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Title: Characterization, Classification, and Genesis of Seismocardiographic Signals.
Creator: Taebi, Amirtaha, Mansy, Hansen, Kassab, Alain, Huang, Helen, Vosoughi, Azadeh, University of Central Florida
Abstract / Description: Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction.In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency...
Show moreSeismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction.In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms.Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features.SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG.
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Date Issued: 2018
Identifier: CFE0007106, ucf:51944
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007106

Title: Data-Driven Modeling and Optimization of Building Energy Consumption.
Creator: Grover, Divas, Pourmohammadi Fallah, Yaser, Vosoughi, Azadeh, Zhou, Qun, University of Central Florida
Abstract / Description: Sustainability and reducing energy consumption are targets for building operations. The installation of smart sensors and Building Automation Systems (BAS) makes it possible to study facility operations under different circumstances. These technologies generate large amounts of data. That data can be scrapped and used for the analysis. In this thesis, we focus on the process of data-driven modeling and decision making from scraping the data to simulate the building and optimizing the...
Show moreSustainability and reducing energy consumption are targets for building operations. The installation of smart sensors and Building Automation Systems (BAS) makes it possible to study facility operations under different circumstances. These technologies generate large amounts of data. That data can be scrapped and used for the analysis. In this thesis, we focus on the process of data-driven modeling and decision making from scraping the data to simulate the building and optimizing the operation. The City of Orlando has similar goals of sustainability and reduction of energy consumption so, they provided us access to their BAS for the data and study the operation of its facilities. The data scraped from the City's BAS serves can be used to develop statistical/machine learning methods for decision making. We selected a mid-size pilot building to apply these techniques. The process begins with the collection of data from BAS. An Application Programming Interface (API) is developed to login to the servers and scrape data for all data points and store it on the local machine. Then data is cleaned to analyze and model. The dataset contains various data points ranging from indoor and outdoor temperature to fan speed inside the Air Handling Unit (AHU) which are operated by Variable Frequency Drive (VFD). This whole dataset is a time series and is handled accordingly. The cleaned dataset is analyzed to find different patterns and investigate relations between different data points. The analysis helps us in choosing parameters for models that are developed in the next step. Different statistical models are developed to simulate building and equipment behavior. Finally, the models along with the data are used to optimize the building Operation with the equipment constraints to make decisions for building operation which leads to a reduction in energy consumption while maintaining temperature and pressure inside the building.
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Date Issued: 2019
Identifier: CFE0007810, ucf:52335
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007810

Title: AN ALL-AGAINST-ONE GAME APPROACH FOR THE MULTI-PLAYER PURSUIT-EVASION PROBLEM.
Creator: Talebi, Shahriar, Simaan, Marwan, Qu, Zhihua, Vosoughi, Azadeh, University of Central Florida
Abstract / Description: The traditional pursuit-evasion game considers a situation where one pursuer tries to capture an evader, while the evader is trying to escape. A more general formulation of this problem is to consider multiple pursuers trying to capture one evader. This general multi-pursuer one-evader problem can also be used to model a system of systems in which one of the subsystems decides to dissent (evade) from the others while the others (the pursuer subsystems) try to pursue a strategy to prevent it...
Show moreThe traditional pursuit-evasion game considers a situation where one pursuer tries to capture an evader, while the evader is trying to escape. A more general formulation of this problem is to consider multiple pursuers trying to capture one evader. This general multi-pursuer one-evader problem can also be used to model a system of systems in which one of the subsystems decides to dissent (evade) from the others while the others (the pursuer subsystems) try to pursue a strategy to prevent it from doing so. An important challenge in analyzing these types of problems is to develop strategies for the pursuers along with the advantages and disadvantages of each. In this thesis, we investigate three possible and conceptually different strategies for pursuers: (1) act non-cooperatively as independent pursuers, (2) act cooperatively as a unified team of pursuers, and (3) act individually as greedy pursuers. The evader, on the other hand, will consider strategies against all possible strategies by the pursuers. We assume complete uncertainty in the game i.e. no player knows which strategies the other players are implementing and none of them has information about any of the parameters in the objective functions of the other players. To treat the three pursuers strategies under one general framework, an all-against-one linear quadratic dynamic game is considered and the corresponding closed-loop Nash solution is discussed. Additionally, different necessary and sufficient conditions regarding the stability of the system, and existence and definiteness of the closed-loop Nash strategies under different strategy assumptions are derived. We deal with the uncertainties in the strategies by first developing the Nash strategies for each of the resulting games for all possible options available to both sides. Then we deal with the parameter uncertainties by performing a Monte Carlo analysis to determine probabilities of capture for the pursuers (or escape for the evader) for each resulting game. Results of the Monte Carlo simulation show that in general, pursuers do not always benefit from cooperating as a team and that acting as non-cooperating players may yield a higher probability of capturing of the evader.
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Date Issued: 2017
Identifier: CFE0007135, ucf:52314
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007135

