Tactile perception by friction induced vibrations

Cataloged from PDF version of article.When a finger moves to scan the surface of an object (haptic sensing), the sliding contact generates vibrations that propagate in the finger skin activating the receptors (mechanoreceptors) located in the skin, allowing the brain to identify objects and perceive information about their properties. The information about the surface of the object is transmitted through vibrations induced by friction between the skin and the object scanned by the fingertip. The mechanoreceptors transduce the stress state into electrical impulses that are conveyed to the brain. A clear understanding of the mechanisms of the tactile sensing is fundamental to numerous applications, like the development of artificial tactile sensors for intelligent prostheses or robotic assistants, and in ergonomics. While the correlation between surface roughness and tactile sensation has already been reported in literature, the vibration spectra induced by the finger-surface scanning and the consequent activation of the mechanoreceptors on the skin have received less attention. In this paper, frequency analysis of signals characterizing surface scanning is carried out to investigate the vibration spectrum measured on the finger and to highlight the changes shown in the vibration spectra as a function of characteristic contact parameters such as scanning speed, roughness and surface texture. An experimental set-up is developed to recover the vibration dynamics by detecting the contact force and the induced vibrations; the bench test has been designed to guarantee reproducibility of measurements at the low amplitude of the vibrations of interest, and to perform measurements without introducing external noise. Two different perception mechanisms, as a function of the roughness wavelength, have been pointed out. The spectrum of vibration obtained by scanning textiles has been investigated. (C) 2011 Elsevier Ltd. All rights reserved

DETERMINING THE LOCATIONS OF POTENTIAL FIREFIGHTING TEAMS BY USING GIS TECHNIQUES

Wild forest fires are one of the most important disasters that affect the forest ecosystem especially in the regions with arid climate conditions. Besides, forest fires threats human life and results in seriously property loss. In order to fight forest fires effectively, it is crucial that firefighting team should reach fire location and start fire extinguishing activities within the critical response time. Since firefighting teams are transported to fire locations by fire-trucks, the optimum route with minimum travel time should be determined by considering available road network. “New Service Area” tool under “Network Analyst” extension of ArcGIS can be used to determine a region that can be reached from a point within a specified time period. In this study, it was aimed to evaluate the locations of current firefighting teams and investigate locations of potential firefighting teams using “New Service Area” tool. The study area is located in Mustafakemalpaşa in Bursa where forest lands are sensitive to forest fires at the second degree and there is currently one firefighting team in the area. The results indicated that 31.28% of forest land can be reached by current firefighting team within the critical response time. When including new firefighting teams, it was found that accessible forest lands increased to 71.55%. It can be concluded that locating new firefighting teams should be established in the study area to increase the accessible forested areas on time and GIS-based decision support systems can be effectively used to fight forest fires regarding with disaster management

ROAD EXTRACTION TECHNIQUES FROM REMOTE SENSING IMAGES: A REVIEW

The importance of analysis high resolution satellite imagery plays an important research topic for geographical information analysis of cities. Geospatial data plays an important role in important issues such as governmental, industrial, research topics on traffic management, road monitoring, GNSS navigation, and map updating. In this study, road detection from satellite imagery methods are classified as classification-based, knowledge-based, mathematical morphology and dynamic programming. In the beginning, the road structures including feature and model are analyzed. Then, the advantages and disadvantages of road detection methods are evaluated and summarizes their accuracy and performance based on road detection principles. Therefore, in order to obtain remarkable results for road detection, it is better to use more than one method. In after days, performing a complex road extraction from a satellite image is still a necessary and important research topic

Model validation for a noninvasive arterial stenosis detection problem

Copyright @ 2013 American Institute of Mathematical SciencesA current thrust in medical research is the development of a non-invasive method for detection, localization, and characterization of an arterial stenosis (a blockage or partial blockage in an artery). A method has been proposed to detect shear waves in the chest cavity which have been generated by disturbances in the blood flow resulting from a stenosis. In order to develop this methodology further, we use both one-dimensional pressure and shear wave experimental data from novel acoustic phantoms to validate corresponding viscoelastic mathematical models, which were developed in a concept paper [8] and refined herein. We estimate model parameters which give a good fit (in a sense to be precisely defined) to the experimental data, and use asymptotic error theory to provide confidence intervals for parameter estimates. Finally, since a robust error model is necessary for accurate parameter estimates and confidence analysis, we include a comparison of absolute and relative models for measurement error.The National Institute of Allergy and Infectious Diseases, the Air Force Office of Scientific Research, the Deopartment of Education and the Engineering and Physical Sciences Research Council (EPSRC)

Emigration, remittances, and the subjective well-being of those staying behind

© 2018, The Author(s). We offer the first global perspective on the well-being consequences of emigration for those staying behind using several subjective well-being measures (evaluations of best possible life, positive affect, stress, and depression). Using the Gallup World Poll data for 114 countries during 2009–2011, we find that having family members abroad is associated with greater evaluative well-being and positive affect, and receiving remittances is linked with further increases in evaluative well-being, especially in poorer contexts—both across and within countries. We also document that having household members abroad is linked with increased stress and depression, which are not offset by remittances. The out-migration of family members appears less traumatic in countries where migration is more common, indicating that people in such contexts might be able to cope better with separation. Overall, subjective well-being measures, which reflect both material and non-material aspects of life, furnish additional insights and a well-rounded picture of the consequences of emigration on migrant family members staying behind relative to standard outcomes employed in the literature, such as the left-behind’s consumption, income, or labor market outcomes

Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease

Brain arteries are routinely imaged in the clinical setting by various modalities, e.g., time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression, and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time-consuming, involve too many processing steps, and/or neglect image intensity information. In this study, we present a deep learning-based solution for the anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture using imaging data of 242 patients with cerebrovascular disease to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability, and fast predictions, our method could be highly applicable in the clinical setting

Dissipation of vibration in rough contact

The relationship which links the normal vibration occurring during the sliding of rough surfaces and the nominal contact area is investigated. Two regimes are found. In the first one, the vibrational level does not depend on the contact area, while in the second one, it is propor- tional to the contact area. A theoretical model is proposed. It is based on the assumption that the vibrational level results from a competition between two processes of vibration damping, the internal damping of the material and the contact damping occurring at the interface

A Large Hadron Electron Collider at CERN

This document provides a brief overview of the recently published report on the design of the Large Hadron Electron Collider (LHeC), which comprises its physics programme, accelerator physics, technology and main detector concepts. The LHeC exploits and develops challenging, though principally existing, accelerator and detector technologies. This summary is complemented by brief illustrations of some of the highlights of the physics programme, which relies on a vastly extended kinematic range, luminosity and unprecedented precision in deep inelastic scattering. Illustrations are provided regarding high precision QCD, new physics (Higgs, SUSY) and electron-ion physics. The LHeC is designed to run synchronously with the LHC in the twenties and to achieve an integrated luminosity of O(100) fb$^{-1}$. It will become the cleanest high resolution microscope of mankind and will substantially extend as well as complement the investigation of the physics of the TeV energy scale, which has been enabled by the LHC

Are mice good models for human neuromuscular disease? Comparing muscle excursions in walking between mice and humans

The mouse is one of the most widely used animal models to study neuromuscular diseases and test new therapeutic strategies. However, findings from successful pre-clinical studies using mouse models frequently fail to translate to humans due to various factors. Differences in muscle function between the two species could be crucial but often have been overlooked. The purpose of this study was to evaluate and compare muscle excursions in walking between mice and humans