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In this paper, we propose the concept of "doping watermarking", whose principle is to add an imperceptible noise to an host signal in order to improve its properties. Especially, our aim is to reduce the spectral support of the probability density function (PDF) of an audio signal in order to match the conditions of the quantization theorem. In this context, we develop a specific audiowatermarking algorithm and test its performance on real audio signals. This watermark allows to recover the PDF of a digital signal from a sub-quantized version of the signal, with very low error.Index Terms-quantization theorem, sub-quantization, audiowatermarking, speech and audio processing.

This paper addresses the issues of the Linear Parameter Varying (LPV) modelling and control of flexible-link robot manipulators. The LPV formalism allows the synthesis of nonlinear control laws and the assessment of their closed-loop stability and performances in a simple and effective manner, based on the use of Linear Matrix Inequalities (LMI). Following the quasi-LPV modelling approach, an LPV model of a flexible manipulator is obtained, starting from the nonlinear dynamic model stemming from Euler-Lagrange equations. Based on this LPV model, which has a rational dependence in terms of the varying parameters, two different methods for the synthesis of LPV controllers are explored. They guarantee the asymptotic stability and some level of closed-loop L 2 -gain performance on a bounded parametric set. The first method exploits a descriptor representation that simplifies the rational dependence of the LPV model, whereas the second one manages the troublesome rational dependence by using dilated LMI conditions and taking the particular structure of the model into account.The resulting controllers involve the measured state variables only, namely the joint positions and velocities. Simulation results are presented that illustrate the validity of the proposed control methodology. Comparisons with an inversion-based nonlinear control method are performed in the presence of velocity measurement noise, model uncertainties and high-frequency inputs.