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Neural Network Classifiers for Object Detection in Optical and Infrared Images
This thesis presents a series of neural network classifiers for object detection in both optical and infrared images. The focus of this work is on efficient and accurate solutions. The thesis discusses the evolution of the highly efficient and tiny network Binary Classification Vision Transformer (BC-ViT) and how through thoughtful modifications and improvements, the BC-ViT can be utilized for tasks of increasing complexity. Chapter 2 discusses the creation of BC-ViT and its initial use case for underwater image classification of optical images. The BC-ViT is able to complete its task with an accuracy of 99.29\% while being comprised of a mere 15,981 total trainable parameters. Chapter 3, Waste Multi-Class Vision Transformer (WMC-ViT), introduces the usefulness of mindful algorithm design for the realm of multi-class classification on a mutually exclusive dataset. WMC-ViT shows that the task oriented design strategy allowed for a network to achieve an accuracy score of 94.27\% on a five class problem while still maintaining a tiny parameter count of 35,492. The final chapter demonstrates that by utilizing functional blocks of BC-ViT, a simple and effective target detection algorithm for infrared images can be created. The Edge Infrared Vision Transformer (EIR-ViT) showed admirable results with a high IoU of 79\% coupled with a massive reduction in parameters compared to current state of the art solutions at roughly 120,000. This thesis ends with summary of Chapters 2, 3, and 4 that further discusses the implications this design strategy offers.
Conditional Disclosure of Secrets and Storage over Graphs
In the era of big data, it is essential to implement practical security and privacy measures to ensure the lawful use of data and provide users with trust and assurance. In the dissertation, I address this issue through several key steps. Firstly, I delve into the problem of conditional secret disclosure, representing it using graphs to determine the most efficient approach for storing and disclosing secrets. Secondly, I extend the conditional disclosure of secrets problem from a single secret to multiple secrets and from a bipartite graph to an arbitrary graph. Thirdly, I remove security constraints to observe how they affect the efficiency of storage and recovery. In our final paper, I explore the secure summation problem, aiming to determine the capacity of total noise. Throughout the dissertation, I leverage information-theoretic tools to address security and privacy concerns.
On the Fundamental Limits of Secure Summation and MDS Variable Generation
Secure multiparty computation refers to the problem where a number of users wish to securely compute a function on their inputs without revealing any unnecessary information. This dissertation focuses on the fundamental limits of secure summation under different constraints. We first focus on the minimal model of secure computation, in which two users each hold an input and wish to securely compute a function of their inputs at the server. We propose a novel scheme base on the algebraic structure of finite field and modulo ring of integers. Then we extend the minimal model of secure computation, in which K users wish to securely compute the sum of their inputs at the server. We prove a folklore result on the limits of communication cost and randomness cost. Then we characterized the optimal communication cost with user dropouts constraint, when some users may lose connection to the server and the server wishes to compute the sum of remaining inputs. Next, we characterize the optimal communication and randomness cost for symmetric groupwise keys and find the feasibility condition for arbitrary groupwise keys. Last, we study the secure summation with user selection, such that the server may select any subset of users to compute the sum of their inputs. This leads us to the MDS variable generation problem. We characterize the optimal individual key rate and the result is interestingly the harmonic number.
PM2.5 Particle Sensing and Fit Factor Test of a Respirator with SAW-Based Sensor
PM2.5 particle sensing has been done using surface acoustic wave based sensor for two different frequencies. Due to mass loading and elasticity loading on the sensor's surface, the center frequency of the sensor shifts. The particle concentration can be tracked based on that frequency shift. The fit factor test has been conducted using higher frequency SAW sensor. The consist results has been achieved for particle sensing and fit factor test with SAW based sensor.
Distributed Source Coding with LDPC Codes: Algorithms and Applications
The syndrome source coding for lossless data compression with side information based on fixed-length linear block codes is the main emphasis of this work. We demonstrate that the source entropy rate can be achieved for syndrome source coding with side information when the sources are correlated. Next, we examine employing LDPC codes to apply the channel and syndrome concepts in order to satisfy the Slepian Wolf limit. Our findings indicate that irregular codes perform significantly better when the compression ratio is larger. Additionally, we looked at how well different applications performed when running on two different mobile networks. We have tested those applications which are used in our day-to-day life. Our main focus is to make wireless communication much easier. We know that nowadays data is increasing which led to increase in the transfer of data. There are a lot of errors while doing so like channel error, bit error rate, jitter, etc. To overcome such kind of problems compression and decompression should be done effectively without any complexity to achieve a high performance ratio.
Intelligent ECG Acquisition and Processing System for Improved Sudden Cardiac Arrest (SCA) Prediction
The survival rate for a suddent cardiac arrest (SCA) is incredibly low, with less than one in ten surviving; most SCAs occur outside of a hospital setting. There is a need to develop an effective and efficient system that can sense, communicate and remediate potential SCA situations on a near real-time basis. This research presents a novel Zeolite-PDMS-based optically unobtrusive flexible dry electrodes for biosignal acquisition from various subjects while at rest and in motion. Two zeolite crystals (4A and 13X) are used to fabricate the electrodes. Three different sizes and two different filler concentrations are compared to identify the better performing electrode suited for electrocardiogram (ECG) data acquisition. A low-power, low-noise amplifier with chopper modulation is designed and implemented using the standard 180nm CMOS process. A commercial off-the-shelf (COTS) based wireless system is designed for transmitting ECG signals. Further, this dissertation provides a framework for Machine Learning Classification algorithms on large, open-source Arrhythmia and SCA datasets. Supervised models with features as the input data and deep learning models with raw ECG as input are compared using different methods. The machine learning tool classifies the datasets within a few minutes, saving time and effort for the physicians. The experimental results show promising progress towards advancing the development of a wireless ECG recording system combined with efficient machine learning models that can positively impact SCA outcomes.
Machine Learning Improvements for Data Partitioning and Classification Applied to Cardiac Arrhythmia Signals
This thesis creates a new method for the ethical splitting of data as well as improvements to neural network architectures to increase performance. Ethical dataset splitting should be based on statistics from the data, this prevents artificial manipulation of the data that helps or hurts the performance of a network. This bias introduced to the dataset can also be present by using the popular method of randomly splitting data into datasets. To remove bias from dataset splitting, the splits of a dataset must be based on statistics from the data. Improving neural network architectures to increase performance is very important for a wide range of applications, especially for classification of heartbeats. Every improvement matters, especially when the application means that any errors could put the life of a person in danger. These advancements being applied to heartbeat classification have exciting implications for saving thousands of lives and billions of dollars. The presented methods can also be expanded to a wide variety of applications and adapted to different types of data as increasing performance and splitting up datasets is important in all fields of machine learning.
An Optimized Control System for the Independent Control of the Inputs of Doherty Power Amplifier
This thesis presents an optimized drive signal control system for a 2.5 GHz Doherty power amplifier (PA). The designed system enables independent control of the amplitudes and phases of the drive signals fed to the inputs of two parallel PAs. This control system is demonstrated here for Doherty PA architecture with a combiner network which is used as an impedance inversion between the path of two parallel connected PAs. Independent control of the inputs is achieved by incorporating a variable attenuator (VA) and a variable phase shifter (VPS) in each of the two parallel paths. Integrating VA and VPS allows driving varying power levels with an arbitrary phase difference between the individual parallel PAs. A Combiner network consists of a quarter-wave transmission line at the output of the main power amplifier, which is used to invert the impedance between the main and peaking transistor. The specific VA (Qorvo QPC6614) and VPS (Qorvo QPC2108) components that are used for the test system provide an amplitude attenuation range from 0.5 dB to 31.5 dB with a step size of 0.5 dB and a phase range from 0◦ to 360◦ for a step size of 5.6◦at the intended operating frequency of 2.5 GHz, offering the benefit of characterizing the behavior of PAs under test for an extensive range of drive signals to optimize the output performance such as power added efficiency (PAE) or adjacent channel leakage ratio (ACLR). For demonstration, the designed drive signal control system is integrated with two parallel GaN transistor-based PAs (Qorvo QPD0005) with a P1dB of 37.7 dBm. Each PA is preceded by a drive amplifier with a gain of 17.8 dB to boost the power fed into the PA. The control system incorporates various custom-designed components such as a 20 dB directional coupler, a 3 dB Wilkinson power …
Analysis of Compressive Sensing and Hardware Implementation of Orthogonal Matching Pursuit
My thesis is to understand the concept of compressive sensing algorithms. Compressive sensing will be a future alternate technique for the Nyquist rate, specific to some applications where sparsity property plays a major role. Software implementation of compressive sensing (CS) takes more time to reconstruct a signal from CS measurements, so we use the orthogonal matching pursuit and basis pursuit algorithms. We have used an image size of 256x256 is used for reconstruction and also implemented a field-programmable gate array (FPGA) of the orthogonal matching pursuit using an image.
Applications of Machine Learning for Remote Sensing and Environmental Monitoring
This thesis covers applications of machine learning to the fields of remote sensing and environmental monitoring. First, a generalized background on the concepts, tools, and methods used throughout the remainder of the research project are introduced. Chapter 3 covers the implementation of artificial neural networks to improve low-cost particulate matter sensing networks using collocated high-quality sensors with varying dataset parameters. In Chapter 4, an attention-enhanced LSTM-Convolutional neural network is presented to reconstruct satellite-based aerosol optical depth data lost to atmospheric interference. Chapter 5 applies attention mechanisms and convolutional neural networks to the reconstruction and upsampling of satellite-based land surface temperature maps. Chapter 6 presents a model employing geospatial techniques and machine learning methods with a combination of ground-based and remote sensing data to produce a daily ultra-high resolution 30 meter mapping of the PM2.5 concentration across Denton County, Texas.
Occupancy Monitoring Using Low Resolution Thermal Imaging Sensors
Occupancy monitoring is an important research problem with a broad range of applications in security, surveillance, and resource management in smart building environments. As a result, it has immediate solutions to solving some of society's most pressing issues. For example, HVAC and lighting systems in the US consume approximately 45-50% of the total energy a building uses. Smart buildings can reduce wasted energy by incorporating networkable occupancy sensors to obtain real-time occupancy data for the facilities. Therefore, occupancy monitoring systems can enable significant cost savings and carbon reduction. In addition, workplaces have quickly adapted and implemented COVID-19 safety measures by preventing overcrowding using real-time information on people density. While there are many sensors, RGB cameras have proven to be the most accurate. However, cameras create privacy concerns. Hence, our research aims to design an efficient occupancy monitoring system with minimal privacy invasion. We conducted a systematic study on sensor characterization using various low-resolution infrared sensors and proposed a unified processing algorithms pipeline for occupancy estimation. This research also investigates low-resolution thermal imaging sensors with a chessboard reading pattern, focusing on algorithm design issues and proposing solutions when detecting moving objects. Our proposed approach achieves about 99% accuracy in occupancy estimation, enabling many practical smart building applications. We also added additional sensors to our system using sensor fusion technology to boost its functionality and demonstrated the system's feasibility by deploying it in different real-time application scenarios.
Efficient Convolutional Neural Networks for Image Processing Applications
Modern machine learning techniques focus on extremely deep and multi-pathed networks, resulting in large memory and computational requirements. This thesis explores techniques for designing efficient convolutional networks including pixel shuffling, depthwise convolutions, and various activation fucntions. These techniques are then applied to two image processing domains: single-image super-resolution and image compression. The super-resolution model, TinyPSSR, is one-third the size of the next smallest model in literature while performing similar to or better than other larger models on representative test sets. The efficient deep image compression model is significantly smaller than any other model in literature and performs similarly in both computational cost and reconstruction quality to the JPEG standard.
Advances to Convolutional Neural Network Architectures for Prediction and Classification with Applications in the First Dimensional Space
In the vast field of signal processing, machine learning is rapidly expanding its domain into all realms. As a constituent of this expansion, this thesis presents contributive work on advancements in machine learning algorithms by building on the shoulder of giants. The first chapter of this thesis contains enhancements to a CNN (convolutional neural network) for better classification of heartbeat arrhythmia. The network goes through a two stage development, the first being augmentations to the network and the second being the implementation of dropout. Chapter 2 involves the combination of CNN and LSTM (long short term memory) networks for the task of short-term energy use data regression. Exploiting the benefits of two of the most powerful neural networks, a unique, novel neural network is created to effectually predict future energy use. The final section concludes this work with directions for future works.
Mixed Reality Tailored to the Visually-Impaired
The goal of the proposed device and software architecture is to apply the functionality of mixed reality (MR) in order to make a virtual environment that is more accessible to the visually-impaired. We propose a glove-based system for MR that will use finger and hand movement tracking along with tactile feedback so that the visually-impaired can interact with and obtain a more detailed sense of virtual objects and potentially even virtual environments. The software architecture makes current MR frameworks more accessible by augmenting the existing software and extensive 3D model libraries with both the interfacing of the glove-based system and the audibly navigable user interface (UI) of a virtual environment we have developed. We implemented a circuit with finger flexion/extension tracking for all 5 fingers of a single hand and variable vibration intensities for the vibromotors on all 5 fingertips of a single hand. The virtual environment can be hosted on a Windows 10 application. The virtual hand and its fingers can be moved with the system's input and the virtual fingertips touching the virtual objects trigger vibration motors (vibromotors) to vibrate while the virtual objects are being touched. A rudimentary implementation of picking up and moving virtual objects inside the virtual environment is also implemented. In addition to the vibromotor responses, text to speech (TTS) is also implemented in the application for when virtual fingertips touch virtual objects and other relevant events in the virtual environment.
