Signal Processing Commons^™

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 …

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 …

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 …

Aerospace Vehicle Comprising Module For Method Of Terrain, Terrain Activity And Material Classification, Matthew E. Nussbaum, Marissa S. Herron

AFIT Patents

A method of classifying terrain, terrain activity and materials through panchromatic imagery a module programmed to provide such classification and aerospace vehicles comprising such module is provided. Panchromatic images of known materials terrains and terrain activities are taken and processed to form a multiband texture cube, that due it amount of data, is stored as a computer data base. New panchromatic images of unclassified materials, terrains and/or terrain activities are processed and compared via computer with such database that allows for inexpensive, quick and efficient classification of such new images.

Scalability And Connectivity Challenges For The Future Of Digital Radio Communication, Christopher Bayliss

Honors Program: Senior Projects (Public)

This thesis explores future challenges in digital radio communication, particularly focusing on scalability and connectivity issues that could impede technological advancements. With the increasing production of digital technology and reliance on radio frequencies, there is a need for innovative solutions to overcome limitations in spectrum availability, interference management, and bandwidth constraints. This work evaluates current modulation techniques, spectrum allocation strategies, and advanced communication technologies such as cognitive radio systems and reconfigurable intelligent surfaces. Through a comprehensive literature review and analysis, this thesis identifies promising developments, including dynamic spectrum sharing and symbiotic radio systems, that could significantly enhance spectrum efficiency and …

Affordable Bioprint Head-Adapter For 3d Printers, Andrew Ceralde, Dominic Drake, Noah Engles, Mohammad Alwan, Nevada Perry

Honors Capstones

Introduction: Addressing the need for affordable and accessible bioprinting technology, the Northern Illinois University bioprinting research and design project aims to democratize the field by developing an open source bioprint head. The initiative targets the prohibitive cost of commercial bioprinters by proposing an optimal design that can be integrated with widely available 3D printers, like the Creality Ender-3. This approach seeks to enable the widespread adoption of bioprinting technology, particularly for applications in tissue engineering and regenerative medicine.

Methods: Leveraging SolidWorks for design, the project employs a methodology that combines a precision extrusion system with thermal and UV crosslinking capabilities. …

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.

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

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 mesh network. Aggregated results will be displayed to the user through a Graphical User Interface at the fusion center.

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

School of Computing: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

Machine Learning And Geostatistical Approaches For Discovery Of Weather And Climate Events Related To El Niño Phenomena, Sachi Perera

Computational and Data Sciences (PhD) Dissertations

El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA …

Database And Machine Learning Model For Classifying Autism Spectrum Disorder From Smartphone Based Electroretinography, Rory Harris

Honors Scholar Theses

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that negatively affects a patient’s cognitive and communication aptitude and, therefore, can severely impact that patient’s quality of life. Because of this, early diagnosis is paramount. In recent studies, electroretinography (ERG), which is a measure of the retina’s electrical response to a brief flash of light into the eye, has shown promise in detecting ASD. Access to these scans can provide early diagnosis, improving well-being. Current ERG devices are very expensive due to their on board processing capabilities. This paper aims to create an ERG device using a smartphone as the main …

Developing General Purpose Apps To Automate Image Analysis Of Wave-Augmented-Varicose-Explosion Atomization And Other Multi-Phase Interfacial Flows, Ethan Newkirk

Senior Honors Theses

Atomization involves disrupting a flow of contiguous liquid into small droplets ranging from one submicron to several hundred microns (micrometers) in diameter through the processes of exerting sufficient forces that disrupt the retaining surface tensions of the liquid. Understanding this phenomenon requires high-speed imaging from physical models or rigorous multiphase computational fluid dynamics models. We produce a MATLAB application that utilizes various methods of image analysis to quickly analyze and store mathematical data from detailed image analyses. We present a user with numerous tools and capabilities that provide results that deviate from 1.8% to 8.9% of the original image sequence …

Features Of Magnetoelastic Converters Of Mechanical Quantities Of Transformer Type, Sulton Amirov, Kamila Jurayeva

Chemical Technology, Control and Management

The article discusses the features of a magnetoelastic transformer type transducer. In magnetoelastic transformer-type transducers, mutual inductance is assumed as a variable value, which is a function of the value of the applied mechanical force. Hence, the transformation coefficients of such converters are also variable. There are also choke type converters in which a change in the magnetic permeability of the magnetic circuit leads to a change in the total electrical resistance of the choke. For further research, transformer-type converters were selected, which make it possible to widely implement techniques for improving basic characteristics.

