RESUMEN
RATIONALE: Improving the analytical performance of linear ion traps (LITs) is crucial for the advancement of high-performance LIT mass spectrometers. In this study, a double resonant excitation method was employed in an asymmetric LIT to achieve high ion unidirectional ejection efficiency and enhanced mass resolution. METHODS: The asymmetric trapping field was generated by stretching one x electrode with a distance α. The double resonant excitation was achieved by applying an alternating voltage out of phase and a supplementary alternating voltage in phase to the x and y electrode pairs of the LIT, respectively. Numerical simulations of ion trajectories were performed to validate the effectiveness of this method. RESULTS: The mass resolution of the asymmetric LIT with double resonant excitation could be improved to ~3800, which was over two times compared to that with only dipolar resonant excitation, while both reached ~90% in ion unidirectional ejection efficiency. CONCLUSIONS: By employing the double resonant excitation method, the mass resolution could be improved significantly in the asymmetric LIT, while maintaining a considerably high ion unidirectional ejection efficiency. This method might provide a general solution for enhancing ion detection efficiency and mass resolution of LIT mass spectrometers.
RESUMEN
A randomized search algorithm for sparse representations of EEG event-related potentials (ERPs) and their statistically independent components is presented. This algorithm combines greedy matching pursuit (MP) technique with covariance matrix adaptation evolution strategy (CMA-ES) to select small number of signal atoms from over-complete wavelet and chirplet dictionaries that offer best approximations of quasi-sparse ERP signals. During the search process, adaptive pruning of signal parameters was used to eliminate redundant or degenerative atoms. As a result, the CMA-ES/MP algorithm is capable of producing accurate efficient and consistent sparse representations of ERP signals and their ICA components. This paper explains the working principles of the algorithm and presents the preliminary results of its use.