Automatic OSPF Topology map generation using information of the OSPF database

Nowadays, different technologies provide support to different areas of our diverse lifestyles around the world. Highly reputable companies are doing important research on the connectivity of multiple devices to the different types of network. The open Short Path First Protocol (OSPF) is one of the most widespread routing protocols in communications. Through experience, it has been demonstrated that this protocol usually has flaws, either by causes outside the protocol or by configuration, so network administrators should monitor and revise the operation of that protocol. Generally, when you start tracking a network already deployed that uses the OSPF routing protocol, there is very little documented information or even, none of the existing topology, not only because of the dynamism of this connectivity protocol, but also because of the lack of documentation of the installation itself. This project proposes the development of a tool for the generation of a map of the topology of a simple area of the OSPF routing protocol, which will facilitate the establishment of an OSPF area's topology documentation.Keywords: Network logical topology, OSPF, network modelling and mapping

Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images

The use of semantic representations to achieve place understanding has been widely studied using indoor information. This kind of data can then be used for navigation, localization, and place identification using mobile devices. Nevertheless, applying this approach to outdoor data involves certain non-trivial procedures, such as gathering the information. This problem can be solved by using map APIs which allow images to be taken from the dataset captured to add to the map of a city. In this paper, we seek to leverage such APIs that collect images of city streets to generate a semantic representation of the city, built using a clustering algorithm and semantic descriptors. The main contribution of this work is to provide a new approach to generate a map with semantic information for each area of the city. The proposed method can automatically assign a semantic label for the cluster on the map. This method can be useful in smart cities and autonomous driving approaches due to the categorization of the zones in a city. The results show the robustness of the proposed pipeline and the advantages of using Google Street View images, semantic descriptors, and machine learning algorithms to generate semantic maps of outdoor places. These maps properly encode the zones existing in the selected city and are able to provide new zones between current ones.This work has been supported by the Spanish Grant PID2019-104818RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. José Carlos Rangel and Edmanuel Cruz were supported by the Sistema Nacional de Investigación (SNI) of SENACYT, Panama

Situación actual de la aplicación y potenciales usos de la visión artificial en la entomología agrícola en Panamá

Variables such as climate change, droughts, hurricanes, deforestation, and the indiscriminate use of pesticides constantly trigger the presence of insect pests in the country, as well as potential invasive pests that find the necessary conditions to establish themselves and cause damage to sensitive crops in the country. Agricultural entomology focuses on the study of insects associated with agriculture-related plants, both harmful and beneficial, giving increasing importance to the environment and relying on available biotechnological advances to conserve and increase biodiversity in agricultural areas. On the other hand, there are several new technologies that can be used in agricultural entomology, such as computer vision. Computer vision or machine vision is the area of research around how computers view and understand digital images and/or videos.  This study aims to give an approach to the current situation and potential uses of computer vision in agricultural entomology and to examine in a timely manner how it can be applied in Panama. The challenges and techniques of computer vision and artificial intelligence applied to entomology are evaluated through the selection of current bibliography published on the subject. This work also aims to identify gaps and opportunities with a view to becoming an updated reference for future work. Several bibliographic references were analysed, from which the information contained therein was extracted and the applicability of the different techniques in Panama is presented.Variables como el cambio climático, las sequías, los huracanes, la deforestación y el uso indiscriminado de plaguicidas desencadenan constantemente la presencia de insectos plagas en el país, así como de potenciales plagas invasoras que encuentran las condiciones necesarias para establecerse y causar daños en los cultivos sensibles del país. La entomología agrícola se centra en el estudio de los insectos asociados a las plantas relacionadas con la agricultura, tanto los perjudiciales como los beneficiosos, dando cada vez más importancia al medio ambiente y apoyándose en los avances biotecnológicos disponibles para conservar y aumentar la biodiversidad en las zonas agrícolas. Por otra parte, hay una serie de nuevas tecnologías que pueden utilizarse en la entomología agrícola, como la visión artificial. La visión por ordenador o visión artificial es el área de investigación en torno a cómo los ordenadores ven y entienden las imágenes digitales y/o videos.  Este estudio pretende dar un enfoque de la situación actual y potenciales usos de la visión por ordenador en la entomología agrícola y examinar de manera puntual cómo puede aplicarse en Panamá. Se evalúa los retos y las técnicas de visión por ordenador e inteligencia artificial aplicadas a la entomología mediante la selección de bibliografía actual publicada sobre la temática. Asimismo, este trabajo pretende identificar las lagunas y las oportunidades con vistas a convertirse en una referencia actualizada para futuros trabajos. Se analizaron varias referencias bibliográficas, de los cuales se extrajo la información contenida y se expone la aplicabilidad de las distintas técnicas en Panamá

EVA: EVAluating at-home rehabilitation exercises using augmented reality and low-cost sensors

