ETCM 2017 Program Layout (Summer School in Dark Blue)
ETCM 2017 Accepted Papers with Abstracts
Analysis of Uplink Capacity in IEEE 802.16 Transparent and Non-Transparent Modes
Abstract: This paper presents a performance analysis of uplink capacity in a Mobile Multihop Relay (MR), by using Transparent Relay Station (T-RS) and Non-Transparent Relay Station (NT-RS) modes in conformance with IEEE 802.16. The work presents different simulation scenarios for evaluating uplink capacity in MR by varying the position and the number of RS in transparent and non-transparent modes in an urban environment associated with COST 231 model propagation. The results obtained show that IEEE 802.16 has better performance by using RS in non-transparent mode. It was distinguished that the maximum network efficiency reaches a value of up to 90%, and the increment of the RSs in each mode allowed to increase the efficiency of network by at least 4% in transparent mode per each RS, whilst in non-transparent mode for each additional RS the efficiency increases by around 5%.
An Efficient Residential LTE Small Cell Using a “Designated” Wireless Local Loop Band
Abstract: Mobile Network Operators (MNOs) as well as Fixed Network Operators (FNOs) are actively searching for solutions to satisfy the exponentially growing users’ demand for broadband services, such as high definition video content and real time applications, at a reasonable CAPEX investment. At the same time, the MNOs have to deal with the scarcity and expensiveness of the new “licensed” radio bands, and the FNOs have to deal with the high congestion of the current “unlicensed” radio bands such as the Industrial, Scientific and Medical (ISM) radio band (WiFi). However, some of the Wireless Local Loop (WLL) radio bands, such as the DECT band (1880/1900 MHz), due to their “designated” band status, attributed by the competent regulator entities, are much less congested. In this paper, we propose to re-engineer the Customer Premises Equipment (CPE) that the FNOs provide to the end user, so that it continues to offer the traditional short-range Cordless Telephone service to a few DECT handsets but at the same time introduces a new residential Long Term Evolution (LTE) small cell service, operating as an “underlay” application in the same “designated” DECT band, for the broadband hungry devices. We do not modify the WiFi service that is also included in the CPE. We are aware that the deployment of our proposal might require a revision of the “designation” of the DECT band by the Electronic Communications Committee (ECC). The CPE we propose is capable to dynamically adjust the assignment of the radio resource, with a 2 MHz granularity, to the Cordless Telephony service and the new LTE small cell service, as a function of the user’s DECT service needs. Under some conditions the whole DECT band could be used to provide exclusively DECT services, as in the case of the current situation, and under some other conditions, the DECT band could be used to share it between the DECT service that has the highest priority and the new LTE small cell service. We believe that a configuration where the DECT service has the warranted capability to simultaneously offer the Cordless Telephony service to 12 DECT handsets should be suitable in most of the cases. In this situation, only 2 MHz bandwidth are enough to transport the audio conversations with high quality, and therefore, the remaining 18 MHz can be assigned for the residential LTE small cell that could provide user data rates from 45 Mbps to 450 Mbps to the customer applications.
Artificial Neural Networks and Digital Image Processing: An approach for Indirect Weight Measurement
Abstract: A person’s weight is an important variable in determining the correct dosage of medication and anesthetics. However, weight is difficult to measure when people are in a medical emergency; thus, in such situations, weight is only subjectively estimated. We propose an image processing technique for the indirect measurement of the weight of a person in such scenarios. This algorithm computes the area that the subject fills in a normalized image; and a non-linear model relates this value to weight. The perspective of the camera and the noise in the image are also corrected. For the model, both Least Mean Square (LMS) and Artificial Neural Network (ANN) fitting techniques were explored. A mean relative error of 5.8% was obtained for the ANN method under k-Fold cross validation. Lack of complexity allows for the proposed algorithm to be implemented on smartphones, thereby making this technology accessible.
On the Design of Silicon Microring Filters with Wide Free Spectral Range for the O-band
Abstract: An optical add-drop multiplexer (OADM) filter configuration based on a single microring is designed, fabricated and experimentally demonstrated for O-band communications. The influence of the waveguide width is carefully analyzed to design the optimum coupling factor while keeping the minimum ring length. In such a way, the FSR is maximized while insertion losses below 1dB and extinctions ratios higher than 20dB are ensured.
Reliability Model for a Static Var Compensator
Abstract: This paper presents a reliability model of a Static Var Compensator (SVC) using an innovative algorithm based on sequential Monte Carlo simulation and Markov chains. The method employs the equivalent circuit of a SVC and takes the failure rate and repair time of each component as input in order to compute the failure rate and repair time of the whole SVC system. The specific contribution of this investigation is that it presents a mathematical pathway to model operating conditions of a SVC subject to individual operating states of its components, resulting in a comprehensive reliability model.
Comparative study for DC Motor Position Controllers
Abstract: Numerous techniques for controlling Direct Current motors have been developed along the technological evolution. Overshoot Percentage, Torque Load Rejection, Armature Resistance variation, Time Rise, Time Settling and Steady-state error are essential considerations to determine an appropriate controller for DC Motor Position Control. Proportional, Integrative, Derivative Control (PID) and Fuzzy Logic Controller provide suitable responses, however, the responses are not ideal because of considerably slow response time or when Torque Load is increased. Accordingly, with these drawbacks, researchers have implemented a system that merges the PID and Fuzzy Logic Controller: Self-Tuning Controller. This configuration establishes, as a supervisor, the Fuzzy Logic Controller in order to set the PID coefficients using a linguistic idea of the variables’ behaviors. The purpose of this examination is to provide a comparative study between PID, Fuzzy PI and Fuzzy Self-Tuning PID Controllers regarding the parameters mentioned before in order to present the advantages or disadvantages of each.
Deep Learning Object-Recognition in a Design-to-Robotic-Production and -Operation Implementation
Abstract: This paper presents a new instance in a series of discrete proof-of-concept implementations of comprehensively intelligent built-environments based on Design-to-Robotic-Production and -Operation (D2RP&O) principles developed at Delft University of Technology (TUD). With respect to D2RP, the featured implementation presents a customized design-to-production framework informed by optimization strategies based on point clouds. With respect to D2RO, said implementation builds on a previously developed highly heterogeneous, partially meshed, self-healing, and Machine Learning (ML) enabled Wireless Sensor and Actuator Network (WSAN). In this instance, a computer vision mechanism based on open-source Deep Learning (DL) / Convolutional Neural Networks (CNNs) for object-recognition is added to the inherited ecosystem. This mechanism is integrated into the system’s Fall-Detection and -Intervention System in order to enable decentralized detection of three types of events and to instantiate corresponding interventions. The first type pertains to human-centered activities / accidents, where cellular- and internet-based intervention notifications are generated in response. The second pertains to object-centered events that require the physical intervention of an automated robotic agent. Finally, the third pertains to object-centered events that elicit visual / aural notification cues for human feedback. These features, in conjunction with their enabling architectures, are intended as essential components in the on-going development of highly sophisticated alternatives to existing Ambient Intelligence (AmI) solutions.
Performance Metrics for Diversity-Combining Techniques over Nakagami-m Fading
Abstract: In wireless communication systems, antenna diversity is an important technique to tackle deep fading. Diversity techniques mitigate the efects of fading by generating several copies of the signal transferred over M dierent theoretically independent channels. In this paper we review the most signicant metrics used in the performance analysis of diversity
combining techniques over Nakagami-m fading channels. These well-known metrics are Signal-to-Noise Ratio (SNR), Outage Probability (OP), Bit Error Rate (BER), Level Crossing Rate (LRC) and Average Fade Duration (AFD). In addition, we summarize how they are obtained from the first and second-order statistics of the resulting combined signal. The goal of this
paper is to provide an straightforward guide on these metrics to ease the comparison studies among wireless communication systems. Moreover, we provide handy hints and references of the situation of this research area.
Sliding Mode Formation Control of Mobile Robots with Input Delays
Abstract: Group of robots have become important platforms in applications such as search and rescue, active mapping, and load transportation. Maintaining the group formation while tracking prescribed trajectories is critical in this applications. The performance of the formation can be compromised due to time-delays present in the natural response of the internal actuators. Furthermore, the stability of the system can be compromised. In this work, we proposed a cascade control scheme to tackle an input delay in the kinematics of the mobile robots. An inner velocity control loop is designed based on an approximation for the input delay, while an outer loop takes care of maintaining the shape and posture of the formation. We validate our approach by comparing the performance of our control methodology in simulation with an standard PI and a sliding controller under the assumption of different values input delays.
Sliding-Mode Control in a Cascade Scheme for a PMSG based Wind Energy Conversion System
Abstract: In this work, Sliding-Mode Control (SMC) is employed in a cascade scheme in order to perform the maximum power point tracking (MPPT) of a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS). In addition, SMC is used on the DC-AC power converter to achieve a suitable energy conversion and also the integration to a distribution network. Firstly, the WECS scheme is defined with a wind turbine generator (WTG) connected to an uncontrolled AC-DC rectifier, a controlled DC-DC chopper and a controlled DC-AC three-phase voltage source inverter. Then, the employed cascade scheme for each controlled power converter is detailed. SMC is achieved by using space state small-signal average equations of the system. Finally, WECS performance is analyzed on different wind speeds and load profiles. The results are exposed remarking proper dynamic and steady-state responses.
Automatic system for the analysis of flexion angle of the knee using a probabilistic model
Abstract: In human locomotion, the way of walking depends of multiple factors; some of them, alien to the person: treading surface, climatic variables, available space and others. Factors related to morphology also generate difficulties or anomalies in the people movement. This paper analyze the human march and generates a model of human walk based on captured data from people walking using two Kinect cameras. For modeling, we take as reference the points from the hip, knee and ankle; additionally pressure data were captured in two platforms of weight to obtain the pressure exerted in each foot; we calculate the probabilistic model based on the results of 30 persons. To evaluate its efficiency, the model was tested in 100 persons. The results show that 58% of subjects present an apparent normal gait, 21% apparent normal gait with possible anomaly in the left leg, 18% apparent normal gait with possible anomaly in the right leg and 3% apparent abnormal gait.
Anthropometry-based approach for side-mounted desktop chairs design evaluation for university students in Ecuador
Abstract: It is quite common to find side-mounted desktop chairs as main classroom furniture in Ecuadorian universities. However, there is no evidence this furniture is designed to fit the anthropometric characteristics of the students who use it. In this context, this research aims to find the ideal design parameters dimensions for mounted desktop chairs for university students in Ecuador based on relevant anthropometric information and considering the biomechanics of students in sitting position. A sample of 15 mounted desktop chair models used in 10 Ecuadorian universities was evaluated. As a result, every furniture presented mismatch in seat height, desktop height and under desktop height, as well as in the width and depth of the tablet. Students who use this furniture could be at risk to develop health issues because of their daily exposure to poor postures as a result of inadequate furniture design.
An aide Diagnosis System Based on k-means for insulin resistance assesment in eldery people from the Ecuadorian highlands
Abstract: The lack of standardized cut-off values for the surrogate methods to diagnose Insulin resistance (IR) and the fact that the sensitivity of these methods have been studied in specific populations have limited their use in clinical routine.
We developed a system that could aide to diagnosis IR in elderly people, analyzing four surrogate methods of IR estimation using a k-means clustering algorithm. Study subjects included 119 nondiabetic participants over 65 year old from Ecuadorian highlands. Blood tests included a two-point oral glucose test tolerance. The k-means clustering algorithm, was applied in one-dimensional experiments for the Homa\–IR, Quicki, Avignon and Matsuda. The population was divided into three clusters: C-N, with normal values and C-IR with IR and C-L with values in between. The number of individuals classified in each C-IR was very different according to each method. With the cut-off values obtained, for each method, the system for the evaluation of IR in elderly people was developed. Our work is intended to aid physicians in the early detection of IR by using information from diverse methods.
Dynamic Obstacle Avoidance based on Null-Space for Quadcopter’s Formation
Abstract: The aim of this paper is to design, simulate and develop a dynamic obstacle avoidance controller based on Null-Space for the trajectory tracking of a formation of three quadcopters built on their kinematic model, to evade grounded and aerial mobile obstacles. The Null-Space based controller permits to change the priority of the task to achieve, allowing to define rigid or flexible formation depending on the priority task of the controller. The stability analysis of the control system shows that such a system is stable and simulation results validate the proposed control system.
Telerehabilitation Platform for Hip Surgery Recovery
Abstract: The enhancement of ubiquitous and pervasive computing brings new perspectives in terms of medical rehabilitations. In that sense, the present study proposes a Web-based platform to promote the reeducation of patients after hip replacement surgery. This project focuses on two fundamental aspects in the development of a suitable telerehabilitation application, which are: (i) being based on an affordable technology and (ii) providing the patients with a real-time assessment of the correctness of their movements. A comparative test shows that the movement’s evaluation carried out by therapists is consistent with the output of the automatic assessment module. Improvements of the algorithm are discussed, in order to increase the accuracy and depth of the analysis.
Analysis of Heart Rate Variability Parameters for Metabolic Dysfunctions Diagnosis
Abstract: Obesity, metabolic syndrome (MS) and insulin resistance (IR) are diseases related to lifestyle, they have become a social and public health problem. There are numerous diagnostic criteria of MS, the most used is the diagnostic criterion according to NCEP-ATP III and the NCEP-ATP III revised version. Otherwise, obesity and IR are diagnosed through HOMA-IR and body mass index (BMI), respectively. These methods have diagnostic limitations; in HOMA-IR case there can be false negatives in incipient stages of the disease. BMI may show false positives in subjects with a high percentage of muscle mass. In addition to the anthropometric and biochemical variables, other types of parameters have been studied for the diagnosis of obesity, MS and IR; studies reveal that heart rate variability (HRV) parameters can discriminate between diabetic, MS and control subjects. The aim of this research is to propose dimensionless indexes that can be used to diagnose subjects with MS, IR and obesity using HRV parameters (RR, RMSSD, SD, HF and LF). For this purpose, seven dimensionless indexes, designed from the π Vaschy-Buckingham theorem, were assessed using ROC curves and a database of 40 subjects. The index π1, built with the variables: HF and RMSSD; obtained a better performance as classifier of MS, IR and obesity, presenting an area under the ROC curve greater than 0.70, a sensitivity and specificity greater than 0.70 in each pathology. The π1 dimensionless index designed in this study is a simple method that allows diagnosing three pathologies from a non-invasive test such as electrocardiogram.
Reserved, On Demand or Serverless: Model-based simulations for cloud budget planning
Abstract: Cloud computing providers offer a wide variety of pricing models, complicating the client decision, as no single model is the cheapest in all scenarios. In addition, small to medium-sized organizations frequently lack personnel that can navigate the intricacies of each pricing model, and as a result, end up opting for a sub-optimal strategy, leading to overpaying for computing resources or not being able to meet performance goals. In this paper, we: (1) present the results of a study that shows that, in Ecuador, a considerable percentage of companies choose conservative pricing strategies, (2) present a case study that shows that the conservative pricing strategy is suboptimal under certain workloads, and (3) propose a set of models, a tool and a process that can be used by tenants to properly plan and budget their cloud computing costs. Our tool is based on M(t)/M/* queuing theory models and is easy to configure and use. Note that, even though we are motivated by our study of adoption of cloud computing technologies in Ecuador, our tool and process are widely applicable and not restricted to the Ecuadorian context.
On Programming an MP-TCP Analyzer Plugin using OpenDayLight Beryllium as the SDN Controller
Abstract: MP-TCP (MultiPath-TCP) is a protocol that sends data through multiple paths in hosts that have several network interfaces by creating a set of TCP (Transmission Control Protocol) connections. We propose using the principles of SDN (Software Defined Networking) for monitoring MP-TCP traffic in a network. For coding the Analyzer, the key ideas of the proposed solution must first be fully understood and are described along with the programming aspects of the development of a plugin for analyzing MP-TCP messages using OpenDayLight (ODL) Beryllium as the SDN controller. The architecture and services provided by ODL are also described. Services are used trough the available API for developing our plugin. The plugin must generate both proactive and reactive rules that should be installed in the network devices so that MP-TCP messages are sent to the controller for processing and displaying to the network administrator. Results of tests obtained with the Analyzer when using physical and virtual switches in a linear topology are presented.
Morphological Filter to Remove Harmonics of Electrical Signal
Abstract: Currently mathematical morphology is used in the analysis of electric signals due to the clear understanding of its processes and results. The aim of this paper is to describe the use of this method for filtering harmonic signals using four basic operations of mathematical morphology such as: dilatation, erosion, opening and closing, including necessary simulations and analysis to confirm its correct functioning. The presented work is applied to real signals measured in the laboratory of electric machines at Universidad Politécnica Salesiana Cuenca. Results show the effectiveness of the algorithm in removing harmonics from the input signal in order to get a signal as similar as possible to the fundamental component
Learning Image Vegetation Index through a Conditional Generative Adversarial Network
Abstract: This paper proposes a novel approach to generate Normalized Difference Vegetation Index (NDVI) from just a near
infrared (NIR) image. NDVI values are obtained by using images from the visible and infrared spectral bands. The proposed
approach is based on the usage of a Conditional Generative Adversarial Network (CGAN) architecture model. In the first
stage it learns how to generate the NDVI index from the given input image. Three different architectures are evaluated, flat,
siamese and triplet models. In the evaluated models, the final layer of the architecture considers the infrared image to enhance
the details, resulting in a sharp NVDI image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated index cames from the training dataset, rather than the index automatically generated. In the experiments phase the three generative adversarial models were tested with the objective of determining which one generates NDVI values with the greatest similarity to the ones numerically calculated from the usage of visible and infrared images (ground truth). Experimental results with a large set of real images are provided showing that triplet model is the best one that reaches the best performance.
Using D-FACTS in Microgrids for Power Quality Improvement: A Review
Abstract: Microgrids have attracted much attention in recent years due to their ability to integrate distributed energy resources, storage devices, and loads as well as to operate in gridconnected mode or in islanded mode. Microgrids are expected to
provide high quality power with high efficiency, reliability, and security. However, the inherent intermittent nature of the
renewable sources and switching nature of the power electronic interfaces adversely affect the power quality in the microgrid.
This paper presents a review of power quality enhancement techniques commonly used in microgrids. Among these
techniques Distribution Flexible Alternating Current Transmission System (D-FACTS) devices show great promise in
strengthening AC transmission systems stability and control; providing speed and flexibility for several applications. An
extensive overview of the state-of-the-art D-FACTS devices will be presented. As of today, costs and reliability concerns are the main barriers for the integration of these technologies. Wide deployment of these devices will depend on their ability to
overcome their limitations through the maturity of the devices and economies of scale.
A Survey of Battery Energy Storage System (BESS), Applications and Environmental Impacts in Power Systems
Abstract: A brief discussion is presented regarding the current development and applications of Battery Energy Storage Systems (BESS) from the recent achievements in both the academic research and commercial sectors. It is reviewed the architecture of BESS, the applications in grid scale and its benefits of implementing it in power systems. BESS can help to improve the penetration levels of RES (renewable energy resources), and it is listed some of the most relevant application where BESS plays an important role. Also, it is summarized the criteria used to assess the environmental impacts of BESS and how it is compared with conventional units. Finally, some study cases are presented and where BESS have been tested.
Frequency Characteristic of Generator Park of the National Interconnected System of Ecuador
Abstract: This paper presents the frequency characteristic curve of a real hydrothermal system using speed drop of each generator for determinate the speed droop equivalent. The elaboration of the frequency characteristic curve is based on the parameters of each unit. The determination the frequency characteristic curve is building for each individuality speed droop into limits of capacity. The frequency characteristic curve is no lineal due of characteristic technique of generators and variability of demand. Power electric system (SEP) has different dispatch for hydrological cycle of hydroelectric power unit that determine of unit commitment of thermic generator, thought. Speed droop is different since it more dispatch unit. The proposed methodology is implemented in MATLAB® and EXCEL® and its performance is evaluated using the National Interconnected System of Ecuador (S.N.I for its acronym in Spanish) to determine frequency characteristic curve the economic load dispatch of thermal units on weekdays of each period hydrological.
