Signal Processing Commons^™

Performance Enhancement Of Wide-Range Perception Issues For Autonomous Vehicles, Suvash Sharma

Theses and Dissertations

Due to the mission-critical nature of the autonomous driving application, underlying algorithms for scene understanding should be given special care during their development. Mostly, they should be designed with precise consideration of accuracy and run-time. Accuracy should be considered strictly which if compromised leads to faulty interpretation of the environment that may ultimately result in accidental scenarios. On the other hand, run-time holds an important position as the delayed understanding of the scene would hamper the real-time response of the vehicle which again leads to unforeseen accidental cases. These factors come as the functions of several factors such as the …

Challenges And Signal Processing Of High Strain Rate Mechanical Testing, Barae Lamdini

Theses and Dissertations

Dynamic testing provides valuable insight into the behavior of materials undergoing fast deformation. During Split-Hopkinson Pressure Bar testing, stress waves are measured using strain gauges as voltage variations that are usually very small. Therefore, an amplifier is required to amplify the data and analyze it. One of the few available amplifiers designed for this purpose is provided by Vishay Micro-Measurements which limits the user’s options when it comes to research or industry. Among the challenges of implementing the Hopkinson technology in the industry are the size and cost of the amplifier. In this work, we propose a novel design of …

Analysis And Implementation Of Low Fidelity Radar-Based Remote Sensing For Unmanned Aircraft Systems, Matthew Duck

Theses and Dissertations

Radar-based remote sensing is consistently growing, and new technologies and subsequent techniques for characterization are changing the feasibility of understanding the environment. The emergence of easily accessible unmanned aircraft system (UAS) has broadened the scope of possibilities for efficiently surveying the world. The continued development of low-cost sensing systems has greatly increased the accessibility to characterize physical phenomena. In this thesis, we explore the viability and implementation of using UAS as a means of radar-based remote sensing for ground penetrating radar (GPR) and polarimetric scatterometry. Additionally, in this thesis, we investigate the capabilities and implementations of low-cost microwave technologies for …

Automotive Sensor Fusion Systems For Traffic Aware Adaptive Cruise Control, Jonah T. Gandy

Theses and Dissertations

The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility …

A Harmonic Radar System For Honey Bee Tracking To Better Understand Colony Collapse Disorder, William B. Woo

Theses and Dissertations

Honey bees are some of the most important pollinators for agriculture in the world and are pivotal to the health of worldwide ecosystems. Like all insects, bees struggle with exposure to parasites, diseases, and other environmental factors that can negatively affect the overall health of the colony. Recently, a new unexplainable phenomenon called Colony Collapse Disorder (CCD) has been wreaking havoc on bee populations worldwide. As a result, a system capable of tracking bees is required to understand the different contributions of chemicals, parasites, etc. to CCD. This research seeks to show data supporting the development of systems for an …

Spatial-Spectral Analysis In Dimensionality Reduction For Hyperspectral Image Classification, Chiranjibi Shah

Theses and Dissertations

This dissertation develops new algorithms with different techniques in utilizing spatial and spectral information for hyperspectral image classification. It is necessary to perform spatial and spectral analysis and conduct dimensionality reduction (DR) for effective feature extraction, because hyperspectral imagery consists of a large number of spatial pixels along with hundreds of spectral dimensions.

In the first proposed method, it employs spatial-aware collaboration-competition preserving graph embedding by imposing a spatial regularization term along with Tikhonov regularization in the objective function for DR of hyperspectral imagery. Moreover, Collaboration representation (CR) is an efficient classifier but without using spatial information. Thus, structure-aware collaborative …

Recognizing Traffic Signaling Gestures Through Automotive Sensors., Benjamin James Bartlett

Theses and Dissertations

As technology advances with each new day, so do the applications and uses of the different modalities of technology, including transportation, particularly in ADAS vehicles. These systems allow the vehicle to avoid collisions, change lanes, adjust the vehicle’s speed, and more without the need of driver input. However, each sensor type has a weakness, and most advanced driver- assisted system (ADAS) vehicles rely heavily on sensors, such as RGB cameras, radars, and LiDAR sensors. These visual-based sensors may collect very noisy data in cloudy, raining, foggy, or other obscuring phenomena. Radar, on the other hand, does not rely on visual …

Real Time Audio Processing Using A Low-Power Digital Signal Processor, Aaron Norlinger

Honors Program: Senior Projects (Public)

This project focused on the creation of a series of audio processing functions that could run in real time on the ezDSP5502 processor. The Digital Signal Processor (DSP) being used for this project is an industry standard for lowpower signal processing applications. The overall goal was to have a code base that could sample audio in real time from any source, filter it in a variety of ways, run a Fast Fourier Transform on the audio, display the resulting frequency data to an LCD screen, and then output the filtered audio to an external speaker. This general process is used …

Design Of Hardware To Aid Smartphone-Based Oscilloscope App, Riddock Moran

Honors Program: Senior Projects (Public)

A smartphone-based oscilloscope improves on traditional lab oscilloscopes in accessibility and portability but faces several performance limitations compared to traditional oscilloscopes. Among these, an oscilloscope app that uses the phone’s audio to read voltage signals will have a sampling rate and voltage bottlenecked by the capabilities of the audio codec, which will rarely exceed a rate of 48 kHz and 1 volt, respectively. Additionally, smartphones lack the ability to read line-in audio, allowing only one channel input through the microphone. Direct connections to an audio source may not be possible due to requiring an audio jack connection, and different poles …

Electromechanical Fatigue Properties Of Dielectric Elastomer Stretch Sensors Under Orthopaedic Loading Conditions, Andrea Karen Persons

