Signal Processing Commons^™

Bearing Health Detector, Natalie N. Tokhmakhian, Alexander J. Davis

Electrical Engineering

Bearings, a common component in rotating machinery, are essential components of modern rotating machines; thus, monitoring their health is crucial when reducing downtime and boosting production efficiency. The Bearing Health Detector (BHD), a hand-held device, captures and processes the sound of a machine under test in real time and estimates the level of wear and tear by comparing the sound to previous tests. The BHD encompasses audiences involved with roller bearings in rotating machinery and is designed to provide the diagnosis of wear through the universal detection of good, satisfactory, and very poor with the following color scheme: green, yellow, …

Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner

Electrical & Computer Engineering Faculty Publications

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

Deep Learning Based Speech Enhancement And Its Application To Speech Recognition, Ju Lin

All Dissertations

Speech enhancement is the task that aims to improve the quality and the intelligibility of a speech signal that is degraded by ambient noise and room reverberation. Speech enhancement algorithms are used extensively in many audio- and communication systems, including mobile handsets, speech recognition, speaker verification systems and hearing aids. Recently, deep learning has achieved great success in many applications, such as computer vision, nature language processing and speech recognition. Speech enhancement methods have been introduced that use deep-learning techniques, as these techniques are capable of learning complex hierarchical functions using large-scale training data. This dissertation investigates the deep learning …

Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing, Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola

Faculty Publications

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed …

Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde

Electronic Theses and Dissertations

In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and …

Dismount Threat Recognition Through Automatic Pose Identification, Andrew M. Freeman

Theses and Dissertations

The U.S. military has an increased need to rapidly identify nonconventional adversaries. Dismount detection systems are being developed to provide more information on and identify any potential threats. Current work in this area utilizes multispectral imagery to exploit the spectral properties of exposed skin and clothing. These methods are useful in the location and tracking of dismounts, but they do not directly discern a dismount's level of threat. Analyzing the actions that precede hostile events yields information about how the event occurred and uncovers warning signs that are useful in the prediction and prevention of future events. A dismount's posturing, …

Solving The Vehicle Re-Identification Problem By Using Neural Networks, Tanweer Rashid

Computational Modeling & Simulation Engineering Theses & Dissertations

Vehicle re-identification is the process by which vehicle attributes measured at one point on a road network are compared to vehicle attributes measured at another point in an effort to match vehicles without using any unique identifiers such as license plate numbers. A match is made if the two measurements are estimated to belong to the same vehicle. Vehicle attributes can be sensor readings such as loop induction signatures, or they can also be actual vehicle characteristics such as length, weight, number of axles, etc. This research makes use of vehicle length, travel time, axle spacing and axle weights for …

Gaussian Mixture Models And Neural Networks For Automatic Speaker Identification, Usha Gayatri Chalkapally

Electrical & Computer Engineering Theses & Dissertations

Automatic Speaker Recognition is the process of automatically recognizing who is speaking on the basis of individual information contained in speech signals. This technique of Automatic Speaker Recognition makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.

In this thesis, the techniques of Gaussian Mixture Models and Neural Networks for Automatic Speaker Identification are presented. Algorithms for Speaker Identification using Gaussian Mixture Models were developed, …

Fingerprint Recognition Using Neural Networks, Eng Hoe Kennie Yeoh

Student Works (2000-2009)

Traditional methods of fingerprint verification uses either complicated feature detection algorithms that are not specific to each fingerprint, or compare two fingerprint images directly using image processing toots. The former involves very complicated calculations and tedious algorithms, and the latter tend to work poorly. In this paper it is described a new method which takes the middle ground. This paper studies the implementation of the Fast Fourier Transform and Artificial Neural Networks into the recognition of fingerprints. With tests conducted on the implementation of the Fourier Transform as a method of fingerprint feature extraction, the use of the Fourier Transform …

Electromyography (Emg) Signal Classification By Artificial Neural Networks, Behnam Dashtipour

Electrical & Computer Engineering Theses & Dissertations

EMG signal processing is one of the active fields of biomedical signal processing. One unanswered question is how to determine whether a muscle is fatigued by analyzing the EMG signal. Fatigue detection could be useful in several different practical situations. There are several studies which show there are differences between EMG signal features before fatigue and after fatigue. Generally studies are based on an analytical analysis of the EMG signal instead of a quantitative analysis. In all previous studies in EMG signal processing for fatigue/nonfatigue detection, the result is that there exist some differences between the EMG signal before muscle …

Cepstral And Auditory Model Features For Speaker Recognition, John M. Colombi

Theses and Dissertations

The TIMIT and KING databases, as well as a ten day AFIT speaker corpus, are used to compare proven spectral processing techniques to an auditory neural representation for speaker identification. The feature sets compared were Linear Predictive Coding (LPC) cepstral coefficients and auditory nerve firing rates using the Payton model. This auditory model provides for the mechanisms found in the human middle and inner auditory periphery as well as neural transduction. Clustering algorithms were used to generate speaker specific codebooks - one statistically based and the other a neural approach. These algorithms are the Linde-Buzo-Gray (LBG) algorithm and a Kohonen …