Doctoral Dissertations

Keywords and Phrases

Computer Vision; Deep Learning; Digital Communications; Machine Learning; Signal Processing; Statistical Signal Analysis

Abstract

"This work applies machine learning (ML) techniques to selected computer vision and digital communication problems. Machine learning algorithms can be trained to perform a specific task without explicit programming. This research applies ML to the problems of: plant identification from images of leaves, channel state information (CSI) estimation for wireless multiple-input-multiple-output (MIMO) systems, and gain estimation for a multi-user software-defined radio (SDR) application.

In the first task, two methods for plant species identification from leaf images are developed. One of the methods uses hand-crafted features extracted from leaf images to train a support vector machine classifier. The other method combines five publicly available leaf datasets: Flavia, Folio, LeafSnap, Swedish, and Middle European Woods 2014, to create a new data set named F2LSM. To create a benchmark, multiple end-to-end convolutional neural network classifiers are trained to classify images in the F2LSM dataset.

The second application of ML is a novel CSI estimation technique for MIMO communication systems. The approach uses atmospheric conditions, the position of the transmitter and receiver, and the relative motion of the transmitter and receiver as features for an artificial neural network (ANN).

The third study uses two ML methods to estimate gain for a multi-user SDR system in an aircraft, where a single SDR must generate a composite waveform for multiple communication links. An accurate estimate of the composite waveform’s peak is required to set the digital-to-analog converter’s gain to a value that will avoid clipping, while minimizing quantization noise. One of the methods uses an ANN to estimate the waveform’s peak and statistical moments. The other method uses an ANN to estimate the statistical distribution parameters that closely represent the voltage distribution of the waveform"--Abstract, page iv.

Advisor(s)

Kosbar, Kurt Louis

Committee Member(s)

Moss, Randy Hays, 1953-
Adekpedjou, Akim
Zhang, Jiangfan
Sedigh, Sahra

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2022

Journal article titles appearing in thesis/dissertation

  • Fast classification of leaf images for agricultural remote sensing applications
  • Plant identification in a combined-imbalanced leaf dataset
  • CSI estimation using artificial neural network
  • Rapid gain estimation for multi-user software defined radio applications
  • Distribution-based gain estimation for aeronautical software-defined radio applications

Pagination

xiii, 109 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2022 Viraj K. Gajjar, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12159

Electronic OCLC #

1344518721

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