Signal Processing Commons^™

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 …

Design, Fabrication, & Laboratory Testing Of A Strain Gage Instrumentation System For Marine Applications, Salem C. Homrighausen

UNF Graduate Theses and Dissertations

A strain gage-based instrumentation system was designed and fabricated to measure the moment present at the base of a mock, diesel-engine snorkel for a submerged vehicle designed to operate in the surf zone. Afterwards, a lab experiment was designed and executed to determine the accuracy of the instrument and a 2-dimensional, matrix-vector equation was formulated to relate pairs of voltages, Vx and Vy, to moments, Mx and My for the experiment. Three versions of this model were built. The first model was created using traditional statics equations governing strain, stress, moment, force. The second and third models were built directly …

Ai-Driven Dynamic Pilot Placement For 5g Mmwave Massive Mimo: A Random Forest Regression Approach, Mohammad R. Abou Yassin, Soubhi Abo Chahine, Hamza Issa

BAU Journal - Science and Technology

Efficient pilot placement in 5G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is critical to enhancing performance, achieving high spectral efficiency (SE), low bit error rate (BER), reduced pilot overhead, and minimized latency. However, this requires pilot symbols transmission, which occupies spectral resources and results in reducing spectral efficiency (SE). This paper proposes a novel dynamic pilot placement (DPP) framework, optimized using a Random Forest Regression (RFR) approach, to enhance system performance. Unlike traditional static and semi-static pilot allocation methods, the DPP approach dynamically adjusts pilot positions based on real-time channel state information (CSI) and system requirements, reducing interference and …

An Experimental Investigation Of Signal Processing Techniques For Vibration-Based Structural Health Monitoring In Residential Buildings Subjected To Base Excitation, Fedaa Ali

Electrical Engineering Theses

Coastal regions, particularly in the southeastern United States, are consistently confronted with the ongoing threat of hurricane-induced damage to their buildings. Consequently, there is a pressing need to concentrate efforts on the evaluation and prediction of structural integrity and reliability in such environments. This is paramount for minimizing losses and enhancing public safety in the face of these challenging climatic conditions. Current structural health monitoring systems are typically customized for specific buildings, rendering them excessively expensive and impractical for residential structures. This research presents a comprehensive study of signal processing techniques and damage detection for an economical yet efficient structural …

Identifying Subject Bias In Wifi-Based Human Activity Recognition Evaluation Methods, Amany Elkelany, Robert J. Ross, Susan Mckeever

Conference papers

WiFi-based Human Activity Recognition (HAR) has emerged as a promising approach for monitoring and analysing human activities in a non-intrusive manner, leveraging WiFi signals for activity classification. Despite advancements, existing WiFi-based HAR research lacks consideration of subject (human) bias. This results in learning models performing well on individuals used in the training samples but failing to generalise to new/unseen subjects, in contrast to known good practices in machine learning. In this paper, we address this oversight directly by systematically examining the evaluation methodology for the WiFi-based HAR context. Specifically, we investigate the impact of Leave-One-Subject-Out Cross-Validation (LOSOCV) in a hybrid …

Exploring Smart Thermostat, Don P. Dang

2024 Fall Honors Capstone Projects - Archive

This study examines the security, privacy, and compatibility challenges associated with smart thermostats in smart home systems. Smart thermostats, as part of the growing Internet of Things (IoT) ecosystem, face vulnerabilities such as unauthorized access, data breaches, and inconsistent security protocols. Using a mixed-methods approach, this research evaluates encryption techniques, communication protocols (Zigbee, Z-Wave, Wi Fi), and user behaviors that impact system security and efficiency. Key findings indicate that 35% of users are concerned about hacking risks, while 25% express data privacy concerns. Many users lack awareness of security measures, such as firmware updates and password management, which increases vulnerabilities. …

Hardware Applications Of High-Speed Fault Detection Algorithms, Daniel Zintsmaster

All Theses

The growing need for a sustainable electric energy infrastructure has driven research into control, protection, and optimization of power systems. A key challenge is that the changing grid must operate at increasingly higher speeds, but many current hardware devices cannot meet these demands. This thesis focuses on power system protection, aiming to design a hardware solution that can detect and isolate faults in microseconds, ensuring faster, more reliable grid operations. The first hardware developed was for a low-voltage direct current (LVdc) microgrid, which faces challenges due to a lack of protection schemes and novel speed requirements. This thesis presents protection …

Co-Emulation Of Robotics And Software-Defined Radio Based 5g Wireless Communications., Bhaskara Venkata Raju Garuda

Electronic Theses and Dissertations

The convergence of robotics and 5G wireless communication technologies has opened new avenues for real-time, dynamic robotic applications. This dissertation introduces a novel framework that integrates the Robot Operating System (ROS), Software-Defined Radios (SDRs), and 5G wireless networks to achieve seamless coemulation of robotic systems. The research emphasizes the unique features of 5G, such as ultra-low latency and high throughput, which enable critical applications like remote surgery, industrial automation, and autonomous vehicles. The methodology combines ROS for robotic control, SDRs for programmable communication channels, and 5G testbeds for high-speed, reliable data transmission. The experimental evaluation focuses on both position-based and …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

Automated Solutions For Hydroponic Plant Growth, Sydney Mcclure, Adam Lachguar

Sustainability Conference

Within the past year, Project H.O.M.E. has been focusing on the design and development of a semi-automatic hydroponic system specifically for sustaining plant life in Martian-like conditions. Given the significance of extended space-based travel, where the duration of human life in space is a crucial factor, growing food becomes imperative. This project has integrated electrical engineering and computer science, with features like automated pH testing and sensor-based evaluations. Key functionalities, including timed watering and automatic adjustments, were coded to enhance plant care. Initially, the project’s comprehensive research and strategic planning resulted in detailed blueprints and computer-aided design models for the …

Biocorrosion Analysis Via Multiscale Time Series Analysis, Victor Hugo Mendoza Vejar, Eliseo Hernandez Martinez, Hector Puebla

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch, Anthony L. Franz, Eugene V. Dao, Dong L. Wu, Nimalan Swarnalingam, Cornelius C. J. H. Salinas, Daniel J. Emmons

Faculty Publications

Intense ionization enhancements in the Earth’s ionosphere, known as sporadic-E (E_s), can degrade and severely disrupt the propagation of radio signals. Although many previous studies have analyzed the characteristics and morphologies of sporadic-E, few efforts have attempted to model global E_s occurrence rates (ORs) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E occurrence rates using a Karhunen–Loéve Expansion (KLE) of a global OR climatology built with Global Navigation Satellite System radio occultation (GNSS-RO) and ionosonde observations. Using an fbE ≥ threshold of 3 MHz, the model outputs a blanketing sporadic-E …

Real-Time Synthesis Of A Nonuniformly Correlated, Partially Coherent Beam Using An Optical Coordinate Tansformation, Milo W. Hyde Iv

Faculty Publications

We design, build, and validate an optical system for generating light beams with complex spatial coherence properties in real time. Beams of this type self-focus and are resistant to turbulence degradation, making them potentially useful in applications such as optical communications. We begin with a general theoretical analysis of our proposed design. Our approach starts by generating a Schell-model (uniformly correlated or shift-invariant) source by spatially filtering incoherent light. We then pass this light through an optical coordinate transformer, which converts the Schell-model source into a nonuniformly correlated field. After the general analysis, we discuss system engineering, including trade-offs among …

Design And Implementation Of An Inverted Short Baseline Acoustic Positioning System, Jakob Frabosilio

Master's Theses

This document details the design, implementation, testing, and analysis of an inverted short baseline acoustic positioning system. The system presented here is an above-water, air-based prototype for an underwater acoustic positioning system; it is designed to determine the position of remotely-operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) in the global frame using a method that does not drift over time.

A ground-truth positioning system is constructed using a stacked hexapod platform actuator, which mimics the motion of an AUV and provides the true position of an ultrasonic microphone array. An ultrasonic transmitter sends a pulse of sound towards …

Scene Decomposed Blind Deconvolution And Neural Network Based Multi-Frame Image Restoration Techniques For Astronomical Imagery, Joshua S. Sprang

Theses and Dissertations

Ground based astronomical imaging is an important method in gaining situational awareness of orbiting and far off objects in space. This method of imaging is accessible to everyone that can look up into the sky, but the accessibility to digital telescope systems allows for more exciting methods of extracting information. A use case for these telescopes is finding nearby objects to larger brighter known objects. The number satellites in low-earth orbit and geosynchronous earth orbit is becoming more congested as these orbits increase in population. Tens of thousands of satellites and debris now exist in this orbit, with the number …

Design Of A Portable Fast Scan Cyclic Voltammetry Device Utilizing Pulse Width Modulation For Waveform Generation, Nora Szymkowski

Masters Theses

Fast Scan Cyclic Voltammetry (FSCV) is a widely used electrochemical technique for real-time measurement of the brain’s chemical messengers, including the molecule dopamine, with high temporal resolution. Currently the financial burden of performing FSCV is quite high, ranging from $8,000 to $20,000+ making the barrier to entry nearly insurmountable for laboratories and classrooms at small institutions. The purpose of this project was to develop a Do-It-Yourself (DIY), portable, and cost-effective FSCV system for use in laboratory and classroom settings. The project aimed to create a compact and cost-effective system that could be used by researchers and educators to study dopamine …

Novel Sensors, Algorithms And Metrics For Human-Robot Interaction., Henry Lee Reynolds

Electronic Theses and Dissertations

The increased presence and deployment of robotics in sectors such as the medical field results in the demand for robots to, directly and indirectly, interface with people and their environment, making human-robot interaction (HRI) a vital thrust of robotics research. Assistive robots, for example, aid humans in accomplishing tasks or by providing support in the workforce. As the demand for nurses and the aging population increases, the assistive robots deployed will be deeply rooted in environments that require constant interaction with humans. This work contributed to improving aspects of HRI through 1) Expanding accessibility of the methods used for interfacing …

Computer Vision Algorithms For Assessment Of Surgical Suturing Skill Using Hand And Needle Motion, Jianxin Gao

All Dissertations

Surgical suturing skill assessment is a crucial part of surgical education. Vascular surgery educators have developed a simulation-based examination called Fundamentals of Vascular Surgery, which includes a clock-face model for assessing open surgical suturing skills. The clock-face model, however, requires the valuable time of expert surgeons to determine examinees' skills. Moreover, expert surgeons have different judgments for appropriate sutures, which leads to inconsistent grading. These limitations motivate us to use sensors to measure examinees' needle motions and hand motions during the clock-face suturing exercises, and then use the measurements for objective suturing skill assessment.

To assess suturing skills based on …

Modeling, Optimization, And Characterization For Choke Horn Antennas, Ibrahim Nasser I Alquaydheb

Graduate Theses and Dissertations

This dissertation presents a comprehensive study focusing on the modeling, optimization, and characterization of choke horn antennas (CHAs). An analytical model designed to capture the parameters of CHA and derive the total radiated fields from the choke and waveguide elements is primarily focused on in this work. Compared to the use of simulation software, such as ANSYS HFSS (High Frequency Structure Simulator) or CST Studio, which employs numerical methods to simulate and calculate antenna performance, numerous advantages are offered by the analytical model. Analytical models provide deeper insight into electromagnetic interactions and the principles governing antenna behavior, leading to a …

Authenticated Diagnosing Of Covid-19 Using Deep Learning-Based Ct Image Encryption Approach, Mohamed Attia Abdelgwad, Amira Hassan Abed, Mahmoud Bahloul

Future Computing and Informatics Journal

Researchers are motivated to use artificial intelligence in biometrics, medical imaging encryption, as well as cybersecurity due to its rapid progress. An encryption method for CT scans—which are used to diagnose COVID-19 disease—is proposed in this study. The suggested encryption method creates a connection among an individual's face picture and CT image to increase confidentiality. The simple CT picture is first enhanced with a host image. An encryption key is multiplied by the final result. This key is produced by applying a Convolutional Neural Network (CNN) to recognize characteristics from people's face photographs. Additionally, a straightforward CNN with three convolutional …

Segmentation And Classification Of Left Ventricular Abnormalities In Cardiac Mri Using Initial Point Prediction Based Deformable Model, Md. Asadur Rahman, Md. Al Noman, A. B. M. Aowlad Hossain

Future Computing and Informatics Journal

The shape of the left ventricle (LV) of a cardiac magnetic resonance image (CMRI) helps physicians to diagnose different cardiac abnormalities. The similarity of pixel intensity and shape of LV with neighbor tissues, the imprecision of boundaries, and the presence of noise are the challenges to accurate segmentation of LV. This paper contributes to the successful implementation of an automatic edge contouring method to segment LV area from CMRI and detect whether the ventricle belongs to abnormalities. This method proposes the regression-based artificial neural network to predict the possible initial position of the deformable edge-based active contour model for precise …

Multi-Classification Model For Brain Tumor Early Prediction Based On Deep Learning Techniques, Abdelrahman T. Elgohr, Mohamed S. Elhadidy, Mahmoud Elazab Dr, Raneem Ahmed Hegazii, Moataz M. El Sherbiny

Journal of Engineering Research

Brain tumor early prediction is a critical task in medical imaging, as early detection and classification of tumors can significantly improve patient outcomes and treatment planning. In this study, we propose multi-classification models based on deep learning techniques for early prediction of brain tumors using magnetic resonance imaging (MRI) scans. Specifically, we investigate the effectiveness of Convolutional Neural Networks (CNN) in the You Only Look Once (YOLO) approach for an accurate classification of brain tumors into multiple classes based on their morphological characteristics. The proposed model is designed to extract spatial features from MRI images, capturing local patterns and structures …

Using A Neural Network To Remove Noise From Images, Anvar Asatilloyevich Ravshanov

Chemical Technology, Control and Management

This article proposes modern approaches to the problem of noise reduction in images using neural networks and also analyses the possibilities of noise reduction using neural networks. The convolutional neural network model and the Mediana, Sobel filter were considered for image denoising. The quality improvement of the trained neural network and the comparison with classical noise reduction methods have been carried out.

Shape Memory Alloy Capsule Micropump For Drug Delivery Applications, Youssef Mohamed Kotb

Theses and Dissertations

Implantable drug delivery devices have many benefits over traditional drug administration techniques and have attracted a lot of attention in recent years. By delivering the medication directly to the tissue, they enable the use of larger localized concentrations, enhancing the efficacy of the treatment. Passive-release drug delivery systems, one of the various ways to provide medication, are great inventions. However, they cannot dispense the medication on demand since they are nonprogrammable. Therefore, active actuators are more advantageous in delivery applications. Smart material actuators, however, have greatly increased in popularity for manufacturing wearable and implantable micropumps due to their high energy …

Technologies For Wearable Seizure Detection: A Systematic Review, Rhema Losli

University Honors Theses

Knowing when a seizure occurred is helpful because this information can be used to evaluate the effectiveness of seizure interventions and possibly alert caregivers to emergency situations. The current practice for recording seizures outside of a hospital and without sensors is through keeping a self-reported seizure diary. This practice may be unreliable if the diary is not updated or the person having the seizure does not realize it is happening. Wearable seizure detectors aim to solve this problem by reliably recording when a seizure happened and either sending out an alert or storing the data for later analysis. In this …

Enhancing Direction Finding Accuracy In Perturbed Digital Arrays Via Rf Ranging-Based Self Calibration, Ariel Freiman

Master's Theses

Direction finding with radio-frequency (RF) waves have numerous applications in radio navigation, wireless localization, emergency aid, and air traffic control, among others. Direction-finding using digital arrays outperforms traditional analog techniques but requires precise knowledge of the location of the array elements to obtain accurate results. Array perturbations can lead to algorithm failures and false detection, compromising direction-finding capabilities.

This research proposes implementing a Matched Filter - Least Square (MF-LS) algorithm for Two-Way Ranging (TWR) to enhance direction-finding accuracy in arrays with perturbed element locations. The MF-LS algorithm leverages the properties of matched filters to accurately determine element positions by measuring …

Harmonic Flow Modeling And Analysis Of A Green Hybrid Ac/Dc Seaport Power System, Krishan Kaushal Ram, Taufik, Helen Yu, Siddharth Vyas

Master's Theses

This thesis aims to design and develop a model for a proposed Green Seaport power system and perform harmonic analysis. The model was developed using MATLAB Simulink and tests were performed by dividing the system into several Battery Energy Storage System (BESS) operating modes such as simultaneous charging and discharging, simultaneous charging, and simultaneous discharging. For each mode, BESS state of charge and other factors such as solar irradiance for the PV system, load levels and power factor were varied to observe the impact on system’s voltage Total Harmonic Distortion (THD) level and current Total Demand Distortion (TDD). 62 separate …

Design Of High Efficiency Doherty Power Amplifier, Kobi G. Kelly, Severin Pindell

Electrical Engineering

The project includes design and fabrication of a high efficiency power amplifier for a student design competition held at International Microwave Symposium (IMS) 2023. Efficient power amplifiers are critical for base station communication requiring efficient use of available power. The final design optimizes power added efficiency (PAE) and linearity. The amplifier will operate at 2.45 GHz. Competitive PAE above 50%, and C/I above 30 dB is achieved by leveraging a Doherty class amplifier using accurate discrete CGH4006P transistor models to simulate an efficient and linear design. Unique design features include optimal transistor bias point selection and power split ratios between …

Impact Of Operational Ladar Occlusions On Point Cloud Instance Segmentation, Andrew D. Gibson

Theses and Dissertations

Data exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the …

Remote Profiling Of Atmospheric Turbulence: Enhanced Resolution With Stacked Rayleigh Beacons In Tardis, Benjamin C. Wilson

Theses and Dissertations

A stacked beacon turbulence profiling methodology has been introduced and applied to have increased profiling on the Turbulence and Aerosol Research and Investigation System (TARDIS). The model was derived and demonstrated the applicability through discussion on data processing and inversion processes. The methodology was applied for two different nighttime experiments, one in Summer and one in Fall. C 2 n profiles were derived into the 1300 m altitude ranges for both nights and the summer experiment was compared to a co-located DELTA Sky measurements and LEEDR generated Climatological profiles. The comparison implied promise in the methodology with additional work needed …