Amadeo Arguelles

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• Computer Sciences, PhD
• Researcher at National Polytechnic Institute

This study explores the effectiveness of an integrated heuristic-machine learning approach in forecasting solar radiation in various Köppen climate zones. Our objective was to refine the model selection process for solar zoning, which involves characterizing solar radiation patterns in various geographic regions. We evaluated 107 heuristic models i...

Purpose Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditio...

This study proposes a pedagogical model for the creation and development of case studies, following a narrative scheme, that will allow the challenges and issues surrounding the megatrends of the 4th Industrial Revolution, specifically with the megatrend "People and the Internet". This proposal will be framed in an online learning environment, it h...

In the fast-growing automotive technology sector, it has become increasingly clear that there is a need for cars with smarter and more interactive systems. This article presents the Intelligent Driving Assistance System (IDAS), an artificial intelligence system that enables the driver to use voice commands to access various features of a car. The p...

We present and approach for monitoring and built a dataset of regional historical air quality data in Mexico City. We design a hybrid air quality network prototype that combines mobile and stationary sensors to collect street-level data on particulate matter (PM2.5 and PM10). The network is composed of mobile monitoring modules, both stationary at...

This paper describes an application of Wireless Sensor Network and Associative Models to monitor and forecast air quality in Smart Cities. The modifications that were made to the Gamma Classifier provide the foundation for this proposal. The improved model proposes a different way to measure similarity between patterns in the training set, reduces...

This paper presents the Ramath mobile application (app) and its second version using a head mounted display (HMD). Both use augmented reality with the support of game play mechanics in a non-game application, oriented to help 12 to 15 years old students (male and female) to learn several math subjects. We associate concepts such as «gamification» a...

Introduction: Wearable assistive devices for the visually impaired whose technology is based on video camera devices represent a challenge in rapid evolution, where one of the main problems is to find computer vision algorithms that can be implemented in low-cost embedded devices. Objectives and Methods: This work presents a Tiny You Only Look Once...

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical va...

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these technologies allow the following of different health conditions, their...

The impact of the air pollution phenomenon has been long studied, but most often with a fragmented approach, without closely looking at the relationship between different components that characterize it, such as sensor-based data, health data from institutional databases, and data on how it is perceived by human beings in social media. The research...

Education in Latin America turned to the use of distance communication strategies in the scenario's face of health, social, and economic consequences resulting from the COVID-19 pandemic. This abrupt change meant reviewing a set of conditions that drastically affected quality teaching and education in the region. This chapter addresses the conditio...

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, assoc...

An intelligent campus has the purpose of improving the quality of life of students, making intensive, global, sustainable and efficient use of information technologies to interconnect all the actors and services for the benefit of the entire community, to establish an intelligent environment of teaching, learning and living [2]. In such smart envir...

Early breast cancer diagnosis is crucial, as it can prevent further complications and save the life of the patient by treating the disease at its most curable stage. In this paper, we propose a new artificial immune system model for associative classification with competitive performance for breast cancer detection. The proposed model has its found...

Cloud computing continues to be an important technology in higher education. This domain is a rapidly evolving space, and continues to gain momentum as a primary infrastructure topology for technological advances across emergent industries. The on-the-cloud paradigm provides numerous affordances and new methods of working in industry, and also for...

Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammo...

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 th...

Cloud Computing is a rapidly evolving field that is triggering a wave of innovations in various domains such as machine learning and artificial intelligence. Cloud skills are becoming essential for any technology-related profession. Furthermore, the accelerated adoption of cloud technologies by industry is increasing the demand for cloud-trained pr...

Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user inte...

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm...

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input control of prosthetic devices has become a hot topic of research. Challenge of classifying this signals relies on the accuracy of the proposed algorithm and the poss...

The current paper contains the theoretical foundation for the off-the-mainstream model known as Alpha-Beta associative memories (\(\alpha \beta \) model). This is an unconventional computation model designed to operate as an associative memory, whose main application is the solution of pattern recognition tasks, particularly for pattern recall and...

This study reports the design and implementation of a pattern recognition algorithm aimed to classify electroencephalographic (EEG) signals based on a class of dynamic neural networks (NN) described by time delay differential equations (TDNN). This kind of NN introduces the signal windowing process used in different pattern classification methods....