Title: Leveraging the Intrinsic Switching Behaviors of Spintronic Devices for Digital and Neuromorphic Circuits.
Creator: Pyle, Steven, DeMara, Ronald, Vosoughi, Azadeh, Chanda, Debashis, University of Central Florida
Abstract / Description: With semiconductor technology scaling approaching atomic limits, novel approaches utilizing new memory and computation elements are sought in order to realize increased density, enhanced functionality, and new computational paradigms. Spintronic devices offer intriguing avenues to improve digital circuits by leveraging non-volatility to reduce static power dissipation and vertical integration for increased density. Novel hybrid spintronic-CMOS digital circuits are developed herein that...
Show moreWith semiconductor technology scaling approaching atomic limits, novel approaches utilizing new memory and computation elements are sought in order to realize increased density, enhanced functionality, and new computational paradigms. Spintronic devices offer intriguing avenues to improve digital circuits by leveraging non-volatility to reduce static power dissipation and vertical integration for increased density. Novel hybrid spintronic-CMOS digital circuits are developed herein that illustrate enhanced functionality at reduced static power consumption and area cost. The developed spin-CMOS D Flip-Flop offers improved power-gating strategies by achieving instant store/restore capabilities while using 10 fewer transistors than typical CMOS-only implementations. The spin-CMOS Muller C-Element developed herein improves asynchronous pipelines by reducing the area overhead while adding enhanced functionality such as instant data store/restore and delay-element-free bundled data asynchronous pipelines.Spintronic devices also provide improved scaling for neuromorphic circuits by enabling compact and low power neuron and non-volatile synapse implementations while enabling new neuromorphic paradigms leveraging the stochastic behavior of spintronic devices to realize stochastic spiking neurons, which are more akin to biological neurons and commensurate with theories from computational neuroscience and probabilistic learning rules. Spintronic-based Probabilistic Activation Function circuits are utilized herein to provide a compact and low-power neuron for Binarized Neural Networks. Two implementations of stochastic spiking neurons with alternative speed, power, and area benefits are realized. Finally, a comprehensive neuromorphic architecture comprising stochastic spiking neurons, low-precision synapses with Probabilistic Hebbian Plasticity, and a novel non-volatile homeostasis mechanism is realized for subthreshold ultra-low-power unsupervised learning with robustness to process variations. Along with several case studies, implications for future spintronic digital and neuromorphic circuits are presented.
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Date Issued: 2019
Identifier: CFE0007514, ucf:52658
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007514

Title: Real-time SIL Emulation Architecture for Cooperative Automated Vehicles.
Creator: Gupta, Nitish, Pourmohammadi Fallah, Yaser, Rahnavard, Nazanin, Vosoughi, Azadeh, University of Central Florida
Abstract / Description: This thesis presents a robust, flexible and real-time architecture for Software-in-the-Loop (SIL) testing of connected vehicle safety applications. Emerging connected and automated vehicles (CAV) use sensing, communication and computing technologies in the design of a host of new safety applications. Testing and verification of these applications is a major concern for the automotive industry. The CAV safety applications work by sharing their state and movement information over wireless...
Show moreThis thesis presents a robust, flexible and real-time architecture for Software-in-the-Loop (SIL) testing of connected vehicle safety applications. Emerging connected and automated vehicles (CAV) use sensing, communication and computing technologies in the design of a host of new safety applications. Testing and verification of these applications is a major concern for the automotive industry. The CAV safety applications work by sharing their state and movement information over wireless communication links. Vehicular communication has fueled the development of various Cooperative Vehicle Safety (CVS) applications. Development of safety applications for CAV requires testing in many different scenarios. However, the recreation of test scenarios for evaluating safety applications is a very challenging task. This is mainly due to the randomness in communication, difficulty in recreating vehicle movements precisely, and safety concerns for certain scenarios. We propose to develop a standalone Remote Vehicle Emulator (RVE) that can reproduce V2V messages of remote vehicles from simulations or from previous tests, while also emulating the over the air behavior of multiple communicating nodes. This is expected to significantly accelerate the development cycle. RVE is a unique and easily configurable emulation cum simulation setup to allow Software in the Loop (SIL) testing of connected vehicle applications in a realistic and safe manner. It will help in tailoring numerous test scenarios, expediting algorithm development and validation as well as increase the probability of finding failure modes. This, in turn, will help improve the quality of safety applications while saving testing time and reducing cost.The RVE architecture consists of two modules, the Mobility Generator, and the Communication emulator. Both of these modules consist of a sequence of events that are handled based on the type of testing to be carried out. The communication emulator simulates the behavior of MAC layer while also considering the channel model to increase the probability of successful transmission. It then produces over the air messages that resemble the output of multiple nodes transmitting, including corrupted messages due to collisions. The algorithm that goes inside the emulator has been optimized so as to minimize the communication latency and make this a realistic and real-time safety testing tool. Finally, we provide a multi-metric experimental evaluation wherein we verified the simulation results with an identically configured ns3 simulator. With the aim to improve the quality of testing of CVS applications, this unique architecture would serve as a fundamental design for the future of CVS application testing.
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Date Issued: 2018
Identifier: CFE0007185, ucf:52280
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007185

