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International audience; Water losses in Water Distribution Networks (WDNs) are inevitable. This is due to joints interconnections, ageing infrastructure and excessive pressure at lower demand. Pressure control has been showing promising results as a means of minimising water loss. Furthermore, it has been shown that pressure information at critical nodes is often adequate to ensure effective control in the system. In this work, a greedy algorithm for the identification of critical nodes is presented. An emulator for the WDN solution is put forward and used to simulate the dynamics of the WDN. A model-free control scheme based on reinforcement learning is used to interact with the proposed emulator to determine optimal pressure reducing valve settings based on the pressure information from the critical node. Results show that flows through the pipes and nodal pressure heads can be reduced using this scheme. The reduction in flows and nodal pressure leads to reduced leakage flows from the system. Moreover, the control scheme used in this work relies on the current operation of the system, unlike traditional machine learning methods that require prior knowledge about the system.

International audience; Pressure control in water distribution networks (WDNs) is one of the interventions commonly employed to improve the reliability and sustainability of water supply. Various approaches have been proposed to solve the problem of pressure control. However, most schemes that have been proposed rely on the accuracy of a model in order to precisely control a real WDN. Therefore, any deviation between a model and real WDN parameters could render the results of control schemes useless. As a result, this work proposes the utilisation of the reinforcement learning (RL) technique to control nodes pressure in WDNs without solving the model. Quadratic approximation emulators of WDNs and RL agents are used in the proposed scheme. The effectiveness of the proposed scheme is tested on two WDNs networks and the results are compared with the conventional optimisation scheme that is commonly used for simulation cases. The results show that the proposed scheme is able to achieve the desired results when compared to the benchmark optimisation procedure. However, unlike the optimisation procedure, the proposed scheme achieved the results without the numerical solution of the WDNs. Therefore, this scheme could be used in situations where the model of a network is not well defined.

International audience; In many developing countries, society is characterized by increasing population growth, urbanization, and industrialization, which have an effect on the environment. The growing population demands improved and safe ways of transporting people, goods, and services from one point to another at a minimal impact on the environment. The high demand for energy is another concern for any developing nation, most especially for a country where rail cars are powered by electricity. The weight of the rail cars contributes to the total carbon dioxide (CO2) emission. In order to reduce the energy demand, CO2 emission and increase return on investment, there is a need for improved composite materials. The current research focused on the production of composites made from polypropylene, sisal fiber and nanoparticle. The fiber was treated and the content was varied from 10, 20, 30 and 40 wt%, while the clay and the compatibilizer were kept constant at 3 and 5 wt% respectively. There was an improvement in the mechanical and thermal properties of the composites. Based on the improvement observed, the material can be used for the interior component of the rail vehicle.

International audience; Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Rie-mannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.

To optimize performance of ad hoc networks, the contradicting objectives: energy conservation and high data rate realization need to be jointly considered. In such a case, network interference need to be controlled in a manner that users transmit at minimum power enough to sustain the transmission. This paper proposes a joint power and rate adaptation in ad hoc network based on coupled interference minimization (PRIM) where the formulated network utility maximization (NUM) is solved using reverse engineering based on Karush-Kuhn-Tucker (KKT) conditions. In that way, users determine their transmit power and data rate based on their local observations (coupled interference). Pricing mechanism is employed in PRIM to restrict users from self-interest behaviours. It is demonstrated theoretically that PRIM satisfies the conditions of the super-modular games such that the local optimality is always the global optimality. Simulation results have shown that adapting transmits power and data rates based on the coupled interference (i.e. link dynamics) can improve the performance of ad hoc networks.

The present paper focuses on sharpness enhancement and noise removal in two dimensional gray scale images. In the grid smoothing approach, the image is represented by a graph in which the nodes represent the pixels and the edges reflect the connectivity. A cost function is defined using the spatial coordinates of the nodes and the gray levels present in the image. The minimisation of the cost function leads to new spatial coordinates for each node. Using an adequate cost function, the grid is compressed in the regions with large gradient values and relaxed in the other regions. The result is a grid which fits accurately the objects in the image. In the presented framework, the noise in the initial image is removed using a mesh smoothing approach. The edges are then enhanced using the grid smoothing. If the level of noise is low, the grid smoothing is applied directly to the image. The mathematical framework of the method is introduced in the paper. The processing chain is tested on natural images.

In the 21st century, energy demand and the attendant environmental degradation, are among the most challenging issues. The concern is due to the high dependence, globally on fossil fuels as a form of energy generation. Over 6.5 billion people worldwide require approximately 13 Terawatts of energy for their day-to-day needs. In order to achieve the required energy demand, there is a need to diversify into other forms of energy; in this case, renewable energy. In so doing, there is the need to study, extensively, alternative materials and sources needed for energy generation, storage, distribution and application. There has been a significant advancement in energy generation, conversion and storage, such as fuel cells and solar cells, photovoltaic cells, supercapacitors, batteries, etc. The emergence of nanostructured and composite materials has resulted in some significant contributions towards the improvement in the energy industry development. Renewable energy, such as wind and solar energies, depend considerably, on the environmental conditions, which are not always stable. Hence, in order to harness the energy from these sources and to adequately store such energy, there is a need for a high-performance energy conversion and storage system for the energy generation process. In this regard, carbon nanomaterials, metallic sulphides, titanium oxide and many other nanostructured materials have been studied, to a large extent, for energy conversions and storage devices. The importance of nanostructured and composite materials has shown, from researches, to resolve the issues surrounding energy from generation to storage. Keywords: Nanostructured, Composites, Renewable energy, Energy application

A two-junction micro p+np+ silicon avalanche-mode light-emitting device (Si AMLED) is analyzed for its dispersion characteristics, which generally resulted in different wavelengths of light (colors) being emitted at different angles from the surface of the device. The SiAMLED is integrated into on-chip bipolar radio frequency-integrated circuitry at micron dimensions. LEDs have high-frequency modulation frequencies reaching into the GHz range. Such devices, which are of micron dimension, operate at 8 to 10 V, 1 μA to 2 mA. The emission wavelength is in the 450- to 850-nm range, emission spot sizes are about 1 μm2, and emission intensities are up to 200 nW . μm − 2. The observed geometrical-chromatic dispersion characteristics range from 0.01 deg / nm wavelength for green radiation at a 5 deg exit angle to the normal of the device to 0.16 deg / nm wavelength for blue radiation at a 60 deg exit angle to the normal of the surface of the device. The high dispersion characteristics of the emitted radiation are attributed to the positioning of the optical source ∼1 μm subsurface to the silicon–silicon oxide interface, as well as to the high-refractive index differences between silicon and the surrounding lower refractive index silicon oxide layers. It is believed that the identified dispersion characteristics will have interesting and futuristic on-chip electro-optic applications for on-chip micro-optical wavelength dispersers, futuristic optical communication demultiplexers, along with on-chip microgas and biosensor applications.

International audience; Modern water distribution networks (WDNs) are complex and difficult to manage due to increased level of urbanization, varying consumer demands, ageing infrastructure, operational costs, and inadequate water resources. The management problems in such complex networks may be classified into short-term, medium-term, and long-term, depending on the duration at which the problems are solved or considered. To address the management problems associated with WDNs, mathematical models facilitate analysis and improvement of the performance of water infrastructure at minimum operational cost, and have been used by researchers, water utility managers, and operators. This paper presents a detailed review of the management problems and essential mathematical models that are used to address these problems at various phases of WDNs. In addition, it also discusses the main approaches to address these management problems to meet customer demands at the required pressure in terms of adequate water quantity and quality. Key challenges that are associated with the management of WDNs are discussed. Also, new directions for future research studies are suggested to enable water utility managers and researchers to improve the performance of water distribution networks.

International audience; A reconfigurable assembly fixture is a major and important component of a reconfigurable assembly system. It is required for the assembly of a variety of press brake models in order to reduce the assembly time and overall production time. The stages and requirements for the design of an assembly fixture and understanding of the assembly process for press brake models were used to design a reconfigurable assembly fixture. A detailed design analysis of parts of the fixture and the hydraulic system is considered and presented in this article. The stress and displacement analysis of the parts is executed using Solidworks express simulation. The parameters of the hydraulic components were determined from force requirements, and the hydraulic system was modelled physically using Matlab Simscape hydraulics. The response of the hydraulic system was obtained for each actuator in the system in order to depict the output of the actuators from the spool displacement of the valves. Stress analysis conducted on parts of the fixture showed that it can withstand maximum stresses that are lesser than the yield strength of the material used for the part. It was also established that synchronization of hydraulic actuators can best be achieved by the use of a sine input to the electrohydraulic valve. An experimental investigation was done using FESTO hydraulic test bench in order to observe the synchronized extension and retraction of the hydraulic actuators. The simulation of the hydraulic system, electric system and the programmable logic controller was prepared using automation studio. The design is envisaged to provide the industries with relevant information on accurate location and gripping of press brake frames rather than turning and repositioning of the frame in order to fit other parts during assembly. The article provides relevant information on the design analysis of a reconfigurable assembly fixture for press brakes which is novel because articles on reconfigurable assembly fixtures have not considered its application to press brake assembly.

International audience; This article presents a new method for fitting a digital line or plane to a given set of points in a 2D or 3D image in the presence of noise by maximizing the number of inliers, namely the consensus set. By using a digital model instead of a continuous one, we show that we can generate all possible consensus sets for model fitting. We present a deterministic algorithm that efficiently searches the optimal solution with time complexity O(Nd log N) for dimension d, where d = 2,3, together with space complexity O(N) where N is the number of points.

This paper analyses the kernel density estimation on spaces of Gaussian distributions endowed with different metrics. Explicit expressions of kernels are provided for the case of the 2-Wasserstein metric on multivariate Gaussian distributions and for the Fisher metric on multivariate centred distributions. Under the Fisher metric, the space of multivariate centred Gaussian distributions is isometric to the space of symmetric positive definite matrices under the affine-invariant metric and the space of univariate Gaussian distributions is isometric to the hyperbolic space. Thus kernel are also valid on these spaces. The density estimation is successfully applied to a classification problem of electro-encephalographic signals.

International audience; Brain Computer Interfaces (BCI) based on electroencephalog-raphy (EEG) rely on multichannel brain signal processing. Most of the state-of-the-art approaches deal with covariance matrices , and indeed Riemannian geometry has provided a substantial framework for developing new algorithms. Most notably , a straightforward algorithm such as Minimum Distance to Mean yields competitive results when applied with a Riemannian distance. This applicative contribution aims at assessing the impact of several distances on real EEG dataset , as the invariances embedded in those distances have an influence on the classification accuracy . Euclidean and Riemannian distances and means are compared both in term of quality of results and of computational load .

International audience; Brain Computer Interfaces (BCI) rely on brain waves signal, such as electro-encephalogram (EEG) recording, to endow a disabled user with non-muscular communication. Given the very low signal-to-noise ratio of EEG, a signal enhancement phase is crucial for ensuring decent performances in BCI systems. Several methods have been proposed for EEG signal enhancement, such as Independent Component Analysis, Common Spatial Pattern, and Principal Component Analysis. We show that Canonical Correlation Analysis (CCA), initially introduced to SSVEP-based BCI as a feature extraction method, is a good candidate for such preprocessing state. Evaluation is performed on a recording from 5 subjects during a BCI task based on Steady-State Visual Evoked Potentials (SSVEP). We demonstrate that CCA significantly improves classification performances in SSVEP-based BCIs.

International audience; Detecting change-points in time series is at the heart of numerous applications, as abrupt changes in signal properties are quite common in natural and industrial processes. In this paper we propose an algorithm for a particular change- point detection problem where the frequency band of the signal changes at some points in the time axis. Apart from detecting the change-points, the proposed algorithm is also able to estimate the frequency bands. The main idea of the algorithm is to consider a simple local bandlimited model to represent the input signal in each sliding time window. The local model consists of a sum of two exponentials that, in the frequency domain, reads as the transfer function of a second order bandpass filter. Relying on the operational calculus, we obtain an explicit estimation of the parameters, that indicate the cut-off frequencies associated to each time window. The implementation is done in discrete time domain where the cut-off frequencies are computed as a combination of outputs of several FIR filters, providing a low computational cost online estimation. Experimental results show the efficiency and the stability of this algorithm even in presence of a moderate amount of noise.; La détection de ruptures dans les séries chronologiques est un problème important dans de nombreuses applications étant donné que l'occurrence de tels phénomènes représente souvent une information pertinente. Dans cet article, nous proposons un algorithme pour un problème de détection de ruptures correspondant à des changement brusque de bande de fréquence du signal observé. En plus de détecter les instants de ruptures, l'algorithme proposé est aussi en mesure d'estimer les bandes de fréquences. L'idée principale de l'algorithme est de considérer un modèle à bande limitée, local, simple pour représenter le signal dans chaque fenêtre temporelle glissante. Le modèle local consiste en une somme de deux exponentielles qui, dans le domaine fréquentiel, se lit comme la fonction de transfert d'un filtre passe-bande du second ordre. S'appuyant sur le calcul opérationnel, on obtient une estimation explicite des paramètres, qui indiquent les fréquences de coupure associé à chaque fenêtre de temps. La mise en oeuvre se fait en ligne, dans le domaine temporel, avec une charge de calcul faible. Les résultats expérimentaux illustrent les performances de cet algorithme, en présence de bruit.

International audience; The classification of subscriber types in mobile cellular networks is valuable for network service providers since it provides a mechanism to plan network services by better understanding subscriber behaviour in a network. Mobile networks contain vast repositories of data that store valuable information regarding subscriber behaviour. In this paper, a new approach for subscriber classification in mobile cellular networks is proposed. The proposed approach considers network traffic generated from a mobile cellular network operator in South Africa. The proposed approach makes use of a difference histogram approach for feature extraction and a fuzzy c-means clustering algorithm to classify traffic data into subscriber classes. To validate the proposed approach, a comparative analysis of two different multi-resolution feature extraction approaches, the empirical mode decomposition (EMD) approach and the discrete wavelet packet transform (DWPT) are compared with results obtained with the difference histogram (DH) approach. It is shown that the difference histogram provides better clustering results when compared to the two multi-resolution approaches demonstrating the potential of the difference histogram approach.

International audience; IP multimedia subsystem was introduced by the third generation partnership project (3GPP) to enable 3G mobile systems to provide ubiquitous cellular access to all the services that the Internet provides, with improved quality of service, appropriate charging and integration of services. For users to access the IMS services, IMS capable User Equipments are required. In this paper we present an IMS Client compliant to 3GPP, European telecommunications standards institute and Internet engineering task force (IETF) specifications. Though some work has been done in the development of the IMS Client, such clients cannot be deployed in the mobile devices. Our IMS client is developed under Java 2 micro-edition (J2ME) platform so it can be deployed in the mobile devices. The implementation and demonstration of the IMS client on the IMS Testbed are presented. Our proposed IMS client could perform the user registration and Instant messaging communication.

International audience; Wireless power transfer system is a modern technology which allows the transfer of electric power between the air-cored coils of its transformer via high frequency magnetic fields. However, due to its coil separation distance and misalignment, maximum power transfer is not guaranteed. Based on a more efficient and general model available in the literature, rederived mathematical models for evaluating the mutual inductance between circular coils with and without lateral and angular misalignment are presented. Rather than presenting results numerically, the computed results are graphically implemented using MATLAB codes. The results are compared with the published ones and clarification regarding the errors made are presented. In conclusion, this study shows that power transfer efficiency of the system can be improved if a higher frequency alternating current is supplied to the primary coil, the reactive parts of the coils are compensated with capacitors and ferrite cores are added to the coils.