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, …

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.

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.

Article presenting 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, results of traffic flow simulations are presented.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 …

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 …

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.

This thesis offers the design and improvement of a 2 GHz to 20 GHz low noise amplifier (LNA) utilizing pHEMT technology. The pHEMT technology allows the LNA to generate a boosted signal at a lower noise figure (NF) while consuming less power and achieving smooth overall gain. The design achieves an overall gain (S21) of ≥ 10 dB with an NF ≤ 2 dB while consuming ≤ 30 mA of power while using commercial off-the-shelf (COTS) components.

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.

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.

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.

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.

Although wireless sensor networks have been successfully used for environmental monitoring, one of the major challenges that this technology has been facing is supplying continuous and reliable electrical power during long-term field deployment. Batteries require repetitive visits to the deployment site to replace them once discharged; admittedly, they can be recharged from solar panels, but this only works in open areas where solar radiation is unrestricted. This dissertation introduces a novel approach to design and implement a reliable efficient solar energy harvester to continuously, and autonomously, provide power to wireless sensor nodes for long-term applications. The system uses supercapacitors charged by a solar panel and is designed to reduce power consumption to very low levels. Field tests were conducted for more than a year of continuous operation and under a variety of conditions, including areas under dense foliage. The resulting long-term field data demonstrates the feasibility and sustainability of the harvester system for challenging applications. In addition, we analyzed solar radiation data and supercapacitor charging behavior and showed that the harvester system can operate battery free, running on the power provided by supercapacitors. A battery is included only for backup in case the supercapacitor storage fails. The proposed approach provides …

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 …

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.