Document Type


Date of Award


Degree Name

Master of Science in Electrical Engineering - (M.S.)


Electrical and Computer Engineering

First Advisor

Nirwan Ansari

Second Advisor

Edwin Hou

Third Advisor

Irving Y. Wang


Stack filters are a class of sliding?window nonlinear digital filters that possess the weak superposition property(threshold decomposition) and the ordering property known as the stacking property. They have been demonstrated to be robust in suppressing noise. Two methods are introduced in this thesis to adaptively configure a stack filter. One is by employing the Least Mean Square(LMS) algorithm and the other is based on Perceptron learning.

Experimental results are presented to demonstrate the effectiveness of our methods to noise suppression.



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