Email: pamela.godoy@sdas-group.com

She is an engineer in Electronics and Communication Networks, holds a Master's Degree in Information Systems Management and Business Intelligence. She has experience in Telecommunications in the area of infrastructure, access and transmissions, voice and data core. She worked as a professor and researcher at the Faculty of Engineering and Applied Sciences, in the careers: Engineering in Electronics and Communication Networks and Engineering in Telecommunications, at Universidad Técnica del Norte, Ibarra - Ecuador. Teaching Experience: Electrical Circuits, Digital Systems, Numerical Methods, Probability and Stochastic Processes, Project Management. In addition, she has business work experience in Information Technology Support and Telecommunications for the main telephone operators in the country. Intern at the University of La Plata - Argentina.

Research Interests

Telecommunications, machine learning, embedded systems, intelligent systems, neural networks

Publications

1. A New Approach to Supervised Data Analysis in Embedded Systems Environments: A case of study
   Science and Information Conferences 2019
   Pamela E. Godoy-Trujillo, Paul D. Rosero-Montalvo, Luis E. Suárez-Zambrano, Diego H. Peluffo- Ordoñez
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   Abstract

   Nowadays, the implementation of embedded systems with sensors for massive data collection has become widely used for their flexibility and improvement in decision making. However, this process can be affected by errors in reading, attrition of systems, among others. For this, a selection approach of supervised algorithms with a prototypes selection criterion is presented, which allows an adequate embedded system performance. To do that a quality measure was established which compromises between the data reduction of the training set, algorithm processing time and the classification performance. As a result, it was determined that the algorithm for the data selection is Condensed Nearest Neighbors (CNN) and the classification algorithm is k-Nearest Neighbour (k-NN).

2. Interactive Visualization Interfaces for Big Data Analysis Using Combination of Dimensionality Reduction Methods: A Brief Review
   Technology, Sustainability and Educational Innovation 2019
   Ana C. Umaquinga-Criollo, Diego H. Peluffo-Ordóñez, Paúl D. Rosero-Montalvo, Pamela E. Godoy-Trujillo, Henry Benítez-Pereira.
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   Abstract

   The Big Data analysis allows to generate knowledge based on mathematical models that surpass human capabilities, and therefore it is necessary to have robust computer systems. In this connection, the dimensionality reduction (DR) allows to perform approximations to make data perceptible in a simple and compact way while also the computational cost is reduced. Additionally, interactive interfaces enable the user to work with algorithms involving complex mathematical and statistical processes typically aimed at providing weighting factors to each RD algorithm to find the best way to represent data at a low dimension. In this study, a bibliographic re-view of the different models of interactive interfaces for the analysis of Big Data using RD is presented, by considering different, existing proposals and approaches on how to display the information. Particularly, those approaches based on mental processes and uses of color along with an intuitive handling are of special interest.

3. Hardware and Software Filter Design for ECG Signal Acquisition and Processing
   International Conference on Applied Technologies
   Paul D. Rosero-Montalvo, Edgar Maya-Olalla, Marcelo Zambrano Vizuete, Pamela Godoy-Trujillo, Ana Checa-Ramirez, Julio Andrade-Collahuazo, Christian Montalvo-Loza.
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   Abstract

   Heart data acquisition is actually common these days to prevent its atypical functioning. However, it presents some issues like noise or the overlapping of spectra of the rest of the human body signals. Consequently, the electrocardiogram (ECG) is the most common way of heart monitoring. However, ECG electronic systems have not a portability criterion. To do so, the present system shows filter hardware and software design to improve ECG data acquisition through signal processing. For the one hand, it presents the sensor design, signal coupling, and band-pass filter Sallen-key structure. For another hand, the system implements a function transfer with Triangular and Gaussian filter stages. As a remarkable result, the output signal is a significant noise reduction.

4. Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques
   Ecuador Technical Chapters Meeting 2018
   Paul D. Rosero-Montalvo, Pamela Godoy-Trujillo, Edison Flores-Bosmediano, Jorge Carrascal-García, Santiago Otero-Potosi, Henry Benitez-Pereira, Diego H. Peluffo-Ordóñez.
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   Abstract

   We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.

5. Elderly Fall Detection Using Data Classification on a Portable Embeddedd System
   Ecuador Technical Chapters Meeting 2017
   P.D. Rosero-Montalvo, D.H. Peluffo-Ordóñez, Pamela Godoy, K. Ponce, E.A. Rosero, C.A. Vásquez, F. Cuzme, S.C Flores, Z. A. Mera
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   Abstract

   The area of research on the detection of falls in the elderly allows to prevent major ailments to a person and not receiving timely medical attention. Although different systems have been proposed for the detection of falls, there are some open problems such as: cost, computational load, precision, portability, among others. This paper presents an alternative approach based on the acquisition of speed variation of the person on the X, Y and Z axes using an accelerometer and machine learning techniques. Since the information acquired by the sensor is very variant, with noise and high volume of data, a prototype selection stage is carried out using confidence intervals and techniques of Leaving-One-Out. Subsequently, automatic detection is performed using the K-nearest neighbors (K-NN) classifier. As a result of fall detection 95% accuracy is achieved in experiments from 5 trials and already used in reality by an older adult, the system has a time of 30 ms for position selection and the detection of drop is maintained in a 92% right.