Title: Self-Scaling Evolution of Analog Computation Circuits.
Creator: Pyle, Steven, DeMara, Ronald, Vosoughi, Azadeh, Chanda, Debashis, University of Central Florida
Abstract / Description: Energy and performance improvements of continuous-time analog-based computation for selected applications offer an avenue to continue improving the computational ability of tomorrow's electronic devices at current technology scaling limits. However, analog computation is plagued by the difficulty of designing complex computational circuits, programmability, as well as the inherent lack of accuracy and precision when compared to digital implementations. In this thesis, evolutionary algorithm...
Show moreEnergy and performance improvements of continuous-time analog-based computation for selected applications offer an avenue to continue improving the computational ability of tomorrow's electronic devices at current technology scaling limits. However, analog computation is plagued by the difficulty of designing complex computational circuits, programmability, as well as the inherent lack of accuracy and precision when compared to digital implementations. In this thesis, evolutionary algorithm-based techniques are utilized within a reconfigurable analog fabric to realize an automated method of designing analog-based computational circuits while adapting the functional range to improve performance. A Self-Scaling Genetic Algorithm is proposed to adapt solutions to computationally-tractable ranges in hardware-constrained analog reconfigurable fabrics. It operates by utilizing a Particle Swarm Optimization (PSO) algorithm that operates synergistically with a Genetic Algorithm (GA) to adaptively scale and translate the functional range of computational circuits composed of high-level or low-level Computational Analog Elements to improve performance and realize functionality otherwise unobtainable on the intrinsic platform. The technique is demonstrated by evolving square, square-root, cube, and cube-root analog computational circuits on the Cypress PSoC-5LP System-on-Chip. Results indicate that the Self-Scaling Genetic Algorithm improves our error metric on average 7.18-fold, up to 12.92-fold for computational circuits that produce outputs beyond device range. Results were also favorable compared to previous works, which utilized extrinsic evolution of circuits with much greater complexity than was possible on the PSoC-5LP.
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Date Issued: 2015
Identifier: CFE0005866, ucf:50873
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005866

Title: Motor imagery classification using sparse representation of EEG signals.
Creator: Saidi, Pouria, Atia, George, Vosoughi, Azadeh, Berman, Steven, University of Central Florida
Abstract / Description: The human brain is unquestionably the most complex organ of the body as it controls and processes its movement and senses. A healthy brain is able to generate responses to the signals it receives, and transmit messages to the body. Some neural disorders can impair the communication between the brain and the body preventing the transmission of these messages. Brain Computer Interfaces (BCIs) are devices that hold immense potential to assist patients with such disorders by analyzing brain...
Show moreThe human brain is unquestionably the most complex organ of the body as it controls and processes its movement and senses. A healthy brain is able to generate responses to the signals it receives, and transmit messages to the body. Some neural disorders can impair the communication between the brain and the body preventing the transmission of these messages. Brain Computer Interfaces (BCIs) are devices that hold immense potential to assist patients with such disorders by analyzing brain signals, translating and classifying various brain responses, and relaying them to external devices and potentially back to the body. Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major, yet very challenging, step in developing Brain Computer Interfaces (BCIs). Of primary importance is to use less data and computationally efficient algorithms to support real-time BCI. To this end, in this thesis we explore and develop algorithms that exploit the sparse characteristics of EEGs to classify these signals. Different feature vectors are extracted from EEG trials recorded by electrodes placed on the scalp.In this thesis, features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the EEG training signals for each class. This is used to classify the signals based on the pattern of their sparse representation using a minimum-residual decision rule.We first attempt to use all the available electrodes to verify the effectiveness of the proposed methods. To support real time BCI, the electrodes are reduced to those near the sensorimotor cortex which are believed to be crucial for motor preparation and imagination.In a second approach, we try to incorporate the effect of spatial correlation across the neighboring electrodes near the sensorimotor cortex. To this end, instead of considering one feature vector at a time, we use a collection of feature vectors simultaneously to find the joint sparse representation of these vectors. Although we were not able to see much improvement with respect to the first approach, we envision that such improvements could be achieved using more refined models that can be subject of future works. The performance of the proposed approaches is evaluated using different features, including wavelet coefficients, energy of the signals in different frequency sub-bands, and also entropy of the signals. The results obtained from real data demonstrate that the combination of energy and entropy features enable efficient classification of motor imagery EEG trials related to hand and foot movements. This underscores the relevance of the energies and their distribution in different frequency sub-bands for classifying movement-specific EEG patterns in agreement with the existence of different levels within the alpha band. The proposed approach is also shown to outperform the state-of-the-art algorithm that uses feature vectors obtained from energies of multiple spatial projections.
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Date Issued: 2015
Identifier: CFE0005882, ucf:50884
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005882

Title: COMPRESSIVE AND CODED CHANGE DETECTION: THEORY AND APPLICATION TO STRUCTURAL HEALTH MONITORING.
Creator: Sarayanibafghi, Omid, Atia, George, Vosoughi, Azadeh, Rahnavard, Nazanin, University of Central Florida
Abstract / Description: In traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this thesis the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered when decision makers only have access to compressed measurements, i.e., each measurement is derived by a subset of features. Herein, we propose a new framework that is termed Compressed Change Detection. The main...
Show moreIn traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this thesis the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered when decision makers only have access to compressed measurements, i.e., each measurement is derived by a subset of features. Herein, we propose a new framework that is termed Compressed Change Detection. The main approach relies on integrating ideas from the theory of identifying codes with change point detection in sequential analysis. If the stochastic properties of certain features change, then the changes can be detected by examining the covering set of an identifying code of measurements. In particular, given a large number N of features, the goal is to detect a small set of features that undergoes a statistical change using a small number of measurements. Sufficient conditions are derived for the probability of false alarm and isolation to approach zero in the asymptotic regime where N is large.As an application of compressed change detection, the problem of detection of a sparse number of damages in a structure for Structural Health Monitoring (SHM) is considered. Since only a small number of damage scenarios can occur simultaneously, change detection is applied to responses of pairs of sensors that form an identifying code over a learned damage-sensing graph. Generalizations of the proposed framework with multiple concurrent changes and for arbitrary graph topologies are presented.
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Date Issued: 2016
Identifier: CFE0006387, ucf:51507
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006387

Title: Reliable Spectrum Hole Detection in Spectrum-Heterogeneous Mobile Cognitive Radio Networks via Sequential Bayesian Non-parametric Clustering.
Creator: Zaeemzadeh, Alireza, Rahnavard, Nazanin, Vosoughi, Azadeh, Qi, GuoJun, University of Central Florida
Abstract / Description: In this work, the problem of detecting radio spectrum opportunities in spectrum-heterogeneous cognitive radio networks is addressed. Spectrum opportunities are the frequency channels that are underutilized by the primary licensed users. Thus, by enabling the unlicensed users to detect and utilize them, we can improve the efficiency, reliability, and the flexibility of the radio spectrum usage. The main objective of this work is to discover the spectrum opportunities in time, space, and...
Show moreIn this work, the problem of detecting radio spectrum opportunities in spectrum-heterogeneous cognitive radio networks is addressed. Spectrum opportunities are the frequency channels that are underutilized by the primary licensed users. Thus, by enabling the unlicensed users to detect and utilize them, we can improve the efficiency, reliability, and the flexibility of the radio spectrum usage. The main objective of this work is to discover the spectrum opportunities in time, space, and frequency domains, by proposing a low-cost and practical framework. Spectrum-heterogeneous networks are the networks in which different sensors experience different spectrum opportunities. Thus, the sensing data from sensors cannot be combined to reach consensus and to detect the spectrum opportunities. Moreover, unreliable data, caused by noise or malicious attacks, will deteriorate the performance of the decision-making process. The problem becomes even more challenging when the locations of the sensors are unknown. In this work, a probabilistic model is proposed to cluster the sensors based on their readings, not requiring any knowledge of location of the sensors. The complexity of the model, which is the number of clusters, is automatically inferred from the sensing data. The processing node, also referred to as the base station or the fusion center, infers the probability distributions of cluster memberships, channel availabilities, and devices' reliability in an online manner. After receiving each chunk of sensing data, the probability distributions are updated, without requiring to repeat the computations on previous sensing data. All the update rules are derived mathematically, by employing Bayesian data analysis techniques and variational inference.Furthermore, the inferred probability distributions are employed to assign unique spectrum opportunities to each of the sensors. To avoid interference among the sensors, physically adjacent devices should not utilize the same channels. However, since the location of the devices is not known, cluster membership information is used as a measure of adjacency. This is based on the assumption that the measurements of the devices are spatially correlated. Thus, adjacent devices, which experience similar spectrum opportunities, belong to the same cluster. Then, the problem is mapped into a energy minimization problem and solved via graph cuts. The goal of the proposed graph-theory-based method is to assign each device an available channel, while avoiding interference among neighboring devices. The numerical simulations illustrates the effectiveness of the proposed methods, compared to the existing frameworks.
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Date Issued: 2017
Identifier: CFE0006963, ucf:51639
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006963