Meteorological Picture Reception System using Software Defined Radio (SDR)
Abstract: In this paper is shown the development of a low cost prototype that allows the visualization of meteorological images though the acquisition and the processing of a signal in Automatic Picture Transmission (APT) format, which is transmitted by National Oceanic and Atmospheric Administration (NOAA) polar orbit satellites. The deployment of the system is based on Software Defined Radio (SDR) technology, and for signal processing has been used the GNU Radio toolkit. The whole system is built with open-source hardware and software.
Optimal dimensioning of multiple PON in regions with sparse users and heterogeneous bitrate demands
Abstract: In this paper we cover the optimal dimensioning of multiple PON deployment in large regions with low users’ density and different bit rate demands by means of an optimization framework called Optimal Topology Search. We compare the costs of deployment of different PON technologies in different bit rate demand scenarios and different cities’ densities in order to find the best technology choice for a specific users’ distribution and bitrate demand.
Integrated long cavity mode locked ring laser with output boost amplifier
Abstract: We report an integrated 30 mm long cavity mode locked ring laser at 1555 nm, with a low repetition rate at 2.7 GHz which acts an Optical Frequency Comb Generator (OFCG). The device uses InP-based active-passive integration technology and an output boost amplifier. Passive (PML) and hybrid mode locked (HML) operation are experimentally demonstrated, with picosecond pulses of 4.65 ps and 4.23 ps pulse-widths respectively. The device exhibits a very narrow RF linewidth of the beat note of few KHz. Also we evaluate the phase noise performance of the fundamental signal generated in both regimes PML and HML.
Optimization Techniques in distribution networks with the presence of DGs
Abstract: This paper presents the advantages of optimization algorithms and logical analysis algorithms for voltage and reactive power control on distribution networks with the presence of Distributed Generators (DGs). The problem is formulated as a multi-objective model that minimizes the variations of the voltage on the pilot bus using Pareto optimization, Pareto and Fuzzy, and Pareto and Fuzzy-PI. The proposed technique is applied to the IEEE 13-node test feeder with variable load and one DG. The results demonstrate the efficiency on the voltage variation when the techniques are combined.
A performance comparison between OFDM and FBMC in PLC applications
Abstract: Power Line Communication (PLC) can be a viable alternative for broadband data transmission, mainly by utilizing the infrastructure of the existing electrical networks, reducing costs in its implementation. However, the power grid is a hostile medium for data transmission, presenting impedance mismatches, noise interference and multipath signal propagation, characterizing the PLC channel model. With the objective of obtaining a better spectral efficiency and increasing the data transmission rate in PLC networks, this work studied the performance of the Filter Bank Multicarrier (FBMC) submitted to the PLC channel model, comparing the results with the Orthogonal Frequency Division Multiplexing (OFDM), which is the modulation used in the physical layer of the IEEE 1901 standard. The results shows that the FBMC is more robust to the PLC channel model, providing an increase in the data transmission rate of up to 25% in relation to OFDM and can be implemented in the physical layer of the IEEE 1901 standard
Analysis of web-based learning systems by data mining
Abstract: This article describes the trend in the use of learning systems that aims to analyze information generated by students. This information is obtained from Learning Management Systems (LMS). The objective is to improve the quality of education and to allow institutions to offer the student a personalized education. With regard to the analysis of information a description of the algorithms associated with data mining is provided. Another aspect considered are the tools that are used to manage data mining algorithms and to present the information required by an educational institution. Data from these systems can be evaluated in such a way as to convert the information collected into useful information to provide an education tailored to the needs of each student. This approach seeks to improve the effectiveness and efficiency of education by recognizing patterns in student performance. This article presents a study of the learning systems implemented in LMS, and the use of data mining techniques for data analysis. This analysis is done specifically in a case study applied to the e-learning platform Moodle. The aim is to provide stakeholders with guidance on the use of information and communication technology (ICT) tools.
Development of a didactic platform for teaching the Einthoven´s Triangle
Abstract: The Einthoven´s Triangle is a fundamental concept in electrophysiology and biomedical instrumentation. However, it is complicated to understand for students, because the concept is very abstract. Therefore, we present the development of a didactic platform for teaching this concept interactively. The developed platform is constituted by the hardware for data acquisition, the software for signal processing and a user graphical interface. Its use let the students identify the different points where electrodes have to be connected to get the signal of the different leads and visualize those signals. Additionally, the graphical interface allows the selection of any two leads for the automatic deduction of the signal of the dipole that represents the heart as a unique source of current.
Operational Framework Proposal for ESPOL University 2.0 Smart Campus Implementation
Abstract: Ecuadorian universities will have in the future an increasingly high demand of research students that necessitates multi-purpose technological infrastructures with high technology equipment. Thus, it is compulsory to rethink the planning and expansion schemes on which the university assets are updated and adopt a novel model of education platform known as UNIVERSITY 2.0. This approach requires the definition of a referential administrative framework to make the transition between current and future processes to be focused on IT solutions, efficiency, optimal track and use of resources, energy departments, etc., all defined as open divisions that could align the campus as an autonomous city. Hence, integrated technological solutions for all careers will conform the operational platform from which entrepreneur initiatives will deploy industry solutions. This paper presents the framework to line up local universities towards future challenges.
Computer Vision for detection of body expressions of children with cerebral palsy
Abstract: This article is the result of an investigation to improve the communication with a case study that suffers Cerebral Palsy through the use of Computer Vision. At present, there is a 15% of the world’s population that suffers some form of disability which disables them from complying with the common activities of any person in a social environment. The technology can help to facilitate the implementation of processes that support people with special needs to improve their lifestyle. For conducting the investigation it was necessary the development of a prototype that detects body expressions using the OpenCV library. The performance of the classifiers is promising.
Plot Features from Vibration Signal for Gearbox Fault Diagnosis
Abstract: This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.
Performance analysis of the effects caused by HPA models on an OFDM signal with high PAPR
Abstract: This paper presents an analysis of the Orthogonal Frequency Division Multiplexing (OFDM) signal with high Peak-to-Average Power Ratio (PAPR) that passes through different high power amplifiers (HPA) models. First, we present the main characteristics of the OFDM signal and the mathematical models of the HPAs. Then, we evaluate the performance of the OFDM signal with several HPA models in terms of the PSD (Power Spectral Density) and BER (Bit Error Rate) for different types of modulation and number of subcarriers. Finally, we show that for an OFDM-based system with N = 64 subcarriers and QSPK modulation, the Rapp Model outperforms the others whereas the Saleh model performs the worst with respect to the ideal HPA.
Towards a Methodologhy to Extract Forensics Information from the Smartphone Sensors: Finding Evidence
Abstract: Currently smartphones have been acquired by a big segment of the population, their capabilities have been increased. Nowadays, it can be found different functionalities and sensors included in these artifacts. Therefore, a lot of studies can be extracted from users which have one of these devices. In this paper we present a new methodology that uses the information obtained from the sensors of smartphones in order to use the generated information as digital evidence. The methodology developed allows the gathering of information by using in conjunction with the accelerometer, gyroscope, GPS, step counter, and the date and time stamp. These data can be used for the detection of motor activity and unusual movements. The study shows that the proposed methodology is a viable option to be used as digital evidence in criminal cases.
Security of Mobile Cloud Computing: A Systematic Mapping Study
Abstract: Cloud Computing has gained more prominence in organizations in the last years. Meanwhile, ubiquity and mobility are two main features that have emerged from the popularization of mobile devices. Therefore, the combination of ubiquitous mobile network and cloud computing generates Mobile Cloud Computing technology. This next-generation technology has led to the development of applications based on mobile cloud computing, which required specific security features because of their specific requirements. This paper presents the results of a systematic mapping done for mobile cloud computing technology. This study breaks down the security sub-characteristic from the ISO / IEC 25010 and we relate them to the information found in our study. Where 83 papers were analyzed and evaluated. The novelties found in this study allows the quantification and detection of security problems that are not being addressed by the researchers and can help as a basis for further research.
UAV Motion Planning and Obstacle Avoidance Based on Adaptive 3D Cell Decomposition: Continuous Space vs Discrete Space
Abstract: One important challenge in the current research of UAVs is the obstacle avoidance feature, a highly demanded capability in all kinds of UAV applications. Different algorithms that perform sampling tasks in continuous or discrete spaces are widely used in path planning. The Probabilistic Roadmap (PRM) and Rapidly Exploring Random Tree (RRT) variants have a stochastic essence, which allows them to obtain a response even in wide and complex environments. However, they present disadvantages related to computational cost and time convergence of results. In this paper a survey on sampling-based planners is presented, comparing algorithms with continuous and discrete sampling in 3D environments. On the other hand, two new variants are introduced: the Exact Cell Decomposition on Probabilistic Roadmap (ECD-PRM), as an extension of PRM method, and the Modified Adaptive Cell Decomposition (MACD) performing a reduced space decomposition. The comparison is performed attending to several criteria such as path cost, number of generated nodes and number of control points in the final path, whenever it has been reached.
Photonic microwave filter based on SBS and balanced detection
Abstract: In this paper, we present a new filter architecture to enhance the out-of-band rejection of SBS-based passband filters with the microwave signal amplitude modulated. The new architecture is based on SBS and balanced detection (between the SBS-filtered signal and the original signal). The out-of-band rejection is compared to conventional SBS-based photonic microwave filters relying on amplitude and phase modulation, reaching an improvement of 17 dB compared to amplitude modulation.
A Control Theory Approach for Managing Cloud Computing Resources: A proof-of-concept on memory partitioning
Abstract: Autonomic cloud services need to adjust the number and partitioning of resources depending on observed workload changes. The goal is to maximize system performance while minimizing costs and meeting service level objectives (SLOs). This problem is well suited for the use of control engineering principles, where the system can be designed as a closed-loop controller. In this position paper, we discuss why control engineering approaches are suitable in this context and illustrate our argument using a motivating problem: dynamic memory partitioning for cloud caches.
