Sign least mean squares-based deconvolution technique for ultrasonic iris applications

Аннотация

Sign LMS algorithms, members of the simplified adaptive least-mean squares class, have been developed to reduce computational complexity and simplify hardware implementation. These advantages make them suitable to utilize in ultrasonic IRIS units, a class of applications requiring simple and efficient signal processing algorithms. This paper proposes a specific sign LMS adaptive filters-based deconvolution technique for IRIS applications. It extracts only two of the interface echoes replications for enhanced resolution and quality-enriched presentation; this technique is named "selective deconvolution". Resolution enhancement and presentation's quality enrichment performance among the different sign LMS algorithms were investigated by experiments, and based on performance, the methods themselves were compared. Computational requirements are also presented. The proposed technique with the various adaptive sign LMS filters gave satisfactory results; sign data LMS was found to be the best technique for IRIS applications.