Novel Algorithms and Hardware Architectures for Computational Subsystems Used in Cryptography and Error Correction Coding
A modified, single error-correcting, and double error detecting Hamming code, hereafter referred to as modified SEC-DED Hamming code, is proposed in this research. The code requires fewer logic gates to implement than the SEC-DED Hamming code. Also, unlike the popular Hsiao's code, the proposed code can determine the error in the received word from its syndrome location in the parity check matrix. A detailed analysis of the area and power utilization by the encoder and decoder circuits of the modified SEC-DED Hamming code is also discussed. Results demonstrate that this code is an excellent alternative to Hsiao's code as the area and power values are very similar. In addition, the ability to locate the error in the received word from its syndrome is also of particular interest. Primitive polynomials play a crucial role in the hardware realizations for error-correcting codes. This research describes an implementation of a scalable primitive polynomial circuit with coefficients in GF(2). The standard cell area and power values for various degrees of the circuit are analyzed. The physical design of a degree 6 primitive polynomial computation circuit is also provided. In addition to the codes, a background of the already existing SPX GCD computation algorithm is provided. Its implementation revealed that the combinational implementation of the SPX algorithm utilizes a significantly lesser area than Euclid's algorithm. The FSMD implementation of the SPX algorithm reduces both dynamic and leakage power consumption. The physical design of the GCD computation using the SPX algorithm is also provided.
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 REWOD sensor.
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 improving the closed-loop performance, we propose a design of the truncated predictor feedback method with time-varying feedback parameters and give the potential range of choosing the time-varying feedback parameters to replace the traditional constant low gain parameters.
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.
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 design is simulated to compare the S21 at different positions on the TX.
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.
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.
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.
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.
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.
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.
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.
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.
Analysis of the Integration of LEO Satellite Constellations into 5G Networks
Low Earth orbit (LEO) satellite systems have been proposed as a resource for combating the challenges in 5G network coverage and expanding connectivity to a global realm. This research focuses on the current architecture of LEO satellite constellations, with an emphasis on satellite coverage, visibility patterns and coordination schemes. Key-elements of integrating LEO satellites into the eMBB component of 5G are presented and a breakdown of potential link channel characteristics and physical layer performance metrics are described. The produced information allows for a justified analysis on the conceptualized integration.
Air Corridors: Concept, Design, Simulation, and Rules of Engagement
Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then, a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, the results of simulations of traffic flows are presented.
The Convolutional Recurrent Structure in Computer Vision Applications
By organically fusing the methods of convolutional neural network (CNN) and recurrent neural network (RNN), this dissertation focuses on the application of optical character recognition and image classification processing. The first part of this dissertation presents an end-to-end novel receipt recognition system for capturing effective information from receipts (CEIR). The main contributions of this research part are divided into three parts. First, this research develops a preprocessing method for receipt images. Second, the modified connectionist text proposal network is introduced to execute text detection. Third, the CEIR combines the convolutional recurrent neural network with the connectionist temporal classification with maximum entropy regularization as a loss function to update the weights in networks and extract the characters from receipt. The CEIR system is validated with the scanned receipts optical character recognition and information extraction (SROIE) database. Furthermore, the CEIR system has strong robustness and can be extended to a variety of different scenarios beyond receipts. For the convolutional recurrent structure application of land use image classification, this dissertation comes up with a novel deep learning model for land use classification, the convolutional recurrent land use classifier (CRLUC), which further improves the accuracy in classifying remote sensing land use images. Besides, the convolutional fully-connected neural networks with hard sample memory pool structure (CFMP) is invented to tackle the remote sensing land use image classification tasks. The CRLUC and CFMP algorithm performances are tested in popular datasets. Experimental studies show the proposed algorithms can classify images with higher accuracy and fewer training episodes compared to popular image classification algorithms.
Group Testing: A Practical Approach
Broadly defined, group testing is the study of finding defective items in a large set. In the medical infection setting, that implies classifying each member of a population as infected or uninfected, while minimizing the total number of tests.
Algebraic Trait for Structurally Balanced Property of Node and Its Applications in System Behaviors
This thesis targets at providing an algebraic method to indicate network behaviors. Furthermore, for a signed-average consensus problem of the system behaviors, event-triggering signed-average algorithms are designed to reduce the communication overheads. In Chapter 1, the background is introduced, and the problem is formulated. In Chapter 2, notations and basics of graph theory are presented. It is known that the terminal value of the system state is determined by the initial state, left eigenvector and right eigenvector associated with zero eigenvalue of the Laplacian matrix. Since there is no mathematical expression of right eigenvector, in Chapter 3, mathematical expression of right eigenvector is given. In Chapter 4, algebraic trait for structurally balanced property of a node is proposed. In Chapter 5, a method for characterization of collective behaviors under directed signed networks is developed. In Chapter 6, dynamic event-triggering signed-average algorithms are proposed and proved for the purpose of relieving the communication burden between agents. Chapter 7 summarizes the thesis and gives future directions.
