Real-Time LiDAR-based Power Lines Detection for Unmanned Aerial Vehicles

The growing dependence of modern-day societies on electricity leads to the increasing importance of effective monitoring and maintenance of power lines. Due to the population’s renouncement to the installation of new electric power lines, the existing ones are constantly operating at maximum capacity. This leaves no room for breakdowns, as it leads to major economic losses for the electrical companies and blackouts for the consumers. Endowing Unmanned Aerial Vehicles (UAVs) with the appropriate sensors for inspection the power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual detection methods are usually applied to locate the power lines and their components. Although, they are generally too sensitive to atmospheric conditions and noisy background. Poor light conditions or a background rich in edges may compromise their results. In order to overcome those limitations, this dissertation addresses the problem of power line detection and modeling based on the use of a Light Detection And Ranging (LiDAR) sensor. A novel approach to the power line detection was developed, the Power Line LiDARbased Detection and Modeling (PL2DM). It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. In the segmentation, the breaking cluster points are detected by an analysis of their planar properties. Exporting the potential power line points to a further step, it performs a scan based straight line detection. The final model of the power line is obtained by matching and grouping the several line segments detected using their collinearity properties. Horizontally, the power lines are modeled as a straight line, while vertically are approximated to a catenary curve. The algorithm was tested with a real dataset, showing promising results both in terms of outputs and processing time. From there, it was demonstrated that the proposed algorithm can be applied to real-time operations of the UAV, adding object-based perception capabilities for other layers of processing.A crescente dependência das sociedades modernas no uso de eletricidade conduz a uma crescente importância da eficiência da monitorização e manutenção das linhas elétricas. A renitência das populações `a instalação de novas linhas elétricas faz com que as existentes estejam constantemente a operar na sua máxima capacidade. Isto faz com que não possam existir falhas, uma vez que resultariam em grandes perdas económicas para as companhias elétricas e em falhas energéticas para os consumidores. Equipando um Unmanned Aerial Vehicle (UAV) com os sensores adequados `a inspeção de linhas elétricas, podem ser reduzidos os custos e riscos de operação associados `as inspeções tradicionais, baseadas em patrulhas pedonais e no uso de um helicóptero. No entanto, isto implica o desenvolvimento de algoritmos para que o processo de inspeção seja fiável e autónomo. As linhas elétricas e os componentes associados são geralmente localizados através de métodos de deteção visual. Estes m´métodos são, geralmente, muito sensíveis `as condições atmosféricas e a fundos ruidosos. Condições de luz deficientes ou fundos ricos em contrastes são alguns dos fatores que podem comprometer os seus resultados. De forma a ultrapassar essas limitações, esta dissertação endereça o problema da deteção e modelação de linhas elétricas, tendo por base o uso de um sensor Light Detection And Ranging (LiDAR). Foi desenvolvida uma nova abordagem aos métodos de deteção de linhas elétricas, o Power Line LiDAR-based Detection and Modeling (PL2DM). Esta abordagem ´e baseada numa análise individual de varrimentos, em que ´e feita uma comparação minimalista de todos os pontos, presentes numa dada nuvem de pontos, com uma vizinhança adaptativa. Na segmentação, os pontos de quebra dos grupos criados são detetados tendo em conta as suas propriedades planares. Passando os pontos passíveis de pertencerem a linhas elétricas para o processamento seguinte, é realizada, em cada varrimento, uma deteção de linhas retas. O modelo final das linhas elétricas é obtido a partir da associação e agrupamento dos diversos segmentos de reta detetados, tendo por base a sua colinearidade. Na sua projeção horizontal, as linhas elétricas são modeladas como linhas retas. Verticalmente, são aproximadas ao modelo de uma curva catenária. O algoritmo foi testado com um conjunto de dados reais, tendo mostrado resultados promissores, tanto em termos de dados gerados como de tempo de processamento. Com isso, ficou demonstrado que o algoritmo proposto pode ser aplicado nas operações do UAV em tempo real, adicionando capacidades de perceção baseada em objetos para outras camadas de processamento