Over one billion people in the world live with some form of disability. This is incessantly increasing due to aging population and chronic diseases. Among the emerging social needs, rehabilitation services are the most required. However, they are scarce and expensive what considerably limits access to them. In this paper, we propose EVA, an augmented reality platform to engage and supervise rehabilitation sessions at home using low-cost sensors. It also stores the user’s statistics and allows therapists to tailor the exercise programs according to their performance. This system has been evaluated in both qualitative and quantitative ways obtaining very promising results.This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds. Edmanuel Cruz is funded by a Panamenian grant for Ph.D. studies IFARHU and SENACYT 270-2016-207. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243 and FPU16/00887. Thanks also to Nvidia for the generous donation of a Titan Xp and a Quadro P6000

Enhancing perception for the visually impaired with deep learning techniques and low-cost wearable sensors

As estimated by the World Health Organization, there are millions of people who lives with some form of vision impairment. As a consequence, some of them present mobility problems in outdoor environments. With the aim of helping them, we propose in this work a system which is capable of delivering the position of potential obstacles in outdoor scenarios. Our approach is based on non-intrusive wearable devices and focuses also on being low-cost. First, a depth map of the scene is estimated from a color image, which provides 3D information of the environment. Then, an urban object detector is in charge of detecting the semantics of the objects in the scene. Finally, the three-dimensional and semantic data is summarized in a simpler representation of the potential obstacles the users have in front of them. This information is transmitted to the user through spoken or haptic feedback. Our system is able to run at about 3.8 fps and achieved a 87.99% mean accuracy in obstacle presence detection. Finally, we deployed our system in a pilot test which involved an actual person with vision impairment, who validated the effectiveness of our proposal for improving its navigation capabilities in outdoors.This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds, the University of Alicante project GRE16-19, and by the Valencian Government project GV/2018/022. Edmanuel Cruz is funded by a Panamenian grant for PhD studies IFARHU & SENACYT 270-2016-207. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243. Thanks also to Nvidia for the generous donation of a Titan Xp and a Quadro P6000

Geoffrey: An Automated Schedule System on a Social Robot for the Intellectually Challenged

The accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goalsThe accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goal

UASOL, a large-scale high-resolution outdoor stereo dataset

In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite there already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work.This work was supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds. It was funded by the University of Alicante project GRE16-19, and by the Valencian Government project GV/2018/022. Edmanuel Cruz is funded by a Panamanian grant for PhD studies IFARHU & SENACYT 270-2016-207. This work was also supported by a Spanish grant for PhD studies ACIF/2017/243. Thanks also to NVIDIA for the generous donation of a Titan Xp and a Quadro P6000

Towards a robotic personal trainer for the elderly

The use of robots in the environment of the elderly has grown significantly in recent years. The idea is to try to increase the comfort and well-being of older people through the employment of some kind of automated processes that simplify daily work. In this paper we present a prototype of a personal robotic trainer which, together with a non-invasive sensor, allows caregivers to monitor certain physical activities in order to improve their performance. In addition, the proposed system also takes into account how the person feels during the performance of the physical exercises and thus, determine more precisely if the exercise is appropriate or not for a specific person.This work was partly supported by the Spanish Government (RTI2018-095390-B-C31) and FCT—Fundação para a Ciência e Tecnologia through the Post-Docscholarship SFRH/BPD/102696/2014 (A. Costa) and UID/CEC/00319/2019

Geoffrey: An Automated Schedule System on a Social Robot for the Intellectually Challenged

The accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goals.This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with FEDER funds. Edmanuel Cruz is funded by a Panamanian grant for PhD studies IFARHU & SENACYT 270-2016-207. Jose Carlos Rangel was supported by the National System of Research (SNI) of the SENACYT of Panama. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243 and FPU16/00887

Robotics semantic localization using deep learning techniques

The tremendous technological advance experienced in recent years has allowed the development and implementation of algorithms capable of performing different tasks that help humans in their daily lives. Scene recognition is one of the fields most benefited by these advances. Scene recognition gives different systems the ability to define a context for the identification or recognition of objects or places. In this same line of research, semantic localization allows a robot to identify a place semantically. Semantic classification is currently an exciting topic and it is the main goal of a large number of works. Within this context, it is a challenge for a system or for a mobile robot to identify semantically an environment either because the environment is visually different or has been gradually modified. Changing environments are challenging scenarios because, in real-world applications, the system must be able to adapt to these environments. This research focuses on recent techniques for categorizing places that take advantage of DL to produce a semantic definition for a zone. As a contribution to the solution of this problem, in this work, a method capable of updating a previously trained model is designed. This method was used as a module of an agenda system to help people with cognitive problems in their daily tasks. An augmented reality mobile phone application was designed which uses DL techniques to locate a customer’s location and provide useful information, thus improving their shopping experience. These solutions will be described and explained in detail throughout the following document