Asynchronous Level Crossing ADC for Biomedical Recording Applications
This thesis focuses on the recording challenges faced in biomedical systems. More specifically, the challenges in neural signal recording are explored. Instead of the typical synchronous ADC system, a level crossing ADC is detailed as it has gained recent interest for low-power biomedical systems. These systems take advantage of the time-sparse nature of the signals found in this application. A 10-bit design is presented to help capture the lower amplitude action potentials (APs) in neural signals. The design also achieves a full-scale bandwidth of 1.2 kHz, an ENOB of 9.81, a power consumption of 13.5 microwatts, operating at a supply voltage of 1.8 V. This design was simulated in Cadence using 180 nm CMOS technology.
Estimation of Drone Location Using Received Signal Strength Indicator
The main objective of this thesis is to propose a UAV (also called as drones) location estimation system based on LoRaWAN using received signal strength indicator in a GPS denied environment. The drones are finding new applications in areas such as surveillance, search, rescue missions, package delivery, and precision agriculture. Nearly all applications require the localization of UAV during flight. Localization is the method of determining a UAVs physical position using a real or virtual coordinate system. This thesis proposes a LoRaWAN-based UAV location method and presents experimental findings from a prototype. The thesis mainly consists of two different sections: one is the distance estimation and the other is the location estimation. First, the distance is estimated based on the mean RSSI values which are recorded at the ground stations using the path loss model. Later using the slant distance estimation technique, the path loss model parameters L and C are estimated whose values are unknown at the beginning. These values completely depend on the environment. Finally, the trilateration system architecture is employed to find the 3-D location of the UAV.
Group Testing with Greedy Algorithm
Group testing is all about identifying properties of a set of elements by testing them.
Design of Low-Power Front End Compressive Sensing Circuitry and Energy Harvesting Transducer Modeling for Self-Powered Motion Sensor
Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as a neural signal, electrocardiogram (ECG), and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increases, data volume is increased resulting in impermissible transmitting power. This thesis work presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. This thesis work also includes the (reverse electrowetting-on-dielectric) REWOD based energy harvesting model for self-powered bio-sensor which utilizes the electrical energy generated through the process of conversion of mechanical energy to electrical energy. This REWOD based energy harvesting model can be a good alternative to battery usage, particularly for the bio-wearable applications. The comparative analysis of the results generated for voltage, current and capacitance of the rough surface model is compared to that of results of planar surface REWOD.
Formation Control and Path Planning Strategies for Unmanned Aerial Vehicle Swarms
This dissertation focuses on the path planning of unmanned aerial vehicle (UAV) swarms under distributed and hybrid control scenarios. It presents two such models and analyzes them both from theory and practice. In the first method, a distributed formation control strategy for UAV swarm based on consensus law is presented. This model makes use of the fundamental concepts of leader-follower structure, social potential functions, and algebraic graph theory to jointly address flocking and de-confliction in the formation control problem. The impact of network topology on formation control is analyzed. It is shown that the degree distribution of the network representing the multi-agent system defines the rate at which formation is attained. Conditions for convergence and stability are derived. In the second method, a hybrid framework for path planning and coverage area by UAV swarms is presented. This strategy significantly improves the current labor-intensive and resource-constraint operations in aquaculture farms. To monitor the farms periodically, an optimized back-and-forth flight path based on the Shamos algorithm is utilized. A trajectory tracking strategy for UAV swarms under uncertain wind conditions is presented.
Electrical Equivalent Modeling of the Reverse Electrowetting-on-Dielectric (REWOD) Based Transducer along with Highly Efficient Energy Harvesting Circuit Design towards Self-Powered Motion Sensor
Among various energy harvesting technologies reverse electrowetting-on-dielectric energy harvesting (REWOD) has been proved to harvest energy from low frequency motion such as many human motion activities (e.g. walking, running, jogging etc.). Voltage rectification and DC-DC boosting of low magnitude AC voltage from REWOD can be used to reliably self-power the wearable sensors. In this work, a commercial component-based rectifier and DC-DC converter is designed and experimentally verified, for further miniaturization standard 180 nm CMOS process is used to design the rectifier and the DC-DC boost converter.This work also includes the MATLAB based model for REWOD energy harvester for various REWOD models. In REWOD energy harvesting, a mechanical input during the motion causes the electrolyte placed in between two dissimilar electrodes to squeeze back and forth thereby periodically changing the effective interfacial area, hence generating alternating current. The alternating current is given to the rectifier design. There is no realistic model that has been developed yet for this technique. Thereby, a MATLAB based REWOD model is developed for the realistic simulation of the REWOD phenomenon. In the work, a comparison of different REWOD models such as planar surface, rough surface and porous models are performed demonstrating the variations in capacitance, current and voltage.