High-resolution seismocardiogram acquisition and analysis system

Several devices and measurement approaches have recently been developed to perform ballistocardiogram (BCG) and seismocardiogram (SCG) measurements. The development of a wireless acquisition system (hardware and software), incorporating a novel high-resolution micro-electro-mechanical system (MEMS) accelerometer for SCG and BCG signals acquisition and data treatment is presented in this paper. A small accelerometer, with a sensitivity of up to 0.164 µs/µg and a noise density below 6.5 µg/ Hz is presented and used in a wireless acquisition system for BCG and SCG measurement applications. The wireless acquisition system also incorporates electrocardiogram (ECG) signals acquisition, and the developed software enables the real-time acquisition and visualization of SCG and ECG signals (sensor positioned on chest). It then calculates metrics related to cardiac performance as well as the correlation of data from previously performed sessions with echocardiogram (ECHO) parameters. A preliminarily clinical study of over 22 subjects (including healthy subjects and cardiovascular patients) was performed to test the capability of the developed system. Data correlation between this measurement system and echocardiogram exams is also performed. The high resolution of the MEMS accelerometer used provides a better signal for SCG wave recognition, enabling a more consistent study of the diagnostic capability of this technique in clinical analysis.This work is supported by FCT with the reference project UID/EEA/04436/2013, COMPETE 2020 with the code POCI-01-0145-FEDER-006941

Fish assemblages in a coastal bay adjacent to a network of marine protected areas in southern Brazil

A Baía Norte em Santa Catarina, é considerada como uma típica baía costeira, rodeada por uma rede de Áreas Marinhas Protegidas. Este estudo teve como objetivos descrever a composição e estrutura da comunidade de peixes, testar a influência dos padrões espaciais e sazonais e avaliar a influência das variáveis abióticas nos padrões de abundância. Amostragens sazonais foram realizadas por meio de arrasto de fundo em seis áreas predefinidas da Baía Norte no verão, outono, inverno e primavera no ano de 2005. Simultaneamente, foram coletados dados do ambiente com uma sonda multiparâmetro. Diferenças temporais e espaciais na abundância de peixes foram testados por uma análise PERMANOVA. Para visualizar graficamente as diferenças encontradas na PERMANOVA, foi realizada a análise canônica de coordenadas principais (CAP). Para avaliar a influência de variáveis ambientais sobre a fauna de peixes, foi aplicado um DISTLM usando critério de informação de Akaike (AIC). Foram coletados 9.888 espécimes, distribuídos em 27 famílias e 62 espécies. Citharichthys spilopterus foi a espécie dominante. A PERMANOVA detectou diferenças na abundância entre estações, áreas e interação entre todos os fatores. O DISTLM selecionou a temperatura e pH. Os resultados enfatizam a sazonalidade como um fator importante para a estruturação da fauna de peixes do local de estudo.Baía Norte (North Bay) in Santa Catarina State is considered a typical coastal bay and is surrounded by a network of Marine Protected Areas. The objectives of this study were to describe the composition of the demersal fish assemblage, identify seasonal and spatial structures on a fine scale and evaluate the role of habitat descriptors and abiotic variables affecting the fish assemblage structure. Seasonal samplings were conducted in 2005, using bottom trawls in six pre-established areas in Baía Norte in summer, fall, winter and spring. Simultaneously with each trawl, environmental data were collected with a multiparameter probe. Temporal and spatial differences in fish abundance were tested by a PERMANOVA. To illustratethe differences detected graphically we ran a canonical analysis of principal coordinates (CAP). The influence of environmental variables on the fish fauna was evaluated using a Distant Based Linear Model (DistLM) with Akaike's information criterion (AIC). A total of 9,888 specimens, distributed in 27 families and 62 species, were collected. Citharichthys spilopterus was the most abundant species. PERMANOVA detected differences for abundance between seasons, areas and interaction among all the factors. The DISTLM selected temperature and pH. The results highlight seasonality as an important factor in the structuring of fish fauna of the study place

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost