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

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

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

Hybrid Matched Filter Detection Spectrum Sensing

The radio frequency spectrum is getting more congested day by day due to the growth of wireless devices, applications, and the arrival of fifth generation (5G) mobile communications. This happens because the radio spectrum is a natural resource that has a restricted existence. Access to all devices can be granted, but in a more efficient way. To resolve the issue, cognitive radio technology has come out as a way, because it is possible to sense the radio spectrum in the neighboring. Spectrum sensing has been recognized as an important technology, in cognitive radio networks, to allow secondary users (SUs) to detect spectrum holes and opportunistically access primary licensed spectrum band without harmful interference. This paper considers the Energy Detection (ED) and Matched Filter Detection (MFD) spectrum sensing techniques as the baseline for a study where the so-called Hybrid Matched Filter Detection (Hybrid MFD) was proposed. Apart from an analytical approach, Monte Carlo simulations have been performed in MATLAB. These simulations aimed at understanding how the variation of parameters like the probability of false alarm, the signal-to-noise ratio (SNR) and the number of samples, can affect the probability of miss-detection. Simulation results show that i) higher probability of miss-detection is achieved for the ED spectrum sensing technique when compared to the MFD and Hybrid MFD techniques; ii) More importantly, the proposed Hybrid MFD technique outperforms MFD in terms of the ability to detect the presence of a primary user in licensed spectrum, for a probability of false alarm slightly lower than 0.5, low number of samples and low signal-to-noise ratio.info:eu-repo/semantics/publishedVersio

Rastreabilidade e autenticidade do vinho: abordagens para a avaliação da origem geográfica, casta e ano de vindima

The aim of this work is to identify and discuss physicochemical wine characteristics, to provide to some extent a link to the vintage, variety, and/or geographical origin. Bibliographic datasets were attempted to provide the main information for topic comprehension, identifying the sources of wine compositional variability and how these can be expressed in terms of the belonging categories. Since all the environmental and technological conditions which vineyard and wine are subjected are rarely known, different sources were inspected. Great importance was given to the study of isotopic composition because of its importance in food frauds detection history. The interaction of the plant genotype with the environmental conditions of the vintage is the main responsible for the wines organic and inorganic fraction variability in terms of both total and relative content. This phenotypical expression, together with human and abiotic variability sources, has been examined since it contains to some extent the information for the discrimination of wines according to their category. Recently, new proton nuclear magnetic resonance (1H NMR) spectroscopy techniques have been under study and, used concurrently to chemometric data management procedures, showed to be an interesting and promising tool for wine characterization according to both vintage and varietyinfo:eu-repo/semantics/publishedVersio

Capture of UAVs through GPS spoofing using low-cost SDR platforms

The increased use of unmanned aerial vehicles (UAVs), better known as drones, by civilians has grown exponentially and their autonomous flight control systems have improved significantly, which has resulted in a greater number of accidents and dangerous situations. To help resolve this problem, in this paper, we address the use of low-cost Software Defined Radio (SDR) platforms for simulating a global navigation satellite system (GNSS), more specifically the global positioning system (GPS), in order to transmit false signals and induce a location error on the targeted GPS receiver. Using this approach, a defensive system can be implemented which can divert, or even take control of unauthorized UAVs whose flight path depends on the information obtained by the GPS system.info:eu-repo/semantics/acceptedVersio

Neural architecture search for 1D CNNs - Different approaches tests and measurements

In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided.info:eu-repo/semantics/publishedVersio

On-line optical monitoring of the mixing performance in co-rotating twin-screw extruders