Design of a low-cost, portable, and automated cardiopulmonary resuscitation device for emergency scenarios in Ecuador
Abstract: Approximately 90% of patients who suffer out-of-hospital cardiac arrest die. In these cases, when a cardiopulmonary resuscitation (CPR) maneuver is needed, rescuer fatigue is a problem because more than 2 minutes of continuous CPR translates in defective delivery of the resuscitation technique. In rural areas, this problem has two main components: first, lack of access to trained professionals with adequate emergency medical equipment, and second, the long delay to receive immediate medical care. After the 7.8- magnitude earthquake in Ecuador on April 2016, first responders were unable to deliver CPR to hundreds of victims, resulting in preventable deaths. Though automated CPR devices such as the AutoPulse, ROSC-U, and LUCAS-2 exist, the prohibitive cost of such devices (more than $12,000) make them inaccessible for hospitals in Ecuador. Therefore, there is a dire need for a low-cost, portable, automated CPR device to combat lack of access to emergency medical equipment and rescuer fatigue, while keeping patients alive long enough to endure transportation to a medical facility. To address this need, we have designed and manufactured a portable automated CPR device that costs $1290 and weights 4.5 kg. The device can be powered by any car or boat battery, and is equipped with a backup battery. Effective CPR can be administered following the American Heart Association guidelines, using a dual crank mechanism driven by a drill motor to provide at least 5-6 cm of chest displacement at 120 compressions per minute . The device is easy to use, and can be operable on-site with minimal training to emergency personnel.
Comparative analysis of meteorological monitoring using an integrated low-cost environmental unit based on the internet of things (IoT) with an automatic meteorological station (AWS)
Abstract: Meteorological information is important for decision making when related to natural phenomena, research in meteorology and climatology, etc. Considering that the cost of implementing monitoring stations can be high, studies have been carried out using low-cost equipment for meteorological monitoring. However, the information is unreliable because it does not follow a regulated standard and rises the uncertainty of how accurate the measurements are. This article studies the implementation of the Bosch BME280 integrated environmental unit which includes referential meteorological monitoring sensors for the estimation of temperature, relative humidity, and barometric pressure, used in mobile applications within the internet of things (IoT). The measurements produced by the Bosch unit are compared with those delivered by an automatic meteorological station (AWS) to determine the accuracy and reliability of the generated data. The outcomes show that the records present a constant phase shift, which, when determined and corrected, results in a similarity between measurements greater than 95%.
Computer Vision classifier and platform for automatic counting: more than cars
Abstract: Abstract—Data on urban mobility is traditionally obtained by polling or by people observing and reporting the counting. Even though this task is expensive, labor intensive, and prone to subjectivity, the argument supporting this practice is the high cost of electronic devices such as infrared sensors, loop inductors, or piezo-electric sensors. This project proposes a
collective monitoring platform for data collection to overcome the aforementioned issues. Data collection will be through a more sophisticated yet less expensive process. We develop a software that processes existing video streams from security cameras to collect urban mobility data. Our software works not only for cameras from public institutions but also from urban and cycling activists. For privacy reasons, only the transportations counting total are shared. Camera owners do not need to share video flows into our web server. The web server meets OGC standards for information storage and also allows consultation and public Access to gathered data. The existence of geographically distributed and temporally continuous data about the number of cyclists, pedestrians, cars, and buses is expected to reveal the real use of existing infrastructure. The geo-referenced data obtained from our pilot study is available at http://www.tivo.ec:8080/cliente. OpenCV library is used for processing, counting, and generating results in a Raspberry Pi. Results of classification accuracy are not yet available. Nevertheless, accuracy of counting is about 83% and the classification discriminates with success the cars but lack
in sorting of other categories yet.
Current Challenges of Interactive Digital Television
Abstract: This article presents a high level overview of the current challenges that Interactive Digital Television involves
due to the changes in the user’s behaviour during the modern TV watching experience. We present a short review of several
works found in the literature that have addressed specific issues related to improve interactivity experience, moreover, we have
envisioned a system architecture that evidence how secondary screen accompanying devices such as mobile phones or tablets
can be used to facilitate the interactivity process from the technical and operative points of view. This architecture supports
innovative interactive applications for automatic ad recognition utilizing a second screen device as a friendly user interface
A Centralized Control of Movements Using a Collision Avoidance Algorithm for a Swarm of Autonomous Agents
Abstract: This work proposes a method for moving a swarm of autonomous Unmanned Aerial Vehicles to accomplish an specific task. The approach uses a centralized strategy which considers a trajectory calculation and collision avoidance. The solution was implemented in a simulated scenario as well as in a real controlled environment using a swarm of nano drones, together with a setup supported by a motion capture system. The solution was tested while planting seeds in a field composed by a grid of points that represent the places to be sown. Experiments were performed for testing the robustness of the solution as well as the collision avoidance algorithm while increasing the number of agents to perform the task.
Passive Chipless RFID Tag Using Fractals: A Design Based Simulation
Abstract: This paper presents the design and simulation of a passive chipless RFID Tag using fractal shapes. To design the fractal antenna the vicsek fractal was choosen on its different iterations. In order to obtain data regarding its detection, simulations were done covering the frequency range of 1GHz to 20GHz. The simulation will have two stages: the first one, the tag will be illuminated by two horn antennas to obtain the return loss and forward transmission parameters, that will show the medium disturbance generated by the TAG induction. The second stage, is the monostatic Radar Cross Section (RCS) using a probe, located in a certain distance away from the chipless fractal TAG. Finally, the results shows that fractal antenna design after multiple iterations, improves their detection.
Model Predictive Control Tuning Based on Extended Kalman Filter
Abstract: The Model Predictive Control (MPC) is a regulation technique that has had several applications in industry and robotics. Nevertheless, there is not a tuning algorithm that allows MPC to reach control design approaches, because most of the
work on this field is heuristic. This article shows an alternative way to tune a MPC using an Extended Kalman Filter (EKF). The algorithm is implemented in a continuous stirred tank reactor (CSTR) in order to regulate temperature and molar concentration of the reactant.
Applying artificial vision techniques and artificial neural networks to autonomous quadcopter landing
Abstract: Autonomous navigation of drones continues to be a very broad field of research in which different methods have been proposed, using technologies such as geolocation and artificial vision. This work presents a method that combines artificial vision techniques of and artificial neural networks (ANN) to achieve an autonomous landing of a quadcopter based on the references obtained by detecting and locating a helipad near the end point of its programmed path. This method let the system to be independent of the data thrown by the GPS and using a control based on visual characteristics in the last stage of flight instead. Images from a camera mounted on the quadcopter will be used to locate the helipad. Later, two artificial neural networks will operate in cascade to identify the marker and determine its position. This information will allow the drone to locate itself on the helipad and update its landing routine autonomously. Additionally, an optical flow predictor, specifically Lucas Kanade, has been implemented to track the marker as a function of the strong characteristics obtained from the region of interest delivered by the ANN. This process is performed with the aim of reducing the computational cost of the proposed method and improving its execution time. To validate the proposed method, flight tests were carried out in which the landing point was to be located in various types of terrain, achieving 70% of success on highly roughened surfaces and 100% in homogeneous surfaces.
An Evaluation of Cache Management Policies under Workloads with Malicious Requests
Abstract: We study the performance of cache admission and eviction policies under workloads with malicious requests (i.e., requests that try to poison or pollute the cache with useless information). The eviction policies we have chosen for this study are: LRU, SLRU, and SLRU2. We also evaluated W-TinyLFU, a frequency based cache admission policy that seeks to increase the effectiveness of the cache, specially under skewed access distributions. We implemented the policies in a caching simulator. To evaluate the policies, we used both synthetic (Zipfian) and real workloads (replaying real traces from Yahoo, YouTube, and a feature animation company), and modified versions of these workloads that contained malicious accesses. To the best of our knowledge, this is the first systematic study of the performance of caching policies under workloads with malicious requests. Our results show that W-TinyLFU improves the performance of eviction policies such as LRU and SLRU for both real and synthetic workloads including those under the presence of malicious requests.
GreenFarm-DM: A tool for analyzing vegetable crops data from a greenhouse using data mining techniques (First trial)
Abstract: This work shows the use of Big Data and Data Mining techniques on vegetable crops data from a greenhouse by implementing the ﬁrst version of a software tool, so called GreenFarm-DM. Such a tool is aimed at analyzing the factors that inﬂuence the growth of the crops, and determine a predictive model of soil moisture. Within a greenhouse, the variables that affect crop growth are: relative humidity, soil moisture, ambient temperature, and levels of illumination and CO2. These parameters are essential for photosynthesis, i.e. during processes where plants acquire the most nutrients, and therefore, if performing a good control on these parameters, plants might grow healthier and produce better fruits. The process of analysis of such factors in a data mining context requires designing an analysis system and establishing an objective variable to be predicted by the system. In this case, in order to optimize water resource expenditure, soil moisture has been chosen as the target variable. The proposed analysis system is developed in a user interface implemented in Java and NetBeans IDE 8.2, and consists mainly of two stages. One of them is the classiﬁcation through algorithm C4.5 (chosen for the ﬁrst trial), which uses a decision tree based on the data entropy, and allows to visualize the results graphically. The second main stage is the prediction, in which, from the classiﬁcation results obtained in the previous stage, the target variable is predicted from information of a new set of data. In other words, the interface builds a predictive model to determine the behavior of soil moisture.
Development of a testing environment for particle detectors used by Lago Project
Abstract: In this work, we describe the development of a Testing environment, both hardware and software, for the new generation of particle detectors used by the LAGO Collaboration. The test environment consists of two printed circuit boards (PCBs), an interface board to interconnect RedPitaya (SoC used for data acquisition) with a photomultiplier tube (PMT) and environmental sensors, and a biasing board used to interconnect RedPitaya to a Silicon Photomultiplier (SiPM). A Software module in LabView and Matlab was developed to emulate RedPitaya, this SW saves and process data acquired through a digital oscilloscope. We show a first application of this test environment: setting the working point of the PMT and SiPM.
Footprint Analysis Using a Low Cost Photo-Podoscope
Abstract: Footprint analysis is usually performed using a rudimentary process or traditional podoscope. The footprint evaluation is performed without incorporation of the measured data within the patient medical record. In this work, the footprint analysis is performed using a photopodoscope connected to a personal computer where image processing techniques allow accurate estimation of clinical indices, as well as incorporation of demographic and clinical data within patient medical record, that can be readily available through Internet. Validation of the proposed equipment is performed using a group of elderly people and considering as the ground truth the traditional assessment of footprint indices by expert medical staff. Results show errors for the estimation of the Hernandez-Corvo index as low as 5.86% in a group of 18 elderly people.