Theses and Dissertations

Fatigue testing of stretch sensors often focuses on high amplitude, low-cycle fatigue (LCF) behavior; however, when used for orthopaedic, athletic, or ergonomic assessments, stretch sensors are subjected to low amplitude, high-cycle fatigue (HCF) conditions. As an added layer of complexity, the fatigue testing of stretch sensors is not only focused on the life of the material comprising the sensor, but also on the reliability of the signal produced during the extension and relaxation of the sensor. Research into the development of a smart sock that can be used to measure the range of motion (ROM) of the ankle joint during …

Poly Camtracker, Nickolas W. Ogilvie, Shawn W. Thai

Electrical Engineering

With the onset of the post-pandemic virtual classroom world, more and more classes are attempting hybrid-style instruction. To accomplish that, recordings of lectures are widely used, with presentation-sharing & audio recording capability. However, video recordings of lectures, with the ability to see the board, lab equipment, and the professor, are the optimal way to give the virtual students the best experience. Right now, technology is readily available for static camera recordings of lecture halls, but this may not suffice when it comes to larger lecture halls and lab demonstrations, where directed shots of equipment or the whiteboard are needed in …

Locating Unknown Interference Sources With Time Difference Of Arrival Estimates, Chia Ying Kuo

McKelvey School of Engineering Graduate Student Theses & Dissertations

Adaptive spectrum sharing between different systems and operators is being deployed in order to make use of the wireless spectrum more efficiently. However, when the spectrum is shared, it can create situations in which an operator is unable to determine the identity of an interferer transmitting an unknown signal. This is the situation in which the POWDER testbed found itself in, starting in late 2021. This thesis provides general-purpose tools for operators to locate an unknown signal source in real-world outdoor environments. We used cross-correlation between the signals measured at multiple time-synchronized base stations to estimate the time difference of …

State Estimation—Beyond Gaussian Filtering, Haozhan Meng

LSU New Orleans Theses and Dissertations

This dissertation considers the state estimation problems with symmetric Gaussian/asymmetric skew-Gaussian assumption under linear/nonlinear systems. It consists of three parts. The first part proposes a new recursive finite-dimensional exact density filter based on the linear skew-Gaussian system. The second part adopts a skew-symmetric representation (SSR) of distribution for nonlinear skew-Gaussian estimation. The third part gives an optimized Gauss-Hermite quadrature (GHQ) rule for numerical integration with respect to Gaussian integrals and applies it to nonlinear Gaussian filters.

We first develop a linear system model driven by skew-Gaussian processes and present the exact filter for the posterior density with fixed dimensional recursive …

Design & Analysis Of Mixed-Mode Integrated Circuit For Pulse-Shape Discrimination, Bryan Orabutt

McKelvey School of Engineering Graduate Student Theses & Dissertations

In nuclear science experiments it is usually necessary to determine the type of radiation, its energy and direction with considerable accuracy. The detection of neutrons and discriminating them from gamma rays is particularly difficult. A popular method of doing so is to measure characteristics intrinsic to the pulse shape of each radiation type in order to perform pulse-shape discrimination (PSD).

Historically, PSD capable systems have been designed with two approaches in mind: specialized analog circuitry, or digital signal processing (DSP). In this work we propose a PSD capable circuit topology using techniques from both the analog and DSP domains. We …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Speaker Diarization And Identification From Single-Channel Classroom Audio Recording Using Virtual Microphones, Antonio Gomez

Electrical and Computer Engineering ETDs

Speaker identification in noisy audio recordings, specifically those from collaborative learning environments, can be extremely challenging. There is a need to identify individual students talking in small groups from other students talking at the same time. To solve the problem, we assume the use of a single microphone per student group without any access to previous large datasets for training.

This dissertation proposes a method of speaker identification using cross-correlation patterns associated to an array of virtual microphones, centered around the physical microphone. The virtual microphones are simulated by using approximate speaker geometry observed from a video recording. The patterns …

Multicarrier Modulation Using Discrete Fractional Fourier Transform, Amir Raeisi Nafchi

Electrical and Computer Engineering ETDs

The focus of the research was to investigate the application of the discrete fractional Fourier transform (DFRFT) in communication systems. We investigated the compactness of the Gauss-Hermite like eigenvectors of the DFRFT and showed how a multi-carrier modulation system could benefit from it. This led to identifying an affine DFRFT. We proved the circular convolution property for the proposed DFRFT. Using this affine transform, we were able to design an orthogonal frequency division multiplexer (OFDM) communication system. In the process of implementing the OFDM, we developed a method for fast computation of the DFRFT using the chirp-z transform. Using the …

Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

Multimodal hyperspectral and lidar data sets provide complementary spectral and structural data. Joint processing and exploitation to produce semantically labeled pixel maps through semantic segmentation has proven useful for a variety of decision tasks. In this work, we identify two areas of improvement over previous approaches and present a proof of concept network implementing these improvements. First, rather than using a late fusion style architecture as in prior work, our approach implements a composite style fusion architecture to allow for the simultaneous generation of multimodal features and the learning of fused features during encoding. Second, our approach processes the higher …

Dc Microgrid Fault Detection Using Multiresolution Analysis Of Traveling Waves, Rudy Montoya

Mechanical Engineering ETDs

Fast detection and isolation of faults in a DC microgrid is of particular importance. Fast tripping protection (i) increases the lifetime of power electronics (PE) switches by avoiding high fault current magnitudes and (ii) enhances the controllability of PE converters. This thesis proposes a traveling wave (TW) based scheme for fast tripping protection of DC microgrids. The proposed scheme utilizes a discrete wavelet transform (DWT) to calculate the high-frequency components of DC fault currents. Multiresolution analysis (MRA) using DWT is utilized to detect TW components for different frequency ranges. The Parseval energy calculated from the MRA coefficients are then used …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …