Assessing UAV platform types and optical sensor specifications

Photogrammetric acquisition with unmanned aerial vehicles (UAV) has grown extensively over the last couple of years. Such mobile platforms and their processing software have matured, resulting in a market which offers off-the-shelf mapping solutions to surveying companies and geospatial enterprises. Different approaches in platform type and optical instruments exist, though its resulting products have similar specifications. To demonstrate differences in acquisitioning practice, a case study over an open mine was flown with two different off-the-shelf UAVs (a fixed-wing and a multi-rotor). The resulting imagery is analyzed to clarify the differences in collection quality. We look at image settings, and stress the fact of photographic experience if manual setting are applied. For mapping production it might be safest to set the camera on automatic. Furthermore, we try to estimate if blur is present due to image motion. A subtle trend seems to be present, for the fast flying platform though its extent is of similar order to the slow moving one. It shows both systems operate at their limits. Finally, the lens distortion is assessed with special attention to chromatic aberration. Here we see that through calibration such aberrations could be present, however detecting this phenomena directly on imagery is not straightforward. For such effects a normal lens is sufficient, though a better lens and collimator does give significant improvement

Towards real-time deep learning-based network intrusion detection on FPGA

Computer Science

Machine learning for misuse-based network intrusion detection: overview, unified evaluation and feature choice comparison framework

Computer Science

Distributional impacts of cash allowances for children: A microsimulation analysis for Russia and Europe

This article analyses programmes of cash allowances for children and compares their effectiveness in combating child poverty in Russia and four European Union (EU) countries representing alternative family policy models – Sweden, Germany, Belgium and the United Kingdom. Using microsimulation models, this article estimates the potential gains if the Russian system were re-designed along the policy parameters of these countries and vice versa. The results confirm that the poverty impact of the programme design is smaller than that of the level of spending. Other conditions being equal, the best distributional outcomes for children are achieved by applying the mix of universal and means-tested child benefits, such as those employed by the United Kingdom and Belgium. At the same time, the Russian design of child allowances does not appear to be less effective in terms of its impact on child poverty when transferred to European countries in place of their current arrangements

Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention

In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an image and to identify their parts for potential interactions. The method can recognize objects from arbitrary viewpoints and generalizes to instances that have never been observed during training, even if they are partially occluded and appear against cluttered backgrounds. Our approach builds on the implicit shape model of Leibe et al. We extend it to couple recognition to the provision of meta-dat

How much confidence can we have in EU-SILC? Complex sample designs and the standard error of the Europe 2020 poverty indicators

If estimates are based on samples, they should be accompanied by appropriate standard errors and confidence intervals. This is true for scientific research in general, and is even more important if estimates are used to inform and evaluate policy measures such as those aimed at attaining the Europe 2020 poverty reduction target. In this article I pay explicit attention to the calculation of standard errors and confidence intervals, with an application to the European Union Statistics on Income and Living Conditions (EU-SILC). The estimation of accurate standard errors requires among others good documentation and proper sample design variables in the dataset. However, this information is not always available. Therefore, I complement the existing documentation on the sample design of EU-SILC and test the effect on estimated standard errors of various simplifying assumptions with regard to the sample design. It is shown that accounting for clustering within households is of paramount importance. Although this results in many cases in a good approximation of the standard error, taking as much as possible account of the entire sample design generally leads to more accurate estimates, even if sample design variables are partially lacking. The effect is illustrated for the official Europe 2020 indicators of poverty and social exclusion and for all European countries included in the EU-SILC 2008 dataset. The findings are not only relevant for EU-SILC users, but also for users of other surveys on income and living conditions which lack accurate sample design variables

How much confidence can we have in EU-SILC? Complex sample designs and the standard error of the Europe 2020 poverty indicators

If estimates are based on samples, they should be accompanied by appropriate standard errors and confidence intervals. This is true for scientific research in general, and is even more important if estimates are used to inform and evaluate policy measures such as those aimed at attaining the Europe 2020 poverty reduction target. In this article I pay explicit attention to the calculation of standard errors and confidence intervals, with an application to the European Union Statistics on Income and Living Conditions (EU-SILC). The estimation of accurate standard errors requires among others good documentation and proper sample design variables in the dataset. However, this information is not always available. Therefore, I complement the existing documentation on the sample design of EU-SILC and test the effect on estimated standard errors of various simplifying assumptions with regard to the sample design. It is shown that accounting for clustering within households is of paramount importance. Although this results in many cases in a good approximation of the standard error, taking as much as possible account of the entire sample design generally leads to more accurate estimates, even if sample design variables are partially lacking. The effect is illustrated for the official Europe 2020 indicators of poverty and social exclusion and for all European countries included in the EU-SILC 2008 dataset. The findings are not only relevant for EU-SILC users, but also for users of other surveys on income and living conditions which lack accurate sample design variables

Poverty in the enlarged European Union. A discussion about definitions and reference Groups

The enlargement of the European Union (EU) has stirred discussion about the relevance of the traditional EU poverty indicator which measures poverty in relative and national terms. As a result of diverging living conditions of the poor in the old and new member states, some authors have argued in favour of alternative poverty measures. One line of thought is that the reference groups people use for evaluating their living standard have Europeanised and that a poverty measure should incorporate this evolution. Bowing on the rich literature on the conceptualisation of poverty and reference group theory, we argue that a distinction must be made between privately oriented reference groups and publicly oriented reference groups. Although research has mainly focused on the former type of reference groups, only the latter offers a norm to define the poverty threshold. We conclude with a research proposal to construct poverty lines driven by publicly oriented reference groups

Can low-income households afford a healthy diet? Insufficient income as a driver of food insecurity in Europe

In Europe, food insecurity is still a serious concern for individual and public health. Although progress has been made in reducing undernourishment, other types of malnutrition such as obesity and overweight are on the rise. Policies that aim at improving healthy eating and addressing food insecurity tend to focus on food aid, nutritional education and financial incentives. These policies are generally not targeted at the problem of insufficient income as a key barrier to access a healthy diet. In this paper, we present new evidence which shows that insufficient household income and inadequate minimum income policies constitute a remaining concern for accessing a healthy diet. We make use of estimates of the minimum cost of a healthy diet in 24 European countries, in accordance with national food-based dietary guidelines. We use these unique data to (1) estimate the proportion of people living in (sub)urban areas with insufficient income to access a healthy diet, before and after housing costs, based on representative income survey data (EU-SILC), and, (2) compare the cost of a healthy diet with the level of minimum income schemes for specific household types using microsimulation techniques. We find that in 16 out of 24 countries at least 10% of the population in (sub)urban areas risks to be confronted with income-related food insecurity. Our findings show that policies directed at tackling food insecurity should be embedded in broader economic and social policies that promote an adequate income for all, and limit the cost of other essential goods and services