By Stergios Stergiopoulos
Advances in electronic sign processing algorithms and desktop expertise have mixed to supply real-time platforms with features a ways past these of simply few years in the past. Nonlinear, adaptive equipment for sign processing have emerged to supply larger array achieve functionality, notwithstanding, they lack the robustness of traditional algorithms. The problem continues to be to improve 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 delivering a great advent to the foundations and functions of complex sign processing, it develops a accepted 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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Extra info for Advanced Signal Processing Handbook Theory And Implementation For Radar, Sonar, And Medical Imaging Real-Time
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The LMS algorithm is relatively simple to implement. Yet it is powerful enough to evaluate the practical benefits that may result from the application of adaptivity to the problem at hand. Moreover, it provides a practical frame of reference for assessing any further improvement that may be attained through the use of more sophisticated adaptive filtering algorithms. Finally, the study must include tests with real-life data, for which there is no substitute. Practical applications of adaptive filtering are very diverse, with each application having peculiarities of its own.
The noise may arise from a variety of sources. For example, the data may have been derived by means of noisy sensors or may represent a useful signal component that has been corrupted by transmission through a communication channel. In any event, we may use a filter to perform three basic informationprocessing tasks. 1. Filtering means the extraction of information about a quantity of interest at time t by using data measured up to and including time t. 2. Smoothing differs from filtering in that information about the quantity of interest need not be available at time t, and data measured later than time t can be used in obtaining this information.