Electrical and Computer Engineering Commons^™

Constructing Orthonormal Bases With The Residuals Of Successive Approximations, An Introduction To Multiresolution Analysis, Elijah J. Guptill

Master's Theses

Wavelets and wavelet analysis are used in the study of signal processing, quantum field theory, functional analysis, multifractal analysis, and various other areas of mathematics. Multiresolution analysis provides a framework for building a wavelet basis of $\mathcal{L}^{2}(\mathbb{R})$ from a scaling function $\phi$, whose dyadic dilations and translations, $\{2^{j /2}\phi(2^{j}x-k):j,k\in \mathbb{Z}\}$, approximate $\mathcal{L}^{2}(\mathbb{R})$. One of the key properties of  $\phi$ is that it must satisfy $\phi(x)=\sum_{k\in \mathbb{Z}}{p_{k}2^{j /2}\phi(2^{j}x-k)}$ with respect to the norm on $\mathcal{L}^{2}(\mathbb{R})$. This equation is called a two-scale difference equation. Such equations enforce a regularity on the ordinary generating function $2^{-1 /2}\sum_{k\in \mathbb{Z}}{p_{k}z^{k}}$, known as the quadrature condition. …

Engr 204: 8-Week Op-Amp Major Project Series, Liubov Kreminska

Open Educational Resources

This submission outlines a collection of electronic design projects that can be completed within an 8-week timeframe, progressing from beginner-friendly circuits to advanced real-world applications involving op-amp design. Each project includes a week-by-week plan, required equipment, and key technical challenges. Through these projects, students gradually develop competence in signal processing, circuit stability, noise reduction, calibration, filter design, audio electronics, and bio-signal amplification. The structured timeline ensures systematic learning while allowing hands-on experimentation, troubleshooting, and refinement. The projects serve both as academic exercises and as practical foundations for more advanced electronic system design.

Research And Development Of Intelligent Measurement Systems, Odil Abdujalilovich Jumaev, Mahmudov Giyosjon Baqoyevich

Chemical Technology, Control and Management

The article discusses modern methods for developing intelligent measuring systems. Intelligent measuring systems are systems based on intelligent technologies that not only accurately measure physical or chemical quantities, but also have the ability to self-analyze, diagnose and make management decisions. The article comprehensively examines the architecture, components of such systems, the organization of their software and hardware, the relationship of sensors and artificial intelligence algorithms. It also analyzes the practical application and prospects of intelligent measuring systems in such areas as industry, medicine, energy, ecology, transport.

Computational Modeling For Automatic Superconducting Cavity Fault Prediction And Classification Using Time Series Signals, Md Monibor Rahman

Electrical & Computer Engineering Theses & Dissertations

Processing multivariate time series signals collected from sensor networks is challenging because of complex temporal dependencies and non-stationarity. With the advent of artificial intelligence (AI) like machine learning and deep learning, it has become possible to process sensor-driven time series data more effectively than traditional statistical methods.

This dissertation aims to develop machine learning and deep learning models to address machine fault diagnosis using multivariate time series signals collected from the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. The first goal of the proposed work is to develop deep learning–based classification models and an unsupervised fault clustering approach …

Deep Learning-Based Gain Estimation For Multi-User Software-Defined Radios In Aircraft Communications, Viraj K. Gajjar, Kurt L. Kosbar

Electrical and Computer Engineering Faculty Research & Creative Works

It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoing composite signal would be passed through a digital-to-analog converter (DAC) before being up-converted and passed through a radio frequency (RF) amplifier. To prevent non-linear distortion in the RF amplifier, it is important to know the peak voltage of the composite. While this is reasonably straightforward when a single modulation is used, it is more challenging when working with composite signals. This paper describes …

Identification Of Fiducial Points In Seismocardiographic Cycles Using Manual And Automated Annotation Methods, Jasmine-Vy T. Truong

Honors Undergraduate Theses

There is currently a need for complementary methods for non-invasive cardiac monitoring. Seismocardiography (SCG), the measurement of cardiac-induced vibrations at the chest surface, has shown potential clinical utility. Improving the reliability of detecting fiducial points of electrocardiography (ECG) and SCG, which collectively capture the electro-mechanical cardiac activities, could expand ECG/SCG utility as a low-cost, accessible tool for clinical assessment. This study identifies commonly accepted criteria for fiducial point detection in SCG and ECG through an extensive literature review and signal processing techniques. The previous criteria were evaluated to identify their strengths and weaknesses. Based on the findings, an improved set …

Heartfev1: A Mobile Electrocardiogram Based System For Inferring Forced Expiratory Volume In One Second From Patients With Chronic Obstructive Pulmonary Disease, Maria Nyamukuru

Dartmouth College Ph.D Dissertations

Chronic Obstructive Pulmonary Disease (COPD), characterized by chronic airway inflammation and airflow obstruction, is the third leading cause of death globally. Patients with COPD experience exacerbated symptoms like breathlessness and cough, significantly impacting their quality of life and leading to costly hospitalizations. Early detection of COPD exacerbations is crucial for mitigating these negative effects.