The use of real-time techniques to evaluate the global mixing performance of co-rotating twin-screw extruders is well consolidated, but much less is reported on the specific contribution of individual screw zones. This work uses on-line flow turbidity and birefringence to ascertain the mixing performance of kneading blocks with different geometries. For this purpose, one of the barrel segments of the extruder was modified in order to incorporate four sampling devices and slit dies containing optical windows were attached to them. The experiments consisted in reaching steady extrusion and then adding a small amount of tracer. Upon opening each sampling device, material was laterally detoured from the local screw channel, and its turbidity and birefringence were measured by the optical detector. Residence time distribution curves (RTD) were obtained at various axial positions along three different kneading blocks and under a range of screw speeds. It is hypothesized that K, a parameter related to the area under each RTD curve, is a good indicator of dispersive mixing, whereas variance can be used to assess distributive mixing. The experimental data confirmed that these mixing indices are sensitive to changes in processing conditions, and that they translate the expected behavior of each kneading block geometry.This study was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 scholarship (00889834001-08) to F.O.C. Bernardo, PVE 30484/2013-01 to J.A.C., Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and PQ scholarship (311790/2013-5) to S.V.C

Effective GPS jamming techniques for UAVs using low-cost SDR platforms

Lately, a rising number of incidents between unmanned aerial vehicles (UAVs) and airplanes have been reported in airports and airfields. In order to help cope with the problem of unauthorized UAV operations, in this paper we evaluate the use of low cost SDR platforms (software defined radio) for the implementation of a jammer able to generate an effective interfering signal aimed at the GPS navigation system. Using a programmable BladeRF x40 platform from Nuand and the GNU radio software development toolkit, several interference techniques were studied and evaluated, considering the spectral efficiency, energy efficiency and complexity. It was shown that the tested approaches are capable of stopping the reliable reception of the radionavigation signal in real-life scenarios, neutralizing the capacity for autonomous operation of the vehicle.info:eu-repo/semantics/acceptedVersio