Development and Application of Novel Computer Vision and Machine Learning Techniques
The following thesis proposes solutions to problems in two main areas of focus, computer vision and machine learning. Chapter 2 utilizes traditional computer vision methods implemented in a novel manner to successfully identify overlays contained in broadcast footage. The remaining chapters explore machine learning algorithms and apply them in various manners to big data, multi-channel image data, and ECG data. L1 and L2 principal component analysis (PCA) algorithms are implemented and tested against each other in Python, providing a metric for future implementations. Selected algorithms from this set are then applied in conjunction with other methods to solve three distinct problems. The first problem is that of big data error detection, where PCA is effectively paired with statistical signal processing methods to create a weighted controlled algorithm. Problem 2 is an implementation of image fusion built to detect and remove noise from multispectral satellite imagery, that performs at a high level. The final problem examines ECG medical data classification. PCA is integrated into a neural network solution that achieves a small performance degradation while requiring less then 20% of the full data size.
Traditional and Deep Learning Approaches to Color Image Compression and Pattern Recognition Problems
This thesis includes three separate research projects focusing on computer vision principles and deep learning pattern recognition problems. Chapter 3 entails color quantization applications using traditional Kmeans clustering techniques and random selection of color techniques within the red, green, blue (RGB) color space to maintain a high-quality image while significantly reducing image file size. Chapter 4 consists of a handwriting character recognition algorithm using backpropagation to classify 70,000 handwritten values from US Census Bureau employees and high school students. Chapter 5 proposes a novel classification technique for 109,446 unique heartbeat samples to identify areas of interest and assist medical professionals in diagnosing heart problems.
Object Detection for Aerial View Images: Dataset and Learning Rate
In recent years, deep learning based computer vision technology has developed rapidly. This is not only due to the improvement of computing power, but also due to the emergence of high-quality datasets. The combination of object detectors and drones has great potential in the field of rescue and disaster relief. We created an image dataset specifically for vision applications on drone platforms. The dataset contains 5000 images, and each image is carefully labeled according to the PASCAL VOC standard. This specific dataset will be very important for developing deep learning algorithms for drone applications. In object detection models, loss function plays a vital role. Considering the uneven distribution of large and small objects in the dataset, we propose adjustment coefficients based on the frequencies of objects of different sizes to adjust the loss function, and finally improve the accuracy of the model.
Efficient Linear Secure Computation and Symmetric Private Information Retrieval Protocols
Security and privacy are of paramount importance in the modern information age. Secure multi-party computation and private information retrieval are canonical and representative problems in cryptography that capture the key challenges in understanding the fundamentals of security and privacy. In this dissertation, we use information theoretic tools to tackle these two classical cryptographic primitives. In the first part, we consider the secure multi-party computation problem, where multiple users, each holding an independent message, wish to compute a function on the messages without revealing any additional information. We present an efficient protocol in terms of randomness cost to securely compute a vector linear function. In the second part, we discuss the symmetric private information retrieval problem, where a user wishes to retrieve one message from a number of replicated databases while keeping the desired message index a secret from each individual database. Further, the user learns nothing about the other messages. We present an optimal protocol that achieves the minimum upload cost for symmetric private information retrieval, i.e., the queries sent from the user to the databases have the minimum number of bits.
Light Matter Interactions in Two-Dimensional Semiconducting Tungsten Diselenide for Next Generation Quantum-Based Optoelectronic Devices
In this work, we explored one material from the broad family of 2D semiconductors, namely WSe2 to serve as an enabler for advanced, low-power, high-performance nanoelectronics and optoelectronic devices. A 2D WSe2 based field-effect-transistor (FET) was designed and fabricated using electron-beam lithography, that revealed an ultra-high mobility of ~ 625 cm2/V-s, with tunable charge transport behavior in the WSe2 channel, making it a promising candidate for high speed Si-based complimentary-metal-oxide-semiconductor (CMOS) technology. Furthermore, optoelectronic properties in 2D WSe2 based photodetectors and 2D WSe2/2D MoS2 based p-n junction diodes were also analyzed, where the photoresponsivity R and external quantum efficiency were exceptional. The monolayer WSe2 based photodetector, fabricated with Al metal contacts, showed a high R ~502 AW-1 under white light illumination. The EQE was also found to vary from 2.74×101 % - 4.02×103 % within the 400 nm -1100 nm spectral range of the tunable laser source. The interfacial metal-2D WSe2 junction characteristics, which promotes the use of such devices for end-use optoelectronics and quantum scale systems, were also studied and the interfacial stated density Dit in Al/2D WSe2 junction was computed to be the lowest reported to date ~ 3.45×1012 cm-2 eV-1. We also examined the large exciton binding energy present in WSe2 through temperature-dependent Raman and photoluminescence spectroscopy, where localized exciton states perpetuated at 78 K that are gaining increasing attention for single photon emitters for quantum information processing. The exciton and phonon dynamics in 2D WSe2 were further analyzed to unveil other multi-body states besides localized excitons, such as trions whose population densities also evolved with temperature. The phonon lifetime, which is another interesting aspect of phonon dynamics, is calculated in 2D layered WSe2 using Raman spectroscopy for the first time and the influence of external stimuli such as temperature and laser power on the phonon behavior …
The Role of Eigenvalues of Parity Check Matrix in Low-Density Parity Check Codes
The new developments in coding theory research have revolutionized the application of coding to practical systems. Low-Density Parity Check (LDPC) codes form a class of Shannon limit approaching codes opted for digital communication systems that require high reliability. This thesis investigates the underlying relationship between the spectral properties of the parity check matrix and LDPC decoding convergence. The bit error rate of an LDPC code is plotted for the parity check matrix that has different Second Smallest Eigenvalue Modulus (SSEM) of its corresponding Laplacian matrix. It is found that for a given (n,k) LDPC code, large SSEM has better error floor performance than low SSEM. The value of SSEM decreases as the sparseness in a parity-check matrix is increased. It was also found from the simulation that long LDPC codes have better error floor performance than short codes. This thesis outlines an approach to analyze LDPC decoding based on the eigenvalue analysis of the corresponding parity check matrix.
