Laboratoire d'Ingénierie des Systèmes

This paper studies the acoustical properties of hard-backed porous layers with periodically embedded air filled Helmholtz resonators. It is demonstrated that some enhancements in the acoustic absorption coefficient can be achieved in the viscous and inertial regimes at wavelengths much larger than the layer thickness. This enhancement is attributed to the excitation of two specific modes: Helmholtz resonance in the viscous regime and a trapped mode in the inertial regime. The enhancement in the absorption that is attributed to the Helmholtz resonance can be further improved when a small amount of porous material is removed from the resonator necks. In this way the frequency range in which these porous materials exhibit high values of the absorption coefficient can be extended by using Helmholtz resonators with a range of carefully tuned neck lengths.

BACKGROUND: In order to select for quantitative plant resistance to pathogens, high throughput approaches that can precisely quantify disease severity are needed. Automation and use of calibrated image analysis should provide more accurate, objective and faster analyses than visual assessments. In contrast to conventional visible imaging, chlorophyll fluorescence imaging is not sensitive to environmental light variations and provides single-channel images prone to a segmentation analysis by simple thresholding approaches. Among the various parameters used in chlorophyll fluorescence imaging, the maximum quantum yield of photosystem II photochemistry (Fv/Fm) is well adapted to phenotyping disease severity. Fv/Fm is an indicator of plant stress that displays a robust contrast between infected and healthy tissues. In the present paper, we aimed at the segmentation of Fv/Fm images to quantify disease severity. RESULTS: Based on the Fv/Fm values of each pixel of the image, a thresholding approach was developed to delimit diseased areas. A first step consisted in setting up thresholds to reproduce visual observations by trained raters of symptoms caused by Xanthomonas fuscans subsp. fuscans (Xff) CFBP4834-R on Phaseolus vulgaris cv. Flavert. In order to develop a thresholding approach valuable on any cultivars or species, a second step was based on modeling pixel-wise Fv/Fm-distributions as mixtures of Gaussian distributions. Such a modeling may discriminate various stages of the symptom development but over-weights artifacts that can occur on mock-inoculated samples. Therefore, we developed a thresholding approach based on the probability of misclassification of a healthy pixel. Then, a clustering step is performed on the diseased areas to discriminate between various stages of alteration of plant tissues. Notably, the use of chlorophyll fluorescence imaging could detect pre-symptomatic area. The interest of this image analysis procedure for assessing the levels of quantitative resistance is illustrated with the quantitation of disease severity on five commercial varieties of bean inoculated with Xff CFBP4834-R. CONCLUSIONS: In this paper, we describe an image analysis procedure for quantifying the leaf area impacted by the pathogen. In a perspective of high throughput phenotyping, the procedure was automated with the software R downloadable at http://www.r-project.org/. The R script is available at http://lisa.univ-angers.fr/PHENOTIC/telechargements.html.

Summary Approximate Bayesian computation ( ABC ), a type of likelihood‐free inference, is a family of statistical techniques to perform parameter estimation and model selection. It is increasingly used in ecology and evolution, where the models used can be too complex to be handled with standard likelihood techniques. The essence of ABC techniques is to compare simulation outputs to observed data, in order to select the parameter values of the simulations which best fit the data. ABC techniques are thus computationally demanding. This constitutes a key limitation to their implementation. We introduce the R package ‘Easy ABC ’ that enables one to launch a series of simulations from the R platform and to retrieve the simulation outputs in an appropriate format for post‐processing. The ‘Easy ABC ’ package further implements several efficient parameter sampling schemes to speed up the ABC procedure: on top of the standard prior sampling, it implements various algorithms to perform sequential ( ABC ‐sequential) and Markov chain Monte Carlo ( ABC ‐ MCMC ) sampling schemes. The package functions can furthermore make use of parallel computing. The R package ‘Easy ABC ’ complements the package ‘abc’ which enables various post‐processing of simulation outputs. ‘Easy ABC ’ makes several state‐of‐the‐art ABC implementations available to the large community of R users in the fields of ecology and evolution. It is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/EasyABC/index.html .

The absorption properties of a metaporous material made of non-resonant simple shape three-dimensional rigid inclusions (cube, cylinder, sphere, cone, and ring torus) embedded in a rigidly backed rigid-frame porous material are studied. A nearly total absorption can be obtained for a frequency lower than the quarter-wavelength resonance frequency due to the excitation of a trapped mode. To be correctly excited, this mode requires a filling fraction larger in three-dimensions than in two-dimensions for purely convex (cube, cylinder, sphere, and cone) shapes. At long wavelengths compared to the spatial period, a cube is found to be the best purely convex inclusion shape to embed in a cubic unit cell, while the embedment of a sphere or a cone cannot lead to an optimal absorption for some porous material properties and dimensions of the unit cell. At a fixed position of purely convex shape inclusion barycenter, the absorption coefficient only depends on the filling fraction and does not depend on the shape below the Bragg frequency arising from the interaction between the inclusion and its image with respect to the rigid backing. The influence of the incidence angle and of the material properties, namely, the flow resistivity is also shown. The results of the modeling are validated experimentally in the case of cubic and cylindrical inclusions.

In clinical and research applications, the assessment of vascular function has become of major importance to evaluate and follow the evolution of cardiovascular pathologies, diabetes, hypertension, or foot ulcers. Therefore, the development of engineering methodologies able to monitor noninvasively blood vessel activities-such as endothelial function-is a significant and emerging challenge. Laser-based techniques have been used to respond-as much as possible-to these requirements. Among them, laser Doppler flowmetry (LDF) and laser Doppler imaging (LDI) were proposed a few decades ago. They provide interesting vascular information but possess drawbacks that prevent an easy use in some clinical situations. Recently, the laser speckle contrast imaging (LSCI) technique, a noninvasive camera-based tool, was commercialized and overcomes some of the LDF and LDI weaknesses. Our paper describes how-using engineering methodologies-LDF, LDI, and LSCI can meet the challenging clinician needs in assessing vascular function, with a special focus on the state of the art and future trends.

This note deals with the model-reference control of timed event graphs using the dioid algebra and the residuation theory. It proposes a control structure based on a precompensator and a feedback controller to improve the controlled system performance. It is shown that this approach always leads to an optimal behavior of the closed-loop system. An example is given to illustrate the proposed approach.

This paper explores the dynamics of attitude change in two dimensions resulting from social interaction. We add a rejection mechanism into the 2D bounded confidence (BC) model proposed by Deffuant et al. (2001). Individuals are characterized by two-dimensional continuous attitudes, each associated with an uncertainty u, supposed constant in this first study. Individuals interact through random pairs. If their attitudes are closer than u on both dimensions, or further than u on both dimensions, or closer than u on one dimension and not further than u + δ u on the other dimension, then the rules of the BC model apply. But if their attitudes are closer than u on one dimension and further than u + δ u on the other dimension, then the individuals are in a dissonant state. They tend to solve this problem by shifting away their close attitudes. The model shows metastable clusters, which maintain themselves through opposite influences of competitor clusters. Our analysis and first experiments support the hypothesis that, for a large range of uncertainty values, the number of clusters grows linearly with the inverse of the uncertainty, whereas this growth is quadratic in the BC model.

A linear system theory has been developed for the class of discrete-event systems subject to synchronization. This paper presents the just-in-time control of such systems when reference input is updated and/or in the presence of uncontrollable input(s), the proposed controls are the solutions to an optimization problem under equality constraint.

This note proposes an internal model control for linear discrete-event systems over max-algebra. We concentrate on the controller block of this control structure.

The problem considered is state estimation in the presence of unknown state and measurement noise, each noise component being assumed to belong to some known interval. In such a bounded-error context, most available results are for linear models, and the purpose of the present paper is to deal with the nonlinear case. Based on interval analysis and the notion of set inversion, a new state estimator is presented, which evaluates a set estimate guaranteed to contain all values of the state that are consistent with the available observations, given the noise bounds and a set containing the initial value of the state. To the best of our knowledge, it is the first time that such a guaranteed estimator is made available. The precision of the set estimate can be improved, at the cost of more computation. The theoretical properties of the estimator are studied, and computer implementation has received special attention. A simple illustrative example is treated.

