Autonomous configuration of communication systems for IoT smart nodes supported by machine learning

Machine Learning brings intelligence services to IoT systems, with Edge Computing contributing for edge nodes to be part of these services, allowing data to be processed directly in the nodes in real time. This paper introduces a new way of creating a self-configurable IoT node, in terms of communications, supported by machine learning and edge computing, in order to achieve a better efficiency in terms of power consumption, as well as a comparison between regression models and between deploying them in edge or cloud fashions, with a real case implementation. The correct choice of protocol and configuration parameters can make the difference between a device battery lasting 100 times more. The proposed method predicts the energy consumption and quality of signal using regressions based on node location, distance and obstacles and the transmission power used. With an accuracy of 99.88% and a margin of error of 1.504 mA for energy consumption and 98.68% and a margin of error of 1.9558 dBm for link quality, allowing the node to use the best transmission power values for reliability and energy efficiency. With this it is possible to achieve a network that can reduce up to 68% the energy consumption of nodes while only compromising in 7% the quality of the network. Besides that, edge computing proves to be a better solution when energy efficient nodes are needed, as less messages are exchanged, and the reduced latency allows nodes to be configured in less time.info:eu-repo/semantics/publishedVersio

Precise water leak detection using machine learning and real-time sensor data

Water is a crucial natural resource, and it is widely mishandled, with an estimated one third of world water utilities having loss of water of around 40% due to leakage. This paper presents a proposal for a system based on a wireless sensor network designed to monitor water distribution systems, such as irrigation systems, which, with the help of an autonomous learning algorithm, allows for precise location of water leaks. The complete system architecture is detailed, including hardware, communication, and data analysis. A study to discover the best machine learning algorithm between random forest, decision trees, neural networks, and Support Vector Machine (SVM) to fit leak detection is presented, including the methodology, training, and validation as well as the obtained results. Finally, the developed system is validated in a real-case implementation that shows that it is able to detect leaks with a 75% accuracy.info:eu-repo/semantics/publishedVersio

Improve irrigation timing decision for agriculture using real time data and machine learning

With the constant evolution of technology and the constant appearance of new solutions that, when combined, manage to achieve sustainability, the exploration of these systems is increasingly a path to take. This paper presents a study of machine learning algorithms with the objective of predicting the most suitable time of day for water administration to an agricultural field. With the use of a high amount of data previously collected through a Wireless Sensors Network (WSN) spread in an agricultural field it becomes possible to explore technologies that allow to predict the best time for water management in order to eliminate the scheduled irrigation that often leads to the waste of water being the main objective of the system to save this same natural resource.info:eu-repo/semantics/acceptedVersio

Design and implementation of an IoT gateway to create smart environments

The paper presents a proposal of a practical implementation for an IoT gateway dedicated to real-time monitoring and remote control of a swimming pool. Based on a Raspberry Pi, the gateway allows bidirectional communication and data exchange between the user and the sensor network implemented on the environment using an Arduino.info:eu-repo/semantics/publishedVersio

Sustainable irrigation system for farming supported by machine learning and real-time sensor data

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.info:eu-repo/semantics/publishedVersio

Influence of growing sites and physicochemical features on the incidence of lenticel breakdown in 'Gala' and 'Galaxy' apples.

The origin of lenticels during apple fruit development growing in different altitudes, the physicochemical and physiological fruit features at harvest and storage, were studied in order to correlate these aspects to lenticel breakdown. "Gala" and "Galaxy" apples were harvested from orchards located at three traditional growing regions in Brazil: Vacaria, RS (971 m a.s.l.), Fraiburgo, SC (1048 m a.s.l.) and São Joaquim, SC (1353 m a.s.l.), during the 2008/09 season. The fruit were kept in cold storage (CS) at 0 ◦C and 90% RH for up to 120 days or at controlled atmosphere (CA) at 1.5% O2, 2.5% CO2, 0 ◦C and 90% RH for up to 210 days. Colour index was higher in ?Galaxy? apples and in fruit grown in São Joaquim, where the minimum temperatures were approximately 10 ◦C. The firmness was reduced at CS and CA, butit remained greater than 40 N in all the treatments. Acidity, except for ?Galaxy? apples, and sugars were influenced by the climatic conditions of the growing sites. There was no relationship between the lenticels dyeing and the incidence of lenticel breakdown. Contrary to the expectations, higher incidence of the disorder was noticed in ?Gala? apples grown in São Joaquim (SC) at the end of the both types of storage. The lenticel breakdown was featured by symptoms such as concentric depression around the lenticels, cell obliteration of the epidermis and subepidermal layers, with wide spaces formed by cell lysis. The origin of lenticels has no influence on the disorder development. It was not possible to establish a relationship among physicochemical, physiological and anatomical features from distinct growing sites and the predisposition to lenticel breakdown disorder

Encuentros y devenires en el espacio híbrido para nuestra formación

En la sociedad del siglo XXI emerge un planteamiento de recursión organizativa como producto y productor de y en la institución-sociedad-mundo, y que influye directamente en la formación del profesorado. El entorno analizado parte de los encuentros y devenires generados en el espacio híbrido entre la escuela Príncep de Viana, la Facultad de Ciencias de la Educación, el Centro de Arte la Panera y otros recursos comunitarios para la formación de los profesionales implicados: maestros, profesionales de centros de arte y museos, profesores de la universidad, estudiantes futuros maestros y otros profesionales de la educación. Nuestro objetivo es mostrar cómo a partir de los encuentros y los devenires en el espacio híbrido tomamos decisiones para reconstruir las actividades de enseñanza-aprendizaje del Proyecto Àlber en el marco del Educ-arte – Educa (r) t y contribuir a la formación de los implicado

Oxidative stress modulates the expression of VEGF isoforms in the diabetic retina

Funding: This work was supported by the Portuguese Foundation for Science and Technology (FCT) with individual grants to S. Simão (SFRH/ BPD/78404/2011), D. Bitoque (SFRH/BD/52424/2013), S. Calado (SFRH/BD/76873/2011), GA Silva (EXPL-BIM-MEC-1433-2013, PIRG05-GA-2009-249314–EyeSee). FCT Research Center Grant UID/ BIM/04773/2013 CBMR 1334.publishersversionpublishe