Papers by Dr. Marco Aurelio Nuño Maganda
CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers, 2012
Since mobile devices were conceived and commercialized, their market has grown exponentially, so ... more Since mobile devices were conceived and commercialized, their market has grown exponentially, so as its problems related to secure data residing in them. Elliptic curve cryptography (ECC) is an approach to public key cryptography (PCK) based on the algebraic structure of elliptic curves over finite fields. It represents the most suitable choice for implementing cryptography in mobile devices since it uses smaller key sizes compared with others traditional public key cryptosystems without decreasing the security level. In this work we present the design of software modules for ECC over .Net Compact Framework (.Net CF) 3.5 well suited for mobile and embedded devices with Windows CE as operating system. The main cores are modules for finite field arithmetic and elliptic curve cryptographic schemes defined over the prime field Zp. These modules are not available neither in the programming language nor the .Net CF. We evaluated the performance of our implementations using the Personal Di...
Electronics, Sep 14, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Health informatics journal, Apr 1, 2024
Digital image segmentation is one of the most important stages in the implementation of an Automa... more Digital image segmentation is one of the most important stages in the implementation of an Automatic Fingerprint Identification System. This work describes a strategy for image segmentation of latent fingerprints using a proper combination of operators achieving better results than other approaches reported in the literature. Latent fingerprint images are low quality images that make more difficult the segmentation process. The proposed segmentation strategy is based on the gradient magnitude of the image and the detection of regions. This strategy was implemented in Matlab and Java, and was tested using fingerprint images of the Fingerprint Verification Competition databases, which are commonly used for these purposes. The results achieved show a significant improvement compared with representative algorithms of literature, such as those based on the variance of image.
Physics Education
Basic science courses, such as physics, are fundamental to precollege studies. For instance, in d... more Basic science courses, such as physics, are fundamental to precollege studies. For instance, in different engineering applications, electricity and magnetism require comprehending subjects such as electrical circuits and power electronics. However, abstract concepts such as electric or magnetic fields are often difficult to explain without visual tools. Computer programs designed for learning physics fundamentals are useful in this area because they allow the visualization, interaction and reinforcement of main concepts in a better way than using only blackboard. In this work, an interactive MATLAB based program to visualize a magnetic field in 3D was presented. The measurements were sampled by a computer-controlled Cartesian robot with a Hall effect sensor. The magnetic field was generated by an energized electromagnet to show the relationship between electricity and magnetism. The designed graphical user interface aims to stimulate interest in electromagnetism in precollege students.
CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers, 2012
Accurate streamflow prediction is a fundamental task for integrated water resources management an... more Accurate streamflow prediction is a fundamental task for integrated water resources management and flood risk mitigation. The purpose of this study is to forecast the water inflow to lake Como, (Italy) using different machine learning algorithms. The forecast is done for different days ranging from one day to three days. These models are evaluated by three statistical measures including Mean Absolute Error, Root Mean Squared Error, and the Nash-Sutcliffe Efficiency Coefficient. The experimental results show that Neural Network performs better for streamflow estimation with MAE and RMSE followed by Support Vector Regression and Random Forest.
2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS), 2014
Detect, classify and keep track, in real-time, on different kinds of objects or vehicles that are... more Detect, classify and keep track, in real-time, on different kinds of objects or vehicles that are moving on a road is crucial for traffic managements systems, among other research areas. In this paper, a vision based system to detect, track, count and classify moving vehicles, on any kind of road, is shown. The data acquisition system consists of a HD-RGB camera placed on the road, while the information processing is performed by clustering and classification algorithms. The system obtained an efficiency score over the 95 percent in test cases, as well, the correct classification of 85 percent of the test objects. Also, the system achieves 30 fps in image processing with a resolution of 1280×720.
Academia Journals, 2019
Resumen-Para que las células de nuestro cuerpo sean capaces de subsistir necesitan de una cantida... more Resumen-Para que las células de nuestro cuerpo sean capaces de subsistir necesitan de una cantidad mínima de energía, la cual obtienen de los alimentos que ingerimos diariamente. El metabolismo basal, es el gasto energético que presenta el cuerpo humano estando en reposo, sin actividad. Su cálculo, nos permite conocer la cantidad de calorías que debe consumir el cuerpo humano diariamente para mantener una buena salud. El presente artículo, muestra la forma de estimar los parámetros necesarios para el cálculo de la tasa metabólica basal de una persona, utilizando para ello técnicas de inteligencia artificial mediante modelos pre-entrenados de Deep Learning y el uso de las API de síntesis de voz de Google, así como también el uso de sensores que permitan su estimación. Conociendo la tasa metabólica basal de una persona, es posible calcular el contenido calórico diario que debe consumir para mantener una buena salud y controlar la obesidad. Palabras clave-tasa metabólica basal, inteligencia artificial, deep learning, sensores, raspberry pi.
IEEE Latin America Transactions, 2018
With the constant increase in the volume of information available on the Web, it is more dificult... more With the constant increase in the volume of information available on the Web, it is more dificult to find the specific information related to a given domain. Users are facing the problem of information overload, in which a query about a specialized subject (local information, e-commerce: hotels, airlines, car rental; science: biology, mathematics, medicine, etc.) on a web search engine, it returns a lot of web pages or results that in most of the cases are outside the domain of interest. This is one reason why the vertical search tools have become a necessity for users that seek specific-domain information from diferent databases available in the Web through input sources called Web Query Interfaces (ICWs). This paper describes an approach for automatic integration of ICWs, a crucial task to construct vertical search tools. The proposed methodology is validated by realizing a vertical search prototype called VSearch that allows users to transparently query multiple web databases in a specific-domain through a unified ICW. The proposed approach for automatic ICWs integration is based on: i) a hierarchical model called AEV for modeling the visual content of ICW; ii) semantic clustering for the identification of relationships between fields in ICWs; and iii) a field homogenization and unification process of AEV schemes for the construction of a unified ICW. The VSearch prototype was implemented and evaluated using a study case. The experimental results demonstrate the high precision in the integration phase and an efective methodology to create a functional vertical search tool for a given domain.
2009 5th Southern Conference on Programmable Logic (SPL), 2009
ABSTRACT Recently, spiking neural networks (SNNs) have obtained the interest of machine learning ... more ABSTRACT Recently, spiking neural networks (SNNs) have obtained the interest of machine learning researchers due to the rich dynamics shown by these information processing models. One of the most important problems that must be addressed for implementing efficient SNNs is the information encoding. In this paper, an implementation of a high-performance hardware architecture for population information coding based on Gaussian receptive fields (GRFs) is proposed. This architecture can be useful for data classifying and clustering applications, because this coding scheme has been used in the past, and an efficient mapping of this technique in hardware can improve the actual performance of these applications. The GRFs information coding can be efficiently implemented on FPGA technology, because it contains several operations that can be computed in parallel like the exponential function. The proposed hardware architecture was implemented, tested and validated with several random datasets. The proposed hardware core is the first step for implementing successfully classifiers like SpikeProp algorithm. Synthesis and timing results for the proposed hardware architecture are presented.
2007 International Conference on Field-Programmable Technology, 2007
In this paper the design of a dedicated high-performance hardware architecture for the SpikeProp ... more In this paper the design of a dedicated high-performance hardware architecture for the SpikeProp algorithm is described. The proposed architecture performs the two main phases in spiking neural networks (SNNs) processing: recall and learning. The proposed architecture is flexible with respect to the number of neurons, the data precision to be used and the number of processing elements. Tradeoffs and
Computational Intelligence and Neuroscience
Brain–computer interfaces are systems capable of mapping brain activity to specific commands, whi... more Brain–computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain–computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing t...
2009 International Joint Conference on Neural Networks, 2009
Spiking Neural Networks (SNNs) have become an important research theme due to new discoveries and... more Spiking Neural Networks (SNNs) have become an important research theme due to new discoveries and advances in neurophysiology, which states that information among neurons is interchanged via pulses or spikes. FPGAs are widely used for implementing high performance digital hardware systems, due to its flexibility and because they are suitable for the implementation of systems with high degree of parallelism.
Scientific Reports, 2022
A practical solution to the problems caused by the water, air, and soil pollution produced by the... more A practical solution to the problems caused by the water, air, and soil pollution produced by the large volumes of waste is recycling. Plastic and glass bottle recycling is a practical solution but sometimes unfeasible in underdeveloped countries. In this paper, we propose a high-performance real-time hardware architecture for bottle classification, that process input image bottles to generate a bottle color as output. The proposed architecture was implemented on a Spartan-6 Field Programmable Gate Array, using a Hardware Description Language. The proposed system was tested for several input resolutions up to 1080 p, but it is flexible enough to support input video resolutions up to 8 K. There is no evidence of a high-performance bottle classification system in the state-of-the-art. The main contribution of this paper is the implementation and integration of a set of dedicated image processing blocks in a high-performance real-time bottle classification system. These hardware module...
