The identification of the inter-electrode gap size in the high frequency group pulse micro-electrochemical machining （HGPECM） is mainly discussed. The auto-regressive（AR） model of group pulse current flowing acros...The identification of the inter-electrode gap size in the high frequency group pulse micro-electrochemical machining （HGPECM） is mainly discussed. The auto-regressive（AR） model of group pulse current flowing across the cathode and the anode are created under different situations with different processing parameters and inter-electrode gap size. The AR model based on the current signals indicates that the order of the AR model is obviously different relating to the different processing conditions and the inter-electrode gap size; Moreover, it is different about the stability of the dynamic system, i.e. the white noise response of the Green＇s function of the dynamic system is diverse. In addition, power spectrum method is used in the analysis of the dynamic time series about the current signals with different inter-electrode gap size, the results show that there exists a strongest power spectrum peak, characteristic power spectrum（CPS）, to the current signals related to the different inter-electrode gap size in the range of 0～5 kHz. Therefore, the CPS of current signals can implement the identification of the inter-electrode gap.展开更多
A large class of multimedia and biomedical signals can be modeled as Autoregressive (AR) random processes. Performance of watermarking embedding algorithms utilizing this host model is still left unexplored. The autho...A large class of multimedia and biomedical signals can be modeled as Autoregressive (AR) random processes. Performance of watermarking embedding algorithms utilizing this host model is still left unexplored. The authors investigate the decoding performance of Spread Spectrum (SS) embedding algorithm in the standard Additive White Gaussian Noise (AWGN) channel with the host signal being modeled as AR process. The SS embedding algorithm also use linear interference cancelation in the subspace spanned by watermark pattern. They study the influence of design parameters on the decoding performance. The analytic result is verified by Monte Carlo simulation on synthesized AR process. The result may be helpful to design watermarking system for speech, biomedical and image signals.展开更多
基金This project is supported by the 10th Five-year Plan Pre-research Project Foundation of China Weapon Industry Company, China(No.42001080701).
文摘The identification of the inter-electrode gap size in the high frequency group pulse micro-electrochemical machining （HGPECM） is mainly discussed. The auto-regressive（AR） model of group pulse current flowing across the cathode and the anode are created under different situations with different processing parameters and inter-electrode gap size. The AR model based on the current signals indicates that the order of the AR model is obviously different relating to the different processing conditions and the inter-electrode gap size; Moreover, it is different about the stability of the dynamic system, i.e. the white noise response of the Green＇s function of the dynamic system is diverse. In addition, power spectrum method is used in the analysis of the dynamic time series about the current signals with different inter-electrode gap size, the results show that there exists a strongest power spectrum peak, characteristic power spectrum（CPS）, to the current signals related to the different inter-electrode gap size in the range of 0～5 kHz. Therefore, the CPS of current signals can implement the identification of the inter-electrode gap.
基金supported by research project of“SUSTSpring Bud”:the research on decoder under desynchronization attack for data hiding systems
文摘A large class of multimedia and biomedical signals can be modeled as Autoregressive (AR) random processes. Performance of watermarking embedding algorithms utilizing this host model is still left unexplored. The authors investigate the decoding performance of Spread Spectrum (SS) embedding algorithm in the standard Additive White Gaussian Noise (AWGN) channel with the host signal being modeled as AR process. The SS embedding algorithm also use linear interference cancelation in the subspace spanned by watermark pattern. They study the influence of design parameters on the decoding performance. The analytic result is verified by Monte Carlo simulation on synthesized AR process. The result may be helpful to design watermarking system for speech, biomedical and image signals.
