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)
The aim of this work is to propose a fast and reliable design flow for the implementation of some image analysis algorithms on an adaptive architecture using an FPGA platform. This adaptive architecture is designed in a Globally Asynchronous Locally Synchronous (GALS) approach so that the hardware resources are stand-alone modules. Any modification only(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)
The effects of nuclear radiations on conventional thermocouples (type K, C and N) mainly used in irradiation experiments may create significant drifts of the signals. In order to solve these difficulties, the Commissariat à l'Energie Atomique has started to develop and qualify in laboratory conditions a miniature device, which combines a noise(More)