Search

En este documento se desarrollan algunos métodos específicos para llevar a cabo un clasificador que sea capaz de identificar manatíes adultos y cría de distintos audios. La duración de las vocalizaciones se evaluó con un método de eliminación de ruido basado en eliminación de silencios, para luego aprovechar esta señal y evaluar uno de los principales parámetros, la frecuencia fundamental la cual se calculó con dos métodos (cepstrum y THD) y de esta manera se compararon los valores obtenidos y se evaluaron en un clasificador de mínima distancia.

With the globalization and proliferation of COVID-19, new challenges have emerged in higher education regarding the use and integration of new technologies. In this context, the University is called upon to organize, distribute and design the best strategies to overcome the challenge of distance education in times of pandemic. This document proposes elements that support the implementation of a Digital Transformation Plan of the Panama Technological University. The digital resource platform developed for teachers and researchers to share with their students, digital resources such as: manuals, tools, videos, Webinars, podcasts and various experiences that contribute to the support of the teaching-learning process is presented. The structure of the platform is presented, as well as an evaluation by registered users. With the globalization and proliferation of COVID-19, new challenges have emerged in higher education regarding the use and integration of new technologies. In this context, the University is called upon to organize, distribute and design the best strategies to overcome the challenge of distance education in times of pandemic. This document proposes elements that support the implementation of a Digital Transformation Plan of the Panama Technological University. The digital resource platform developed for teachers and researchers to share with their students, digital resources such as: manuals, tools, videos, Webinars, podcasts and various experiences that contribute to the support of the teaching-learning process is presented. The structure of the platform is presented, as well as an evaluation by registered users.

In signal processing, ARMA processes are widely used to model short-memory processes. In various applications, comparing or classifying ARMA processes is required. In this paper, our purpose is to provide analytical expressions of the divergence rates of the Kullback-Leibler divergence, the Renyi divergence (RD) of order α and their symmetric versions for two Gaussian ARMA processes, by taking advantage of results such as the Yule-Walker equations and notions such as inverse filtering. The divergence rates can be interpreted as the sum of different quantities: power of one ARMA process filtered by the inverse filter associated with the second ARMA process, cepstrum, etc. Finally, illustrations show that the ranges of values taken by the divergence rates of the RD are sensitive to α, especially when the latter is close to 1.

This chapter focuses on the contributions made in the development of assistive technologies for the navigation of blind and visually impaired (BVI) individuals. A special interest is placed on vision-based systems that make use of image (RGB) and depth (D) information to assist their indoor navigation. Many commercial RGB-D cameras exist on the market, but for many years the Microsoft Kinect has been used as a tool for research in this field. Therefore, first-hand experience and advances on the use of Kinect for the development of an indoor navigation aid system for BVI individuals is presented. Limitations that can be encountered in building such a system are addressed at length. Finally, an overview of novel avenues of research in indoor navigation for BVI individuals such as integration of computer vision algorithms, deep learning for the classification of objects, and recent developments with stereo depth vision are discussed.

Recent advances in applications of Brain-Computer Interfaces (BCIs) for control and communication in more complex systems have demonstrated their potential for the augmentation of human cognition. This paper aims to develop a BrainComputer Interface application capable of monitoring the focus level of a user in real time and produce a binarized output signal based on such electrophysiological activity to show that a real-time, low-cost BCI can be implemented in industrialized settings. This can be achieved by applying a signal processing stage consisting of a DC offset subtraction layer, a bandpass filter bank and a number of thresholds tuned for each individual user. Experiments performed showed that while it is possible to monitor the desired neurological features, a number of undesired artifacts present at all times in brain signals make online analysis especially challenging.

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used as biomarkers for taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both principal component analysis (PCA) and linear discriminant analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens.Author SummaryHard ticks (Ixodidae) are external parasites that feed on the blood of almost every species of terrestrial vertebrate on earth, including humans. Due to a complete dependency on blood, both sexes and even immature stages, are capable of transmitting disease agents to their hosts, causing distress and sometimes death. Despite the public health significance of ixodid ticks, accurate species identification remains problematic. Vector species identification is core to developing effective vector control schemes. Herein, we provide the first report of MALDI identification of several species of field-collected Neotropical tick specimens preserved in ethanol for up to four years. Our methodology shows that identification does not depend on a commercial reference library of lab-reared samples, but with the help of machine learning it can rely on a self-curated reference library. In addition, our approach offers greater accuracy and lower cost per sample than conventional and modern identification approaches such as morphology and molecular barcoding.

This work presents a methodology to automatically detect and identify manatee vocalizations in continuous passive acoustic underwater recordings. Given that vocalizations of each manatee present a slightly different frequency content, it is possible to identify individuals using a non-invasive acoustic approach. The recordings are processed in four stages, including detection, denoising, classification, and manatee counting and identification by vocalization clustering. The main contribution of this work is considering the vocalization spectrogram as an image (i.e., two-dimensional pattern) and representing it in terms of principal component analysis coefficients that feed a clustering approach. A performance study is carried out for each stage of the scheme. The methodology is tested to analyze three years of recordings from two wetlands in Panama to support ongoing efforts to estimate the manatee population.