The most critical element for early detection of COPD exacerbations is daily monitoring of lung function, particularly forced expiratory volume in one second (FEV1), a key metric of lung function. By tracking declines in FEV1, COPD exacerbations can be predicted up to two weeks in advance, …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Detection Of Unauthorized Transmissions In A Frequency Spectrum Using A Wireless Sensor Network, Benjamin Roehrig, Joel Brinkman, Dylan Zupec, Jannette Gonzalez

Honors Capstones

A prototype for a wireless sensor network has been designed to detect and identify unauthorized wireless transmissions in a frequency spectrum. This prototype design is intended to detect unauthorized transmissions within the FM band of frequencies independently at individual nodes with Software Defined Radio receivers and transmit that information to a fusion center for aggregation using a Bluetooth Low Energy mesh network. Aggregated results will be displayed to the user through a Graphical User Interface at the fusion center.

High Bandwidth Hybrid Current Sensor Readout Interface For Power Applications, Asma Mahar

Graduate Theses and Dissertations

High-density and power-efficient circuits have become realizable with the advent of wide bandgap semiconductor devices due to their high switching speed and lower conduction/switching losses. The operation of these circuits requires current information for protection, monitoring, and control. For the realization and a further boost in the efficiency of such power circuits, small size, non-invasive, high bandwidth current sensors with noise immunity, accuracy, and linearity are required. Various hybrid sensors have been reported to meet the requirements of emerging power circuits. A combination of a magneto resistor or Hall sensor and a coil is used to cover a wide frequency …

Enhancing Rotating Machinery Fault Diagnosis: A Dual-Head Attention Mechanism In Deep Learning Neural Networks, Qing Snyder

Dissertations

Rotating machinery is crucial to production efficiency and safety in manufacturing industries for an extended time. Ensuring machinery reliability necessitates effective diagnostic systems, particularly for rotating bearings, the key components of such equipment. Fault diagnosis in rotating machinery is essential to prevent failures and minimize downtime, thereby playing an important role in industrial operations. The application of advanced neural network techniques in industry has risen recently. Among these, attention-based neural networks, especially the Transformer models, are originally noteworthy for their sequential data handling capability. This research delves into attention-based algorithms for rotating machinery fault diagnosis, signifying a substantial advancement in …

A Practical Low-Dimensional Feature Vector Generation Method Based On Wavelet Transform For Psychophysiological Signals, Erdem Erkan, Yasemi̇n Erkan

Turkish Journal of Electrical Engineering and Computer Sciences

High-dimensional feature vectors entail computational cost and computational complexity. However, a successful classification can be obtained with an optimally sized feature vector consisting of distinctive features. With the widespread use of the internet and mobile devices, the need for systems with low computational costs is increasing day by day. In this study, starting from the idea that each motor imagery is represented as a subject-specific pattern in the brain, we propose a new and practical method that can generate a low-dimensional feature vector based on wavelet transform. The feature vector is obtained from the correlation between each trial and each …

Development Of The Digital Signal Processing For The Space Weather Probes Version 2 Sensor Using The Matlab/Simulink Environment, Benjamin J. Lewis

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Space Weather Probes (SWP) is an instrument that provides measurements of the plasma environment of the ionosphere. SWP was flown on the Scintillation Prediction Observation Task (SPORT) mission, a joint mission between the United States of America and Brazil. This thesis will develop the digital signal processing (DSP) hardware design for the Space Weather Probes version 2 (SWP2). The data from these instruments will be used to determine the density and temperature of the local plasma, as well as the electric field in the local plasma. This thesis presents the design and testing of the DSP designs for all of …

Learning Dynamic Information Of High-Dimensional Signal Time-Series Using Advanced Machine Learning/Artificial Intelligence, Guannan Liu

LSU Doctoral Dissertations

In this dissertation, we propose a novel simulation-based device-free indoor localization and tracking system using the received signal strength indicators (RSSIs) of WiFi signals as the input features. The Feko channel-propagation simulation software is used to process the RSSI maps of the given arbitrary indoor geometry. In order to learn the dynamic information of high-dimensional RSSI time-series, we propose three procedures for the localization and dynamic tracking system.

First, The indoor geometry is partitioned into several equi-size zones and the localization problem is treated as the typical \multi-classification" problem. The advanced machine-learning techniques such as decision tree (DT) classifier, random …

Automotive Collision Warning System Retrofit, Ethan Clark Najmy

Electrical Engineering

In the early 2000s, few automakers began implementing forward collision warning systems in their cars. As technology advanced this system became available on more and more luxury cars. In recent years, this technology has spread to more affordable vehicles driven every day. However, as this technology has only recently advanced to less expensive, more economical cars, older vehicles of the same model may not have this advanced and important safety feature. This project investigates and creates a preliminary design for an affordable, easy-to-install, forward collision warning system that can be retrofitted to vehicles without the system currently installed. Using a …