Telemedicine in Medical Training in Ecuador
Abstract: Telemedicine is becoming increasingly important for medical education in Ecuador. Medical education involves medical practical training supported by Rotating Internship (RI) program at the undergraduate level and the Obligatory Rural Health Service (ORHS) program after obtaining the professional degree. RI and ORHS programs have evidenced the need to develop a direct consultation tool to improve the learning process during the RI stage in hospitals and to access a Continuous Medical Education (CME) for the ORHS program. This article discusses one experience with the use of a Telemedicine Platform (TP) used by undergraduate students and faculty doctors, testing its viability for its future application in medical training during the RI and ORHS programs. An experimental study with 124 students and 6 faculty doctors, from different medical specialties in the context of a telemedicine elective course, was designed. The use and acceptance of the TP were assessed over three 4-month period using auto-generated records of users’ interactions with the platform and questionnaires. The students conducted 262 teleconsultations. The average time to write a student case report was 28.3 ± 15.0 minutes and the average time to write a faculty response was 11.60 ± 5.40 minutes. Faculty doctors highlighted the utility of the TP to transfer medical knowledge and to increase practical training during the last study levels. All surveyed students agreed that the TP was useful for their practical training. The experimental study results showed that the TP is a useful tool to be integrated as a practical teaching methodology and its use may be recommended for medical training in the RI program and to increase access to CME in the ORHS program in Ecuador.
Statistical Characterization of the Finger Tapping Test Using an Android Mobile App
Abstract: In this research, a system to objectively quantify the Finger Tapping Test (FTT) is presented. The main element of this system is the Finger-TApp application, developed in order to record the taps performed by the people evaluated for 10 seconds. The study was performed with 64 healthy participants, aged between 50 and 83 years, men and women. Initially they were analyzed in three age groups, but finding no greater differences between these groups, it was decided to analyze all of them in a single set. With the results of the amount of taps performed by each patient, the mean and standard deviation were calculated; based on these values, the Upper Standard Deviation (USD) and the Lower Standard Deviation (LSD) were calculated. Additionally, it was verified that the distribution of the amount of taps, in the range of ages of the evaluated participants, corresponds to a normal distribution. According to the results obtained, future work is proposed, including improvements in the system and evaluations of people with Parkinson’s disease, in which case the results obtained in the current study will be taken as a normality reference.
T-wave alternans detection and estimation through Cosine Modulated Filter Bank and Modified Media Average
Abstract: T-wave alternans (TWA) is considered as a sudden cardiac death susceptibility index. TWA has been shown to presage lethal ventricular arrhythmias in patients with structural heart disease. TWA appears on the surface of the ECG signal as a fluctuation of ventricular repolarization every two beats. This alternant wave has an amplitude on the microvolts order, being imperceptible the naked eye which makes difficult its detection. In this work, we propose the TWA detection and estimation method through the Cosine Modulated Filter Bank (CMFB) as preprocessing stage to minimize the noise present in ECG signal during routine exercise testing. A TWA detection and estimation by using the Modified Media Average (MMA) method has been established. For evaluation process, the ECG records from Physionet databases were used. The combined CMFB and MMA presents an improvement of 5 dB and 7 dB for probability detection (PD) by using the thresholds of 47 μV and 60 μV respectively than by using only the MMA method. The PD and the mean absolute error (MAE) metrics have been used.
Dynamic obstacle avoidance based on time-variation of a potential field for robots formation
Abstract: This paper presents a new dynamic obstacle avoidance strategy based on time-variation of a potential field, with multiple control objectives. The strategy uses the null space of a Jacobian matrix to achieve the different control objectives in a non-conflicting way while using either a flexible or a rigid formation to avoid static and mobile obstacles. By modifying the priorities of control objectives without changing the controller structure. Overall control system stability is analyzed and proven through Lyapunov theory. Experimental results for a three-robot formation show the performance of the proposed controllers.
Wind Generation Emulator using a DC Machine
Abstract: This work presents the implementation of a system capable of emulate the production curve of a wind turbine using a DC machine. The system consists of an asynchronous machine controlled by a variable frequency drive which receives the speed set point from a computer. This machines was mechanically coupled to a DC generator. A DC / DC power converter was implemented for field weakening control over the DC generator to manipulate the generated voltage. The speed produced by the asynchronous machine emulates the wind resource. For each speed point, a voltage is generated according to the production curve emulated. Finally the generated voltage delivered by the DC machine is regulated to a constant 48V bus with a maximum output power of 105W. This paper describes also the modeling of the DC generator and of the DC / DC power converters.
Mathematical Model of a Planar Four-Link Mechanism for Motion of the Cruciate Ligaments of the Knee Joint; And Validation of the Model Using Video Analysis
Abstract: The mathematical model of four linkages for knee joint requires the joint components to establish a “kind” of a linkage. The linkage was a “cruciate” linkage, besides of the anatomical geometry of cruciate ligaments, the cruciate linkage was established because of the efficiency of motion and force transmission. The joint components make motions of: flexion, extension, abduction, adduction and internal and external rotation. This present paper focused in the flexion and extension motions on the sagittal plane. The Freudenstein’s equation was determined and from that equation was possible obtain the real value of the bond angle and real value of the output angle. Applying the least squares technique to Freudenstein’s equation the lengths of the links were optimized to “n” positions. For the optimization was necessary obtain the smallest error between the desired value of the angle of the coupling link and that which was obtained from experimental data in “The geometry of the knee in the sagittal plane” of J J O’Connor and J W Goodfellow. The standard deviation was 0.6340. Newton Raphson method was used to figure out the non-lineal equations system. Finally, the video analysis technic was used to validate the model that was developed. The percentage errors were between 4 and 16. The mathematical model obtained can be used in PIMI 1504 Project. PIMI 1504 Project is being developed between Escuela Politécnica Nacional and Universidad Politécnica de Valencia.
Optimal-Robust Controller for Furuta Pendulum based on Linear Model
Abstract: An inverted pendulum is an unstable system with highly non-linear dynamic. It is necessary designing controllers that can react appropriately to external disturbances and modelling uncertainties. Therefore, the aim of this article is to
blend a Variable Structure Controller(VSC) with LQR as a sliding surface on the linearized model of the Furuta pendulum (rotary inverted pendulum); the performance of the designed controller is tested by simulation, and the robustness is verified by experimental tests on the inverted pendulum trading platform of National Instruments (ROTPEN).
Network Functions Virtualization: An overview and Open-Source projects
Abstract: Network Functions Virtualization (NFV) has emerging as a networking technology from telecom indrustry to provide agility and flexibility in the deployment of network services and to reduce the Capital Expenditures (CAPEX) and the the Operating Expenses (OPEX) by leveraging virtualization and cloud technologies. NFV decouples the software implementation of network functions from the underlying hardware, and it provides an abstraction of network functions such as: firewalls, deep packet inspectors, load balancers, among others, via software components that can run on general purpose devices that can be located in a variety of telecom infrastructure, including: data centers, network nodes, and end-user facilities. These Virtual Network Functions (VNFs) can easily be created, moved or migrated from one equipment to another without the need to install new specialized hardware, allowing a faster deployment of the services and providing innovation and a great number of opportunities for the world of networked systems. In this paper is provided an overview of NFV, explaining its charatceteristics, enabling technologies, benefits, use cases and challenges, as well as its relationship with another emerging technology as Software Defined Networking (SDN). The architectural framework, several uses case and a list with 179 SND/NFV open-source projects are also provided, at the end it is described the Proof of Concepts (PoCs) and some research lines in this interesting research area.
Analysis of two Control Strategies applied to a Single Phase Active Power Filter
Abstract: This document presents the control system of a single-phase active filter used to mitigate the current harmonic distortion injected into distribution grid by nonlinear loads. The system is based on a cascade control scheme. The internal loop generates the current which is injected into the common point of coupling to eliminate the harmonics produced by non-linear loads. Meanwhile, the external loop regulates the DC voltage of the capacitor that feeds the active filter. In this work, a comparative analysis between two control techniques Proportional – Integral (PI) controller and Sliding Mode Controller (SMC) is carried out. The results for both PI and SMC controller are presented and discussed.
Mammogram Classification using Back-Propagation Neural Networks and Texture Feature Descriptors
Abstract: Breast cancer has an important incidence in women worldwide. Early diagnosis of this illness plays a key role in decreasing its mortality and improves its prognosis. Currently, mammography is considered as the standard examination for detection of breast cancer. However, the identification of breast abnormalities and the classification of masses on mammographic images are not trivial tasks for dense breasts, and is a challenge for artificial intelligence and pattern recognition. This work presents preliminary results of automatic classification of mammographies by texture characterization based mainly on the Haralick’s descriptors. We implement an artificial neural network (ANN) for classification in three classes: normal, benign and cancer using leave one out technique. The set of images for training and testing the ANN, are taken from the Digital Database for Screening Mammography (DDSM). Results show that the percentage of correct classification occurs in average for 84.72% of the data set.
Underlay and Overlay Networks: The approach to solve addressing and segmentation problems in the new networking era
Abstract: This work was developed in order to analyze the structure and operation of Virtual Extensible LAN technology (VXLAN). A simulation/emulation in a virtualized environment will demonstrate the advantages about VXLAN; and based on the results, this investigation could be used as a proposal for the administration and provisioning of Service Providers and Data Center (DC) clients in the future.
PRECISE WEED AND MAIZE CLASSIFICATION THROUGH CONVOLUTIONAL NEURAL NETWORKS
Abstract: Deep Learning is playing an important role in big data processing for more accurate modeling of common productive processes. It is being widely used in artificial vision applications and specifically in pattern recognition. The versatility of deep learning has positioned it as a fit tool used in many fields of application, among which is precision agriculture. This paper presents the development of an algorithm capable of image segmentation and classification. Segmentation is intended to separate the target plant from the original image, while classification is meant to identify what images belong to the two defined classes. It applies a convolutional neural network (CNN) to discriminate maize plants from weeds in real time, at early crop development stages. It was applied to maize crop because it is a common staple crop in the Ecuadorian Highlands. The convolutional neural network has been trained with a dataset generated in the segmentation stage. The performance of the network was analyzed with LeNET, AlexNet, cNET and sNET network architectures. The network architecture that presented the best training results was cNET based on its performance in terms of accuracy (96.4\%) and processing time of 161 miliseconds in a Raspberry Pi CPU. The minimum working filter number for this network architecture was 16.The best performing algorithms and processors have a significant potential for autonomous weed and crop classification systems in a real-time application.
