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Ultrasonic imaging using complementary coded pulses offers the SNR improvements of signal coding without the filter side-lobes introduced by single-transmit codes. Tissue motion between coded pulse emissions, however, can introduce high side-lobes caused by misalignment of complementary filter outputs. This paper presents a method for filtering and motion compensation of complementary coded signals appropriate for use in medical imaging. The method is robust to the effects of non-ideal transducers on the imaging signals, includes mirrored compensation stages to reduce the impact of motion estimation error, and has been shown to reduce side-lobes to levels that compare favorably to systems using FM-coded signals of similar length and bandwidth while providing increased coding gain and range resolution. In addition, motion compensation allows the received data to be used without the frame-rate penalty usually incurred by complementary-coded imaging. The method has been verified using simulated point and speckle targets with both homogeneous and inhomogeneous motion profiles. Selected results have been verified experimentally. [ABSTRACT FROM AUTHOR]

Empirical mode decomposition (EMD) is a relatively new, data-driven adaptive technique for analyzing multicomponent signals. Although it has many interesting features and often exhibits an ability to decompose nonlinear and nonstationary signals, it lacks a strong theoretical basis which would allow a performance analysis and hence the enhancement and optimization of the method in a systematic way. In this paper, the optimization of EMD is attempted in an alternative manner. Using specially defined multicomponent signals, the optimum outputs can be known in advance and used in the optimization of the EMD-free parameters within a genetic algorithm framework. The contributions of this paper are two-fold. First, the optimization of both the interpolation points and the piecewise interpolating polynomials for the formation of the upper and lower envelopes of the signal reveal important characteristics of the method which where previously hidden. Second, basic directions for the estimates of the optimized parameters are developed, leading to significant performance improvements. [ABSTRACT FROM AUTHOR]