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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

Cutno, PatrickAutomatic Modulation Classifier - A Blind Feature-Based Tool
Master of Science, Miami University, 2016, Computational Science and Engineering
Automatic modulation classifiers (AMC) are one of the basic building blocks of electronic warfare receivers and cognitive radios. Although many research papers on AMC algorithms have been published, very few results on their implementation are available. This thesis presents a feature-based AMC built upon a software-defined radio platform. The developed AMC can detect signals over a broad spectrum and classify the modulation used. The modulation schemes considered in this thesis are amplitude modulation (AM), frequency modulation (FM), phase-shift keying (PSK), and quadrature amplitude modulation (QAM). Experimental results demonstrate the validity of the developed AMC algorithm and its implementation.

Committee:

Chi-Hao Cheng, Ph.D (Advisor); Dmitriy Garmatyuk, Ph.D (Committee Member); Jason Pennington, Ph.D (Committee Member)

Subjects:

Communication; Computer Engineering; Computer Science; Electrical Engineering; Engineering; Experiments; Technology

Keywords:

Software Defined Radio; NI USRP-2920; USRP; Modulation; Automatic Modulation Detection; Automatic Modulation Classification; AMC; High Order Statistics; LabVIEW; Implementation; Electronic Warfare; Cognitive Radio; AM; FM; PSK; QAM; Energy Analyzer; SNR