The generation and design of the service architecture of an information system is complex. It depends more on the vision of the service than on the vision of the service inside an information system. An information system is indeed a system of services that can contain thousands of services. The lack of consideration of constraints imposed by the information system makes it difficult to reuse these services. Another strong constraint is that an information system service must support a business service. The proposed approach allows information system services to be generated in accordance with the business services specification and their logical architecture to be automatically designed by respecting a logical architecture pattern of the system-of-services. An information system services generation algorithm allows being consistent with the logical architecture pattern during this generation. The definition of coherence and coupling properties makes it possible to evaluate the relevancy of the system-of-services. A use case shows the value of these properties in making the logical architecture of the service system more relevant to business services.

The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration, the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion.

This paper presents a performance evaluation of stock picking by merging several technical indicators. Several fusion operators have been proposed either in the probabilistic or in the possibilistic framework. The latter fuzzy framework has been introduced to manage the uncertain information embedded in financial time series due to human biases as studied by behavioral finance. Performances of portfolio resulting from the proposed systems are evaluated according to cumulative returns but also through a risk analysis point of view (Sharpe ratio). Two fusion mechanisms (one probabilistic, one possibilistic) aiming at discriminating common information from merged technical indicators, produce the higher portfolio performances. It also appears that selecting specific technical indicators affects the overall performances of the proposed stock picking systems. Indeed, studying the technical indicators selection through a shared/nonshared information point of view reveals that possibilistic framework is more robust to redundant sources than probabilistic framework. Effects of some parameters used in the fusion algorithms (amount of assets, window length analysis, ...) are also investigated. Results from all these tests clearly show the high potentiality of technical indicators fusion to improve portfolio performances. However, these first promising results have to be further inspected within wider contexts, as discussed at the end of the paper.

This paper proposes an approach referred as: iterative refinement of possibility distributions by learning (IRPDL) for pixel-based image classification. The IRPDL approach is based on the use of possibilistic reasoning concepts exploiting expert knowledge sources as well as ground possibilistic seeds learning. The set of seeds is constructed by incrementally updating and refining the possibility distributions. Synthetic images as well as real images from the RIDER Breast MRI database are being used to evaluate the IRPDL performance. Its performance is compared with three relevant reference methods: region growing, semi-supervised fuzzy pattern matching, and Markov random fields. The IRDPL performance (in terms of recognition rate, 87.3%) is close to the Markovian method (88.8%) that is considered to be the reference in pixel-based image classification. IRPDL outperforms the other two methods, respectively, at the recognition rates of 83.9% and 84.7%. In addition, the proposed IRPDL requires fewer parameters for the mathematical representation and presents a reduced computational complexity.

In this paper, an approach for pixel unmixing based on possibilistic similarity is proposed. This approach uses possibility distributions to express both the expert’s semantic knowledge (a priori knowledge) and the contextual information. Dubois-Prade’s probability-possibility transformation is used to construct these possibility distributions starting from statistical information (learning areas delimitated by an expert for each thematic class in the analyzed scene) which serve, first, for the estimation of the probability density functions using the kernel density estimation. The pixel unmixing is then performed based on the possibilistic similarity between a local possibility distribution estimated around the considered pixel and the obtained possibility distributions representing the predefined thematic classes. The obtained similarity values are used in order to obtain the abundances of different classes in the considered pixel. Accuracy analysis of pixels unmixing demonstrates that the proposed approach represents an efficient estimator of their abundances of the predefined thematic classes and, in turn, higher classification accuracy is achieved. Synthetic images are used in order to evaluate the performances of the proposed approach.

Accurately simulating sonar images and swath bathymetry profiles enables to render the reality of the underwater environment. This approach is strongly motivated by the operational constraints for data acquisition. Such a tool is especially attractive for tuning algorithms (i.e. detection and classification ones) according to sensors settings and sea-bottom nature and relief. Therefore, we developed such a simulator relying on acoustical ray tracing techniques tailored for considering specific underwater propagation properties (multi-path sound propagation, sound velocity profiles, adaptive ray oversampling to modelize acoustic lobe configuration). It aims at rendering data similar to what real sensors provide in a natural contest. In the proposed approach, the virtual environment gathers both static and dynamic behaviours, namely simulator inputs may be a priori known or updated all along time. Sea-ground truth is provided by either a real elevation map or fractal simulation allowing a fine macro-relief description (flat, rocky patch, rippled sea-bottom). In addition, micro-relief is modelled through sea-bed textures resulting from adequate spatial layouts of ground resolution cells. Sets of natural or manufactured bottom-laying items may be introduced using mesh descriptions (ship wreck, ...) or structured geometric primitives (anchors, pipelines...). Furthermore, the material of the previous inputs tunes their acoustic response. Taking into account carrier trajectory and attitude along time warrants multi-sensor realistic outputs. A great interest of our parallel and modular implementation is an easier incorporation in a more global rendering system. However, its interface proposes real time visualisation of the acquired environment through windows showing current sonar images, swath bathymetry, vehicle attitude and trajectory. Given this flexibility, a promising 3D sea-bottom and objects reconstruction method already benefit from this simulator virtual reality.