Adaptive Slot Location in the Design of Slotted Microstrip Multi-Frequency Antenna for Radionavigation and Radiolocation Applications
In light of incidents and concerns regarding the vulnerability of the global positioning system (GPS), the main purpose of the thesis is to look at alternative systems for radio guidance and to put up a serious study on such alternatives with receive and transmit antenna. There is also the need to design such antennas with multiple frequencies to offer robustness in the unlikely event that such adversarial attacks on the GPS happen. The basis on which such alternative antennas are designed is a slotted microstrip. The characteristics of the slot or slots on the microstrip are analyzed by mapping their exact locations on the patch and then noting the resultant center frequencies, the return losses, and the bandwidth. The activities associated with this also focus on the design, fabrication, validation, and characterization of one or more slotted antennas prototypes. The measurement of the antenna prototypes does confirm several frequencies that coexist to see applications, in aeronautical radionavigation, fixed-mobile radionavigation, and radiolocation. The antennas could also feature in a wide-area augmentation system (WAAS), satellite ground link system (SGLS) as well as in surveillance and precision approach radars. Some variations of the antenna are deployed in the areas of law enforcement, surveillance, and unmanned aerial vehicle (UAV). Applications of the antenna in an unmanned ground vehicle (UGV) are feasible due to its multiple resonant frequencies. Radiolocation and radionavigation antennas have also been known to be mounted in UAVs or on tethered balloons along the borders of the United States to detect low-flying aircraft in support of drug interdiction programs.
Assistive Navigation Technology for Visually Impaired Individuals
Sight is essential in our daily tasks. Compensatory senses have been used for centuries by visually impaired individuals to navigate independently. The help of technology can minimize some challenges for visually impaired individuals. Assistive navigation technologies facilitate the pathfinding and tracing in indoor scenarios. Different modules are added to assistive navigation technologies to warn about the obstacles not only on the ground but about hanging objects. In this work, we attempt to explore new methods to assist visually impaired individuals in navigating independently in an indoor scenario. We employed a location estimation algorithm based on the fingerprinting method to estimate the initial location of the user. We mitigate the error of estimation with particle filter. The shortest path has been calculated with an A* algorithm. To provide the user with an accident-free experiment, we employed an obstacle avoidance algorithm capable of warning the users about the potential hazards. Finally, to provide an effective means of communication with the user, we employed text-to-speech and speech recognition algorithms. The main contribution of this work is to glue these modules together efficiently and affordably.