Audio Fingerprint Parametrization for Multimedia Advertising Identification
Abstract: Audio Fingerprint is a technique that is being constantly developed and improved for multimedia recognition. Many audio retrieval platforms such as Shazam, Phillips and ACRCloud have achieved success and good results. However, their algorithms cannot be considered like a general method, since the implementation of one of them depends on the target application, which involves a specific parameterization. Besides, the actual variability of audio content on different media is a problem since there is not a general technique capable of recognizing all spectrum of sounds, such as speech, music and noise. Nowadays advertisement monitoring is a field which has a great interest, not only by the amount of marketing information, but also for control agencies. Therefore a well parametrized algorithm oriented to this type of applications is needed. This
article follows step by step a general framework for fingerprint extracting in order to develop a system for advertisements monitoring. The parameterization process uses some spatial and spectral characteristics measured over 600 advertisements that contain various types of sounds. Key factors such as accuracy, process time and granularity are analyzed together in order to enhance the system performance. At the end, the algorithm shows an accuracy of 99% using three seconds of granularity samples, and also the best compromise between time process and performance is achieved. This study suggests a set of parameterization steps which could be successfully implemented in other related audio applications.
Short-Term Active Power Forecasting of a Photovoltaic Power Plant using an Artificial Neural Network
Abstract: The increasing use of solar energy as a renewable source for electricity generation through photovoltaic power plants has led the interest of their power production forecasts tending to facilitate the management and optimization of this valuable renewable resource. This paper presents a short-term active power forecasting model based on an artificial neural network (ANN). The data used correspond to time series of meteorological variables, such as wind speed, radiation, relative humidity, among others, and electrical, such as power. The raw data are preprocessed to account for missing values and outliers, and the design of the artificial neural network considers variable selection to utilize the best input variables to the model, and a suitable number of layers, number of neurons, learning algorithm and transfer function.
Thesaurus-based Named Entity Recognition System for detecting spatio-temporal crime events in Spanish language from Twitter
Abstract: Social networks offer an invaluable amount of data from which useful information can be obtained on the major
issues in society, among which crime stands out. Research about information extraction of criminal events in Social Networks has been done primarily in English language, while in Spanish, the problem has not been addressed. This paper propose a system for extracting spatio-temporally tagged tweets about crime events in Spanish language. In order to do so, it uses a thesaurus of criminality terms and a NER (named entity recognition) system to process the tweets and extract the relevant information. The NER system is based on the implementation OSU Twitter NLP Tools, which has been enhanced for Spanish language. Our results indicate an improved performance in relation to the most relevant tools such as Standford NER and OSU Twitter NLP Tools, achieving 80.95% accuracy, 59.65% completeness and 68.69% F-measure. The end result shows the crime information broken down by place, date and crime committed through a webservice.
Improvement of Knee Prosthesis Mechanism through Experimental Effort Analysis and Finite Element Method
Abstract: Abstract-In this work the improvement of a knee mechanism prosthesis is made, which is oversized and has an extreme weight in relation to the load for it was designed. This additional weight causes discomfort and increase in the consumption of metabolic energy of prosthesis users. To improve the mechanism, an authentication of finite element model of the original device through non-destructive experimental essays to the prototype. Loads were applied ranging from the weight of a 65 kg person to the maximum load of the main structural test of ISO 10328. Strain gages were installed and connected to a system acquisition, filter and amplification signals, designed and constructed for this purpose, with which the device deformations were measured. Then, the model was simulated with experimental loads and a maximum difference was obtained between the experimental and numerical values of 12.43%, which is considered that the model was authorized. Subsequently, geometric modifications are made and through simulations get that the improved model has a better distribution of the safety factor, decrease of safety factors up to 97% and weight of 43%, maintaining the right structural integrity.
TV Program Recommender using User Authentication on Middleware Ginga
Abstract: The system proposed in this article aims to identify and recognize television users with the objective of offering personalized television programming. In this setting, the authentication and recommendation mechanisms used require to collect the necessary information in an implicit manner as much as possible, such that the leisure and entertainment objectives this broadcasting medium brings are not interrupted. The design proposed for the implementation of the interactive application uses an authentication process based on facial recognition and a recommendation algorithm based on contextual information, which is mainly implicitly captured. Experimental obtained results show that the system offers more accurate recommendations when the user follows a visualization pattern
An experimental comparative analisys among different classifiers applied to identify hand movements based on sEMG
Abstract: This paper presents a comparative analysis among different methods of classifiers such as: Feedforward Neural Networks (FFN), Support Vector Machines (SVM), Naïve Bayes Classifier (NBC) and Linear Discriminant Analysis (LDA) applied for pattern recognition on electromyography signals (EMG) of forearm muscles to identify hand’s movements. Movements to be recognized are: closed hand, open hand, hand flexed inwards, hand flexed out and relax position. Signals are obtained from a “Myo Armband” device that has 8 dry sensors, from which a set of features is extracted: Mean Absolute Value (MAV), Root Mean Square (RMS), Variance (VAR) and Standard Deviation (STD). The procedure consists of two stages, first, training and validation, and second, focused on testing the performance of classifiers with test subjects. Finally, the best classification method is presented through the experimental accuracy measurement.
Towards an Indoor Navigation System using Bluetooth Low Energy Beacons
Abstract: Indoor navigation, the ability to find a path to an office, a conference room, or the exit in an unfamiliar building, is one of the most well-known applications of indoor positioning technology. This technology remains relatively underdeveloped due to the inherent difficulty of geolocalization in an indoor environment. Currently, we are working in an indoor navigation system based on Bluetooth Low Energy (BLE) Beacons fingerprinting and we created a testbed deployment of 30 beacons in the Center for Information Technology of ESPOL University. This paper presents a comparison study of three machine learning techniques used to improve BLE fingerprinting accuracy within our testbed. Preliminary results show that Random Forest, 30\% more accurate than Naive Bayes, is able to correctly estimate the location of multiple users with room-level accuracy 91\% of the time.
Finding a Dynamical Model of a Social Norm Physical Activity Intervention
Abstract: Low levels of physical activity in sedentary individuals constitute a major concern in public health. Physical activity interventions can be designed relying on mobile technologies such as smartphones. The purpose of this work is to find a dynamical model of a social norm physical activity intervention relying on Social Cognitive Theory, and using a data set obtained from a previous experiment. The model will serve as a framework for the design of future optimized interventions. To obtain model parameters, two strategies are developed: first, an algorithm is proposed that randomly varies the values of each model parameter around initial guesses. The second approach utilizes traditional system identification concepts to obtain model parameters relying on semi-physical identification routines. For both cases the obtained model is assessed through the computation of percentage fits to a validation data set, and by the development of a correlation analysis.
Processes with variable dead time: Comparison of hybrid control schemes based on internal model
Abstract: The objective of this work consist of combinig two different control strategies. The IMC structure is used firstly with SMC and then with a Linear Quadratic Regulator (LQR) in order to obtain two controllers. The IMC has an ideal structure to design controllers for facing processes with time delay. The combined control structures are applied over a nonlinear system having variable delay. Finally, a feedforward controller is joined to each previous controller for improving their control performance.
Electronic Hybrid Power System for Charging a Lithium-Polymer Battery
Abstract: This project implements a charger for a 20 Ah lithium polymer batteries, using two different sources of energy: the grid and photovoltaic energy. The charge is switched so that, when the grid feeds the battery, the photovoltaic panel (PV) do not and similarly, when the PV feeds the battery, the grid do not. In this project, three algorithms for tracking the maximum power point (MPP) for a Photovoltaic Array are analyzed and simulated, and one of them is implemented. The power converters are modeled and simulated in order to analyze the general behavior of the system and to design the controllers that allow to regulate properly the involved variables.
Dual Band Rectangular Patch Antenna with Less Return Loss for WiMAX and WBAN Aplications
Abstract: Abstract— This paper presents the design, simulation and construction of Dual Band Rectangular Patch Antenna with coaxial feed for WiMAX (3.5-3.7GHz) and WBAN (3.9-4.1GHz) applications. The two frequency bands were obtained by a pair of parallel slots in a copper patch and a rectangular ground plane separated by an FR-4 substrate with a dielectric constant of 4.2 and a thickness of 1.6 mm. The design is analyzed with the HFSS simulator software. In addition, two antennas of similar characteristics have been fabricated and tested with the PXI equipment in the laboratory. The antennas otained a RLR (Return Loss Ratio) lower than -20 dB and the observed maximum bandwidth is 190 MHz for WiMAX (Worldwide Interoperability for Microwave Access) and 220 MHz for WBAN (Wireless Body Area Network). Also, this document shows the result of the efficiency measurement for both antennas.
Three-phase recloser time delays determination in 138 kV and 46 kV lines of the Empresa Eléctrica Quito
Abstract: The present study is performed with the objective of applying an adequate methodology to determine the operating time delays of three-phase breakers, to attain automatic high-speed reclosing in radial and ring lines at the levels of 46 kV and 138 kV of the Empresa Eléctrica Quito (EEQ). This helps maintaining continuity service with acceptable levels of quality after a transient fault has occurred. For this, an analysis is made on the electrical system of the EEQ with emphasis on the voltage levels mentioned above, to determine which lines have been affected due to transient, either radial or ring lines that are important within the electrical system and a reconnection is possible. By analyzing the characteristics of the breakers associated with the lines, both the breaker sequence of operation given in the nameplate and the time established between the opening and closing of the breaker contacts after the occurrence of one failure, coupled with the arc deionization time for each voltage level, allows determining the three-phase reclosers time delay of the lines. Finally, based on the results obtained from simulations, the appropriate reconnection times were determined in the 46 kV and 138 kV lines of the EEQ that ensure continuity and quality of electric service.
