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.     

Multimodal focus attention and stress detection and feedback in an augmented driver simulator

This paper presents a driver simulator, which takes into account the information about the user’s state of mind (level of attention, fatigue state, stress state). The user’s state of mind analysis is based on video data and biological signals. Facial movements such as eyes blinking, yawning, head rotations, etc., are detected on video data: they are used in order to evaluate the fatigue and the attention level of the driver. The user’s electrocardiogram and galvanic skin response are recorded and analyzed in order to evaluate the stress level of the driver. A driver simulator software is modified so that the system is able to appropriately react to these critical situations of fatigue and stress: some audio and visual messages are sent to the driver, wheel vibrations are generated and the driver is supposed to react to the alert messages. A multi-threaded system is proposed to support multi-messages sent by the different modalities. Strategies for data fusion and fission are also provided. Some of these components are integrated within the first prototype of OpenInterface: the multimodal similar platform.     

Characterization of AlPO systems,precursors of the novel basic catalyst family AlPON

XPS and DRIFTS (diffuse reflectance infrared spectroscopy) spectra of AlPO systems, formally AlPO₄-Al₂O₃, obtained by the sol-gel method have been studied in order to understand their geometric and electronic structure. Both DRIFTS and XPS demonstrate that the acidbase character of these samples depends on a structural modification. For low phosphorus content an amorphous spinel-like solid is proposed. This geometric arrangement alters the electronic density of oxide ions and phosphorus cations and hence their Lewis acid-base properties with respect to the amorphous solid having aluminium and phosphorus only in tetrahedral arrangement.     

Effect of porosity and hydrostatic pressure on water absorption in a semicrystalline fluoropolymer

This paper discusses the effect of porosity and hydrostatic pressure on diffusion kinetics and equilibrium water uptake in a semicrystalline fluoropolymer. Water sorption experiments at atmospheric pressure and under water pressures up to 250 MPa were carried out during 18 months at 40 °C on reference and porous samples. Porosity of samples was induced due to a cavitation process occurring at the highest triaxiality area of waisted and notched specimens during tensile tests. Water uptake was found to be very sensitive to porosity, showing an increase in samples with a high void fraction. On the other hand, water content decreased with increasing pressure suggesting a compaction of the porous space in which water can be stored. Two models describing this water uptake behaviour were considered. The first is a classical model which assumes that sorption occurs only by diffusion following Fick’s law. Fick’s model was found to be in agreement with the experimental results. A “Langmuir-type” sorption model was also proposed to describe water uptake in porous samples, considering a two-phase water transport mechanism: one portion of the absorbed water diffuses through the polymer matrix and the other portion is stored in voids. This model was implemented in a user subroutine using ABAQUS? software and simulations were confronted to experimental sorption curves showing satisfactory agreements. The potential of the Langmuir-type sorption model resides on its availability to be coupled to a poro-mechanical model, in order to improve the understanding of coupling between the mechanical behaviour and water sorption mechanism in a porous polymer.     

Short‐Wave Infrared Sensor by the Photothermal Effect of Colloidal Gold Nanorods

Photodetection in the short‐wave infrared (SWIR) spectrum is a challenging task achieved often by costly low bandgap compound semiconductors involving highly toxic elements. In this work, an alternative low‐cost approach is reported for SWIR sensors that rely on the plasmonic‐induced photothermal effect of solution‐processed colloidal gold nanorods (Au NRs). A series of uniform solution‐processed Au NRs of various aspect ratios are prepared exhibiting a strong and well‐defined longitudinal localized surface plasmon resonance (L‐LSPR) maximum from 900 nm to 1.3 µm. A hybrid device structure is fabricated by applying Au NRs on the surface of a thermistor. Under a monochromatic illumination, hybrid Au‐NR/thermistor devices exhibit a clear photoresponse in the form of photoinduced resistance drop in the wavelength window from 1.0 to 1.8 µm. The photoresponsivity of such hybrid devices reaches a maximum value of 4.44 × 10⁷ Ω W^?1 at λ = 1.4 µm (intensity = 0.28 mW cm^?2), a wavelength in agreement with the L‐LSPR of the Au NRs applied. Colloidal Au NRs, capable to perform fast conversion between photon absorption and thermal energy, thus open an interesting avenue for alternative low‐cost SWIR photodetection.     

Discrimination between accelerated life models via Approximate Bayesian Computation

Accelerated life testing (ALT) is widely used in high-reliability product estimation to get relevant information about an item's performance and its failure mechanisms. To analyse the observed ALT data, reliability practitioners need to select a suitable accelerated life model based on the nature of the stress and the physics involved. A statistical model consists of (i) a lifetime distribution that represents the scatter in product life and (ii) a relationship between life and stress. In practice, several accelerated life models could be used for the same failure mode and the choice of the best model is far from trivial. For this reason, an efficient selection procedure to discriminate between a set of competing accelerated life models is of great importance for practitioners. In this paper, accelerated life model selection is approached by using the Approximate Bayesian Computation (ABC) method and a likelihood-based approach for comparison purposes. To demonstrate the efficiency of the ABC method in calibrating and selecting accelerated life model, an extensive Monte Carlo simulation study is carried out using different distances to measure the discrepancy between the empirical and simulated times of failure data. Then, the ABC algorithm is applied to real accelerated fatigue life data in order to select the most likely model among five plausible models. It has been demonstrated that the ABC method outperforms the likelihood-based approach in terms of reliability predictions mainly at lower percentiles particularly useful in reliability engineering and risk assessment applications. Moreover, it has shown that ABC could mitigate the effects of model misspecification through an appropriate choice of the distance function.     

Automatic boosting of cross-product coverage using Bayesian networks

Closing the feedback loop from coverage data to the stimuli generator is one of the main challenges in the verification process. Typically, verification engineers with deep domain knowledge manually prepare a set of stimuli generation directives for that purpose. Bayesian networks based CDG (coverage directed generation) systems have been successfully used to assist the process by automatically closing this feedback loop. However, constructing these CDG systems requires manual effort and a certain amount of domain knowledge from a machine learning specialist. We propose a new method that boosts coverage in the early stages of the verification process with minimal effort, namely a fully automatic construction of a CDG system that requires no domain knowledge. Experimental results on a real-life cross-product coverage model demonstrate the efficiency of the proposed method.     

Designing and evaluating an energy efficient Cloud

Cloud infrastructures have recently become a center of attention. They can support dynamic operational infrastructures adapted to the requirements of distributed applications. As large-scale distributed systems reach enormous sizes in terms of equipment, the energy consumption issue becomes one of the main challenges for large-scale integration. Like any other large-scale distributed system, Clouds face an increasing demand in energy. In this paper, we explore the energy issue by analyzing how much energy virtualized environments cost. We provide an energy-efficient framework dedicated to Cloud architectures and we validate it through different experimentations on a modern multicore platform. We show on a realistic example that our infrastructure could save 25% of the Cloud nodes’ electrical consumption.     

A new framework for sharp and efficient resolution of NCSP with manifolds of solutions

When numerical CSPs are used to solve systems of n equations with n variables, the preconditioned interval Newton operator plays two key roles: First it allows handling the n equations as a global constraint, hence achieving a powerful contraction. Second it can prove rigorously the existence of solutions. However, none of these advantages can be used for under-constrained systems of equations, which have manifolds of solutions. A new framework is proposed in this paper to extend the advantages of the preconditioned interval Newton to under-constrained systems of equations. This is achieved simply by allowing domains of the NCSP to be parallelepipeds, which generalize the boxes usually used as domains.