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Reservoir computing is a new bio-inspired computation paradigm. It exploits a dynamical system driven by a time-dependent input to carry out computation. For efficient information processing, only a few parameters of the reservoir needs to be tuned, which makes it a promising framework for hardware implementation. Recently, electronic, opto-electronic and all-optical experimental reservoir computers were reported. In those implementations, the nonlinear response of the reservoir is provided by active devices such as optoelectronic modulators or optical amplifiers. By contrast, we propose here the first reservoir computer based on a fully passive nonlinearity, namely the saturable absorption of a semiconductor mirror. Our experimental setup constitutes an important step towards the development of ultrafast low-consumption analog computers.
This paper demonstrates a hyperspectral tomographic technique that can recover the temperature and concentration field of gas flows based on multi-mode absorption spectroscopy (MUMAS). This method relies on the recently proposed concept of nonlinear tomography, which can take full advantage of the nonlinear dependency of MUMAS signals on temperature and enables 2D spatial resolution of MUMAS which is naturally a line-of-sight technique. The principles of MUMAS and nonlinear tomography, as well as the mathematical formulation of the inversion problem, are introduced. Proof-of-concept numerical demonstrations are presented using representative flame phantoms and assuming typical laser parameters. The results show that faithful reconstruction of temperature distribution is achievable when a signal-to-noise ratio of 20 is assumed. This method can potentially be extended to simultaneously reconstructing distributions of temperature and the concentration of multiple flame species. 153554b96e
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