Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

Las enseñanzas de peluquería y estética en FP.

Se realiza una aproximación a los conocimientos técnicos que son indispensables poseer si se quiere comprender cómo lograr una belleza personal. Se incluyen algunos ejemplos de procesos que se estudian en peluquería y estética y que tienen su fundamento científico.

Preparation and characterization of the bacterial cellulose/polyurethane nanocomposites

New nanocomposites based on bacterial cellulose nanofibers (BCN) and polyurethane (PU) prepolymer were prepared and characterized by SEM, FT-IR, XRD, and TG/DTG analyses. An improvement of the interface reaction between the BCN and the PU prepolymer was obtained by a solvent exchange process. FT-IR results showed the main urethane band at 2,270 cm-1 to PU prepolymer; however, in nanocomposites new bands appear as disubstituted urea at 1,650 and 1,550 cm-1. In addition, the observed decrease in the intensity of the hydroxyl band (3,500 cm-1) suggests an interaction between BCN hydroxyls and NCO-free groups. The nanocomposites presented a non-crystalline character, significant thermal stability (up to 230 °C) and low water absorption when compared to pristine BCN. © 2013 Akadémiai Kiadó, Budapest, Hungary.

Multiple imputation for body mass index: lessons from the Australian Longitudinal Study on Women's Health

In large epidemiological studies missing data can be a problem, especially if information is sought on a sensitive topic or when a composite measure is calculated from several variables each affected by missing values. Multiple imputation is the method of choice for 'filling in' missing data based on associations among variables. Using an example about body mass index from the Australian Longitudinal Study on Women's Health, we identify a subset of variables that are particularly useful for imputing values for the target variables. Then we illustrate two uses of multiple imputation. The first is to examine and correct for bias when data are not missing completely at random. The second is to impute missing values for an important covariate; in this case omission from the imputation process of variables to be used in the analysis may introduce bias. We conclude with several recommendations for handling issues of missing data. Copyright (C) 2004 John Wiley Sons, Ltd.