First Advisor

Marek Perkowski

Term of Graduation

Spring 2022

Date of Publication


Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Electrical and Computer Engineering


Electrical and Computer Engineering




Decomposition (Mathematics), Partitions (Mathematics), Machine learning



Physical Description

1 online resource (xix, 372 pages)


This dissertation presents a novel design of a hardware classifier based on combining modified Ashenhurst-Curtis Decomposition and multiplexer-based synthesis. The PSUD classifier brings three new contributions: an approach to solve the column multiplicity problem, an approach to encode multiple-valued variables, and a decomposition algorithm based on modified Ashenhurst-Curtis Decomposition. One of the biggest challenges in Boolean function decomposition is the variable partitioning problem. Thus, we introduce a new representation of two combined classifiers for multiple-valued functions to overcome the variable partitioning problem which allows finding the minimal column multiplicity and consequently to find high quality decompositions leading to a good learning accuracy. Another aspect of our approach is that the trained classifier is a Boolean network realized in an FPGA which allows for fast object recognition by a robot. The classifier gives very good accuracy results when tested on multi-valued Machine Learning benchmarks from the UC Irvine repository.


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Persistent Identifier

Available for download on Sunday, June 02, 2024