The GBIF portal contains a description of most of the global biodiversity data. It faces two problems, namely the data availability and a poor expressiveness of queries, mainly due to a growing number of users which keep expressing new needs. To tackle these problems, we envision a scalable and relatively low cost solution. With this in mind, we propose a non-invasive and decentralized architecture for processing GBIF queries over a cloud infrastructure. We define a dynamic strategy for data distribution and queries processing algorithms that fit the GBIF requirements. We demonstrate the feasibility and efficiency of our solution by a prototype implementation which allows for processing extra query types, up to now unsupported by the GBIF portal.
The existence of crosscutting concerns tangled or scattered, complicates the understanding and evolution of object oriented source code. The industrial adoption of aspect-oriented paradigm has led to research new approaches supporting aspect oriented migration. This migration requires the identification of crosscutting concerns, in order to encapsulate them into aspects. We propose in this paper a new approach for the identification of crosscutting concerns at the conceptual level. We materialize this latter by the UML class and sequence diagrams. We use the formal concept analysis to group scattered functionalities in sequence diagrams, and we analyze the order of method calls to detect the tangled ones. Then, we filter all obtained candidate aspects, in order to avoid the mistakes.
In this work, we consider an optimal control problem of a biological sequencing batch reactor for the treatment of pollutants. This model includes two biological reactions, one being aerobic while the other is anoxic. We are first interested in a problem of optimal control in time and then, in both time and energy. The existence of the optimal trajectories is proven and the corresponding optimal controls are derived in each case.
Spatial Metadata are used to describe the existing data sources in order to facilitate their access and sharing between different actors. The problem of exploitation of these metadata arises when they should be catalogued within the framework of a platform for spatial data federation. We describe a service oriented approach for structuring this component, with an implementation based on LDAP. To achieve this, we start from a canonical language that unifies the major known geographic metadata standards, and then we define new classes of LDAP objects which map syntactic units of canonical language.
The growing complexity of new chips and the time-to-market constraints require fundamental changes in the way systems are designed. Systems on Chip (SoC) based on reused components have become an absolute necessity to embedded systems companies that want to remain competitive. However, the design of a SoC is extremely complex because it encompasses a range of difficult problems in hardware and software design. This paper focuses on the design of parallel and multi-frequency applications using flexible components. Flexible parallel components are assembled using a scheduling method which combines the synchronous data-flow principle of balance equations and the polyhedral scheduling technique. Our approach allows a flexible component to be modelled and a full system to be assembled and synthesized with automatically generated wrappers. The work presented here is an extension of previous work. We illustrate our method on a simplified WCDMA system. We discuss the relationship of this approach with multi-clock architecture, latency-insensitive design, multidimensional data-flow systems and stream programming
We consider hard real-time systems composed of periodic tasks and of an aperiodic flow. Each task, either periodic or aperiodic, has a firm deadline. An aperiodic task is accepted within the system only if it can be completed before its deadline, without causing temporal failures for the periodic tasks or for the previously accepted aperiodic tasks. We propose an acceptance test, linear in the number of pending accepted aperiodic tasks. This protocol can be used provided the idle slots left by the periodic tasks are fairly distributed. We then propose a model-driven approach, based on Petri nets, to produce schedules with a fair distribution of the idle slots for systems of non independent periodic tasks.
OFDM based pilots channel estimation methods with processing into the transform domain appear attractive owing to their capacity to highly reduce the noise component effect. However, in current OFDM systems, null subcarriers are placed at the edge of the spectrum in order to assure isolation from interfering signals in neighboring frequency bands; and the presence of these null carriers may lead, if not taken into account, to serious degradation of the estimated channel responses due to the “border effect” phenomenon. In this paper an improved algorithm based on truncated SVD is proposed in order to correctly support the case of null carriers at border spectrum. A method for optimizing the truncation threshold whatever the system parameters is also proposed. To make the truncated SVD channel estimation method applicable to any SISO or MIMO OFDM system and whatever the system parameters, a complexity reduction algorithm based on the distribution of the power in the transfer matrix (based on DFT or DCT) is proposed.
The aboveground biomass estimation is an important question in the scope of Reducing Emission from Deforestation and Forest Degradation (REDD framework of the UNCCC). It is particularly challenging for tropical countries because of the scarcity of accurate ground forest inventory data and of the complexity of the forests. Satellite-borne remote sensing can help solve this problem considering the increasing availability of optical very high spatial resolution images that provide information on the forest structure via texture analysis of the canopy grain. For example, the FOTO (FOurier Texture Ordination) proved relevant for forest biomass prediction in several tropical regions. It uses PCA and linear regression and, in this paper, we suggest applying classification methods such as k-NN (k-nearest neighbors), SVM (support vector machines) and Random Forests to texture descriptors extracted from images via Fourier spectra. Experiments have been carried out on simulated images produced by the software DART (Discrete Anisotropic Radiative Transfer) in reference to information (3D stand mockups) from forests of DRC (Democratic Republic of Congo), CAR (Central African Republic) and Congo. On this basis, we show that some classification techniques may yield a gain in prediction accuracy of 18 to 20%
