By Stergios Stergiopoulos
Advances in electronic sign processing algorithms and laptop expertise have mixed to supply real-time platforms with functions some distance past these of simply few years in the past. Nonlinear, adaptive equipment for sign processing have emerged to supply higher array achieve functionality, although, they lack the robustness of traditional algorithms. The problem is still to boost an idea that exploits the benefits of both-a scheme that integrates those tools in useful, real-time systems.
The complicated sign Processing instruction manual is helping you meet that problem. past providing a very good advent to the rules and purposes of complex sign processing, it develops a everyday processing constitution that takes benefit of the similarities that exist between radar, sonar, and clinical imaging structures and integrates traditional and nonlinear processing schemes
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For a discussion of VLSI implementation of adaptive filters, see Shabhag and Parhi (1994). This book emphasizes the use of pipelining, an architectural technique used for increasing the throughput of an adaptive filtering algorithm. ©2001 CRC Press LLC of concern: numerical stability and numerical accuracy. Numerical stability is an inherent characteristic of an adaptive filtering algorithm. , binary digits used in the numerical representation of data samples and filter coefficients). An adaptive filtering algorithm is said to be numerically robust when it is insensitive to variations in the word length used in its digital implementation.
40. , Chelmsford, MA, 1998. 41. S. Stergiopoulos, R. Alterson, D. Havelock, and J. Grodski, Acoustic Tomography Methods for 3D Imaging of Shallow Buried Objects, 139th Meeting of the Acoustical Society of America, Atlanta, GA, May 2000. 1 The Filtering Problem The term “filter” is often used to describe a device in the form of a piece of physical hardware or software that is applied to a set of noisy data in order to extract information about a prescribed quantity of interest. The noise may arise from a variety of sources.
18, 153–155, 1993. 30. A. , McGraw-Hill, New York, 1993. 31. , Chelmsford, MA, 1997. 32. Y. E. Fortman, Tracking and Data Association, Academic Press, Boston, MA, 1988. 33. S. , Norwood, MA, 1986. 34. W. Cambell, S. Stergiopoulos, and J. Riley, Effects of bearing estimation improvements of nonconventional beamformers on bearing-only tracking, Proc. Oceans ’95 MTS/IEEE, San Diego, CA, 1995. 35. A. S. Walker, Accurate estimation of source bearing from line arrays, Proc. Thirteen Biennial Symposium on Communications, Kingston, Ontario, Canada, 1986.