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 …

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

Hum Synthesizer, Daniel Keith Mcfall, Jason Chang

Electrical Engineering

Most modern music, especially the popular and rising genres such as pop music and rap music are more reliant on music synthesizers than ever before. While other instruments are readily used to sample background noises, the main form of sound generation is becoming more digital rather than using acoustic instruments. However, many people that want to make music aren’t familiarized with traditional music theory and would have difficulty using a traditional synthesizer. The Hum Synthesizer aims to allow a wider audience to access the functions of a traditional synthesizer by using voice controls rather than keyboard controls. If the user …

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 …

A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is …

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 …

Nanomechanical Resonators: Toward Atomic Scale, Bo Xu, Pengcheng Zhang, Jiankai Zhu, Zuheng Liu, Alexander Eichler, Xu-Qian Zheng, Jaesung Lee, Aneesh Dash, Swapnil More, Song Wu, Yanan Yanan, Hao Jia, Akshay Naik, Adrian Bachtold, Rui Yang, Philip X.-L. Feng, Zenghui Wang

Department of Electrical and Computer Engineering: Faculty Publications

The quest for realizing and manipulating ever smaller man-made movable structures and dynamical machines has spurred tremendous endeavors, led to important discoveries, and inspired researchers to venture to new grounds. Scientific feats and technological milestones of miniaturization of mechanical structures have been widely accomplished by advances in machining and sculpturing ever shrinking features out of bulk materials such as silicon. With the flourishing multidisciplinary field of low-dimensional nanomaterials, including one-dimensional (1D) nanowires/nanotubes, and two-dimensional (2D) atomic layers such as graphene/phosphorene, growing interests and sustained efforts have been devoted to creating mechanical devices toward the ultimate limit of miniaturization— genuinely down …

A Primer On Software Defined Radios, Dimitrie C. Popescu, Rolland Vida

Electrical & Computer Engineering Faculty Publications

The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that …

Fourier Analysis And Other Tools For Electrical Engineers: A Practical Handbook, David G. Long

Books

No abstract provided.

Real Time Simulation And Hardware In The Loop Methods For Power Electronics Power Distribution Systems, Michele Difronzo

Theses and Dissertations

System level testing of Power Electronics Power Distribution Systems (PEPDS) can be challenging when fine temporal resolution is required (time step below 100-200ns). In the recent years, our research group has proposed various methods to simulate in real-time PEPDS using FPGAs and time step as small as 50ns. While the proposed methods allow achieving the desired temporal resolution, they are extremely demanding in terms of resources usage and the size of the PEPDS that can be simulated on a single FPGA is strongly limited.

In this dissertation -work that takes as an example application the US Navy electric Ship Zonal …

New Methods In Wavelet Analysis For Applications Of The Wavelet Transform, Jeffrey D. Williams

Theses and Dissertations

A commonality in the many applications and domains where signal processing (SP)is applied is the detection of events. Detection in SP requires the identification of the occurrence of an event, within a signal, and distinguishing the occurrence from no event. In a classical application of SP, seismologists seek to detect abnormalities in an electromagnetic (EM) signal to detect or not detect the occurrence of an earthquake, represented as an anomalous EM pulse. Since many signals are noisy, such as those produced by a seismograph, it can be challenging to distinguish a significant EM pulse from incident noise. In SP, smoothing …

Error Prevention In Sensors And Sensor Systems, Pedro J. Chacon Dominguez

LSU Doctoral Dissertations

Achievements in all fields of engineering and fabrication methods have led towards optimization and integration of multiple sensing devices into a concise system. These advances have caused significant innovation in various commercial, industrial, and research efforts. Integrations of subsystems have important applications for sensor systems in particular. The need for reporting and real time awareness of a device’s condition and surroundings have led to sensor systems being implemented in a wide variety of fields. From environmental sensors for agriculture, to object characterization and biomedical sensing, the application for sensor systems has impacted all modern facets of innovation. With these innovations, …

Deep Learning Assisted Intelligent Visual And Vehicle Tracking Systems, Liang Xu

Theses and Dissertations

Sensor fusion and tracking is the ability to bring together measurements from multiple sensors of the current and past time to estimate the current state of a system. The resulting state estimate is more accurate compared with the direct sensor measurement because it balances between the state prediction based on the assumed motion model and the noisy sensor measurement. Systems can then use the information provided by the sensor fusion and tracking process to support more-intelligent actions and achieve autonomy in a system like an autonomous vehicle. In the past, widely used sensor data are structured, which can be directly …

Clustered Hyperspectral Target Detection, Sean Onufer Stalley

Dissertations and Theses

Aerial target detection is often used to search for relatively small things over large areas of land. Depending on the size and signature of the target, detection can be a very easy or very difficult task. By capturing images with several hundred color channels, hyperspectral sensors provide a new way of looking at this task, both literally and figuratively. Hyperspectral sensors can be used in many aerial target detection tasks such as identifying unhealthy trees in a forest, searching for minerals at a mining site, or finding the sources of chemical leaks at a factory. The high spectral resolution of …