The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).

Emissions of volatile organic compounds (VOCs) from the compost maturation building in a municipal solid waste treatment facility were inventoried by solid phase microextraction and gas chromatography-mass spectrometry. A large diversity of chemical classes and compounds were found. The highest concentrations were found for n-butanol, methyl ethyl ketone and limonene (ppmv level). Also, a range of compounds exceeded their odor threshold evidencing that treatment was needed. Performance of a chemical scrubber followed by two parallel biofilters packed with an advanced packing material and treating an average airflow of 99,300 m(3) h(-1) was assessed in the treatment of the VOCs inventoried. Performance of the odor abatement system was evaluated in terms of removal efficiency by comparing inlet and outlet abundances. Outlet concentrations of selected VOCs permitted to identify critical odorants emitted to the atmosphere. In particular, limonene was found as the most critical VOC in the present study. Only six compounds from the odorant group were removed with efficiencies higher than 90%. Low removal efficiencies were found for most of the compounds present in the emission showing a significant relation with their chemical properties (functionality and solubility) and operational parameters (temperature, pH and inlet concentration). Interestingly, benzaldehyde and benzyl alcohol were found to be produced in the treatment system.

The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problems related to remote sensing imaging. In this paper we propose a novel approach to deal with the problem of modelling multiscale contexts surrounding pixels of different land cover categories. The approach leverages the computation of a heteroscedastic measure of uncertainty when classifying individual pixels in an image. This classification uncertainty measure is used to define a set of memory gates between layers that allow a principled method to select the optimal decision for each pixel.

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