This paper deals with the synthesis of greatest linear causal feedback for discrete-event systems whose behavior is described in dioid. Such a feedback delays as far as possible the input of the system while keeping the same transfer relation between the input and the output. When a feedback exists in the system, the authors show how to compute a greater one without decreasing the system's performance.

We propose an algorithm which performs a progressive approximation of a viability kernel, iteratively using a classification method. We establish the mathematical conditions that the classification method should fulfil to guarantee the convergence to the actual viability kernel. We study more particularly the use of support vector machines (SVMs) as classification techniques. We show that they make possible to use gradient optimisation techniques to find a viable control at each time step, and over several time steps. This allows us to avoid the exponential growth of the computing time with the dimension of the control space. It also provides simple and efficient control procedures. We illustrate the method with some examples inspired from ecology

Today the challenges of sustainable development require new products, services or uses to be developed within the framework of an eco-innovation process integrating environmental and societal approaches. Creating such offers can be sometimes based upon using eco-innovation tools that focus on the idea generation phase and ideation mechanisms that allow stakeholders to redefine problems and develop new eco-creative concepts. This paper is focused on the comparison of the ideation mechanisms during the idea generation phase of eco-innovative concepts, called the eco-ideation phase. For this, it describes a set of academic and industrial studies taking into account the different ideation mechanisms. The tests compare an adapted creativity tool with existing eco-innovation tools regarding their performances during eco-ideation phases. In a second step, additional case studies are performed with the adapted creativity tool for eco-innovation, to assess whether this performance is maintained in various contexts and with various participants (in knowledge and skills). The results showed that the use of appropriate ideation mechanisms ensures a constant rate of idea generation throughout the eco-ideation session and a wide variety of ideas generated.

In this paper, initial localization problems are solved by using set-membership estimation. The method can be used with any robot and any kind of sensor(s), provided that a computable model of the environment/sensor interaction is available. With a pedagogical aim in mind, it is detailed in the case of the localization of a vehicle from range measurements in a polygonal environment. Salient properties of the method are as follows. First, it does not need any explicit management of matching hypotheses. Second, it is able to deal with ambiguous situations where several radically different vehicle configurations are consistent with the measurements. Third, it can be made robust to outliers. Fourth, it can deal with nonlinear observation models without any approximation. Fifth, the result is guaranteed in the sense that no configuration consistent with the data and the hypotheses can be missed.

Abstract Aim Most studies on latitudinal gradients of biodiversity have focused on gradients of species richness. Here we aim to test whether, on top of these strong diversity gradients, processes of community assembly vary along a latitudinal gradient of more than 33°. Location C hina, latitude 18.67–51.86° N . Methods We used species abundance distribution ( SAD ) data collected in 32 forest tree plots, and fitted a non‐neutral model of community assembly to these SADs . We then calculated the fitted deviation from neutrality, δ, and looked for correlations between δ and geographical and environmental data. Results The fitted parameter δ was positive in most plots, and was furthermore positively correlated with latitude and negatively with temperature, indicating a less even abundance distribution and a likely increase in the strength of environmental filtering in regions further from the tropics and with decreasing temperatures. These results imply that on top of reducing the species richness, cold temperature may impact community assembly processes by strengthening local environmental filtering. Main conclusions Our results suggest a latitudinal gradient in tree community assembly process in C hinese forests, in which deviations from neutrality increase with latitude, probably because of an increase in environmental harshness.