The internet of things covers a large number of solutions, among which we have the LoRa technology. The following work present theoretical and technical aspects of the implementation of this technology in a wireless network to monitor environmental parameters. The document covers a brief look of the physical layer of the LoRa network, such as frequency bands and type of modulation, as well as details of components and procedures performed in our network, and results of the performance of the network in terms of signal-to-noise ratio (SNR) as a function of distance.

This paper proposes a new method for identifying ARMA models in the presence of additive white noise. The method operates with two main steps. First, the noisy ARMA model is approximated by the sum of an high-order AR model with an additive white noise. The parameters of the high-order AR model as well as the driving noise and the additive noise variances are estimated by using an errors-in-variables approach. Second, the coefficients of the ARMA model are extracted from those of the AR model previously identified by means of existing techniques. In particular, three different methods are considered and compared for solving the second step. The effectiveness of the described identification procedure has been tested by Monte Carlo simulations and compared with a prediction error method.

In this work, an analysis of the principal characteristics, scientific aims and some initial results, of the Iberoamerican R&D network "Ditecrod" (Project of CYTED) is made. This network is based on an international agreement to propitiate an efficient trans-disciplinary cooperation in a scientific area where currently significant research efforts are being made for their high potential impact on future health and quality of life: The non-invasive Early medical diagnosis of Cardiovascular and Diabetic Foot diseases. Both chronic & degenerative pathologies are endemic today in many American countries. The main project objectives are the research, application and diffusion of new non-invasive diagnostic methods, having a low technologic cost and an easy portability, in relation to nowadays disposable approaches. The main results (in the first year of operation) of this multinational network are described. This project also seeks to promote the cooperation working among 11 working teams integrated in six R&D groups with large experience and previous innovations on the subject, and 2 emerging university groups until now focused mainly on the university teaching; in total 13 teams of 8 countries: 5 of Biomedical Engineering, 4 of Biophysics, 2 hospitals and 2 companies. CONACYT-SaludConsejo Nacional de Ciencia y Tecnologia (CONACyT) [2013-I-201590] R&D Spanish National Plan Retos [DPI2017-90147-R] European doctoral project EraNet-EMHE [200022] Bilateral Cooperation Project [CSIC-COOPB20166] Iberoamerican Network [CYTED-DITECROD-218RT0545] info:eu-repo/semantics/publishedVersion

The purpose of this paper is to share our positive experience about the collaboration launched a few years ago between UTP (Panama) and Bordeaux INP (France) in the field of signal processing. This collaboration involves research and education activities. This has led to numerous internships of French students in Panama, mobilities of researchers, common research papers, and the 1st double diploma signed between France and a country of Central America. Thus, this paper presents the various aspects of the collaboration.

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used for proteomic fingerprinting and taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method to complement other well-established techniques for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens., Author summary Hard ticks (Ixodidae) are external parasites that feed on the blood of almost every species of terrestrial vertebrate on earth, including humans. Due to a complete dependency on blood, both sexes and even immature stages, are capable of transmitting disease agents to their hosts, causing distress and sometimes death. Despite the public health significance of ixodid ticks, accurate species identification remains problematic. Vector species identification is core to developing effective vector control schemes. Herein, we provide the first report of MALDI identification of several species of field-collected Neotropical tick specimens preserved in ethanol for up to five years. Our methodology shows that identification does not depend on a commercial reference library of lab-reared samples, but with the help of machine learning it can rely on a self-curated reference library. In addition, our approach offers a complimentary tool with good accuracy and lower cost per sample than conventional and modern identification approaches such as morphology and molecular barcoding, provided that financial resources are available to invest in the acquisition of the MALDI equipment.

We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94%&ndash, 99% on the training and 92%&ndash, 98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.

The determination of water quality in rivers is a very important task for ensuring the well-being of any population. Moreover, It is very laborious task, involving a combination of chemical and microbiological analysis techniques. A low cost alternative to achieve this same goal is the use of biomonitoring or recognition of macroinvertebrate organisms which presence or absence can be used to determine the water quality. Biomonitoring is often used in rural communities that use of natural streams for consumption and daily life. In those cases community members are often the ones obtaining the quality reports. Although it is an efficient method it has the disadvantage that it requires biological training both for the capture, and identification of macroinvertebrate present in water and watersheds. This work focuses on providing a tool to automate the recognition of freshwater macroinvertebrate images based on deep convolutional networks using the Python programming language, and the Tensorflow and Keras libraries.

