A New Approach to Machine Learning Hardware Classifier Design Based on Functional Decomposition of Multi-Valued Functions

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

A New Approach to Machine Learning Based on Functional Decomposition of Multi -Valued Functions

This paper presents a novel design of a hardware classifier based on combining modified Ashenhurst-Curtis Decomposition and modified multiplexer-based synthesis. The PSUD classifier brings three new contributions: an approach to solve the column multiplicity problem for both types of decomposition, the way how the multiple-valued variables are encoded and used, and a modification of the 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 good learning. The classifier gives very good accuracy results when tested on multi-valued Machine Learning benchmarks from the UC Irvine repository

Implementation of Risk Management System

This paper takes on an existing company that does not have a risk management system in place and attempts to implement it. The company is in the field of manufacturing red bricks. It is planning to expand to take the opportunity presented to it through the huge governmental investments occurring nowadays. The company managed to survive so far but it is tough times due to competition now and the company has to evaluate its options very carefully to succeed. In order to implement a useful Risk Management System, we have to implement different tools needed to support the risk management system such as the Work Breakdown Structure, Responsibility Interface Matrix, Critical Path Method, and Earned Value Analysis. Then we will describe the Risk Management System and provide the templates and tables to assist the project manager within the company to assess risks in an easy and effective manner

New Prodcut Development - Development Log

This is a development log for ETM 547 Winter 2009