AIMS: Our aim was to develop an accurate, non-invasive, blood-test-based method for identifying the main characteristics of liver fibrosis in non-alcoholic fatty liver disease (NAFLD). METHODS: Fibrosis was staged according to NASH-CRN and Metavir systems in 226 patients with NAFLD. A fully automated algorithm measured the fractal dimension (FD) and the area of fibrosis (AOF). Independent predictors of diagnostic targets were determined using bootstrap methods. RESULTS: (i) Development. Significant fibrosis defined by NASH-CRN F ≥2 was diagnosed by weight, glycaemia, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and prothrombin index [area under the receiver operating characteristic (AUROC)=0.867]; significant fibrosis defined by Metavir F ≥2 was diagnosed by weight, age, glycaemia, AST, ALT, ferritin and platelets (FibroMeter AUROC=0.941, P<0.005). AOF was estimated by the combination of hyaluronic acid, glycaemia, AST, ALT, platelets and prothrombin index ((a) R(2) =0.530), while FD was estimated by hyaluronic acid, glycaemia, AST/ALT, weight and platelets ((a) R(2) =0.529). (ii) Evaluation. Although NASH-CRN was a better system for fibrosis staging, Metavir staging was a better reference for blood test. Thus, the patient rate with predictive values ≥90% by tests was 97.3% with Metavir reference vs. 66.5% with NASH-CRN reference (P<10(-3)). FibroMeter showed a significantly higher AUROC than the NAFLD fibrosis score for significant fibrosis, but not for severe fibrosis or cirrhosis, with both staging systems. Relationships between fibrosis lesions were well reflected by blood tests, e.g., the correlation between histological area and FD of fibrosis (r(s) =0.971, P<10(-3)) was well reflected by the relationship between respective blood tests (r(s) =0.852, P<10(-3)). CONCLUSIONS: Different characteristics of fibrosis in NAFLD can be diagnosed and quantified by blood tests with excellent accuracy.

Levelwise algorithms (e.g., the APRIORI algorithm) have been proved effective for association rule mining from sparse data. However, in many practical applications, the computation turns to be intractable for the user-given frequency threshold and th

Everyday action impairment is one of the diagnostic criteria of Alzheimer's disease and is associated with many serious consequences, including loss of functional autonomy and independence. It has been shown that the (re)learning of everyday activities is possible in Alzheimer's disease by using error reduction teaching approaches in naturalistic clinical settings. The purpose of this study is to develop a dual-modal virtual reality platform for training in everyday cooking activities in Alzheimer's disease and to establish its value as a training tool for everyday activities in these patients. Two everyday tasks and two error reduction learning methods were implemented within a virtual kitchen. Two patients with Alzheimer's disease and two healthy elderly controls were tested. All subjects were trained in two learning sessions on two comparable cooking tasks. Within each group (i.e., patients and controls), the order of the training methods was counterbalanced. Repeated measure analysis before and after learning was performed. A questionnaire of presence and a verbal interview were used to obtain information about the subjective responses of the participants to the VR experience. The results in terms of errors, omissions, and perseverations (i.e., repetitive behaviors) indicate that the patients performed worse than the controls before learning, but that they reached a level of performance similar to that of the controls after a short learning session, regardless of the learning method employed. This finding provides preliminary support for the value of the dual-modal virtual reality platform for training in everyday cooking activities in Alzheimer's disease. However, further work is needed before it is ready for clinical application.

Digital Signal and Image Processing Series

When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characterize the set of all acceptable parameter vectors in a guaranteed way, even when the model is nonlinear and the number of data points small. However, when the data may contain outliers, i.e., data points for which these bounds should be violated, this set may turn out to be empty, or at least unrealistically small. The outlier minimal number estimator (OMNE) has been designed to deal with such a situation, by minimizing the number of data points considered as outliers. OMNE has been shown in previous papers to be remarkably robust, even to a majority of outliers. Up to now, it was implemented by random scanning, so its results could not be guaranteed. In this paper, a new algorithm based on set inversion via interval analysis provides a guaranteed OMNE, which is applied to the initial localization of an actual robot in a partially known two-dimensional (2-D) environment. The difficult problems of associating range data to landmarks of the environment and of detecting potential outliers are solved as byproducts of the procedure.