Absolute Stability of Nonlinear Systems with Two Additive Time-varying Delay Components

In this paper, we present a new sufficient condition for absolute stability of Lure system with two additive time-varying delay components. This criterion is expressed as a set of linear matrix inequalities (LMIs), which can be readily tested by using standard numerical software. We use this new criterion to stabilize a class of nonlinear time-delay systems. Some numerical examples are given to illustrate the applicability of the results using standard numerical software.     

Absolute stability of nonlinear systems with two additive time-varying delay components

In this paper, we present a new sufficient condition for absolute stability of Lure system with two additive time-varying delay components. This criterion is expressed as a set of linear matrix inequalities (LMIs), which can be readily tested by using standard numerical software. We use this new criterion to stabilize a class of nonlinear time-delay systems. Some numerical examples are given to illustrate the applicability of the results using standard numerical software.     

Perimeter discovery in wireless sensor networks

In this paper, we focus on the perimeter detection problem using wireless sensor networks, as perimeter detection has a wide range of uses in several areas. We present a decentralized localized algorithm where sensor nodes determine if they are located along the perimeter of a wireless sensor network. Our proposed algorithm uses the location neighborhood information in conjunction with the Barycentric technique to determine if the sensor node enclosed by neighboring nodes, and consequently, if it is located within the interior of the wireless sensor network. We define performance metrics to analyze the performance of our approach and the simulation shows that the algorithm gives fairly accurate results.     

Discrete data clustering using finite mixture models

Finite mixture models have been applied for different computer vision, image processing and pattern recognition tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel, well motivated mixture that we call the multinomial generalized Dirichlet mixture. The novel model is compared with other discrete mixtures. We designed experiments involving spatial color image databases modeling and summarization, and text classification to show the robustness, flexibility and merits of our approach.     

An efficient image-mosaicing method based on multifeature matching

Mosaicing is connecting two or more images and making a new wide area image with no visible seam-lines. Several algorithms have been proposed to construct mosaics from image sequence where the camera motion is more or less complex. Most of these methods are based either on the interest points matching or on theoretical corner models. This paper describes a fully automated image-mosaicing method based on the regions and the Harris points primitives. Indeed, in order to limit the search window of potential homologous points, for each point of interest, regions segmentation and matching steps are being performed. This enables us to improve the reliability and the robustness of the Harris points matching process by estimating the camera motion. The main originality of the proposed system resides in the preliminary manipulation of regions matching, thus making it possible to estimate the rotation, the translation and the scale factor between two successive images of the input sequence. This estimation allows an initial alignment of the images along with the framing of the interest points search window, and therefore reducing considerably the complexity of the interest points matching algorithm. Then, the resolution of a minimization problem, altogether considering the couples of matched-points, permits us to perform the homography. In order to improve the mosaic continuity around junctions, radiometric corrections are applied. The validity of the herewith described method is illustrated by being tested on several sequences of complex and challenging images captured from real-world indoor and outdoor scenes. These simulations proved the validity of the proposed method against camera motions, illumination variations, acquirement conditions, moving objects and image noise. To determine the importance of the regions matching stage in motion estimation, as well as for the framing of the search window associated to a point of interest, we compared the matching points results of this described method with those produced using the zero-mean normalized cross correlation score (without regions matching). We made this comparison in the case of a simple motion (without the presence of a rotation around optical axis and/or a scale factor), in the case of a rotation and in the general case of an homothety. For justifying the effectiveness of this method, we proposed an objective assessment by defining a reconstruction error.

Deep multilayer multiple kernel learning

Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems. With the emergence and the success of the deep learning concept, multilayer of multiple kernel learning (MLMKL) methods were inspired by the idea of deep architecture. They are introduced in order to improve the conventional MKL methods. Such architectures tend to learn deep kernel machines by exploring the combinations of multiple kernels in a multilayer structure. However, existing MLMKL methods often have trouble with the optimization of the network for two or more layers. Additionally, they do not always outperform the simplest method of combining multiple kernels (i.e., MKL). In order to improve the effectiveness of MKL approaches, we introduce, in this paper, a novel backpropagation MLMKL framework. Specifically, we propose to optimize the network over an adaptive backpropagation algorithm. We use the gradient ascent method instead of dual objective function, or the estimation of the leave-one-out error. We test our proposed method through a large set of experiments on a variety of benchmark data sets. We have successfully optimized the system over many layers. Empirical results over an extensive set of experiments show that our algorithm achieves high performance compared to the traditional MKL approach and existing MLMKL methods.     

Log-based mining techniques applied to Web service composition reengineering

Web service compositions are becoming more and more complex, involving numerous interacting ad-hoc services. These services are often implemented as business processes themselves. By analysing such complex web service compositions one is able to better understand, control and eventually re-design them. Our contribution to this problem is a mining algorithm, based on a statistical technique to discover composite web service patterns from execution logs. Our approach is characterised by a “local” pattern’s discovery that covers partial results through a dynamic programming algorithm. Those locally discovered patterns are then composed iteratively until the composite Web service is discovered. The analysis of the disparities between the discovered model and the initial ad-hoc composite model (delta-analysis) enables initial design gaps to be detected and thus to re-engineer the initial Web service composition.     

Toward green dialysis: Focus on water savings

Hemodialysis is one of the most water and energy‐hungry medical procedures, and thus represents a clear opportunity where improvements should be made concerning the consumption and wastage of water. Three levels were investigated on which there are potential savings: the precise adjustment of water production according to specific needs, the reuse of reverse osmosis rejected water, and finally the huge volumes of post‐patient dialysate effluent. The “AURAL” (Association pour l′Utilisation du Rein Artificiel à Lyon), main unit in Lyon, was the site of investigation for this study, which cares for 173 chronic hemodialysis patients. Evaluation of the 3 levels described earlier was undertaken on this particular building, and on the water treatment currently used. Volumes of produced water can be improved by different hydraulic systems or by adjusting the pure water conductivity used for dialysis. Concerning the reject water, reuse for building sanitation became the focus of further attention. The technical feasibility, volume of saved water, and applicable work costs were considered. The results suggest that out of a possible 2834 m³/year of reject water, 1200 m³/year may be reused and return on investment recovered within 5.8 years. Finally, the reprocessing and feasibility of reuse of dialysate effluent were investigated. Initial calculations show that although technical solutions are available, such processing of the wastewater production is not profitable in the short term. Regarding the significant prior authorization and risk management analysis necessary for such a project, this avenue was pursued no further. From the perspective of a “green dialysis,” the reuse of reject water into sanitation is both viable and profitable in our unit, and must be the next step of our project. More widely, improvements can be made by defining a more precise range of pure water conductivity for dialysis and by applying reuse water project to new or to be renovated units.