CONHECIMENTO EM ENFERMAGEM: REPRESENTAÇÕES SOCIAIS CONSTRUÍDAS POR ESTUDANTES DE FORMAÇÃO INICIAL

CONHECIMENTO EM ENFERMAGEM: REPRESENTAÇÕES SOCIAIS CONSTRUÍDAS POR ESTUDANTES DE FORMAÇÃO INICIAL Fonseca, A.; Lopes, M. J.; Sebastião, L., & Magalhães, D. (2013). Conhecimento em enfermagem: representações sociais construídas por estudantes de formação inicial. In Mendes, F; Gemito, L; Cruz, D., & Lopes, M. (org). Enfermagem Contemporânea. Dez Temas, Dez Debates. (pp 30-43), Nº 1. Coleção E-books. Oficinas Temáticas. ISBN:978-989-20-4162-9. Palavras chave: enfermagem; conhecimento; representações sociais; formação inicial. A aquisição e a construção pessoal do conhecimento em enfermagem resultam de processos complexos de compreensão das situações, nas quais, experiência e saber são estruturados e alvo de reflexão. O conhecimento em enfermagem que o estudante constrói ao longo do tempo, consciente da sua responsabilidade pela própria aprendizagem e num processo contínuo de desenvolvimento, há de possibilitar-lhe o desempenho profissional, pois será mobilizável, nas diversas situações, para dar resposta às necessidades de cuidados de enfermagem. A forma como os estudantes se apropriam dos saberes, como se relacionam com eles e como constroem o seu conhecimento em enfermagem está vinculada à representação que têm deste, uma vez que a constatação da realidade presente nas representações sociais alicerça-se solidamente no indivíduo que a possui e baliza o modo como ele se relaciona com o objeto de representação (Moscovici,1976, 2010; Jodelet, 2001; Abric, 1994). Partindo da questão “quais as representações sociais do conhecimento em enfermagem, elaboradas por estudantes de enfermagem em diferentes etapas da sua formação inicial?”, e tendo como referencial teórico-metodológico a Teoria das Representações Sociais proposta por Moscovici (1961), procurou-se: • Identificar as representações sociais de conhecimento em enfermagem, na perspetiva dos estudantes de enfermagem; • Analisar a estrutura das representações sociais de conhecimento em enfermagem, elaboradas por estudantes de enfermagem. Realizou-se um estudo exploratório, no qual, a partir do total de estudantes da Escola Superior de Enfermagem de S. João de Deus da Universidade de Évora, se selecionaram dois grupos: - Grupo A, composto por 33 estudantes do 1º semestre do 1º ano, recém-admitidos na escola e ainda sem participação em atividades no âmbito da sua formação, obviando assim qualquer contaminação das construções previamente elaboradas; - Grupo B , constituído por 39 estudantes do 2º semestre do 4º ano, no último dia da sua formação. Os dados foram recolhidos através de um questionário, previamente testado, que incluía questões para caraterização sociodemográfica e um estímulo indutor – conhecimento em enfermagem -, para que os sujeitos, através da técnica de associação livre de palavras, evocassem as cinco palavras ou expressões que, por ordem decrescente de importância, associavam a este estímulo . Os dados foram categorizados recorrendo ao Microsoft Office Word® e processados no software Evoc® que forneceu estrutura das representações sociais. Cumpriram-se os procedimentos ético-legais, em conformidade com o preconizado pela Comissão de Ética da Área da Saúde e Bem-Estar da Universidade de Évora. Apresentação dos resultados Dos 33 estudantes do 1º ano (Grupo A), 5 eram do sexo masculino e 28 do sexo feminino, com média de idade de 19,5 anos. Dos 39 estudantes do 4º ano (Grupo B), 5 eram do sexo masculino e 34 do sexo feminino, com média de idade de 23,59 anos. Relativamente ao núcleo central da estrutura das representações sociais do objeto em estudo, constata-se que os estudantes do 1º ano, ao estímulo conhecimento em enfermagem, associaram os elementos ajudar; empenho; competência; prática; desenvolvimento; investigação e pessoas. Os estudantes do 4º ano vincularam conhecimento em enfermagem aos elementos sabedoria; cuidados de qualidade; cuidar e responsabilidade. No que concerne à segunda periferia, verifica-se que os estudantes do 1º ano associaram ao estímulo conhecimento em enfermagem os elementos processos de diagnóstico; trabalho; disponibilidade para com os outros e responsabilidade, enquanto que os estudantes do 4º ano associaram prática e reflexão. A produção das representações, em ambos os grupos, divide-se entre elementos que, embora não revelem consensos, se podem integrar nas dimensões científica e clínica. De salientar que, na estrutura das representações sociais, se encontra, no Grupo B, a dimensão reflexiva identificada no elemento reflexão. Considerações finais Do estudo realizado, realça-se que não há consenso nos elementos da estrutura das representações sociais do conhecimento em enfermagem na perspetiva dos dois grupos de estudantes de formação inicial em enfermagem - estudantes recém-admitidos e estudantes finalistas -. Sendo diferentes os elementos do núcleo central das estruturas das representações dos dois grupos, como é o caso, consideram-se diferentes as representações sociais do objeto em estudo construídas por aqueles (Sá, 1998). Tal fato, poderá ser atribuído à natureza das experiências pessoais e académicas obrigatoriamente diferentes dos estudantes a iniciar a sua formação face aos estudantes a terminar o seu percurso académico. Todavia, os diferentes elementos encontrados assumem caraterísticas semelhantes que permitem, como anteriormente se afirmou, o seu agrupamento nas dimensões científica e clínica. Acresce, no grupo de estudantes finalistas e encontrada na segunda periferia, a dimensão reflexiva que poderá ser reveladora da importância atribuída à reflexão como estratégia para estabelecer novas formas de desenvolvimento do conhecimento, capacidade potencialmente desenvolvida nas experiências ocorridas durantes a formação. Face à estrutura das representações sociais do objeto em estudo que se aprendeu, pode-se dizer que os estudantes do 1º ano consideram a investigação promotora do desenvolvimento do conhecimento em enfermagem, indispensável para ajudar as pessoas no contexto de uma prática exercida com competência e empenho. Para os estudantes do 4º ano, cuidados de qualidade são indispensáveis no processo de cuidar e exigem sabedoria e responsabilidade