Da Reforma do Ensino Médio às possibilidades da Educação Pupular: caminhos para uma contraproposta ao Novo Ensino Médio

Artigo submetido para Trabalho de Conclusão de Curso em História - LicenciaturaTrabalho de Conclusão de Curso apresentado ao Instituto Latino-Americano de Arte, Cultura e História da Universidade Federal da Integração Latino- Americana, como requisito parcial à obtenção do título de Licenciado em História. Orientadora: Profa. Dra. Lívia Fernanda MoralesRecentemente, no breve governo Temer, foram aprovadas a Reforma do Ensino Médio brasileiro e a Base Nacional Curricular Comum. Estas medidas fazem parte do avanço da agenda neoliberal e a educação é um campo importante para o estabelecimento deste domínio (Ducasse, 2015; Freitas, 2018). Este artigo tem como objetivo a análise dos possíveis efeitos desta reforma recortando através da tensão entre trabalho intelectual e trabalho manual partindo da abordagem marxista e, em seguida, apresentar caminhos para a construção de uma contraproposta a este Novo Ensino Médio. Através do resgate histórico de experiências da educação popular, apresentamos uma perspectiva de transformação educacional através da cultura junto a princípios pedagógicos para uma educação contra-hegemônica que importe América Latina.Recientemente, en el breve gobierno de Temer, se aprobaron la Reforma de la Escuela Secundaria de Brasil y la Base de Currículo Común Nacional. Estas medidas son parte del avance de la agenda neoliberal y la educación es un campo importante para el establecimiento de este dominio (Ducasse, 2015; Freitas, 2018). Este artículo tiene como objetivo analizar los posibles efectos de esta reforma y luego presentar formas de construir una contrapropuesta a esta nueva escuela secundaria. A través del rescate histórico de experiencias de educación popular, presentamos una perspectiva de transformación educativa a través de la cultura junto con principios pedagógicos para una educación contrahegemónica que es importante en América Latin

Exploring The Use Of SAR Remote Sensing To Detect Microplastics Pollution In The Oceans

The increase in plastic pollution is advancing micro level pollution and the total weight of microplastics (8 tons and in the North Atlantic as 10.4 x 108 tons. The plastic in marine environment will eventually degrade and it will be promptly colonized by bacteria releasing surfactants. Such surfactants will have the effect of damping the capillary and small gravitational waves on the ocean surface. Since SAR is sensitive to roughness induced by capillary waves, it may be exploited to detect bacterial activities related to plastic pollution. In this work we used Sentinel-1A and COSMO SkyMed radar images acquired in the Atlantic and Pacific gyres to detect surfactants that may be associated to plastic pollution. We are using SAR, because the damping properties of surfactants produce dark areas in images. Since area of low backscattering in SAR images could also be produced by other oceanographic/meteorological event, we exploited geophysical remote sensing products associated to time and locations synchronised to SAR acquisitions. Among other products we considered sea surface temperature, surface wind, chlorophyll, surface reflectance, turbidity and wave heights. Additionally we made sure that the areas were not within busy shipping routes. The result of the analysis is that, including effects due to colocation errors of SAR and meteorological data, we could identify a large amount of linear slicks in SAR images that were not directly related to apparent meteorological conditions. Such slicks in the gyres have the appearance of oil slicks, however in some areas they are in large amount and they are not connected to large ship traffic. At the moment these slicks seems to only be visible when the wind conditions are moderate (e.g. 6m/s) as it happen for ordinary oil slicks. Besides the work on radar data, we are making controlled experiments with micro-plastic pollution in sea water, to understand the amount and type of surfactants produced by microbes colonising plastics. The conclusion of our study is that radar remote sensing has the potential to detect plastic pollution areas under sorter meteorological conditions

Detecting microplastics pollution in world oceans using SAR remote sensing

Plastic pollution in the world’s oceans is estimated to have reached 270.000 tones, or 5.25 trillion pieces. This plastic is now ubiquitous, however due to ocean circulation patterns, it accumulates in the ocean gyres, creating “garbage patches”. This plastic debris is colonized by microorganisms which create unique bio-film ecosystems. Microbial colonization is the first step towards disintegration and degradation of plastic materials: a process that releases metabolic by-products from energy synthesis. These by-products include the release of short-chain and more complex carbon molecules in the form of surfactants, which we hypothesize will affect the fluid dynamic properties of waves (change in viscosity and surface tension) and make them detectable by SAR sensor. In this study we used Sentinel-1A and COSMO-SkyMed SAR images in selected sites of both the North Pacific and North Atlantic oceans, close to ocean gyres and away from coastal interference. Together with SAR processing we conducted contextual analysis, using ocean geophysical products of the sea surface temperature, surface wind, chlorophyll, wave heights and wave spectrum of the ocean surface. In addition, we started experiments under controlled conditions to test the behaviour of microbes colonizing the two most common pollutants, polyethylene (PE) and polyethylene terephthalate (PET) microplastics. The analysis of SAR images has shown that a combination of surface wind speed and Langmuir cells- ocean circulation pattern is the main controlling factor in creating the distinct appearance of the sea-slicks and microbial bio-films. The preliminary conclusion of our study is that SAR remote sensing may be able to detect plastic pollution in the open oceans and this method can be extended to other areas

Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images

In this paper, the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal is revisited. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. First, how these methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude) is investigated. Second, the performance of such algorithms in both cases is analyzed. Experimental results carried out on simulated speckled images and on true SAR data are presented and discussed in order to assess the best strategy. From these results, it can be observed that filtering in the amplitude domain yields better performances in terms of objective quality indexes, such as preservation of structural details, as well as in terms of visual inspection of the filtered SAR dat

Blind Speckle Decorrelation for SAR Image Despeckling

In the past few decades, several methods have been developed for despeckling synthetic aperture radar (SAR) images. A considerable number of them have been derived under the assumption of a fully-developed speckle model in which the multiplicative speckle noise is supposed to be a white process. Unfortunately, the transfer function of SAR acquisition systems can introduce a statistical correlation, which decreases the despeckling efficiency of such filters. In this paper, a whitening method is proposed for processing a complex image acquired by a SAR system. We demonstrate that the proposed approach allows the successful application of classical despeckling algorithms. First, we perform an estimation of the SAR system frequency response based on some statistical properties of the acquired image and by using realistic assumptions. Then, a decorrelation process is applied on the acquired image, taking into account the presence of point targets. Finally, the image is despeckled. The experimental results show that the despeckling filters achieve better performance when they are preceded by the proposed whitening method; furthermore, the radiometric characteristics of the image are preserve

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method