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phy) from a hospital scanner compared to an x-ray CAT image and finally the reconstructed image of the model- based processor using the embedded wave equation propaga- tion model. The results demonstrate the advantage of such a sophisticated approach—especially for this application.
Summary
We expressed the basic notion that signal processing is concerned with the extraction of useful information from uncertain measurement data. From this perspective, process- ing is an essential ingredient that the acoustician cannot ignore and therefore must be included in a daily regimen for problem solving. To explain the place that signal processing occupies, we developed a conceptual signal processing framework to demonstrate how signal processing techniques “fit” into this plan. Using a “bottoms-up” perspective we illus- trated conceptually the progression up the so-called signal processing staircase (Fig. 1) to illustrate a variety of acousti- cal processing problems. We have discussed some of the modern techniques in acoustical signal processing employ- ing the philosophy that this approach incorporates more and more of the a priori acoustical information available into the processing scheme that typically takes the form of a mathe- matical model. The incorporation of these models into the processor leads to the model-based approach or equivalently, the physics-based approach to signal processing. We started with a simple representation of the staircase showing that as the models get more complex so does the processor using some simple examples for motivation. We demonstrated some acoustic applications in sonar and nondestructive eval- uation and compared these results to the more classical approaches. We concluded the discussion with a tomograph- ic imaging technique demonstrating the evolution of model- based approaches to complex acoustical problems. The answer to the question of “science or science fiction” there- fore lies in the hands of the acoustician who must be able to sort through the processed results with the aid of statistical testing to assure the validity of the findings.AT
Acknowledgments
This work was performed under the auspices of the U. S. Department of Energy by the Lawrence Livermore National Laboratory under Contract No. DE-AC52-07Na27344.
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