Paper published in Applied Sciences, 2020
This paper presents an educational mobile assistant application for type 1 diabetes patients. The... more This paper presents an educational mobile assistant application for type 1 diabetes patients. The proposed application is based on four mathematical models that describe the glucose-insulin-glucagon dynamics using a compartmental model, with additional equations to reproduce aerobic exercise, gastric glucose absorption by the gut, and subcutaneous insulin absorption. The medical assistant was implemented in Java and deployed and validated on several smartphones with Android OS. Multiple daily doses can be simulated to perform intensive insulin therapy. As a result, the proposed application shows the influence of exercise periods, food intakes, and insulin treatments on the glucose concentrations. Four parameter variations are studied, and their corresponding glucose concentration plots are obtained, which show agreement with simulators of the state of the art. The developed application is focused on type-1 diabetes, but this can be extended to consider type-2 diabetes by modifying the current mathematical models.
Research on computing science, 2010
According to Food and Agriculture Organization, Mexico is one of the top five citrus producers in... more According to Food and Agriculture Organization, Mexico is one of the top five citrus producers in the world. In order to achieve the required quality control to export their products, citrus producers require sorting machines able to classify millions of fruits according to certain characteristics, as their size and color. Computer vision provides image processing tools, as image segmentation, that could be used as first stage in a classification process. Fruit classification must be fast in order to be able to process as much fruits per second as possible. In this paper, an FPGA architecture for image segmentation of orange images based on decision-tree models is proposed. A decision-tree model is proposed as an alternative to global thresholding and adaptive thresholding algorithms. It was observed that in this scenario, global thresholding fails due to the noise produced by the fast moving fruits in a classification line, and adaptive thresholding algorithms are not suitable for ...
Mobile Information Systems
Teaching robotics is a challenge in many universities due to the mathematics concepts used in thi... more Teaching robotics is a challenge in many universities due to the mathematics concepts used in this area. In recent years, augmented reality has improved learning in several engineering areas. In this paper, a platform for teaching robotic arm manipulation concepts is presented. The system includes a homemade robotic arm, a control system, and the RAR@pp. The RAR@pp is focused on learning robotic arm manipulation algorithms by the detection of markers in the robotic arm and displaying in real time the values based on the data obtained by the control system. Details on the design of the platform are presented, and the related results are discussed. Experimental data about the usability of the application are also shown.
Sensors
Indoor positioning is a recent technology that has gained interest in industry and academia thank... more Indoor positioning is a recent technology that has gained interest in industry and academia thanks to the promising results of locating objects, people or robots accurately in indoor environments. One of the utilized technologies is based on algorithms that process the Received Signal Strength Indicator (RSSI) in order to infer location information without previous knowledge of the distribution of the Access Points (APs) in the area of interest. This paper presents the design and implementation of an indoor positioning mobile application, which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from proposed indoor scenarios. Then, from the tested classifiers in the early stage, only the top-five classifiers were integrated with the proposed mobile indoor positioning, based on the accuracy obtained for the test scenarios. The proposed indoor application achieves high classification rates, above 89%, for at least 10 different locations in indoor environments, where each location has a minimum separation of 0.5 m.
2015 IEEE 6th Latin American Symposium on Circuits & Systems (LASCAS), 2015
Mobile artifacts such as smartphones have made possible the development of wearable systems for u... more Mobile artifacts such as smartphones have made possible the development of wearable systems for user activity monitoring and recognition due to the synergy of communication, computation and sensing capabilities in battery-powered systems-on-chip. Due to user acceptability, smartphones are able to measure nonintrusively proprioceptive motion outside of a controlled environment for rather long periods of time using embedded inertial sensors. Though work has been done for accelerometer-based activity recognition, the portability of the smartphone to a single fixed tight position has been a major constraint to easy the interpretation of the collected data. In this paper, a human activity hierarchical recognition system based on time-domain features and neural networks without the need of the smartphone to be constrained to a single fixed body position is presented. Experimental results on Android-capable smartphones on four on-body locations show that the recognition system achieves high classification rates, above 92%, for five activities including static, walking, running, and up-down stairs walking, running continuously in near real-time with reduced power consumption.
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Papers by Dr. Marco Aurelio Nuño Maganda