Marie-Laure Nivet

Use of constraint solving in order to generate test vectors for behavioral validation

Validation of VHDL descriptions at the early phases of the microelectronic design is one of the most time consuming task design. This paper presents a test vector generation method for behavioral VHDL design. This method analyzes control and dependence flow of VHDL program. We use the cyclomatic complexity, that is a software metric based on a graph associated with the control part of software: the control flow graph (CFG). Significant control flow paths are selected using a powerful algorithm: the Pooles algorithm. The execution of this set of paths satisfies the coverage of each decision outcome of the VHDL program. Any additional test path would be a linear combination of the basis paths already tested and therefore considered to be redundant. By considering the selected paths as a group of constraints, test data are generated and solved using constraint programming. These data form the test bench that test the VHDL description.

A Neural Network model forecasting for prediction of hourly ozone concentration in Corsica

This paper presents the first results of a research project aimed at building a pollution peaks predictor using Artificial Neural Networks (ANNs) with data measured locally. We focus more particularly on the ozone concentration prediction in the Corsica Island at horizon “h+1”. We mainly look at the Multi-Layer Perceptron (MLP) network which is the most used of ANNs architectures both in the Environment domain and in the time series forecasting. We have demonstrated that an optimized MLP with endogenous, exogenous and time indicator inputs can forecast hourly ozone concentration with acceptable accuracy. The final results indicate that our predictor has an average Mean Absolute Percentage Error (MAPE) equal to 10.5%. Knowing that the devices measurement accuracy is around 10%, these results are considered as very convincing by “Qualitair Corse”, regional organization responsible for monitoring air quality. We have also tested in real conditions our predictor: indeed, several ozone pollution peaks occurred during the months of June and August 2010. While PREVAIR, the national air quality forecasting and mapping system, cannot predict the Augusts peaks, it appears that our optimized MLP is able to predict them in both cases.

Predictability of PV power grid performance on insular sites without weather stations: use of artificial neural networks

The official meteorological network is poor on the island of Corsica: only three sites being about 50 km apart are equipped with pyranometers which enable measurements by hourly and daily step. These sites are Ajaccio (41°55N and 8°48E, seaside), Bastia (42°33N, 9°29E, seaside) and Corte (42°30N, 9°15E average altitude of 486 meters). This lack of weather station makes difficult the predictability of PV power grid performance. This work intends to study a methodology which can predict global solar irradiation using data available from another location for daily and hourly horizon. In order to achieve this prediction, we have used Artificial Neural Network which is a popular artificial intelligence technique in the forecasting domain. A simulator has been obtained using data available for the station of Ajaccio that is the only station for which we have a lot of data: 16 years from 1972 to 1987. Then we have tested the efficiency of this simulator in two places with different geographical features: Corte, a mountainous region and Bastia, a coastal region. On daily horizon, the relocation has implied fewer errors than a “naive” prediction method based on the persistence (RMSE=1468 Vs 1383Wh/m² to Bastia and 1325 Vs 1213Wh/m² to Corte). On hourly case, the results were still satisfactory, and widely better than persistence (RMSE=138.8 Vs 109.3 Wh/m² to Bastia and 135.1 Vs 114.7 Wh/m² to Corte). The last experiment was to evaluate the accuracy of our simulator on a PV power grid localized at 10 km from the station of Ajaccio. We got errors very suitable (nRMSE=27.9%, RMSE=99.0 W.h) compared to those obtained with the persistence (nRMSE=42.2%, RMSE=149.7 W.h).

Path-oriented test data generation of behavioral VHDL description

The validation of HDL descriptions before their synthesis is one of the principal problems related to the top-down design process of complex circuits. This task can be accomplished according two approaches: formal verification or simulation based validation. Because formal verification, in spite of recent progress, is only feasible for small descriptions, simulation is still the best way to test hardware design. One of the main problems of such approach is to generate test vectors in order to verify design specifications. We think that high level HDL description represents a new source of information about the circuit which may be useful in test data generation field. The approach presented in this paper borrows techniques used successfully in software testing area for test vectors generation. This paper focus on a path-oriented test data generator.

The Role of Renewable Energy Sources in Solving Energy and Water Problems of Mediterranean Sea Islands

According to the Amsterdam Treaty, declaration No. 30, “…insular regions suffer from structural handicaps linked to their island status, the permanence of which impairs their economic and social development”. Considering the above, the present work aims to present different aspects of the current energy and water situation in Mediterranean Sea islands, using as case studies two representative French and Greek island regions. To this end, common problems as well as differences that call upon the elaboration of more case-specific solutions are identified. Accordingly, emphasis is given on future prospects for renewable energy sources and the role of integrated, hybrid solutions including energy storage and desalination aspects.