En este trabajo proponemos la estimacion de frecuencia en sistemas de redes inteligentes en presencia de desbalances de voltaje. Para esta estimacion proponemos la utilizacion de Modelos Multiples Interactivos (IMM) utilizando dos filtros de Kalman extendidos (EKF) operando en paralelo. Los filtros(EKF) son utilizados para modelar dos modos de operacion que hemos considerado para representar el sistema. Uno de los filtros de EKF permite caracterizar un modo de operacion con variaciones minimas o nulas de frecuencia. El segundo filtro EKF caracteriza un modo de operacion en que se dan una cambios de frecuencia mas significativas. Los resultados demuestran que el algoritmo permite detectar el cambio significativo de frecuencia y combinar las salidas de los filtros EKF de manera que tengamos un tiempo de respuesta adecuado a este cambio, es decir, dando mayor ponderacion el filtro correspondiente al segundo modo. En este trabajo comparamos los resultados del enfoque propuesto contrabajos previos utilizando un solo filtro EKF para demostrar sus ventajas en terminos de error de estimacion y tiempo de respuesta en las cambios de frecuencia

En este trabajo presentamos diversos aspectos de la implementacion del primer acuerdo de doble titulacion en ingenieria entre una institucion de educacion superior de Francia y una en Centroamerica. Entre los temas abordados se destacan la definicion de doble titulacion, las diferencias de los sistemas educativos de ambos paises y en general de Europa y America Latina. Ademas de mostrar los beneficios y el impacto esperado de esta iniciativa, se presentan los retos administrativos y legales que se han presentado durante la implementacion de este programa.

The paper deals with the design of Rayleigh fading channel simulators based on the inner-outer factorization. The core of the approach is to approximate the outer spectral factor of the channel power spectral density (PSD) by either finite-order polynomials or rational functions. This, respectively, leads to MA or AR/ARMA models. The parameter estimation operates in two steps: the outer factor, which leads to a minimum-phase filter, is first evaluated inside the unit disk of the z-plane. Then, we propose to compute the Taylor expansion coefficients of the outer factor because they coincide with the model parameters. Unlike other simulation techniques, this has the advantage that the first p parameters remain unchanged when one increases the model order from p to p+1. A comparative study with existing channel simulation approaches points out the relevance of our ARMA model-based method. Moreover, the ARMA model weakens the oscillatory deviations from the theoretical PSD in the case of AR models, or low peaks at the Doppler frequencies for MA models.

This paper shows the results from the computation cost evaluation of three blind source separation algorithms. The algorithms tested were: FastICA, Adaptive Algorithm Based on Natural Gradient, and Adaptive EASI Based on Relative Gradient. The algorithms were chosen for their relative simplicity, and taking into account their hardware implementation feasibility, either on a FPGA or an ASIC, as part of a system for acoustic localization of mobile agents in industrial environments.

En este trabajo se propone una metodologia para el manejo de los servicios parciales en la linea 1 del metro de Panama. Para validar esta propuesta se presentan los resultados una serie de simulaciones que permiten caracterizar el modelo propuesto con esta metodologia a nivel de ingenieria ferroviaria.

Passenger flow modeling and station dwelling time estimation are significant elements for railway mass transit planning, but system operators usually have limited information to model the passenger flow. In this paper, an artificial-intelligence technique known as fuzzy logic is applied for the estimation of the elements of the origin-destination matrix and the dwelling time of stations in a railway transport system. The fuzzy inference engine used in the algorithm is based in the principle of maximum entropy. The approach considers passengers’ preferences to assign a level of congestion in each car of the train in function of the properties of the station platforms. This approach is implemented to estimate the passenger flow and dwelling times of the recently opened Line 1 of the Panama Metro. The dwelling times obtained from the simulation are compared to real measurements to validate the approach., The authors of this paper want to express their gratitude to the National Secretary of Science and Technology (SENACYT) of the Government of the Republic of Panama for funding this study through the R & D project (MDEPRB09-001). Additionally, they want to thank the support received from Technological University of Panama (UTP), the University of Granada, the Fundación Carolina and the Secretaría del Metro de Panamá (SMP).

Resumen– La radio inteligencia es un novedoso enfoque en los sistemas de comunicacion inalambrica que surge para solucionar la saturacion actual del espectro. Existen tres esquemas de acceso para la radio inteligencia: interweave, underlay y overlay. El esquema overlay puede permitir mayores velocidades de transmision y ancho de banda mediante la aplicacion de tecnicas de mitigacion o manejo de interferencias. A traves de estas tecnicas se garantiza la calidad y eficiencia en las transmisiones. Este trabajo presenta un estudio y un analisis de estas tecnicas que podran ser implementadas en los futuros sistemas de comunicaciones inalambricos. Se analizan las caracteristicas de algunas de las tecnicas mas comunes de mitigacion de interferencia en los esquemas overlay para sistemas de radio inteligencia. Los resultados de simulacion que se presentan en terminos de error de bit para sistemas overlay que utilizan tecnicas de precodificacion y filtrado espacial muestran el buen desempeno de estas tecnicas en los sistemas de comunicacion inalambricos.

There is much interest in the application of subspace-based signal processing techniques for wavefield imaging of scatterers from the scattering matrix [1], [2], [3], [4], [5], [6], [7]. These approaches are part of the modern focus on so-called “qualitative” methods in inverse scattering [8]. Most of these methods are valid in the exact scattering regime including multiple scattering in which the inverse scattering problem is nonlinear.