Mohamed Bedoui Hedi

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Being able to analyze and interpret signal coming from electroencephalogram (EEG) recording can be of high interest for many applications including medical diagnosis and Brain-Computer Interfaces. Indeed, human experts are today able to extract from this signal many hints related to physiological as well as cognitive states of the recorded subject and it(More)
Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both(More)
Using artificial neural networks for Electroencephalogram (EEG) signal interpretation is a very challenging tasks for several reasons. The first class of reasons refers to the nature of data. Such signals are complex and difficult to process. The second class of reasons refers to the nature of underlying knowledge. Expertise is manifold and difficult to(More)
Multidimensional retiming is an efficient optimization approach that ensures increasing a parallelism level in order to optimize the execution time. Two existing techniques called incremental and chained multidimensional retiming are based on this approach, which aim at achieving a full parallelism on loop body in order to schedule applications with a(More)
Nested loops present the most critical sections in several embedded real-time applications. To attain a higher performance, several optimization techniques are employed in order to increase parallelism. However, due to the tight requirements, they are either unable to achieve any execution time constraint or achieve it with a high code size, which presents(More)