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Spectrum Analysis and Prediction Using Long Short Term Memory Neural Networks and Cognitive Radios

Description: One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence o… more
Date: December 2017
Creator: Hernandez Villapol, Jorge Luis
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

A Neural Network Configuration Compiler Based on the Adaptrode Neuronal Model

Description: A useful compiler has been designed that takes a high level neural network specification and constructs a low level configuration file explicitly specifying all network parameters and connections. The neural network model for which this compiler was designed is the adaptrode neuronal model, and the configuration file created can be used by the Adnet simulation engine to perform network experiments. The specification language is very flexible and provides a general framework from which almost an… more
Date: December 1992
Creator: McMichael, Lonny D. (Lonny Dean)
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

Computational Complexity of Hopfield Networks

Description: There are three main results in this dissertation. They are PLS-completeness of discrete Hopfield network convergence with eight different restrictions, (degree 3, bipartite and degree 3, 8-neighbor mesh, dual of the knight's graph, hypercube, butterfly, cube-connected cycles and shuffle-exchange), exponential convergence behavior of discrete Hopfield network, and simulation of Turing machines by discrete Hopfield Network.
Date: August 1998
Creator: Tseng, Hung-Li
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

A Multi-Time Scale Learning Mechanism for Neuromimic Processing

Description: Learning and representing and reasoning about temporal relations, particularly causal relations, is a deep problem in artificial intelligence (AI). Learning such representations in the real world is complicated by the fact that phenomena are subject to multiple time scale influences and may operate with a strange attractor dynamic. This dissertation proposes a new computational learning mechanism, the adaptrode, which, used in a neuromimic processing architecture may help to solve some of these… more
Date: August 1994
Creator: Mobus, George E. (George Edward)
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing

Description: Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biogra… more
Date: August 1995
Creator: Scarborough, David J. (David James)
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
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Classification by Neural Network and Statistical Models in Tandem: Does Integration Enhance Performance?

Description: The major purposes of the current research are twofold. The first purpose is to present a composite approach to the general classification problem by using outputs from various parametric statistical procedures and neural networks. The second purpose is to compare several parametric and neural network models on a transportation planning related classification problem and five simulated classification problems.
Date: December 1998
Creator: Mitchell, David
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm

Description: Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can … more
Date: December 1993
Creator: Civelek, Ferda N. (Ferda Nur)
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

A Heuristic Procedure for Specifying Parameters in Neural Network Models for Shewhart X-bar Control Chart Applications

Description: This study develops a heuristic procedure for specifying parameters for a neural network configuration (learning rate, momentum, and the number of neurons in a single hidden layer) in Shewhart X-bar control chart applications. Also, this study examines the replicability of the neural network solution when the neural network is retrained several times with different initial weights.
Date: December 1993
Creator: Nam, Kyungdoo T.
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

Freeform Cursive Handwriting Recognition Using a Clustered Neural Network

Description: Optical character recognition (OCR) software has advanced greatly in recent years. Machine-printed text can be scanned and converted to searchable text with word accuracy rates around 98%. Reasonably neat hand-printed text can be recognized with about 85% word accuracy. However, cursive handwriting still remains a challenge, with state-of-the-art performance still around 75%. Algorithms based on hidden Markov models have been only moderately successful, while recurrent neural networks have deli… more
Date: August 2015
Creator: Bristow, Kelly H.
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

Toward Supporting Fine-Grained, Structured, Meaningful and Engaging Feedback in Educational Applications

Description: Recent advancements in machine learning have started to put their mark on educational technology. Technology is evolving fast and, as people adopt it, schools and universities must also keep up (nearly 70% of primary and secondary schools in the UK are now using tablets for various purposes). As these numbers are likely going to follow the same increasing trend, it is imperative for schools to adapt and benefit from the advantages offered by technology: real-time processing of data, availabilit… more
Date: December 2018
Creator: Bulgarov, Florin Adrian
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

An Exploration of the Word2vec Algorithm: Creating a Vector Representation of a Language Vocabulary that Encodes Meaning and Usage Patterns in the Vector Space Structure

Description: This thesis is an exloration and exposition of a highly efficient shallow neural network algorithm called word2vec, which was developed by T. Mikolov et al. in order to create vector representations of a language vocabulary such that information about the meaning and usage of the vocabulary words is encoded in the vector space structure. Chapter 1 introduces natural language processing, vector representations of language vocabularies, and the word2vec algorithm. Chapter 2 reviews the basic math… more
Date: May 2016
Creator: Le, Thu Anh
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
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Real-Time Finger Spelling American Sign Language Recognition Using Deep Convolutional Neural Networks

Description: This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of fou… more
Date: December 2018
Creator: Viswavarapu, Lokesh Kumar
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
open access

On-Loom Fabric Defect Inspection Using Contact Image Sensors and Activation Layer Embedded Convolutional Neural Network

Description: Malfunctions on loom machines are the main causes of faulty fabric production. An on-loom fabric inspection system is a real-time monitoring device that enables immediate defect detection for human intervention. This dissertation presented a solution for the on-loom fabric defect inspection, including the new hardware design—the configurable contact image sensor (CIS) module—for on-loom fabric scanning and the defect detection algorithms. The main contributions of this work include (1) creating… more
Date: December 2018
Creator: Ouyang, Wenbin
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
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