Papers by Amadeo Arguelles
Technological Spotlights of Digital Transformation
Information Technology Trends for a Global and Interdisciplinary Research Community
Information management in pandemic conditions involves accelerating the adoption of the digital t... more Information management in pandemic conditions involves accelerating the adoption of the digital transformation in manufacturing and service sectors that impact the economy. The risks of maintaining a dismissive view are already visible on the labor and economic side. The chapter presents a review of information and communication technologies, associated with the drive for health-related areas, automotive manufacturing, robots, self-driven cars, and retail sales to conclude with education. In certain scenarios, it leads the reader to look at a set of solutions that address his or her position on the issues addressed in the chapter. The conditions that affect the acceptance of the digital transformation and what adverse effects cause its late inclusion are described.
Automatic electroencephalographic information classifier based on recurrent neural networks
International Journal of Machine Learning and Cybernetics
The aim of this study was to design an automatic classifier for electroencephalographic informati... more The aim of this study was to design an automatic classifier for electroencephalographic information (EEGI) registered in evoked potentials experiments. The classifier used a parallel associative memory based on recurrent neural networks (RNNs). Each RNN was trained to classify signals belonging to an individual class. A recurrent method based on the application of Lyapunov controlled functions served to design the training procedure of each RNN in the classifier. A parallel structure of RNN with fixed weights (obtained after training process) performed the validation stage. This structure formed a classifier assemble. The selected class assigned to a new segment of EEGI signal is estimated by the minimum value of the least mean square error among the RNNs forming the assemble. The generalization-regularization and a k-fold cross validation ($$k=5$$k=5) were the validation methods evaluating the classifier efficiency. The confusion matrix method justified the application of the classification method introduced in this study. The EEGI obtained from two different annotated databases served to test the classifier based on RNNs. The first database contained signals divided in five different classes and collected from patients suffering from epilepsy. The second database has 90 signals divided in three classes that corresponded to EEGI signals corresponding to 3 different visual evoked potentials. The pattern classifier achieved a maximum correct classification percentage of 97.2% using the information of both databases. This value prevailed over results reported in similar studies using the first database. In comparison with other pattern recognition algorithms, the proposed RNNs based classifier attained similar or even better correct classification results.
FPGA Implementation of Parallel Alpha-Beta Associative Memories
Image Analysis and Recognition, 2008
Associative memories have a number of properties, including a rapid, compute efficient best-match... more Associative memories have a number of properties, including a rapid, compute efficient best-match and intrinsic noise tolerance that make them ideal for many applications. However, a significant bottleneck to the use of associative memories in real-time systems is the amount of data that requires processing. Notwithstanding, Alpha-Beta Associative Memories have been widely used for color matching in industrial processes [1], text translation [2] and image retrieval applications [3]. The aim of this paper is to present the work that produced a ...
A Real Time Artificial Vision Implementation for Quality Inspection of Industrial Products
… , 2008. CERMA'08, Sep 30, 2008
Michel A. Aguilar-Torres, Amadeo J. Argüelles-Cruz, Cornelio Yánez-Márquez Centro de Investigació... more Michel A. Aguilar-Torres, Amadeo J. Argüelles-Cruz, Cornelio Yánez-Márquez Centro de Investigación en Computación, Instituto Politécnico Nacional Col. Nueva Industrial Vallejo, CP 07738, México DF maguilara07@sagitario.cic.ipn.mx, jamadeo@cic.ipn.mx, cyanez@cic.ipn.mx ... This document introduces the implementation of an industrial machine vision system developed in LabView, same that helps classify the quality of mayonnaise trays. The methodology and the algorithms used as well as the results are described here.
Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal
Time-delay systems have been successfully used to represent the complexity of some dynamic system... more Time-delay systems have been successfully used to represent the complexity of some dynamic systems. Time-delay is often used for modeling many real systems. Among others, biological and chemical plants have been described using time-delay terms with better results than those models that have not consider them. However, getting those models represented a challenge and sometimes the results were not so satisfactory. Non-parametric modeling offered an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems. This article introduces the design of a specific class of non-parametric model for uncertain time-delay system based on CNN considering the so-called delayed learning laws analysis. The convergence analysis as well as the learning laws were produced by means of a Lyapunov-Krasovskii functional. Three examples were developed to demonstrate the effectiveness of the modeling process forced by the identifier proposed in this study. The first example was a simple nonlinear model used as benchmark example. The second example regarded the human immunodeficiency virus dynamic behavior is used to show the performance of the suggested non-parametric identifier based on CNN for no fictitious neither academic models. Finally, a third example describing the evolution of hepatitis B virus served to test the identifier presented in this study and was also useful to provide evidence of its superior performance against a non-delayed identifier based on CNN.
Adaptive Identifier for Uncertain Complex Nonlinear Systems Based on Continuous Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2014
This paper presents the design of a complex-valued differential neural network identifier for unc... more This paper presents the design of a complex-valued differential neural network identifier for uncertain nonlinear systems defined in the complex domain. This design includes the construction of an adaptive algorithm to adjust the parameters included in the identifier. The algorithm is obtained based on a special class of controlled Lyapunov functions. The quality of the identification process is characterized using the practical stability framework. Indeed, the region where the identification error converges is derived by the same Lyapunov method. This zone is defined by the power of uncertainties and perturbations affecting the complex-valued uncertain dynamics. Moreover, this convergence zone is reduced to its lowest possible value using ideas related to the so-called ellipsoid methodology. Two simple but informative numerical examples are developed to show how the identifier proposed in this paper can be used to approximate uncertain nonlinear systems valued in the complex domain.
Computers in Human Behavior, 2014
Nowadays, we are immersed in the social and mobile networks era. As a positive consequence of thi... more Nowadays, we are immersed in the social and mobile networks era. As a positive consequence of this, collaborative and mobile learning in educational environments have been encouraged thanks to the use of computing for human learning. By coupling the advantages of collaborative and mobile learning, the teaching-learning processes involved in postgraduate courses may be greatly enhanced. The pedagogical experiences in this regard lived by the authors in the Alpha-Beta Research Group when coupling collaborative and mobile learning in the context of postgraduate level courses, are presented in this paper.
IFIP Advances in Information and Communication Technology, 2011
Artificial Intelligence has been present since more than two decades ago, in the treatment of dat... more Artificial Intelligence has been present since more than two decades ago, in the treatment of data concerning the protection of the environment; in particular, various groups of researchers have used genetic algorithms and artificial neural networks in the analysis of data related to the atmospheric sciences and the environment. However, in this kind of applications has been conspicuously absent from the associative models, by virtue of which the classic associative techniques exhibit very low yields. This article presents the results of applying Alpha-Beta associative models in the analysis and prediction of the levels of Carbon Monoxide (CO) and Nitrogen Oxides (NO x ) in Mexico City.
GNSS Receiver Based on a SDR Architecture Using FPGA Devices
This paper presents the development of a FPGA based GNSS receiver. The developed prototype is bas... more This paper presents the development of a FPGA based GNSS receiver. The developed prototype is based on a Software Radio Architecture and integrates all the main GPS signal processing algorithms as IP modules do. Furthermore, description of a developed system for the acquisition, tracking and position computation algorithms is described. The obtained results display that the positioning obtained with this prototype is closer to the real user position.
Optimized Associative Memories for Feature Selection
Performance in most pattern classifiers is improved when redundant or irrelevant features are rem... more Performance in most pattern classifiers is improved when redundant or irrelevant features are removed, however, this is mainly achieved by high demanding computational methods or successive classifiers construction. This paper shows how Associative Memories can be used to get a mask value which represents a subset of features that clearly identifies irrelevant or redundant information for classification purposes, therefore, classification accuracy is improved while significant computational costs in the learning phase are reduced. An optimal subset of features allows register size optimization, which contributes not only to significant power savings but to a smaller amount of synthesized logic, furthermore, improved hardware architectures are achieved due to functional units size reduction, as a result, it is possible to implement parallel and cascade schemes for pattern classifiers on the same ASIC.
Pollutants Time-Series Prediction using the Gamma Classifier
In this work we predict time series of air pollution data taken in Mexico City and the Valley of ... more In this work we predict time series of air pollution data taken in Mexico City and the Valley of Mexico, by using the Gamma Classifier which is a novel intelligent associative mathematical model, coupled with an emergent coding technique. Historical and current data about the concentration of specific pollutants, in the form of time series, were used. The pollutants of interest are: carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), and nitrogen oxides (NOx, including both nitrogen monoxide, NO, and nitrogen dioxide, NO2.
Computer Methods and Programs in Biomedicine
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 6 ( 2 0 1 2 ) 2... more c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 6 ( 2 0 1 2 ) 287-307 Decision support systems Supervised Machine Learning algorithms Pattern classification a b s t r a c t Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets. (M. Aldape-Pérez). URL: http://www.aldape.org.mx (M. Aldape-Pérez).
CAINN - Weightless Alpha-Beta Neural Network
In this paper, a weightless neural network model is presented, based on the known operations Alph... more In this paper, a weightless neural network model is presented, based on the known operations Alpha and Beta. This weightless Alpha-Beta neural network model is called CAINN - Computing Artificial Intelligence Neural Network.The CAINN's pattern learning and recalling algorithms are created given the Generalized Alpha, Sigma-Alpha, and Sigma-Beta operations, based on the ADAM weightless neural network model. Studies of the CAINN performance are shown. These studies report the reliability of the CAINN model.
Pattern recognition and classification using weightless neural networks (WNN) and Steinbuch Lernmatrix
This proposal presents a novel use of Weightless Neural Networks (WNN) and Steinbuch Lernmatrix f... more This proposal presents a novel use of Weightless Neural Networks (WNN) and Steinbuch Lernmatrix for pattern recognition and classification. High speed of learning, easy of implementation and flexibility given by WNN, combined with the learning capacity, recovery efficiency, noise immunity and fast processing shown by Steinbuch Lernmatrix are key factors considered on the pattern recognition exposed by the suggested model. For experimental purposes, the fundamental pattern sets are built and provided to the model under the learning phase. The additive, subtractive and mixed noises are applied to fundamental patterns to check out the response of the model during the recovery phase. Field Programmable Gate arrays are used in the implementation of such model, since it allows custom user-defined models to be embedded in a reconfigurable hardware platform, and provides block memories and dedicated multipliers suitable for the model.
Prediction of air contaminant concentration based on an associative pattern classifier
Resumen Desde hace poco más de tres lustros, el Reconocimiento de Patrones ha incidido en el trat... more Resumen Desde hace poco más de tres lustros, el Reconocimiento de Patrones ha incidido en el tratamiento de datos concernientes a la protección del medio ambiente; en especial, diversos grupos de investigadores han utilizado algoritmos genéticos y redes neuronales artificiales en la predicción de datos relacionados con las ciencias atmosféricas y el medio ambiente.
Resumen En este trabajo de tesis se presenta un nuevo modelo de redes neuronales sin pesos basado... more Resumen En este trabajo de tesis se presenta un nuevo modelo de redes neuronales sin pesos basado en las operaciones Alfa, Beta y Alfa Generalizada, original de esta tesis. El nuevo modelo de redes neuronales Alfa-Beta sin pesos se ha denominado CAINN, por sus siglas en inglés: Computing Artificial Intelligence Neural Network.
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Papers by Amadeo Arguelles