Identity of identity

Discussing on the subject of Identity, and reaching a common understanding is sometime difficult even between people having a similar background. It represents a formidable challenge when people originate from different disciplines (such as security, law or IT), and when they have totally different backgrounds (such as academics or practitioners). This article presents how FIDIS, a Network of Excellence, is addressing this challenge     

Robust H ∞ static output feedback stabilization of T-S fuzzy systems subject to actuator saturation

This paper proposes a method for designing robust H_?? static output feedback stabilization of Takagi-Sugeno (T-S) fuzzy systems under actuator saturation. In this paper, the input saturation is represented by a polytopic model and the modeling error is assumed a norm-bounded uncertainty. A set invariance condition for robust H_?? static output feedback system under actuator saturation is first established. Then, the estimation of the largest domain of attraction for the system is formulated and solved as a Linear Matrix Inequality (LMI) optimization problem. Two examples are used to demonstrate the effectiveness of the proposed design method.     

Ultrasonic Dispersion of Ceramic Powders

Ultrasonication is very effective in dispersing alumina and barium titanate slurries. When optimum conditions are used, very short durations (2 to 3 min) are enough to achieve well-dispersed, stable suspensions.     

Polymer electrolytes

Summary The room temperature conductivity of lithium salts dissolved in polyethylene oxide, proposed as polymer electrolytes, is shown to decrease drastically upon storage. The crystallization process, which is responsible for that ageing, can be blocked, if two further components are added : an elastomer copolymer butadiene-acrylonitrile and a macromer of polyoxyethylene. In addition, a crosslinking process must be carried out.     

What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.     

Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

Learning state machine-based string edit kernels

During the past few years, several works have been done to derive string kernels from probability distributions. For instance, the Fisher kernel uses a generative model M (e.g. a hidden Markov model) and compares two strings according to how they are generated by M. On the other hand, the marginalized kernels allow the computation of the joint similarity between two instances by summing conditional probabilities. In this paper, we adapt this approach to edit distance-based conditional distributions and we present a way to learn a new string edit kernel. We show that the practical computation of such a kernel between two strings x and x^′ built from an alphabet Σ requires (i) to learn edit probabilities in the form of the parameters of a stochastic state machine and (ii) to calculate an infinite sum over Σ^* by resorting to the intersection of probabilistic automata as done for rational kernels. We show on a handwritten character recognition task that our new kernel outperforms not only the state of the art string kernels and string edit kernels but also the standard edit distance used by a neighborhood-based classifier.     

Action Recognition Using a Bio-Inspired Feedforward Spiking Network

We propose a bio-inspired feedforward spiking network modeling two brain areas dedicated to motion (V1 and MT), and we show how the spiking output can be exploited in a computer vision application: action recognition. In order to analyze spike trains, we consider two characteristics of the neural code: mean firing rate of each neuron and synchrony between neurons. Interestingly, we show that they carry some relevant information for the action recognition application. We compare our results to Jhuang et al. (Proceedings of the 11th international conference on computer vision, pp. 1–8, 2007) on the Weizmann database. As a conclusion, we are convinced that spiking networks represent a powerful alternative framework for real vision applications that will benefit from recent advances in computational neuroscience.     

Towards a continuous microfluidic rheometer

In a previous paper we presented a way to measure the rheological properties of complex fluids on a microfluidic chip (Guillot et al., Langmuir 22:6438, 2006). The principle of our method is to use parallel flows between two immiscible fluids as a pressure sensor. In fact, in a such flow, both fluids flow side by side and the size occupied by each fluid stream depends only on both flow rates and on both viscosities. We use this property to measure the viscosity of one fluid knowing the viscosity of the other one, both flow rates and the relative size of both streams in a cross-section. We showed that using a less viscous fluid as a reference fluid allows to define a mean shear rate with a low standard deviation in the other fluid. This method allows us to measure the flow curve of a fluid with less than 250 μL of fluid. In this paper we implement this principle in a fully automated set up which controls the flow rate, analyzes the picture and calculates the mean shear rate and the viscosity of the studied fluid. We present results obtained for Newtonian fluids and complex fluids using this set up and we compare our data with cone and plate rheometer measurements. By adding a mixing stage in the fluidic network we show how this set up can be used to characterize in a continuous way the evolution of the rheological properties as a function of the formulation composition. We illustrate this by measuring the rheological curve of four formulations of polyethylene oxide solution with only 1.3 mL of concentrated polyethylene oxide solution. This method could be very useful in screening processes where the viscosity range and the behavior of the fluid to an applied stress must be evaluated.