Cédric Bornand

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a r t i c l e i n f o a b s t r a c t Digital watermarking techniques have been proposed for copyright protection and au-thentication of multimedia data. In this paper, we propose a novel Chinese Remainder Theorem (CRT)-based technique for digital watermarking in the Discrete Cosine Transform (DCT) domain that is robust to several common attacks. We(More)
We propose a computationally efficient Legendre neural network (LeNN) for identification of nonlinear dynamic systems. Due to its single-layer architecture, the LeNN offers much less computational complexity than that of a multilayer perceptron (MLP). By taking several plant models of increasing complexity and with extensive simulations we have shown(More)
Smart sensing of environmental parameters is an important task in robotics, process industries, sensor networks and autonomous systems. In this paper, we propose a novel Chebyshev neural network (ChNN) to develop smart sensors which can provide linearized and accurate readout, and can compensate for nonlinear environmental disturbances including additive(More)