A Multisensor Architecture Providing Location-based Services for Smartphones

This paper introduces a multisensor architecture to fuse data acquired from different sensors available in commodity smartphones in order to build accurate location-based services, and pursuing a good balance between accuracy and performance. Using scale invariant features from the images captured using the smartphone camera, we perform a matching process against previously obtained images to determine the current location of the device. Several refinements are introduced to improve the performance and the scalability of our proposal. Location fingerprinting, based on IEEE 802.11, will be used to determine a cluster of physical points, or zone, where the device seems to be according to the received signal strength. In this way, we will reduce the number of images to analyze to those contained in the tentative zone. Additionally, accelerometers will also be considered in order to improve the system performance, by means of a motion estimator. This set of techniques enables a wide range of location-based applications.     

Long‐Term Structural Evolution of an Intercalated Layered Semiconductor

Intercalated molecules can dramatically modify the electronic band structure of layered semiconductors, significantly altering the optical properties of the material. In the layered monochalcogenide Gallium Telluride (GaTe), exposure to air induces a nearly 1 eV reduction of its band gap due to the interlayer diffusion and chemisorption of oxygen. The effect of oxygen chemisorption at the Te‐terminated surfaces on the structure of GaTe, however, is much less known. To better understand the structure–property relationship of intercalated GaTe, a systematic, long‐term, X‐ray diffraction study has been performed on GaTe exfoliated crystals exposed to ambient conditions. It is observed that the structural changes are not limited to a previously observed short‐term increase in lattice expansion. Over the course of months and even years after exfoliation, the oxygen adsorbates continue to modify the structure of GaTe, inducing significant disorder and grain reorientation. It is estimated that approximately one out of every two grains is slightly displaced by the intercalating oxygen, demonstrating a significant increase in grain mosaicity, while still maintaining the original {?2 0 1} out‐of‐plane texture. Correlating these structural transformations to observed changes in electrical and optical properties will enable capitalization of the use of adsorbates to engineer novel properties in these layered materials.     

User encoding for clustering in very sparse recommender systems tasks

Multimedia Tools and Applications - Recommender Systems are a very useful tool which let companies and service providers focus in the preferences of their customers, helping them to avoid an...     

A GIS-based Land Cover Classification Approach Suitable for Fine‐scale Urban Water Management

Water Resources Management - In the context of climate stress, urbanisation and population growth, design and planning tools that assist in decentralised and environmental infrastructural planning...     

A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.     

Automatic bearing fault diagnosis based on one-class ν-SVM

Rolling-element bearings are among the most used elements in industrial machinery, thus an early detection of a defect in these components is necessary to avoid major machine failures. Vibration analysis is a widely used condition monitoring technique for high-speed rotating machinery. Using the information contained in the vibration signals, an automatic method for bearing fault detection and diagnosis is presented in this work. Initially, a one-class ν-SVM is used to discriminate between normal and faulty conditions. In order to build a model of normal operation regime, only data extracted under normal conditions is used. Band-pass filters and Hilbert Transform are then used sequentially to obtain the envelope spectrum of the original raw signal that will finally be used to identify the location of the problem. In order to check the performance of the method, two different data sets are used: (a) real data from a laboratory test-to-failure experiment and (b) data obtained from a fault-seeded bearing test. The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time.     

A review of adaptive online learning for artificial neural networks

In real applications learning algorithms have to address several issues such as, huge amount of data, samples which arrive continuously and underlying data generation processes that evolve over time. Classical learning is not always appropriate to work in these environments since independent and indentically distributed data are assumed. Taking into account the requirements of the learning process, systems should be able to modify both their structures and their parameters. In this survey, our aim is to review the developed methodologies for adaptive learning with artificial neural networks, analyzing the strategies that have been traditionally applied over the years. We focus on sequential learning, the handling of the concept drift problem and the determination of the network structure. Despite the research in this field, there are currently no standard methods to deal with these environments and diverse issues remain an open problem.     

Synthesis at several length scales of zeolite A membranes using a continuous flow method

Zeolite A membranes were synthesized in the inner side of porous TiO₂ and α-Al₂O₃ tubular supports by a continuous method. The methodology was then used at several scales for preparing membranes on TiO₂ and α-Al₂O₃ supports with lengths of 6, 12 and 25 cm. Formation of an homogeneous zeolite film was confirmed by XRD and SEM in all supports. Single-gas permeation experiments (He, H₂, N₂ and n-C₃H₈) indicated that Knudsen diffusion was the predominant mechanism in both supports. All the synthesized membranes present high flux and moderate selectivity to water in water–ethanol mixtures.     

Efficient visualization of 3D models on hardware-limited portable devices

Managing the geometry of a 3D scene efficiently is a key aspect of an interactive 3D application. This aspect is more important if we target at portable devices, which have limited hardware capabilities. Developing new means for improving the interaction with 3D content in mobile devices is key. The aim of this work is to present a technique which can manage the level-of-detail of 3D meshes in portable devices. This solution has been devised considering the restrictions that this kind of devices poses. The results section shows how the integration has been successful while obtaining good performance.