In recent years the world has been a witness to a brutal onslaught of emergent technologies. As such it is not surprising that social networking has permeated through practically every human activity with amazing speed. Educational systems have not lagged behind; and not only is that true, but it is also evident that social networks have ostensibly...

This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis...

This paper shows the application of an embedded system with a wireless sensor network to collect atmospheric pollutants data obtained from sensors placed into micro-climates; such dataset provides the information required to test classification algorithms, that helps to develop applications to improve air quality in specific areas.

In order to improve quality of citizen's life, the Smart Cities concept emerge using technological developments such as wireless networks, sensors, actuators, micro-controllers, cloud databases, and the like. As part of this project, concentrations of three air pollutants (CO, NO2 and O3) were collected in a micro-climate environment.

Recently, the study of dynamic systems and signals in the frequency domain motivates the emergence of new tools. In particular, electrophysiological and communications signals in the complex domain can be analyzed but hardly, they can be modeled. This problem promotes an attractive field of researching in system theory. As a consequence, adaptive a...

The study of the influence of the Green Information Technology (GIT), using their potential to reduce the negative impact of the exhaust emission produced by light-duty vehicles, attending verification compliance to circulate in urban areas, is the main topic of the present contribution. Data collected through surveys answered by car owners in Mexi...

The prototype described in this paper intend to be a tool to correlate different ubiquitous computing devices such as vehicle embedded systems and mobile devices, such as smart phones, to deal with air pollution. This paper presents the results acquired from a prototype based on an embedded system platform and a mobile device using Wi-Fi communicat...

En este artículo se presenta un método de memorias asociativas alfa-beta para imágenes (MAABI), que origina la transformada alfa-beta (TAB) para imágenes. Esta transformada se aplica a sub-bloques de una imagen en escala de grises y genera una memoria heteroasociativa alfa-beta en cada sub-bloque, a partir de una matriz de transformación dada con l...

In this paper, a new method of alpha-beta associative memories for images (MAABI) is presented. This method results in the alpha-beta transform (TAB) for images. The alpha-beta transform presented in this paper is applied to sub-blocks of a gray-scale image and generates an alpha-beta heteroassociative memory on each sub-block using a given transfo...

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 functio...

Time-delay systems have been succesfully used to represent complex dynamical systems. Indeed, time-delay is usually encountered as part of many real systems. Among others, biological and chemical plants have been modeled using Time-delay terms with better results than those models that do not consider them. However, getting those models represents...

Dynamic power controlled routing has become a viable alternative to traditional routing protocols in wireless ad hoc networks, particularly with the goal of making a more efficient power management. The existing schemes in this approach tend to converge to a route comprised of a minimum spanning tree between the source and destination nodes. Howeve...

In this paper, it is presented a novel method based on an alpha-beta convolution model, to be used at transformation stage in an image compression system. This method takes the alpha-beta's associa-tive memories theory and is applied to a set of images in grayscale. Since these associative memories are used for data with binary inputs and outputs ,...

Nowadays classification of electroencephalography (EEG) signals have brought new perspectives in the understanding of the brain. Establishing associated characteristics to certain stimulus in EEG is a monumental work due to complexity of the brain responses. For EEG classification several methods have been proposed. Among various statistical method...

Classification is one of the key issues in medical diagnosis. In this paper, a new tool for engineering education is presented: it is an automatic hepatitis diagnosis system based on associative memories. The characteristic of this approach is twofold: first, learning the fundamental set of associations in order to get an associative memory; second...

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 obtaine...

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 ki...

From a little more than 15 years to this day, several methods and techniques taken from the area of Pattern Recognition have been employed on the treatment of data concerning environmental protection. In particular, diverse research groups have applied genetic algorithms and artificial neural networks to the prediction of data related to atmospheri...

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 us...

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 diagno...

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 us...

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.

An associative memory is a system that relates input patterns and output patterns, furthermore is able to recover the output vector associated although the input pattern was contaminated by some kind of noise. Alpha beta associative memories are robust to subtractive and additive noise and are one of the fastest associative memories besides other q...

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 ope...

A novel weightless neural network model is presented, based on the known operations Alpha and Beta, and three original operations proposed. The new model of weightless neural network has been called CAINN – Computing Artificial Intelligent Neural Network. The experimental aspect is presented by applying the CAINN model to several known databases. A...

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...

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 identifie...

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 fa...