Signal Processing Commons^™

Sparse Methods For Blind Source Separation Of Frequency Hopping Rf Sources, Anushreya Ghosh

Dissertations

Blind source separation (BSS) is performed on frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over a Spatial Channel Model (SCM) in four different propagation environments: (i) line-of-sight (LOS), (ii) single-cluster, (iii) multiple-cluster, and (iv) LOS with interference. The sources are spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by utilizing direction of arrival (DOA) of the sources and clusters. A sparse detection framework is applied to obtain estimates of the sources' FH and DOA patterns. The solutions are binned …

Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens

MODVIS Workshop

No abstract provided.

Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest

Theses and Dissertations

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

Approximate And Sample Entropy Of Center Of Pressure In Unperturbed Tandem Standing: Contribution Of Embedding Dimension And Tolerance, Jayla Mashae Wesley

Masters Theses

Approximate entropy (ApEn) and sample entropy (SampEn) are statistical methods designed to quantify the regularity or predictability of a time series. Although ApEn has been a prominent choice for use, it is currently unclear as to which method and parameter selection combination is optimal for its application in biomechanics. The goal of this thesis was to examine the difference between ApEn and SampEn related to center of pressure (COP) data during standing balance tasks, while also refining tolerance r, to determine entropy optimization. Six participants completed five 30-second, feet together and tandem standing, trials under eyes-open and eyes-closed conditions. Ground …

State Estimation And Data Fusion With Data-Driven Communication, Xiaolei Bian

LSU New Orleans Theses and Dissertations

This dissertation focuses on the systemic design of proper estimation as well as fusion techniques and data-driven communication schemes to infer the state of a dynamic discrete-time linear system over a wireless network. The goal is to effectively extract and share key information over networks to enhance estimates of system states. Such co-design research is drawn by the need to leverage limited resources (communication bandwidth, energy and computational power) for a multitude of data and data sources in networked systems.

We first build the overarching structure by synthesizing communication and estimator/fuser, and research the collective behaviors of the system.

In …

Development Of Medical Device For Volume Status Assessment Using Peripheral Venous Pressure, Audra Beneux

Graduate Theses and Dissertations

The severe side effects of acute dehydration and blood loss may be prevented if assessed and treated quickly. As amplifier technology has improved, small peripheral venous pressure (PVP) signals collected using a transducer on an intravenous catheter can be analyzed to monitor patient volume status. This thesis details the development of an embedded system prototype that quickly and accurately assesses volume status using PVP analysis. The volume prediction algorithm classified 10 seconds of PVP data as “dehydrated” or “resuscitated”. The hardware design consisted of four primary areas: analog signal conditioning, processing, user interaction, and power. After data collection, the signal …

Power Amplifier Based On Composite Injection-Voltaic Transistors, Nodira Batirdjanovna Alimova

Chemical Technology, Control and Management

The problem of high-current radio engineering devices is related to the fact that the use of high-power transistors and other semiconductor devices is limited by such a phenomenon as a secondary breakdown, in which there is a sharp decrease in the voltage on the device with simultaneous internal current lacing, and the device fails. To solve the problem of secondary breakdown, schemes have been proposed that operate stably at reverse voltage values 4-5 times higher than usual and at power dissipation 2-3 times higher than the maximum allowable power for an individual device. The problem is proposed to be solved …

Portable Diffuse Reflectance Spectroscopy For Non-Invasive And Quantitative Assessment Of The Parathyroid Glands Viability During Surgery, Mark Romine, Linh Luong, Alex Moazzen, Katie Cho, Paul Lee

Symposium of Student Scholars

Portable Diffuse Reflectance Spectroscopy for Non-invasive and Quantitative Assessment of the Parathyroid Glands Viability During Surgery

Mark Romine, Linh Luong, Alex Moazzen, Katie Cho and Paul Lee

The parathyroid glands (PTGs) are responsible for the regulation of calcium levels in the blood by secreting a parathyroid hormone. This parathyroid hormone then regulates the body’s absorption, storage, and secretion of calcium, which can directly affect the way muscles and nerves operate. PTGs are often at risk of damage, or accidental removal during thyroid surgeries, because it is challenging to identify PTGs and to determine their viability. Current methods of visual inspections …

Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe

[Archive] Belmont University Research Symposium (BURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Classification Tradeoffs In Multispectral Polarimetric Ladar Architectures, Connor B. Martin

Theses and Dissertations

An end-to-end LADAR system is modeled at the waveform level to perform material classification at a per-pixel basis. A K-Nearest Neighbors machine learning algorithm is chosen to make predictions using polarimetric material characteristics as features. A variable receiver design is modeled to allow for the use of multiple configurations of Polarization State Analyzers. This research investigates the inclusion of multiple wavelengths in the transmitted laser pulse to improve classification accuracy. Additionally, the effects of lowering the receiver’s detector bandwidth are investigated. Through the classification process, transmitting a multispectral laser pulse is shown to improve classification and may improve future LADAR …

Effects Of Calibration Errors On Dropped-Channel Polarimetric Synthetic Aperture Radar, Jacob C. Morrison

Theses and Dissertations

Compressed Sensing (CS) is a mathematical technique that can be applied to sparse data sets to allow for sub-Nyquist sampling. DCPCS is a CS technique that recovers the signal from unmeasured polarisation channels due to antenna crosstalk coupling the information onto the remaining channels. DCPCS reduces data storage/transmission and receiver hardware requirements. This thesis examines the robustness of DCPCS to calibration errors on the antenna crosstalk matrix. Although the antenna design problem is relaxed to a large region of acceptable crosstalk values, very accurate calibration may be required in a monostatic radar. This thesis also looks at the importance of …

Research Of Three-Phases Current’S Transducers Of Filter-Compensation Devices For Control Reactive Power’S Consumption Of Asynchronous Motor, Ilkhomjon Khakimovich Siddikov, Dilyorbek Karimjonov Doniyorbek O'G'Li -, Abdumutal Abdikarimovich Abdigapirov

Chemical Technology, Control and Management

In the article given materials of developing of three-phase electromagnetic current transducers of reactive power, using with asynchronous motor, methods connecting of two sensing element, series, parallel and differential and loops suitable for each phase, dynamic characteristics of output signals of three-phase electromagnetic current transducers asynchronous motor.

On the basis of modern calculation and design complexes are of great importance in the research variable sizes of three-phase current electromagnetic transducers of filter-compensation devices of reactive power of asynchronous motors. Presented mathematical model of research of electrical, electromagnetic and magnetic elements of electromagnetic current transducers in the …

On Refinements To Qmfd Based Chirp Parameter Estimation, Balu Santhanam, Thalanayar Santhanam

Electrical & Computer Engineering Technical Reports

Commuting matrix methods furnish a full basis of orthog- onal eigenvectors for the discrete Fourier transform or its centered version needed for computing the discrete fractional Fourier transform and multicomponent chirp signal analysis. However, these approaches suffer from ill-conditioning issues at higher matrix sizes, and require a computationally expensive eigenvalue decomposition.

In this paper, ill-conditioning issues associated with the QMFD approach developed previously by the authors are addressed via diagonal modification. Further symmetries of the eigenvectors are used to reduce the size of the underlying eigenvalue problem. These modifications are then incorporated into the real-arithmetic implementation of the QMFD approach …

Experimental And Computational Analysis Of Broadband Thz Photoconductive Antennas, Zachary Uttley, Jose Santos Batista, Bilal Pirzada, Magda El-Shenawee

Electrical Engineering Faculty Publications and Presentations

The work presents the fabrication and measurements of four LT-GaAs photoconductive terahertz (THz) antennas with different geometries of metallic electrodes. The goal is to analyze the overall bandwidth of the antennas through a comparison between the spectra of the generated photocurrent in the antenna gap, the radiated electric field THz pulse, and the S₁₁ parameter of the metallic electrodes. The photocurrent density and the S₁₁ parameters are computed using COMSOL multiphysics, while the generated THz pulse was experimentally measured using a time-domain spectroscopy system. The polarizations of the photoconductive antennas are experimentally measured, using x-cut quartz crystal halfwave …

An Epileptic Seizure Detection Method From Eeg Signals Based On A Classifier-Driven Feature Reduction Technique, Raymond N. Kamel

Theses and Dissertations

Epileptic seizure detection can improve the quality of life of epileptic patients, allow for more accurate medication, and minimize the risk of sudden unexpected death in epilepsy (SUDEP). This thesis work aims to develop a robust and stable algorithm for epileptic seizure detection through the classification of EEG signals. To achieve this aim, a methodology is proposed to develop a classifier that can differentiate between the healthy (normal), interictal, and ictal states of EEG signals, while maximizing the classification accuracy and minimizing the computational redundancy. The main pillar upon which this methodology is designed is using a problem-specific classifier-driven feature …

Gamelan Gong Directivity Dataset, Samuel D. Bellows, Dallin T. Harwood, Kent L. Gee, Micah R. Shepherd

Directivity

No abstract provided.

Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland

College of Graduate Studies: Theses & Dissertations

This work proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was initially optimized for use with a 12th order linear shift feedback register maximum length sequence utilizing a minimal polynomial as the characteristic polynomial. This neural network was then scaled to accommodate 7 different sequences, of orders 6 through 12. The neural network was trained for these sequences using training data that is 10 times the length of the sequence. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with …

Accelerating A Software Defined Satnav Receiver Using Multiple Parallel Processing Schemes, Logan Reich, Sanjeev Gunawardena, Michael Braasch

Faculty Publications

Excerpt: Satnav SDRs present many benefits in terms of flexibility and configurability. However, due to the high bandwidth signals involved in satnav SDR processing, the software must be highly optimized for the host platform in order to achieve acceptable runtimes. Modules such as sample decoding, carrier replica generation, carrier wipeoff, and correlation are computationally intensive components that benefit from accelerations.

Live-Sky Gnss Signal Processing Using A Dual-Polarized Antenna Array For Multipath Mitigation, Eric Hahn, Sanjeev Gunawardena, Chris Bartone

Faculty Publications

Excerpt: Multipath results from reflections of Global navigation satellite signals (GNSS) signals arriving at a receiver that are delayed with respect to the desired line-of-sight (LOS) signals. The delayed signals distort the received LOS signals, thereby causing pseudorange and carrier phase measurement errors. Traditional multipath mitigation techniques include antenna gain pattern shaping (primarily to reduce ground multipath) and correlator gating techniques (such as narrow correlator and double-delta correlator [1]).

Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell

Theses and Dissertations--Electrical and Computer Engineering

The future of smart manufacturing relies on predictive maintenance systems that intelligently minimize expensive downtime through timely assessment of machine condition. Deep Learning (DL) has achieved excellent performance in industrial condition monitoring experiments, but the constraints of the manufacturing environment prevent many algorithms from being practically deployed on the factory floor. Ubiquitous sensing from online machines generates high velocity data streams that require new techniques for efficient transmission and storage. Despite these ever-increasing data lakes, many applications still lack the data needed for training DL fault diagnosis and wear tracking models since most data is unlabeled and only from nominal …

Ads-B Communication Interference In Air Traffic Management, George Ray

International Journal of Aviation, Aeronautics, and Aerospace

Automated Dependent Surveillance Broadcast (ADS-B) provides position and state information about aircraft and is becoming an essential component in the global air traffic management system. ADS-B transponders broadcast this key information on a common frequency to both other aircraft and to secondary surveillance radar systems located at ground stations. Both the aircraft transponders and the ground stations work together to assist in managing the commercial airspace. Since the aircraft transponders all broadcast on the same frequency and are in close proximity there is an apparent risk of interference and the garbling of the communications needed to manage the airspace.

The …

Defending Ai-Based Automatic Modulation Recognition Models Against Adversarial Attacks, Haolin Tang, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Automatic Modulation Recognition (AMR) is one of the critical steps in the signal processing chain of wireless networks, which can significantly improve communication performance. AMR detects the modulation scheme of the received signal without any prior information. Recently, many Artificial Intelligence (AI) based AMR methods have been proposed, inspired by the considerable progress of AI methods in various fields. On the one hand, AI-based AMR methods can outperform traditional methods in terms of accuracy and efficiency. On the other hand, they are susceptible to new types of cyberattacks, such as model poisoning or adversarial attacks. This paper explores the vulnerabilities …

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 …

Wifi Sensing At The Edge Towards Scalable On-Device Wireless Sensing Systems, Steven M. Hernandez

Theses and Dissertations

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing …

Eeg-Based Spanish Language Proficiency Classification: An Eeg Power Spectrum And Cross-Spectrum Analysis, Blaise Xavier O'Mara, Skyler Baumer

Honors Theses and Capstones

Second language proficiency may be predicted with electrophysiological techniques. In a machine learning application, this electrophysiological data may be used for language instructors and language students to assess their language learning. This study identifies how electroencephalogram (EEG) power spectrum and cross spectrum data of the brain cortex relates to Spanish second language (L2) proficiency of 20 Spanish language students of varying proficiency levels at the University of New Hampshire. The two metrics for assessing cortical power and processing were event-related desynchronization (ERD)—a measure of relative change in power—of the alpha (8-12 Hz) brain frequency band, and alpha and beta (13-30Hz) …

Evaluation Of Lidar Uncertainty And Applications Towards Slam In Off-Road Environments, Zachary D. Jeffries

Dissertations, Master's Theses and Master's Reports

Safe and robust operation of autonomous ground vehicles in all types of conditions and environment necessitates complex perception systems and unique, innovative solutions. This work addresses automotive lidar and maximizing the performance of a simultaneous localization and mapping stack. An exploratory experiment and an open benchmarking experiment are both presented. Additionally, a popular SLAM application is extended to use the type of information gained from lidar characterization, demonstrating the performance gains and necessity to tightly couple perception software and sensor hardware. The first exploratory experiment collects data from child-sized, low-reflectance targets over a range from 15 m to 35 m. …

Automatic Optical Inspection-Based Pcb Fault Detection Using Image Processing, Shruti Rajiv Vaidya

Dissertations, Master's Theses and Master's Reports

Increased Printed Circuit Board (PCB) route complexity and density combined with the growing demand for low-scale rapid prototyping has increased the desire for Automated Optical Inspection (AOI) that reduces prototyping time and production costs by detecting defects early in the production process. Traditional defect detection method of human visual inspection is not only error prone but is also time-consuming given the growing complex and dense circuitry of modern-day electronics. Electric contact-based testing, either in the form of a bed of nails testing fixture or a flying probe system, is costly for low-rate rapid prototyping. An AOI is a non-contact test …

Perceptual Anthropomorphic Walking Robot Platform For Navigation In Unstructured And Undifferentiated Environments, Luige Vladareanu, Mihai Rădulescu, Marius Pandelea, Hongbo Wang, Florentin Smarandache, Yongfei Feng, Ionel-Alexandru Gal, Alexandra C. Ciocîrlan

Branch Mathematics and Statistics Faculty and Staff Publications

This scientific presentation studies the VIPRO Platform for control of Anthropomorphic Walking Robots (AWR), the architecture control system of the SiMeLA MP robot motion, and shows several experimental results.

Enhancing Vehicular Perception: A Comprehensive Analysis Of Sensor Fusion Performance Through Weighted Averages And Fuzzy C-Means For Optimal Data Association, Zachary Brian Flanigan

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work explores the implementation of sensor fusion and data association for autonomous vehicle design. Advancements in Adaptive Driver Assistance System (ADAS) technology have driven the development of perception algorithms required for higher levels of autonomy in vehicles. Perception algorithms process data collected from radar, camera, and LiDAR sensors to generate a complete model of the ego vehicle’s surrounding environment. Fusion of data from these sensors is important for accurate measurement of longitudinal and lateral distances to surrounding objects. Sensor fusion associates sensor detections to each other through different data association techniques. Data association techniques can consist of independent assignment …