Digital signal processing techniques adopted in the spectroscopy and its relevant devices for chemical analysis have been improved remarkably these days, which means sufficient sensitivity and accuracy of those analyzing devices can be realized withou...
Digital signal processing techniques adopted in the spectroscopy and its relevant devices for chemical analysis have been improved remarkably these days, which means sufficient sensitivity and accuracy of those analyzing devices can be realized without modification or replacement of those hardware and significant reduction in cost caused by those hardware implementation. The technique to combine conventional linear signal processing algorithm and artificial neural networks provides ability of non-linear signal analysis using recognition capability and adaptability by learning of artificial neural networks, which has been not possible by traditional algorithms. These technique can be also used as automation tools.