A Fuzzy Logic Model to Estimate Safe Driving Behavior Based on Traffic Violation
Abstract: Reports from international organizations estimate that death by traffic accidents will increase over the next two decades. One of the main causes of traffic accidents is the traffic violation to road safety norms. Thus, we propose a method to automatically estimate driver’s traffic violation from the vehicle’s data and traffic signs information. Fifteen drivers traveled during 25 min. approximately in a selected route, with 24 recognized traffic signs. From these data, human expertise and the Organic Integral Criminal Code (COIP) information, we create a fuzzy logic model to estimate the traffic violation of drivers. To validate our model, we count all contraventions associated with the penalty it would cause to the driver license’s points, in the same way, a driver would be issued a traffic ticket. Results show a high correlation between the estimation of the traffic violation and contravention points. This methodology serves as a baseline for real-time estimation of traffic violations. Further work will validate this methodology with more drivers to generalize these results.
Wireless Sensor Network Scenario Proposal for Evaluating SINR in Dense Urban Areas
Abstract: Currently wireless technologies continue their huge development, especially in the field of wireless sensor networks (WSNs) due to their multiple benefits and large number of applications. WSNs share characteristics such as: tiny size, low power, low data rate, routing flexibility and low cost; some of the applications involve the medical field and the Internet of Things which gives WSNs an ubiquitous presence conveying to an unprecedented increase in the number of WSNs in the 2.4Ghz band, which in turn leads to dense wireless scenarios and severe interference problems, that deteriorate quality of service (QoS). For an adequate analysis of WSNs under APs interferences in dense urban areas, a scenario proposal is made, and several signal to interference noise ratio (SINR) tests are performed reaching some interesting conclusions that will be fundamental for this and future work.
Rain Rate Estimation using a Microwave Link in Guayaquil City
Abstract: This paper addresses a variation of the ITU-R P.838-3 rain attenuation estimation method to obtain an estimation of the rain rate based on the actual observed attenuation of a microwave radio link. The radio link is 18 kilometers long and uses the 5 GHz U-NII unlicensed band for this purpose. The experiment takes place in the tropical city of Guayaquil, Ecuador. The results are later evaluated by comparing them with the actual average rain rate given by four rain gauges installed along the propagation path. In addition, this research adapts the Synthetic Storm Technique for the use of multiple rain gauges in a region with a considerable level of rainfall spatial diversity to obtain preliminary calculations of radio link attenuation due to rain
Transpiler-Based Architecture for Generating Multi-platform Web Applications
Accuracy of Connected Confidence Left Ventricle Segmentation in 3-D Multi-Slice Computerized Tomography Images
Abstract: Cardiovascular diseases are the main cause of death in the World. This fact has motivated different actions for prevention, diagnosis and monitoring of cardiovascular diseases. In this work, the accuracy of a connected confidence left ventricle segmentation method is performed. This task is accomplished using a software platform for left ventricle segmentation of 3-D cardiac Multi-Slice Computerized Tomography (MSCT) images that is also described. The software platform has as a goal performing research about efficient methods for cardiac image segmentation and quantification. The accuracy assessment of the segmentation method is performed by comparing the estimated segmentation with respect to segmentations manually traced by cardiologists. Results show that the segmentation method provides Dice Similarity coefficients higher than 0.90 with low computational cost. The obtained segmentation is able to include within the left ventricular lumen the papillary trabeculae muscles, enabling further accurate estimation of the left ventricular mass.
Optimum Design and Dimensioning Model of a Mesh-Wi-fi Network for emergency services in protected areas
Abstract: In the design of most of the networks, the deployment cost and the parameters of coverage, interference and capacity are important considerations that must to be taken in count at the moment of dimensioning a network. In this paper, we propose an optimum design and dimensioning model integrating a Wifi-Mesh Network, satisfying the parameters mentioned above to provide emergency services, communication services, and monitoring of environmental conditions in protected areas, where there are no telecommunication services, deploying Wi-Fi Access Points for the access of common users as phones, tablets or computers, and in the core of the network we use Mesh Nodes to communicate the access points between them and to routing the information. Via a case study, we show the comparison between a network with no dimensioning and a network with the proposed design and dimensioning. The proposed optimization model applies linear programming (LP) to solve the different problems of dimensioning.
4X4 MIMO BASED ON QSTBC CODES WIHT ALAMOUTI CODIFICATION AND FEC
Abstract: This paper presents the implementation of a 4×4 MIMO system, using universal software-defined radio peripherals, with Alamouti and QSTBC codes. We also employed FEC and M-QAM modulation in order to increase the transmission reliability at high bit rates. Results show that there’s a strong signal stability when employing this transmission system given that the SNR of the 4-QAM modulation is 24 dB with a BER of 8×10-4. We further improved the transmission results applying a BCH codification.
Design, simulation and analysis of a positive displacement pump controller for biomedical applications requiring pulsatile flow
Abstract: The increased interest in applications of positive displacement pumps in biomedical instrumentation and research demands a challenge in the manufacturing of systems with particular accuracy and reliability. A proof-of-concept control system to drive the electrical actuator of a positive displacement pump is presented. The design of this controller incorporates the Dirac comb function as a generator of the clock/step to command the driver. The implementation is a microcontroller-based stepper motor approach, which consists of a microcontroller, driver, bipolar stepper motor and a basic model of positive displacement pump. The control model is designed and tested for different types of movements as a function of a theoretical torque generated in the pump shaft. The capabilities of this system to produce controllable pulsatile flow movement of the stepper motor operating at different frequencies are demonstrated. At low frequencies, the level of vibration is raised, on the contrary, frequencies greater than 400 Hz produce smooth rotation of the pumping system. Finally, the influence of the pulsatile flow generated for this kind of pump is evaluated in a biomedical example.
Characterizing discussions in Spanish Wikipedia Talk Pages
Abstract: The number of articles in Wikipedia is growing each day; at any moment their content can be edited or discussed in the related talk pages. In this paper, we propose an annotation schema for Wikipedia talk pages in order to determine the type of opinions expressed in them. We apply the annotation schema to a corpus that includes a collection of discussions about 148 topics drawn from 25 Spanish Wikipedia talk pages and make the resulting dataset publicly available for download. Furthermore, we train and evaluate supervised machine learning models to automatically identify the annotation labels, and we achieve an accuracy $F_1=0.71$ in our experiments.
A Practical Approach for NLOS Mitigation in Location-aware Networks
Abstract: This paper introduces a practical two-stage approach to achieve the mitigation of the Non Line Of Sight condition by using space-time diversity in timing-based mobile subscribers positioning systems. The first stage performs a coarse detection of the first (Line Of Sight) arrival, certainly attenuated due to the propagation channel, by performing a serial search over an observation window. Several GLRT and LMPT detectors have been evaluated in order to achieve that the first arrival is not missed. Once the possibly LOS component has been properly detected, a high resolution timing estimation is provided as the second stage. Two methods have been considered for this work: Minimum Variance (MV) and Normalized Minimum Variance (NMV) estimation. A suitable channel model has been provided to evaluate both approaches in terms of SNR and delay spread. These two methods have been compared for different configurations, and their operational characteristics have been built. Finally, these timing-error characteristics have been provided to a link-level simulation platform to obtain their accuracy figures within a Weighted Linear Least Squares (WLLS) positioning system.
EEG signal clustering for motor and imaginary motor tasks on hands and feet
Abstract: Modern technologies uses Brain Computer Interfaces (BCI) to control devices or prosthesis for people with physical impairments. In some cases, EEG data are used to determine the intentionality of the subject when performing motor and imaginary motor tasks. However, EEG signals are very susceptible to noise due to the lower voltage levels that are acquired. We used a data set of 64 EEG recordings of 25 subjects while they were doing motor and imaginary motor movements of hands and feets. Data were preprocessing, including the design of a filter for noise reduction outside the expected frequency spectral that operate the EEG signals. Then, we used features extraction based on spectral density. Finally, the application of five clustering algorithms to detect motor and imaginary motor tasks. Results showed that the k-means, k-medoids and hierarchical clustering algorithms were better in detecting motor activity, and hierarchical clustering for imaginary tasks of hands. Finally, the results show that k-means, k-medoids and Hierarchical clustering algorithms have a better performance detecting motor activity of both hands, but the spectral clustering algorithm has a better performance in the detection of motor tasks of both feet.
Capture and processing of geospatial data with laser scanner system for 3D modeling and virtual reality of Amazonian Caves
Abstract: In recent years there has been an advance in new geotechnologies that contribute to the research in 3D modeling, observing a boom in applications such as Lidar and laser scanning. With the use of these technologies, capable of capturing or obtaining a very close copy of the object of interest, the exploration of subterranean cave environments becomes possible. Due to this process, original information has been possible to be kept in digital files, as there may be deterioration and loss due to improper exploitation or lack of knowledge of the importance of the existing scientific value. In this sense, the goal has been to obtain a three – dimensional model of the Elviandi Cave, which is located in the Province of Napo, Ecuador, with the support of a laser scanner, called the Faro Focus 3D. This has allowed us to rescue the historical value of formations that date back thousands of years, such as stalagmites, stalactites and speleothems. The Mesozoic deposits of the Elviandi Cave has speleologic formations of the Quaternary, with an irregular extension of approximately 450 meters. Consequently, the scan has been meticulous, at intervals of 19 minutes and 19 seconds, taking into account parameters of quality and resolution. Data processing (point cloud) has been performed in the Software Scene, which allowed to join the different scenes in a single cluster. Later the product has been debugged, in order to eliminate (potentially artificial) information that does not belong to the cave. As a main result, we obtained a three-dimensional model of the Elviandi Cave and a visualization in a virtual environment of itself. We concluded that the selected parameters (quality of 6x and resolution of 1/5, dimensionless parameters) produced a balance between manageable size and density of the point cloud.
Improvements in failure detection of DAMADICS control valve using neural networks
Abstract: The present article focuses on the detection and isolation of faults in the DAMADICS valve. For this purpose, the mathematical model of the valve was identified as an ARX type and of the first order, then the blocking; sedimentation and erosion failures were selected valve to perform the experimentation. In order to identify and isolate faults on the valve, a fault detector was designed by parametric estimation of the model, which allowed to determine the values of the parameters “a” and “b”, and their behavior against the failures, giving as a result that they affect the gain from the valve; This detector presents problems in the thresholds of each failure. Then, a radial-based artificial neural network was added as a complement to the fault detector by parameter identification, which allowed correcting the error in the thresholds. As a result, the detector that includes the neural network showed better performance in detecting and isolating faults.
