O estabelecimento de padrões de referência altimétrica utilizando o nivelamento geométrico para a definição de alvos altos e inacessíveis

As estruturas geodésicas altimétricas são fundamentais para os projetos de Engenharia. Neste contexto, pode-se utilizar a tecnologia do nível digital para a determinação de pontos de referência altimétrica através do método do nivelamento geométrico. Outra solução é a utilização da tecnologia da estação total para a determinação de altitudes em pontos situados em locais altos e inacessíveis nas estruturas arquitetônicas, como por exemplo, alvos em estrutura de edificação situados em torres de igrejas e para a realização de uma estrutura geodésica através do método de nivelamento trigonométrico. As estruturas geodésicas altimétricas, implantadas e determinadas pelo método do nivelamento geométrico, materializam, neste trabalho, pontos para o estabelecimento de padrões de referência altimétrica e para a determinação altimétrica de alvos altos e inacessíveis. As estruturas geodésicas foram implantadas e determinadas, no Sítio Histórico de Olinda, empregando-se nível digital de alta precisão e mira de ínvar com código de barras. Este trabalho tem como objetivo definir padrões de referência altimétrica utilizando o método de nivelamento geométrico, resultando na implantação e análise da qualidade de estruturas geodésicas altimétricas

Temporal shoreline series analysis using GNSS

In recent decades, Boa Viagem beach located in the city of Recife-PE and Piedade in Jaboatão dos Guararapes-PE (Brazil) has seen urbanization near the coastline causing changes in social, economic and morphological aspects, where coastal erosion problems are observed. This study uses GNSS (global navigation satellite system) shoreline monitoring approach, which is quicker, and provides continuously updatable data at cm-level accuracy to analyze and determine temporal positional shifts of the shoreline as well as annual average rates through EPR (end point rate). To achieve this, kinematic GNSS survey data for the years 2007, 2009, 2010 and 2012 were used. The results show sectorial trends over the years, with the highest annual retreat rate of 8.16 m /year occurring during the period 2007-2009. Variety of different patterns over the shoreline were also observed. These findings could be essential for decision making in coastal environments

Non-decimated Wavelet Transform for a Shift-invariant Analysis

<div><p>ABSTRACT Due to the ability of time-frequency location, the wavelet transform has been applied in several areas of research involving signal analysis and processing, often replacing the conventional Fourier transform. The discrete wavelet transform has great application potential, being an important tool in signal compression, signal and image processing, smoothing and de-noising data. It also presents advantages over the continuous version because of its easy implementation, good computational performance and perfect reconstruction of the signal upon inversion. Nevertheless, the downsampling required in the computation of the discrete wavelet transform makes it shift variant and not appropriated to some applications, such as for signals or time series analysis. On the other hand, the Non-Decimated Discrete Wavelet Transform is shift-invariant because it eliminates the downsampling and, consequently, is more appropriate for identifying both stationary and non-stationary behaviors in signals. However, the non-decimated wavelet transform has been underused in the literature. This paper intends to show the advantages of using the non-decimated wavelet transform in signal analysis. The main theoretical and practical aspects of the multi-scale analysis of time series from non-decimated wavelets in terms of its formulation using the same pyramidal algorithm of the decimated wavelet transform was presented. Finally, applications with a simulated and real time series compare the performance of the decimated and non-decimated wavelet transform, demonstrating the superiority of non-decimated one, mainly due to the shift-invariant analysis, patterns detection and more perfect reconstruction of a signal.</p></div