Engineering Commons^™

Metric Learning Via Linear Embeddings For Human Motion Recognition, Byoungdoo Kong

Masters Theses

We consider the application of Few-Shot Learning (FSL) and dimensionality reduction to the problem of human motion recognition (HMR). The structure of human motion has unique characteristics such as its dynamic and high-dimensional nature. Recent research on human motion recognition uses deep neural networks with multiple layers. Most importantly, large datasets will need to be collected to use such networks to analyze human motion. This process is both time-consuming and expensive since a large motion capture database must be collected and labeled. Despite significant progress having been made in human motion recognition, state-of-the-art algorithms still misclassify actions because of characteristics …

Fusion Of Audio And Visual Information For Implementing Improved Speech Recognition System, Vikrant Satish Acharya

Masters Theses

Speech recognition is a very useful technology because of its potential to develop applications, which are suitable for various needs of users. This research is an attempt to enhance the performance of a speech recognition system by combining the visual features (lip movement) with audio features. The results were calculated using utterances of numerals collected from participants inclusive of both male and female genders. Discrete Cosine Transform (DCT) coefficients were used for computing visual features and Mel Frequency Cepstral Coefficients (MFCC) were used for computing audio features. The classification was then carried out using Support Vector Machine (SVM). The results …

Evaluating Features For Broad Species Based Classification Of Bird Observations Using Dual-Polarized Doppler Weather Radar, Sheila Werth

Masters Theses

Wind energy is one of the fastest-growing segments of the world energy market; however, wind energy facilities can have detrimental effects on wildlife, especially birds and bats. The ability to monitor vulnerable species in the vicinity of proposed wind sites could enable site selection that favors more vulnerable species, but current monitoring tools lack this classification capability. This work analyzes polarimetric and Doppler measurements of migrating birds for species based variation.

A novel two stage feature extraction technique was developed to enable comparison between birds. Stage one involves mapping time changing radar measurements to the birds behavioral state in time …

Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification, Siwei Feng

Masters Theses

Hyperspectral signature classification is a kind of quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from corresponding hyperspectral signatures containing information like signature energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (NHMC models) to characterize wavelet coefficients which capture the spectrum structural information at multiple levels. Experimental results …

Power Transmission Line Fault Classification Using Support Vector Machines, Zhuokang Jia

Masters Theses

Electrical transmission lines are prone to faults and failures. When a fault occurs, it is impossible most of the times to fix it manually. Many methods have been adopted in the past in order to serve the purpose as fault diagnosing application. In this thesis, I discuss the method of Support Vector Machine (SVM) for fault diagnosis. SVM has the edge of good generalization over other fault diagnosing applications because it is based on pattern recognize algorithms. The aim is to classify the type of fault in the lines. Furthermore, in this work, the current and voltage of each phase …

Classification System For Impedance Spectra, Carl Gordon Sapp

Masters Theses

This thesis documents research, methods, and results to satisfy the requirements for the M.S. degree in Electrical Engineering at the University of Tennessee. This thesis explores two primary steps for proper classification of impedance spectra: data dimension reduction and effectiveness of similarity/dissimilarity measure comparison in classification. To understand the data characteristics and classification thresholds, a circuit model analysis for simulation and unclassifiable determination is studied. The research is conducted using previously collected data of complex valued impedance measurements taken from 1844 similar devices. The results show a classification system capable of proper classification of 99% of data samples with well-separated …