Non-supervised Clasification of Volcanic-Seismic Events for Tungurahua-Ecuador Volcano
Abstract: In this work the state of activity/unrest of Tungurahua volcano (Ecuador) during 2014 was examined through analysis of different seismic events recorded on a permanent geophysical station from Instituto Geof\’isico EPN located at the volcano. In standard volcanic monitoring procedures there exists a classification for seismic events performed in a supervised manner (a human being assigns a class to each event based on perception and some fixed criteria). However, even if this classification yields some information on the possible ongoing volcanic processes inside a volcano, it is not determinant when used as a method to predict an actual volcanic eruption. Therefore, in this work an unsupervised seismic signal classification is proposed so that possible new classes of events are found which could indicate the beginning of eruptive phases or that allow a better description of the volcano’s state. The analysis itself was performed using some unsupervised classification techniques( k-means, archetypal analysis and self-organizing maps) on “discrete”\ seismic signals, signals that lasted less than 2 minutes. To achieve the classification, the Fourier transform was applied to each of the signals so that they were distinguished in regard of their spectral content.
Real-Time Hand Gesture Recognition Using the Myo Armband and Muscle Activity Detection
Abstract: Hand gesture recognition consists of identifying the class and the instant of occurrence of a given movement of the hand. The solutions to this problem have many applications in science and technology. In this paper, we propose a model for hand gesture recognition in real time. This model takes as input the surface electromyography (EMG) measured on the muscles of the forearm by the Myo armband. For any user, the proposed model can learn to recognize any gesture of the hand through a training process. As part of this process a user needs to record 5 times, during 2 s each, the EMG on his forearm, close to the elbow, while performing the gesture to recognize. The k-nearest neighbor and the dynamic time warping algorithms are used for classifying the EMGs seen through a window. As part of the proposed model, we also include a detector of muscle activity that speeds the time of processing up and improves the accuracy of the recognition. We tested the proposed model at recognizing the 5 gestures defined by the proprietary recognition system of the Myo armband, achieving an accuracy of 89.5%. Finally, we also demonstrated that the model proposed in this work outperforms other systems, including the recognition system of the Myo.
Automated Capture of Paper-Based Evaluations to Provide Early Feedback to Students
Abstract: Current Learning Management Systems (LMS) are able to use the data automatically captured from the actions of their users to provide immediate feedback to students and to provide a rich dataset to be mined or analyzed to understand and optimize the learning process. However, in traditional education, not all, or even the majority, of learning products are created or processed through the LMS. Traditional education still uses paper-based assignments and assessments as an integral part of the process. In these cases, the data contained in the LMS is often incomplete and do not provide a holistic view of the students’ activities. To alleviate this problem, this work describe SARA, a system to automatically capture paper-based assignments and evaluations while the instructor is writing feedback and grading them. This information is uploaded automatically to the LMS to become part of both, the feedback provided to students and the data available for analyzing the learning process. This system is based on low-cost hardware and requires little configuration and intervention from the final user to work. An initial evaluation of the system provides evidence of the feasibility and usefulness of SARA in real-world learning environments.
Wireless devices to restart walking during an episode of FOG on patients with Parkinson’s disease
Abstract: Parkinson’s disease (PD) is a chronic and degenerative disorder of body movement, affecting people between the ages of 50 and 60. Freezing of Gait (FOG) is a symptom of the advanced stage of PD, associated with gait disorders with prominent risk falls; these drops cause stress, pain and are the main cause of death by injury. In this research, was developed a hardware-based wireless system non-invasive for the acquisition of real-time data from PD patients presenting with FOG episodes to stimulate walking progression, prevent falls, and improve patients’ lifestyles. This was achieved by placing a stimulator device near the posterior tibial nerve of the lower extremities. The patients’ gait is automatically detected by a three-axis accelerometer coupled to an inertial measurement unit (IMU). To make the data is raised three typical scenarios of occurrence of FOG: walk in a straight line, turns of 180 degrees, up and down steps, the data will be read and stored in a processor and transmitted to the smartphone via Bluetooth. Detected the FOG, generates a signal that produces vibratory stimulation, helping to break the FOG or avoiding it. The features of the devices are: use of surface sensors, low cost, portable, easy use and lightweight, in addition, a smartphone is used for monitoring and processing. For the validation of the system was done analytically based on specificity, sensitivity and effectiveness.
Design and testing of low-cost knee prosthesis
Abstract: One of the policies of the Ecuadorian Government leads to the improvement of the quality of life of its population.
That is way most of the universities have been given the task of carrying out investigations needed to comply with these policies. In the province of Imbabura, one of each 2200 inhabitants have a transfemoral amputation. The Technical University of the North is the only institution located in the region with a capacity of technology development. Due to described above, Mechatronics Engineering and Physiotherapy Schools are a fundamental pillar for development prosthetic devices at low cost, intended for poor people to improve their quality of life. A solution modelled and simulated using a CAD/CAE software based on MEF, is proposed. Gait analysis, patient and prosthesis weights and materials sold in the country, are the criteria considered. The prototype is printed in ceramic powder to evaluate the functionality. Using a 3-axes CNC milling (ROMI D 800) and a
wire cutter (EDM CHMER) the model is manufactured. Testing of engineering based on prosthesis efficiency, degree of
adaptability and resistance of materials, are made. Finally, the device was tested using a patient previously chosen. The results
are that the material selected was the appropriate due to its disposal in the country, the articulation emulates the functional
movements of 80° flexion-extension and the patient chosen indicates its satisfaction using the device
Heat stroke detection system based in IoT
Abstract: The increase of temperature on earth’s surface in recent years has significantly affected the health of humans,
where the concept of heat stroke has become a disturbing situation, especially if we consider the increase in deaths caused
by this condition. The present work shows the design of a system employing temperature and heartbeat sensors using IoT concept for detection heat stroke early in children, elderly people and adults of productive age exposed to high temperatures and high humidity in the southeast of Mexico. The results show that the system proposed is efficient and practically usable in life real.
Evaluation of an SDN-WAN controller applied to services hosted in the cloud
Abstract: Due to constant develop of Internet, wide area networks need to evolve into a dynamic, programmable and centralized system that allows them to adapt to the increasing demands of bandwidth requested by several services, such as IoT ecosystems and connectivity with resident applications in the cloud. In this work we perform the evaluation of SD-WAN in orchestration of a corporate network, which interconnects two software-defined data centers (SDDCs), in which it deploys unified communications services (UCaaS), housed in two private clouds. We evaluated performance of the network controller and the quality of services. The tests we perform in this work are executed in a fully vitalized and hyperconvergent environment.
An Inverted Pendulum Cart Modeled Using the Bond Graph Approach
Abstract: This paper shows the use of the Bond Graph approach to model an Inverted pendulum cart and to design and analyze the limitations of a PID controller. The system dynamic model obtained by the Bond graph approach is the same as obtained analytically using the Lagrange’s Method, but without the need to work with differential equations. In addition, the results show that the PID controller worked well to stabilize one state variable, but it is not useful to stabilize all four state variables in the system.
Elderly Fall Detection Using Data Classification on a Portable Embedded System
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.
Prototype Reduction Algorithms Comparison in Nearest Neighbor Classification for Sensor Data: Empirical Study
Abstract: This work presents a comparative study of prototype selection (PS) algorithms. Such a study is done over data-from-sensor acquired by an embedded system. Particularly, five flexometers are used as sensors, which are located inside a glove aimed to read sign language. Measures were taken to quantify the balance between classification performance and reduction training set data (QCR) with k neighbors equal to 3 and 1 to force the classifier (kNN) to the maximum. Two tests were used: (a)the QCR performance and (b) the embedded system decision in real proves. As result the Random Mutation Hill Climbing (RMHC) algorithm is considered the best option to choose in this data type with removed instances at 87\% and classification performance at 82\% in software tests, also the classifier kNN must be with k=3 to improve the classification performance. In a real situation, with the algorithm implemented. The system makes correct decisions at 81\% with 5 persons doing sign language in real time.
Development of animated facial expressions to express emotions in a robot: RobotIcon
Abstract: Robotics is a field which does not only become more and more important in industry and production but is becoming part of many peoples every day life and as such, the interaction between robots and humans is an essential field of research. In this work, we present a face — being an important part of a natural communication — and show that animated facial expressions can be to express the robot’s emotions. To do so, we developed a set of animated facial expressions to be shown on the screen of the MASHI robot, a telepresence robot with a wide-screen display mounted on top. A survey was conducted to evaluate the recognition value of the facial expressions as well as to evaluate whether displaying the full face or rather just the eye-region affects this recognition rate. The evaluation of this survey shows that most of our emotions are recognized better if a mouth is provided. Yet, the recognition rate is still acceptable without displaying the mouth. In summary, all emotions were well recognized and therefore, displaying these on robots like MASHI will contribute to a more natural interaction.
A Proposal for Implementation of ITIL Incident Management Process in SMEs
Abstract: According to previous findings, Incident Management Process could be the first process to be implemented in the context of an ITIL implementation, however the ten activities defined in the process are too extensive to be implement in a SME. For this reason, it is relevant to develop a strategy for implement all ITIL processes in order to encourage the SMEs starting a formal ITIL implementation. In this article, an implementation strategy using a profile scheme has been developed, this is done by means of two different instruments. Firstly, a deep analysis of selected process and secondly an ISO 2011 structural analysis are revised too. After this, the proposal strategy was applied to Incident Management Process, for this purpose a Delphi study with some experts in the area was conducted in order to validate some constrains related to Incident management activities. As a results, a proposal of strategy for implementing ITIL Incident Management Process is presented.
Improving Positioning of 3D-printed Surgical Guides using Image-processing Techniques
Abstract: Previous studies had tested the use of surgical guides for osteosarcoma resection using anatomical landmarks. These guide-assisted intraoperative techniques have shown improvements in accuracy and precision of the resection process. Due to inevitable errors introduced by human factor, there is still a slight probability that surgeons position the guide with an error beyond the limits established during preoperative planning, yielding in patient risks. The present paper tests image-processing techniques to enhance the accuracy of surgical guide’s positioning during the resection. Experiments were conducted on cadaverous femora and the location of high-contrast marks on the surgical guides were extracted from still photos by boundary tracing. Finally, the mounting angular and translational error was quantified and accounted, and compared to CT-scanning images.