Prospects of electric vehicles in the developing countries : a literature review

Electric mobility offers a low cost of travel along with energy and harmful emissions savings. Nevertheless, a comprehensive literature review is missing for the prospects of electric vehicles in developing countries. Such an overview would be instrumental for policymakers to understand the barriers and opportunities related to different types of electric vehicles (EVs). Considering the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a systematic review was performed of the electronic databases Google Scholar and Web of Science for the years 2010–2020. The electric four-wheelers, hybrid electric vehicles and electric two-wheeler constituted the electric vehicles searched in the databases. Initially, 35 studies identified in the Web of Science that matched the criteria were studied. Later, 105 other relevant reports and articles related to barriers and opportunities were found by using Google Scholar and studied. Results reveal that electric four-wheelers are not a feasible option in developing countries due to their high purchase price. On the contrary, electric two-wheelers may be beneficial as they come with a lower purchase price

Soft Robots for Ocean Exploration and Offshore Operations: A Perspective

The ocean and human activities related to the sea are under increasing pressure due to climate change, widespread pollution, and growth of the offshore energy sector. Data, in under-sampled regions of the ocean and in the offshore patches where the industrial expansion is taking place, are fundamental to manage successfully a sustainable development and to mitigate climate change. Existing technology cannot cope with the vast and harsh environments that need monitoring and sampling the most. The limiting factors are, among others, the spatial scales of the physical domain, the high pressure, and the strong hydrodynamic perturbations, which require vehicles with a combination of persistent autonomy, augmented efficiency, extreme robustness, and advanced control. In light of the most recent developments in soft robotics technologies, we propose that the use of soft robots may aid in addressing the challenges posed by abyssal and wave-dominated environments. Nevertheless, soft robots also allow for fast and low-cost manufacturing, presenting a new potential problem: marine pollution from ubiquitous soft sampling devices. In this study, the technological and scientific gaps are widely discussed, as they represent the driving factors for the development of soft robotics. Offshore industry supports increasing energy demand and the employment of robots on marine assets is growing. Such expansion needs to be sustained by the knowledge of the oceanic environment, where large remote areas are yet to be explored and adequately sampled. We offer our perspective on the development of sustainable soft systems, indicating the characteristics of the existing soft robots that promote underwater maneuverability, locomotion, and sampling. This perspective encourages an interdisciplinary approach to the design of aquatic soft robots and invites a discussion about the industrial and oceanographic needs that call for their application

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

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A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques

A computação visual é uma área do conhecimento que estuda o desenvolvimento de sistemas artificiais capazes de detectar e desenvolver a percepção do meio ambiente através de informações de imagem ou dados multidimensionais. A percepção visual e a manipulação são combinadas em sistemas robóticos através de duas etapas "olhar"e depois "movimentar-se", gerando um laço de controle de feedback visual. Neste contexto, existe um interesse crescimente no uso dessas técnicas em veículos aéreos não tripulados (VANTs), também conhecidos como drones. Essas técnicas são aplicadas para posicionar o drone em modo de vôo autônomo, ou para realizar a detecção de regiões para vigilância aérea ou pontos de interesse. Os sistemas de computação visual geralmente tomam três passos em sua operação, que são: aquisição de dados em forma numérica, processamento de dados e análise de dados. A etapa de aquisição de dados é geralmente realizada por câmeras e sensores de proximidade. Após a aquisição de dados, o computador embarcado realiza o processamento de dados executando algoritmos com técnicas de medição (variáveis, índice e coeficientes), detecção (padrões, objetos ou áreas) ou monitoramento (pessoas, veículos ou animais). Os dados processados são analisados e convertidos em comandos de decisão para o controle para o sistema robótico autônomo Visando realizar a integração dos sistemas de computação visual com as diferentes plataformas de VANTs, este trabalho propõe o desenvolvimento de um framework para controle de missão e guiamento de VANTs baseado em visão computacional. O framework é responsável por gerenciar, codificar, decodificar e interpretar comandos trocados entre as controladoras de voo e os algoritmos de computação visual. Como estudo de caso, foram desenvolvidos dois algoritmos destinados à aplicação em agricultura de precisão. O primeiro algoritmo realiza o cálculo de um coeficiente de reflectância visando a aplicação auto-regulada e eficiente de agroquímicos, e o segundo realiza a identificação das linhas de plantas para realizar o guiamento dos VANTs sobre a plantação. O desempenho do framework e dos algoritmos propostos foi avaliado e comparado com o estado da arte, obtendo resultados satisfatórios na implementação no hardware embarcado.Cumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware

Introduction to modeling of the McKibben Pneumatic artificial muscle with end constraints

The 2016 IEEE International Conference on Information and Automation (IEEE ICIA 2016), Ningbo, China, 31 July-4 August 2016