E-TRoll: Tactile sensing and classification via a simple robotic gripper for extended rolling manipulations

Robotic tactile sensing provides a method of recognizing objects and their properties where vision fails. Prior work on tactile perception in robotic manipulation has frequently focused on exploratory procedures (EPs). However, the also-human-inspired technique of in-hand-manipulation can glean rich data in a fraction of the time of EPs. We propose a simple 3-DOF robotic hand design, optimized for object rolling tasks via a variable-width palm and associated control system. This system dynamically adjusts the distance between the finger bases in response to object behavior. Compared to fixed finger bases, this technique significantly increases the area of the object that is exposed to finger-mounted tactile arrays during a single rolling motion (an increase of over 60% was observed for a cylinder with a 30-millimeter diameter). In addition, this paper presents a feature extraction algorithm for the collected spatiotemporal dataset, which focuses on object corner identification, analysis, and compact representation. This technique drastically reduces the dimensionality of each data sample from 10×1500 time series data to 80 features, which was further reduced by Principal Component Analysis (PCA) to 22 components. An ensemble subspace k-nearest neighbors (KNN) classification model was trained with 90 observations on rolling three different geometric objects, resulting in a three-fold cross-validation accuracy of 95.6% for object shape recognition

Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes.

Aims/hypothesisWhile genome-wide association studies (GWASs) have been successful in identifying novel variants associated with various diseases, it has been much more difficult to determine the biological mechanisms underlying these associations. Expression quantitative trait loci (eQTL) provide another dimension to these data by associating single nucleotide polymorphisms (SNPs) with gene expression. We hypothesised that integrating SNPs known to be associated with type 2 diabetes with eQTLs and coexpression networks would enable the discovery of novel candidate genes for type 2 diabetes.MethodsWe selected 32 SNPs associated with type 2 diabetes in two or more independent GWASs. We used previously described eQTLs mapped from genotype and gene expression data collected from 1,008 morbidly obese patients to find genes with expression associated with these SNPs. We linked these genes to coexpression modules, and ranked the other genes in these modules using an inverse sum score.ResultsWe found 62 genes with expression associated with type 2 diabetes SNPs. We validated our method by linking highly ranked genes in the coexpression modules back to SNPs through a combined eQTL dataset. We showed that the eQTLs highlighted by this method are significantly enriched for association with type 2 diabetes in data from the Wellcome Trust Case Control Consortium (WTCCC, p = 0.026) and the Gene Environment Association Studies (GENEVA, p = 0.042), validating our approach. Many of the highly ranked genes are also involved in the regulation or metabolism of insulin, glucose or lipids.Conclusions/interpretationWe have devised a novel method, involving the integration of datasets of different modalities, to discover novel candidate genes for type 2 diabetes

Experimental investigation of heat transfer effectiveness on finned-tube thermoacoustic heat exchanger

Heat exchangers are some of the most important parts in thermoacoustic devices. In an oscillatory flow condition, the flow and temperature fields around the heat exchangers can be quite complex and it may significantly affect their heat transfer behaviour. As a result, one cannot directly apply the heat transfer correlations for a steady flow to the design of heat exchangers operating in oscillatory flow conditions. However, the fundamental knowledge of heat transfer in oscillatory flow is still not well established. The aim of the current work is to establish the heat transfer correlation for a few selected finned-tube heat exchangers, by measuring the heat transfer from the heat exchangers to the oscillatory flow in a range of operating conditions. Among the main parameters considered are fin spacing, fin length, thermal penetration depth, and gas displacement amplitude, and their effect on the thermal performance of heat exchangers. The results are presented in term of heat transfer effectiveness, which is the ratio of actual heat transfer rate to the maximum possible heat transfer rate. The correlations are proposed between the heat transfer effectiveness and the ratios of the fin length to the displacement amplitude and the fin spacing to the thermal penetration depth. The uncertainties associated with all the measurement data are also considered

Design, Fabrication and Characterization of Low Speed Open-jet Wind Tunnel

A new low-speed open-jet wind tunnel has been designed and constructed at the University of Leeds. A series of Computational Fluid Dynamics (CFD) and experimental evaluations were conducted to determine the flow quality and to verify the wind tunnel suitability for aerodynamic studies. Two sets of results are presented in the current paper. Initially, mean velocity and turbulent intensity measurements in an empty test section using a Pitot-static tube and hot wire anemometer (HWA) were introduced. These results show that flow quality was significantly affected by boundary layer controllers (honeycomb and mesh screens) in the settling chamber and wide angle diffuser. Investigations were also conducted to evaluate the effectiveness of using an array of synthetic jet actuators (SJAs) for flow control in a wake behind a convex "hump" model (section of circular cylinder). These additional tests were conducted to validate the suitability of the wind tunnel for aerodynamics research

A summary of the BARREL campaigns: Technique for studying electron precipitation.

BARREL observed electron precipitation over wide range of energy and timescalesPrecipitating electron distribution is determined using spectroscopy for 19 January 2013 eventBARREL timing data has accuracy within sampling interval of 0.05 s

Persistent reshaping of cohesive sediment towards stable flocs by turbulence.

Cohesive sediment forms flocs of various sizes and structures in the natural turbulent environment. Understanding flocculation is critical in accurately predicting sediment transport and biogeochemical cycles. In addition to aggregation and breakup, turbulence also reshapes flocs toward more stable structures. An Eulerian-Lagrangian framework has been implemented to investigate the effect of turbulence on flocculation by capturing the time-evolution of individual flocs. We have identified two floc reshaping mechanisms, namely breakage-regrowth and restructuring by hydrodynamic drag. Surface erosion is found to be the primary breakup mechanism for strong flocs, while fragile flocs tend to split into fragments of similar sizes. Aggregation of flocs of sizes comparable to or greater than the Kolmogorov scale is modulated by turbulence with lower aggregation efficiency. Our findings highlight the limiting effects of turbulence on both floc size and structure

Distinct motivations to seek out information in healthy individuals and problem gamblers

As massive amounts of information are becoming available to people, understanding the mechanisms underlying information-seeking is more pertinent today than ever. In this study, we investigate the underlying motivations to seek out information in healthy and addicted individuals. We developed a novel decision-making task and a novel computational model which allows dissociating the relative contribution of two motivating factors to seek out information: a desire for novelty and a general desire for knowledge. To investigate whether/how the motivations to seek out information vary between healthy and addicted individuals, in addition to healthy controls we included a sample of individuals with gambling disorder—a form of addiction without the confound of substance consumption and characterized by compulsive gambling. Our results indicate that healthy subjects and problem gamblers adopt distinct information-seeking “modes”. Healthy information-seeking behavior was mostly motivated by a desire for novelty. Problem gamblers, on the contrary, displayed reduced novelty-seeking and an increased desire for accumulating knowledge compared to healthy controls. Our findings not only shed new light on the motivations driving healthy and addicted individuals to seek out information, but they also have important implications for the treatment and diagnosis of behavioral addiction

Thermoacoustic refrigerator driven by a combustion-powered thermoacoustic engine - Demonstrator of device for rural areas of developing countries

This paper presents the design, construction and experimental evaluation of a demonstrator thermoacoustic refrigerator driven by a combustion-powered thermoacoustic engine. The system was developed to be a low-cost device for users based in remote and rural areas of developing countries. It employs a thermoacoustic engine/refrigerator coupling based on a travelling wave looped-tube configuration because of its relatively good thermal performance and construction simplicity. In the present demonstrator, a propane gas burner is used to simulate the thermal input from biomass combustion which is envisaged to be the source of energy for driving the system. Atmospheric air is applied as working fluid, while the operating frequency of the loop is 58.6 Hz. The location of the refrigerator is optimized experimentally to achieve the maximum cooling performance. So far, the lowest temperature achieved at the cold end of the regenerator is -3.6°C, while the maximum COPR achieved is 1.42%

High-Speed, Heavy-Load, and Direction-Controllable Photothermal Pneumatic Floating Robot.

Light-fueled actuators are promising in many fields due to their contactless, easily controllable, and eco-efficiency features. However, their application in liquid environments is complicated by the existing challenges of rapid deformation in liquids, light absorption of the liquid media, and environmental contamination. Here, we design a photothermal pneumatic floating robot (PPFR) using a boat-paddle structure. Light energy is converted into thermal energy of air by an isolated photothermal composite, which is then converted into mechanical energy of liquid to drive the movement of PPFRs. By understanding and controlling the photothermal actuation, the PPFR can achieve an average velocity of 13.1 mm s-1 in water and can be modified for remote on-demand differential steering and self-sustained oscillation. The PPFR may be modified to provide a lifting mechanism, capable of moving 4 times the PPFR mass. Various shapes and materials are suitable for the PPFR, providing a platform for liquid surface transporting, water sampling, pollutant collecting, underwater photography, and photocontrol robots in shallow water

Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images

Thurnhofer-Hemsi K., López-Rubio E., Roé-Vellvé N., Molina-Cabello M.A. (2019) Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer, ChamNowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the min- imization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust t. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech