Month

An Analysis of Compressive Sensing and the Electrocardiogram

As technology has advanced, data has become more and more important. The more breakthroughs are achieved, the more data is needed to support them. As a result, more storage is required in the system's memory. Compression is therefore required. Before it can be stored, the data must be compressed. To ensure that information is not lost, efficient compression is necessary. This also makes sure that there is no redundancy in the data that is being kept and stored. Compressive sensing has emerged as a new field of compression thanks to developments in sparse optimization. Rather than relying just on compression and sensing formulations, the theory blends the two. The objective of this thesis is to analyze the concept of compressive sensing and to study several reconstruction algorithms. Additionally, a few of the algorithms were put into practice. This thesis also included a model of the ECG, which is vital in determining the health of the heart. For the most part, the ECG is utilized to diagnose heart illness, and a modified synthetic ECG can be used to mimic some of these arrhythmias.
Date: May 2022
Creator: Molugu, Shravan
System: The UNT Digital Library
Emotion Recognition Using EEG Signals (open access)

Emotion Recognition Using EEG Signals

Emotions have significant importance in human life in learning, decision-making, daily interaction, and perception of the surrounding environment. Hence, it has become very essential to detect and recognize a person's emotional states and to build a connection between humans and computers. This process is called brain-computer interaction (BCI) and is a vast field of research in neuroscience. Hence, in the past few years, emotion recognition has gained adequate attention in the research community. In this thesis, an emotion recognition system is designed and analyzed using EEG signals. Several existing feature extraction techniques are studied, analyzed, and implemented to extract features from the EEG signals. An SVM classifier is used to classify the features into various emotional states. Four emotional states are detected, namely, happy, sad, anger, and relaxed state. The model is tested, and simulation results are presented with an interpretation. Furthermore, this study has mentioned and discussed the efficacy of the results achieved. The findings from this study could be beneficial in developing emotion-sensitive technologies, such as augmented modes of communication for severely disabled individuals who are unable to communicate their feelings directly.
Date: May 2022
Creator: Choudhary, Sairaj Mahesh
System: The UNT Digital Library

Prisoner's Dilemma in Quantum Perspective

It is known that quantum strategies change the range of possible payoffs for the players in the prisoner's dilemma. In this paper, we examine the effect of the degree of entanglement in determining the payoffs. When both players play quantum strategies, we show that the payoff for both players is unaffected by the entanglement value and it leads to a new Nash equilibrium.
Date: May 2022
Creator: Padakandla Venkata, Charnaditya
System: The UNT Digital Library
Wireless Power Transfer (WPT) System Design for Freely-Moving Animals for Optogenetic Neuromulation Applications (open access)

Wireless Power Transfer (WPT) System Design for Freely-Moving Animals for Optogenetic Neuromulation Applications

Wireless power transfer (WPT) is currently the most efficient way for transmission of power from one port to another, that is popularly used in various applications.This technique can change the previous energy utilization methods in various applications such as electronic devices, implanted medical devices, electrical vehicles and so forth.It mainly helps overcome the limitations of short battery life, limited storage, heavy weight, and high cost of batteries.This paper is based on the design of a transmitter and a receiver to achieve wireless power transfer for applications like optogenetic stimulation in rodents. With inductive coupling, a very high efficiency can be achieved between the transmitting and receiving coils of an antenna at small distances. When the transmitter and receiver are strongly coupled and are working at their resonant frequencies, the range of efficient WPT can be extended. In this work, the simulations are performed in HFSS at a resonating frequency of 13.56 MHz.A 4-port transmitter and a single-port planar receiver model are developed in HFSS, and the simulations are performed to graph the S parameters with a separation distance of 4cm. A Wilkinson power divider is designed using ADS to split the power from the four ports of the transmitter. The …
Date: May 2022
Creator: Sudhakar, Ramya
System: The UNT Digital Library

Low-Power Biopotential Signal Acquisition System for Biomedical Applications

The key requirements of a reliable neural signal recording system include low power to support long-term monitoring, low noise, minimum tissue damage, and wireless transmission. The neural spikes are also detected and sorted on-chip/off-chip to implement closed-loop neuromodulation in a high channel count setup. All these features together constitute an empirical neural recording system for neuroscience research. In this prospectus, we propose to develop a neural signal acquisition system with wireless transmission and feature extraction. We start by designing a prototype entirely built with commercial-off-the-shelf components, which includes recording and wireless transmission of synthetic neural data and feature extraction. We then conduct the CMOS implementation of the low-power multi-channel neural signal recording read-out circuit, which enables the in-vivo recording with a small form factor. Another direction of this thesis is to design a self-powered motion tracking read-out circuit for wearable sensors. As the wearable industry continues to advance, the need for self-powered medical devices is growing significantly. In this line of research, we propose a self-powered motion sensor based on reverse electrowetting-on-dielectric (REWOD) with low-power integrated electronics for remotely monitoring health conditions. We design the low-power read-out circuit for a wide range of input charges, which is generated from the …
Date: May 2022
Creator: Tasneem, Nishat Tarannum
System: The UNT Digital Library
Notch Filter Design for Power Line Interference Artifact Reduction of ECG Signal and Feature Extraction in LabVIEW (open access)

Notch Filter Design for Power Line Interference Artifact Reduction of ECG Signal and Feature Extraction in LabVIEW

Electrocardiogram (ECG) is a biological signal that represents the heart's electrical activity. Interference from power lines introduces a frequency component of 50 to 60 Hz into the signal, which is the principal cause of ECG corruption. By using the Cadence Virtuoso Spectre circuit simulator and typical TSMC RF 180 nm CMOS technology, a notch filter was created to reduce powerline interference. The advantage of utilizing a notch filter for PLI is that noise at 60 Hz is completely eliminated without sacrificing any important information. Additionally, this study contains a MATLAB-based model for, which is used to compute the power spectral density for the obtained time-domain signal. By incorporating power spectral density into data gathering procedures, it is feasible to enhance data collection methodologies, construct models that appropriately account for observed power and aid in the removal of undesired components. NI LabVIEW is used to extract features. The advantage of ECG feature extraction is that it provides information that assists in the identification of cardiac rhythm issues, and gives information about the occurrence of heart attack. In this study, several patient data sets are utilized to extract characteristics and provide information regarding heart condition abnormalities.
Date: May 2022
Creator: Kasidi, Divyasri
System: The UNT Digital Library
Localization of UAVs Using Computer Vision in a GPS-Denied Environment (open access)

Localization of UAVs Using Computer Vision in a GPS-Denied Environment

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.
Date: May 2022
Creator: Aluri, Ram Charan
System: The UNT Digital Library

Wireless Surface Acoustic Wave Sensor for PM2.5 Detection

Currently, there is no equipment to measure the real-time fit of EHMR or N-95masks which are used in harsh environments. Improper fit of these EHMRs or N-95 masks exposes the personnel to hazardous environments. Surface acoustic wave (SAW) sensors have been around for few decades and are being used in various applications. In this work, real-time PM2.5 detection using passive wireless SAW sensors is presented. The design of meander antenna at 433MHz for wireless interrogation of SAW sensor using HFSS and ADS is also presented in this thesis. This works also includes the design of YZ-lithium niobate SAW sensor including COMSOL simulation.
Date: May 2022
Creator: Mamidipally, Sai Karthik
System: The UNT Digital Library
Small-Scale Dual Path Network for Image Classification and Machine Learning Applications to Color Quantization (open access)

Small-Scale Dual Path Network for Image Classification and Machine Learning Applications to Color Quantization

This thesis consists of two projects in the field of machine learning. Previous research in the OSCAR UNT lab based on KMeans color quantization is further developed and applied to individual color channels and segmented input images to explore compression rates while still maintaining high output image quality. The second project implements a small-scale dual path network for image classifiaction utilizing the CIFAR-10 dataset containing 60,000 32x32 pixel images ranging across ten categories.
Date: May 2022
Creator: Murrell, Ethan Davis
System: The UNT Digital Library
Wireless Power Transfer and Power Management Unit Integrated with Low-Power IR-UWB Transmitter for Neuromodulation and Self-Powered Sensor Applications (open access)

Wireless Power Transfer and Power Management Unit Integrated with Low-Power IR-UWB Transmitter for Neuromodulation and Self-Powered Sensor Applications

This dissertation is particularly focused on a novel approach of a wirelessly powered neuromodulation system for chronic patients. The inductively coupled transmitter (TX) and receiver (RX) coils are designed through optimization to achieve maximum efficiency. A power management unit (PMU) consisting of a voltage rectifier, voltage regulator along with a stimulation circuitry is also designed to provide pulse stimulation to genetically modified neurons. For continuous health monitoring purposes, the response from the brain due to stimulation needs to be recorded and transmitted wirelessly outside the brain for analysis. A low-power high-data duty-cycled impulse-radio ultra-wideband (IR-UWB) transmitter is designed and implemented using the standard CMOS process. Another focus of this dissertation is the design of a reverse electrowetting-on-dielectric (REWOD) based energy harvesting circuit for wearable sensor applications which is capable of generating a very low-frequency signal from motion activity such a walking, running, jogging, etc. A commercial off-the-shelf (COTS) based and on-chip based energy harvesting circuit is designed for very low-frequency signals. The experimental results show promising progress towards the advancement in the wirelessly powered neuromodulation system and building the self-powered wearable sensor.
Date: May 2022
Creator: Biswas, Dipon Kumar
System: The UNT Digital Library

Stabilization and Performance Improvement of Control Systems under State Feedback

The feedback control system is defined as the sampling of an output signal and feeding it back to the input, resulting in an error signal that drives the overall system. This dissertation focuses on the stabilization and performance of state feedback control systems. Chapters 3 and 4 focus on the feedback control protocol approaching in the multi-agents system. In particular, the global regulation of distributed optimization problems has been considered. Firstly, we propose a distributed optimization algorithm based on the proportional-integral control strategy and the exponential convergence rate has been delivered. Moreover, a decentralized mechanism has been equipped to the proposed optimization algorithm, which enables an arbitrarily chosen agent in the system can compute the value of the optimal solution by only using the successive local states. After this, we consider the cost function follows the restricted secant inequality. A dynamic event-triggered mechanism design has been proposed. By ensuring the global regulation of the distributed proportional-integral optimization algorithm, the dynamic event-triggered mechanism efficiently reduces the communication frequency among agents. Chapter 5 focuses on the feedback control protocol approaching the single-agent system. Specifically, we investigate the truncated predictor feedback control of the regulation of linear input-delayed systems. For the purpose of …
Date: May 2022
Creator: Yao, Lisha
System: The UNT Digital Library

Advanced Distributed Optimization and Control Algorithms: Theory and Applications

Networked multi-agent systems have attracted lots of researchers to develop algorithms, techniques, and applications.A multi-agent networked system consists of more than one subsystem (agent) to cooperately solve a global problem with only local computations and communications in a fully distributed manner. These networked systems have been investigated in various different areas including signal processing, control system, and machine learning. We can see massive applications using networked systems in reality, for example, persistent surveillance, healthcare, factory manufacturing, data mining, machine learning, power system, transportation system, and many other areas. Considering the nature of those mentioned applications, traditional centralized control and optimization algorithms which require both higher communication and computational capacities are not suitable. Additionally, compared to distributed control and optimization approaches, centralized control, and optimization algorithms cannot be scaled into systems with a large number of agents, or guarantee performance and security. All of the limitations of centralized control and optimization algorithms motivate us to investigate and develop new distributed control and optimization algorithms in networked systems. Moreover, convergence rate and analysis are crucial in control and optimization literature, which motivates us to investigate how to analyze and accerlate the convergence of distributed optimization algorithms.
Date: May 2022
Creator: Zhang, Shengjun
System: The UNT Digital Library