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Imbulgoda Liyangahawatte, Gihan Janith MendisHardware Implementation and Applications of Deep Belief Networks
Master of Science in Engineering, University of Akron, 2016, Electrical Engineering
Deep learning is a subset of machine learning that contributes widely to the contemporary success of artificial intelligence. The essential idea of deep learning is to process complex data by abstracting hierarchical features via deep neural network structure. As one type of deep learning technique, deep belief network (DBN) has been widely used in various application fields. This thesis proposes an approximation based hardware realization of DBNs that requires low hardware complexity. This thesis also explores a set of novel applications of the DBN-based classifier that will benefit from a fast implementation of DBN. In my work, I have explored the application of DBN in the fields of automatic modulation classification method for cognitive radio, Doppler radar sensor for detection and classification of micro unmanned aerial systems, cyber security applications to detect false data injection (FDI) attacks and localize flooding attacks, and applications in social networking for prediction of link properties. The work in this thesis paves the way for further investigation and realization of deep learning techniques to address critical issues in various novel application fields.

Committee:

Jin Wei (Advisor); Arjuna Madanayaka (Committee Co-Chair); Subramaniya Hariharan (Committee Member)

Subjects:

Artificial Intelligence; Computer Engineering; Electrical Engineering; Engineering; Experiments; Information Technology

Keywords:

deep belief networks; multiplierless digital architecture; Xilinx FPGA implementations; low-complexity; applications of deep belief networks; spectral correlation function; modulation classification; drone detection; doppler radar; cyber security

Liu, YeApplication of Convolutional Deep Belief Networks to Domain Adaptation
Master of Science, The Ohio State University, 2014, Computer Science and Engineering
Deep Belief Networks are instances of deep learning that have recently become prominent in machine learning. A recent extension considers adding convolution to the model, yielding better auto feature extraction for domains such as computer vision and natural language processing. The scope of this thesis is to explore the capability of Convolutional Deep Belief Networks (CDBN) in solving domain adaptation problem, for which other machine learning techniques have been provided having some level of both effectiveness and limitation. This thesis is part of a larger project which aims at solving the domain adaptation problems and producing higher accuracy in classification using deep learning algorithms. This project paves the way for further experiments in this direction by yielding promising results when applying CDBN to the domain adaptation problem. It shows that CDBNs are capable of capturing the meaningful features and filtering out irrelevant noise caused by the change of domains.

Committee:

Brian Kulis (Advisor); James Davis (Committee Member)

Subjects:

Computer Science

Keywords:

deep learning, Convolution, Convolutional Deep Belief Networks, features, image classification