Learning of grasp selection based on shape-templates

The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.     

Stability,performance and sensitivity analysis of I.I.D. jump linear systems

This paper presents a symmetric Kronecker product analysis of independent and identically distributed jump linear systems to develop new, lower dimensional equations for the stability and performance analysis of this type of systems than what is currently available. In addition, new closed form expressions characterising multi-parameter relative sensitivity functions for performance metrics are introduced. The analysis technique is illustrated with a distributed fault-tolerant flight control example where the communication links are allowed to fail randomly.     

Identification of Toxin Inhibitors Using a Magnetic Nanosensor‐Based Assay

A magnetic nanosensor‐based method is described to screen a library of drugs for potential binding to toxins. Screening is performed by measuring changes in the magnetic relaxation signal of the nanosensors (bMR nanosensors) in aqueous suspension upon addition of the toxin. The Anthrax lethal factor (ALF) is selected as a model toxin to test the ability of our bMR nanosensor‐based screening method to identify potential inhibitors of the toxin. Out of 30 molecules screened, sulindac, naproxen and fusaric acid are found to bind LF, with dissociation constants in the low micromolar range. Further biological analysis of the free molecules in solution indicate that sulindac and its metabolic products inhibited LF cytotoxicity to macrophages with IC₅₀ values in the micromolar range. Meanwhile, fusaric acid is found to be less effective at inhibiting LF cytotoxicity, while naproxen does not inhibit LF toxicity. Most importantly, when the sulindac and fusaric acid‐bMR nanosensors themselves are tested as LF inhibitors, as opposed to the corresponding free molecules, they are stronger inhibitors of LF with IC₅₀ values in the nanomolar range. Taken together, these studies show that a bMR nanosensors‐based assay can be used to screen known drugs and other small molecules for inhibitor of toxins. The method can be easily modified to screen for inhibitors of other molecular interactions and not only the selected free molecule can be study as potential inhibitors but also the bMR nanosensors themselves achieving greater inhibitory potential.     

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.