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In order to improve the performance of Least Mean Square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on l 0 norm -a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a zero attractor in the iterations, by which the convergence rate of small coefficients, that dominate the sparse system, can be effectively improved. Moreover, using partial updating method, the computational complexity is reduced. The simulations demonstrate that the proposed algorithm can effectively improve the performance of LMS-based identification algorithms on sparse system.

Abstract. We compare a range of powerful compression methods -fractal, wavelets, pyramidal median, JPEG -with compression tools dedicated to astronomy such as HCOMPRESS, FITSPRESS and Mathematical Morphology, and apply these to astronomical images. Quality is quantified from visual appearance, and from photometric and astrometric measurements. Computational requirements of each method are discussed. We also review the implications of Web-based storage and transmission, stressing what we term progressive vision. In summary, no method is perfect, but the PMT method is the best compromise for general astronomical images, combining acceptable photometric and positional precision with good compression capabilities. JPEG is still an excellent method for compression factors less than 40 and has the advantage of being very widely available.