Rotational Magnetoelectric Switching In Orthorhombic Multiferroics, Xu Li, Hao Tian, Hongjun Jiang, Jun-Ming Liu, L. Bellaiche, Yurong Yang

Physics Faculty Publications and Presentations

Controlling the direction of ferromagnetism and antiferromagnetism by an electric field in single-phase multiferroics will open the door to the next generation of devices for spintronics and electronics. The typical magnetoelectric coupling such as the linear magnetoelectric effect is very weak in type-I multiferroics and therefore the magnetoelectric switching is rarely achieved. Here, using first- principles simulations, we propose a magnetoelectric switching mechanism to achieve such highly desired control in orthorhombic multiferroics. One class of two-dimensional proper multiferroics (CrX2Se3 and MnX2Te3, X = Sn, Ge) and perovskite multiferroics (EuTiO3 and BiFeO3/LaFeO3 superlattice) are taken as examples to show the mechanism. …

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 …

Towards A Wearable Device For Measuring Impedance Plethysmography Of The Radial Artery, Pritom Chowdhury

Dartmouth College Master’s Theses

Recent advancements in bioimpedance technology have demonstrated significant promise in the application of cardiac health monitoring. This research explores the design and development of a forearm-based wearable bioimpedance device for non-invasive measurement of heart rate and respiratory rate at an accuracy level comparable to medical-grade monitors. It utilizes a tetrapolar electrode configuration to analyze bioimpedance changes in the radial artery due to blood flow.

An ongoing aspect of this work involves the preliminary development of an embedded framework intended to integrate signal generation, acquisition, and processing within the device to achieve compact and efficient system design, anticipated to contribute to …

Low Cost Magnetometer Calibration And Distributed Simultaneous Multipoint Ionospheric Measurements From A Sounding Rocket Platform, Joshua W. Milford

Doctoral Dissertations and Master's Theses

Low-cost and low-size-weight-and-power (SWaP) magnetometers can provide greater accessibility for distributed simultaneous measurements in the ionosphere, either onboard sounding rockets or on CubeSats. The Space and Atmospheric Instrumentation Laboratory (SAIL) at Embry-Riddle Aeronautical University has launched a multitude of sounding rockets in recent history: one night-time mid-latitude rocket from Wallops Flight Facility in August 2022 and three mid-latitude rockets from White Sands Missile Range during the October 2023 annular solar eclipse. All rockets had a comprehensive suite of instruments for electrodynamics and neutral dynamics measurements. Among this suite was one science-grade three-axis fluxgate magnetometer (Billingsley TFM65VQS / TFM100G2) and up …

Investigation Of Gas Dynamics In Water And Oil-Based Muds Using Das, Dts, And Dss Measurements, Temitayo S. Adeyemi

LSU Master's Theses

Reliable prediction of gas migration velocity, void fraction, and length of gas-affected region in water and oil-based muds is essential for effective planning, control, and optimization of drilling operations. However, there is a gap in our understanding of gas behavior and dynamics in water and oil-based muds. This is a consequence of the use of experimental systems that are not representative of field-scale conditions. This study seeks to bridge the gap via the well-scale deployment of distributed fiber-optic sensors for real-time monitoring of gas behavior and dynamics in water and oil-based mud. The aforementioned parameters were estimated in real-time using …

Comparison Of Conventional And Adaptive Acoustic Beamforming Algorithms Using A Tetrahedral Microphone Array In Noisy Environments, Megan Brittany Ewers

Dissertations and Theses

In situ acoustic measurements are often plagued by interfering sound sources that occur within the measurement environment. Both adaptive and conventional beamforming algorithms, when applied to the outputs of a microphone array arranged in a tetrahedral geometry, are able to capture sound sources in desired directions and reject sound from unwanted directions. Adaptive algorithms may be able to measure a desired sound source with greater spatial precision, but require more calculations and, therefore, computational power. A conventional frequency-domain phase-shift algorithm and a modified adaptive frequency-domain Minimum Variance Distortionless Response (MVDR) algorithm were applied to simulated and recorded signals from a …

A Sindy Hardware Accelerator For Efficient System Identification On Edge Devices, Michael Sean Gallagher

Master's Theses

The SINDy (Sparse Identification of Non-linear Dynamics) algorithm is a method of turning a set of data representing non-linear dynamics into a much smaller set of equations comprised of non-linear functions summed together. This provides a human readable system model the represents the dynamic system analyzed. The SINDy algorithm is important for a variety of applications, including high precision industrial and robotic applications. A Hardware Accelerator was designed to decrease the time spent doing calculations. This thesis proposes an efficient hardware accelerator approach for a broad range of applications that use SINDy and similar system identification algorithms. The accelerator is …