Turbo-code encoders are one of the spreadest family of error correcting codes used in the communication's world, especially in space transmissions. This paper presents an efficient technique to reconstruct turbo-code encoders which allows a passive adversary, with only few bits of an intercepted message encoded by the target turbocode encoder, to determine the parameters of the turbo-code encoder used, and therefore to decode online the communications. Thereby, our results confirm that keeping secret the parameters of turbo-code encoders can not be considered as a cryptographically way to ensure confidentiality. The starting point of our work is algorithms due to Filiol which enable to find the parameters of each convolutional encoder in the turbo-code encoder. Then, we recover the interleaver with two new algorithms, the first one based on the dynamic trie structure and the second one on a first order statistical test. The first algorithm is dedicated to noiseless channels. The asymptotic complexity of the complete process is O(n4) when a n2-bit message is available to attack a n-bit turbo-code encoder. The second algorithm works for every kind of channel and the noise does not matter much. Additionally, we present experimental results which underline the right detection threshold to use to recover the interleaver with a high probability. Furthermore, this method also works for turbo-code encoders composed of punctured convolutional encoders.
In intensive agricultural regions, monitoring land use and cover change represents an important stake. Some land cover changes in agro-systems cause modifications in the management of land use that contribute to increase environmental problems, including an important degradation of water quality. In this context, the identification of land-cover dynamics at high spatial scales constitutes a prior approach for the restoration of water resources.
The modeling approach used to study land use and cover changes at a field-scale is adapted from a vector change analysis method generally applied to assess land cover changes from regional to global scales.
The main objective of this study is to identify vegetation changes at the field scale during winter, in relation with crop successions. Magnitude and direction of the vector of changes with remote sensing data and GIS, calculated on a small watershed located in Western France for a six-year period (1996-2001) indicate both intensity and nature of observed changes in this area. The results allow to qualify accurately (i.e. at the scale of the field) the type of changes, to quantify them and weigh up their intensity. Then, all the results are integrated in a probabilistic model to build-up a short time land use prediction.