Gonzalez, J. S.Nocetti, D. F. G.Ruano, M. Graça2015-04-132015-04-131999-11-290141-9331AUT: MRU00118;http://hdl.handle.net/10400.1/5871Doppler blood flow spectral estimation is a technique for non-invasive cardiovascular disease detection. Blood flow velocity and disturbance may be determined by measuring the spectral mean frequency and bandwidth, respectively. The work presented here, evaluates a high performance parallel-Doppler Signal Processing architecture (SHARC) for the computation of a parametric model-based spectral estimation method known as the modified covariance algorithm. The model-based method incorporates improvement in frequency resolution when compared with Fast Fourier Transform (FFT)-based methods. However, the computational complexity and the need for real-time response of the algorithm, makes necessary the use of high performance processing in order to fulfil such demands. Sequential and parallel implementations of the algorithm are introduced, A performance analysis of the implementations is also presented, demonstrating the effectiveness of the algorithm and the feasibility for real-time response of the system. The results open a greater scope for utilising this architecture in implementing new and more complex methods. The results are applied to the development of a real-time spectrum analyser for pulsed Doppler blood flow instrumentation. (C) 1999 Elsevier Science B.V. All rights reserved.engHigh performance computingParallel processingSignal processingSpectral estimationDoppler ultrasoundSelectionjournal articlehttp://dx.doi.org/10.1016/S0141-9331(99)00041-1