Inkjet Printed Transition Metal Dichalcogenides and Organohalide Perovskites for Photodetectors and Solar Cells
This dissertation is devoted to the development of novel devices for optoelectronic and photovoltaic applications using the promise of inkjet printing with two-dimensional (2D) materials. A systematic approach toward the characterization of the liquid exfoliated 2D inks comprising of graphene, molybdenum disulfide (MoS2), tungsten diselenide (WSe2), and 2D perovskites is discussed at depth. In the first study, the biocompatibility of 2D materials -- graphene and MoS2 -- that were drop cast onto flexible PET and polyimide substrates using mouse embryonic fibroblast (STO) and human esophageal fibroblast (HEF) cell lines, was explored. The polyimide samples for both STO and HEF showed high biocompatibility with a cell survival rate of up to ~ 98% and a confluence rate of 70-98%. An inkjet printed, biocompatible, heterostructure photodetector was constructed using inks of photo-active MoS2 and electrically conducting graphene, which facilitated charge collection of the photocarriers. The importance of such devices stems from their potential utility in age-related-macular degeneration (AMD), which is a condition where the photosensitive retinal tissue degrades with aging, eventually compromising vision. The biocompatible inkjet printed 2D heterojunction devices were photoresponsive to broadband incoming radiation in the visible regime, and the photocurrent scaled proportionally with the incident light intensity, exhibiting a photoresponsivity R ~ 0.30 A/W. Strain-dependent measurements were also conducted with bending, that showed Iph ~ 1.16 µA with strain levels for curvature up to ~ 0.262 cm-1, indicating the feasibility of such devices for large format arrays printed on flexible substrates. Alongside the optoelectronic measurements, temperature-dependent (~ 80 K to 573 K) frequency shifts of the Raman-active E12g and A1g modes of multilayer MoS2 exhibited a red-shift with increasing temperature, where the temperature coefficients for the E12g and A1g modes were determined to be ~ - 0.016 cm-1/K and ~ - 0.014 cm-1/K, respectively. The phonon lifetime τ was …
Interference Alignment through Propagation Delay
With the rapid development of wireless communication technology, the demands for higher communication rates are increasing. Higher communication rate corresponds to higher DoF. Interference alignment, which is an emerging interference management technique, is able to substantially increase the DoF of wireless communication systems. This thesis mainly studies the delay-based interference alignment technique. The key problem lies in the design of the transmission scheme and the appropriate allocation of the propagation delay, so as to achieve the desired DoF of different wireless networks. In addition, through delay-based interference alignment, the achievability of extreme points of the DoF region of different wireless networks can be proved.
Design of a Wearable Flexible Resonant Body Temperature Sensor with Inkjet-Printing
A wearable body temperature sensor would allow for early detection of fever or infection, as well as frequent and accurate hassle-free recording. This thesis explores the design of a body-temperature-sensing device inkjet-printed on a flexible substrate. All structures were first modeled by first-principles, theoretical calculations, and then simulated in HFSS. A variety of planar square inductor geometries were studied before selecting an optimal design. The designs were fabricated using multiple techniques and compared to the simulation results. It was determined that inductance must be carefully measured and documented to ensure good functionality. The same is true for parallel-plate and interdigitated capacitors. While inductance remains relatively constant with temperature, the capacitance of the device with a temperature-sensitive dielectric layer will result in a shift in the resonant frequency as environmental or ambient temperature changes. This resonant frequency can be wirelessly detected, with no battery required for the sensing device, from which the temperature can be deduced. From this work, the optimized version of the design comprises of conductive silver in with a temperature-sensitive graphene oxide layer, intended for inkjet-printing on flexible polyimide substrates. Graphene oxide demonstrates a high dielectric permittivity with good sensing capabilities and high accuracy. This work pushes the state-of-the-art in applying these novel materials and techniques to enable flexible body temperature sensors for future biomedical applications.
High-Performance Detectors Based on the Novel Electronic and Optoelectronic Properties of Crystalline 2D van der Waals Solids
In this work, we study the properties and device applications of MoS2, black phosphorus, MoOx, and NbSe2. We first start with the design, fabrication, and characterization of ultra-high responsivity photodetectors based on mesoscopic multilayer MoS2. The device architecture is comprised of a metal-semiconductor-metal (MSM) photodetector, where Mo was used as the contact metal to suspended MoS2 membranes. The dominant photocurrent mechanism was determined to be the photoconductive effect, while a contribution from the photogating effect was also noted from trap-states that yielded a wide spectral photoresponse from UV-to-IR with an external quantum efficiency (EQE) ~ 104. From time-resolved photocurrent measurements, a fast decay time and response time were obtained with a stream of incoming ON/OFF white light pulses. Another interesting semiconductor 2D material that has attracted special attention due to its small bandgap and ultra-high hole mobility is the black phosphorus. An analysis of the optoelectronic properties and photocurrent generation mechanisms in two-dimensional (2D) multilayer crystallites of black phosphorus (BP) was conducted from 350 K down to cryogenic temperatures using a broad-band white light source. The Mo-BP interface yielded a low Schottky barrier "φ" _"SB" ~ -28.3 meV and a high photoresponsivity R of ~ 2.43 x 105 A/W at a source-drain bias voltage of ~ 0.5 V (300 K, and incident optical power ~ 3.16 μW/cm2). Our report is the first to highlight the empirical use of Mo as a contact metal with BP. From the analysis conducted on the BP devices, the thermally driven photocurrent generation mechanism arising from the photobolometric effect (PBE) dominated the carrier dynamics for T > 181 K since the photocurrent Iph and the bolometric coefficient β undergo a transition in polarity from positive to negative. Our results show the promise of BP to potentially advance thermoelectric and optoelectronic devices stemming from this mono-elemental, …
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