Anne-Claire Legrand

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The aim of this paper is to present an adaptable Fat Tree NoC architecture for Field Programmable Gate Array (FPGA) designed for image analysis applications. Traditional NoCs (Network on Chip) are not optimal for dataflow applications with large amount of data. On the opposite, point to point communications are designed from the algorithm requirements but(More)
An adaptive FPGA architecture based on the NoC (Network-on-Chip) approach is used for the multispectral image correlation. This architecture must contain several distance algorithms depending on the characteristics of spectral images and the precision of the authentication. The analysis of distance algorithms is required which bases on the algorithmic(More)
As an alternative to vector representations, a recent trend in image classification suggests to integrate additional structural information in the description of images in order to enhance classification accuracy. Rather than being represented in a p-dimensional space, images can typically be encoded in the form of strings, trees or graphs and are usually(More)
An efficient Network on Chip (NoC) implemented on Field Programmable Gate Arrays (FPGA) is proposed for the data communication of multispectral image analysis algorithms in an adaptive architecture. The architecture design is based on the linear effort property and reusable IPs. Mul-tispectral image data are several types of data, different length values.(More)