The t... more The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimenta...
Nordic Pulp & Paper Research Journal
Currently, there are two procedures to determine the basis weight in papermaking processes: the m... more Currently, there are two procedures to determine the basis weight in papermaking processes: the measurements made by the quality control laboratory or the measurements made by the quality control system. This research presents an alternative to estimating basis weight-based artificial neural network (ANN) modeling. The NN architecture was constructed by trial and error, obtaining the best results using two hidden layers with 48 and 12 neurons, respectively, in addition to the input and output layers. Mean absolute error and mean absolute percentage error was used for the loss and metric functions, respectively. Python was used in the training, validation, and testing process. The results indicate that the model can reasonably determine the basis weight given the independent variables analyzed here. The R 2 {R^{2}} reached by the model was 94 %, and MAE was 12.40 grams/m2. Using the same dataset, the fine tree regression model showed an R 2 {R^{2}} of 99 % and an MAE of 3.35 grams/m2...
This study consider the problem of high-resolution imaging of the remote sensing (RS) environment... more This study consider the problem of high-resolution imaging of the remote sensing (RS) environment formalized in terms of a nonlinear ill- posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from an extended remotely sensed scene (referred to as the scene image). However, the remote sensing techniques for reconstructive imaging in many RS application areas are relatively unacceptable for being implemented in a (near) real time implementation. In this work, we address a new aggregated descriptive-regularization (DR) method and the Hardware/Software (HW/SW) co-design for the SSP reconstruction from the uncertain speckle-corrupted measurement data in a computationally efficient parallel fashion that meets the (near) real time image processing requirements. The hardware design is performed via efficient systolic arrays (SAs). Finally, the efficiency both in resolution enhancement and in computational complexity reductio...
The paper suggest a novel approach to the problem of high-resolution array radar/SAR imaging as a... more The paper suggest a novel approach to the problem of high-resolution array radar/SAR imaging as an ill-conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties. We explain the theory recently developed by the authors of this presentation that addresses a new fused Bayesian-regularization paradigm for radar/SAR image formation/reconstruction. We show how this theory leads to new adaptive and robustified computational methods that enable one to derive efficient and consistent estimates of the SSP via unifying the Bayesian minimum risk estimation strategy with the ME randomized a priori image model and other projection-type regularization constraints imposed on the solution. We detail such fused Bayesian-regularization (FBR) paradigm and analyze some efficient numerical schemes for computational implementation of the relevant FBR-based methods. Also, we present the results of extended simulation study of the family of the radar image (RI) formation...
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 1, 2017
Journal of Intelligent & Fuzzy Systems, 2018
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
2006 International Conference on Image Processing, 2006
![Research paper thumbnail of Introduction of the IEEE Guadalajara Section Geoscience and Remote Sensing Society Chapter [Chapters]](https://attachments.academia-assets.com/119342044/thumbnails/1.jpg)
](IEEE Geoscience and Remote Sensing Magazine, 2014
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2007
1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.
1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
... Outstanding Citizen - City of Salamanca Mexico, 2008; MS Scholarship 165660 - CONACYT Mexico ... more ... Outstanding Citizen - City of Salamanca Mexico, 2008; MS Scholarship 165660 - CONACYT Mexico 2001-2003; Ph.D. Scholarship 165660 - CONACYT Mexico 2005-2007; Marquis Who's Who in the World - 2007; Best Evaluated Professor - TECMilenio University 2010. ...
In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the... more In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the deaf population in the age group between 3 and 29 years access education with the support of a hearing aid. Additionally, those who have been incorporated frequently face inappropriate educational strategies which poorly develop the use of Mexican Sign Language (MSL) and therefore academical success and opportunities for insertion in the workplace are difficult. This research explores a novel mexican sign language lexicon video dataset containing the dynamical gestures most frequently used by MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. MX-ITESO-100 data set is composed of a lexicon of 100 gestures and 5,000 videos from three participants with different grammatical elements. Additionally, the data set is evaluated in a two-step neural network model with an accuracy greater than 99%. and thus serves as a benchmark for future training ...
Mathematics
The capability analysis of a process against requirements is often an instrument of change. The t... more The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimenta...
Nordic Pulp & Paper Research Journal
Currently, there are two procedures to determine the basis weight in papermaking processes: the m... more Currently, there are two procedures to determine the basis weight in papermaking processes: the measurements made by the quality control laboratory or the measurements made by the quality control system. This research presents an alternative to estimating basis weight-based artificial neural network (ANN) modeling. The NN architecture was constructed by trial and error, obtaining the best results using two hidden layers with 48 and 12 neurons, respectively, in addition to the input and output layers. Mean absolute error and mean absolute percentage error was used for the loss and metric functions, respectively. Python was used in the training, validation, and testing process. The results indicate that the model can reasonably determine the basis weight given the independent variables analyzed here. The R 2 {R^{2}} reached by the model was 94 %, and MAE was 12.40 grams/m2. Using the same dataset, the fine tree regression model showed an R 2 {R^{2}} of 99 % and an MAE of 3.35 grams/m2...
This study consider the problem of high-resolution imaging of the remote sensing (RS) environment... more This study consider the problem of high-resolution imaging of the remote sensing (RS) environment formalized in terms of a nonlinear ill- posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from an extended remotely sensed scene (referred to as the scene image). However, the remote sensing techniques for reconstructive imaging in many RS application areas are relatively unacceptable for being implemented in a (near) real time implementation. In this work, we address a new aggregated descriptive-regularization (DR) method and the Hardware/Software (HW/SW) co-design for the SSP reconstruction from the uncertain speckle-corrupted measurement data in a computationally efficient parallel fashion that meets the (near) real time image processing requirements. The hardware design is performed via efficient systolic arrays (SAs). Finally, the efficiency both in resolution enhancement and in computational complexity reductio...
The paper suggest a novel approach to the problem of high-resolution array radar/SAR imaging as a... more The paper suggest a novel approach to the problem of high-resolution array radar/SAR imaging as an ill-conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties. We explain the theory recently developed by the authors of this presentation that addresses a new fused Bayesian-regularization paradigm for radar/SAR image formation/reconstruction. We show how this theory leads to new adaptive and robustified computational methods that enable one to derive efficient and consistent estimates of the SSP via unifying the Bayesian minimum risk estimation strategy with the ME randomized a priori image model and other projection-type regularization constraints imposed on the solution. We detail such fused Bayesian-regularization (FBR) paradigm and analyze some efficient numerical schemes for computational implementation of the relevant FBR-based methods. Also, we present the results of extended simulation study of the family of the radar image (RI) formation...
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 1, 2017
Journal of Intelligent & Fuzzy Systems, 2018
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
2006 International Conference on Image Processing, 2006
IEEE Geoscience and Remote Sensing Magazine, 2014
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2007
1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.
1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010