Title: Impact of wireless channel uncertainty upon M-ary distributed detection systems.
Creator: Hajibabaei Najafabadi, Zahra, Vosoughi, Azadeh, Rahnavard, Nazanin, Atia, George, University of Central Florida
Abstract / Description: We consider a wireless sensor network (WSN), consisting of several sensors and a fusion center (FC), which is tasked with solving an $M$-ary hypothesis testing problem. Sensors make $M$-ary decisions and transmit their digitally modulated decisions over orthogonal channels, which are subject to Rayleigh fading and noise, to the FC. Adopting Bayesian optimality criterion, we consider training and non-training based distributed detection systems and investigate the effect of imperfect channel...
Show moreWe consider a wireless sensor network (WSN), consisting of several sensors and a fusion center (FC), which is tasked with solving an $M$-ary hypothesis testing problem. Sensors make $M$-ary decisions and transmit their digitally modulated decisions over orthogonal channels, which are subject to Rayleigh fading and noise, to the FC. Adopting Bayesian optimality criterion, we consider training and non-training based distributed detection systems and investigate the effect of imperfect channel state information (CSI) on the optimal maximum a posteriori probability (MAP) fusion rules and detection performance, when the sum of training and data symbol transmit powers is fixed. Our results show that for Rayleigh fading channel, when sensors employ $M$-FSK or binary FSK (BFSK) modulation, the error probability is minimized when training symbol transmit power is zero (regardless of the reception mode at the FC). However, for coherent reception, $M$-PSK and binary PSK (BPSK) modulation the error probability is minimized when half of transmit power is allocated for training symbol. If the channel is Rician fading, regardless of the modulation, the error probability is minimized when training transmit power is zero.
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Date Issued: 2016
Identifier: CFE0006111, ucf:51209
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006111

Title: Robust, Scalable, and Provable Approaches to High Dimensional Unsupervised Learning.
Creator: Rahmani, Mostafa, Atia, George, Vosoughi, Azadeh, Mikhael, Wasfy, Nashed, M, Pensky, Marianna, University of Central Florida
Abstract / Description: This doctoral thesis focuses on three popular unsupervised learning problems: subspace clustering, robust PCA, and column sampling. For the subspace clustering problem, a new transformative idea is presented. The proposed approach, termed Innovation Pursuit, is a new geometrical solution to the subspace clustering problem whereby subspaces are identified based on their relative novelties. A detailed mathematical analysis is provided establishing sufficient conditions for the proposed method...
Show moreThis doctoral thesis focuses on three popular unsupervised learning problems: subspace clustering, robust PCA, and column sampling. For the subspace clustering problem, a new transformative idea is presented. The proposed approach, termed Innovation Pursuit, is a new geometrical solution to the subspace clustering problem whereby subspaces are identified based on their relative novelties. A detailed mathematical analysis is provided establishing sufficient conditions for the proposed method to correctly cluster the data points. The numerical simulations with both real and synthetic data demonstrate that Innovation Pursuit notably outperforms the state-of-the-art subspace clustering algorithms. For the robust PCA problem, we focus on both the outlier detection and the matrix decomposition problems. For the outlier detection problem, we present a new algorithm, termed Coherence Pursuit, in addition to two scalable randomized frameworks for the implementation of outlier detection algorithms. The Coherence Pursuit method is the first provable and non-iterative robust PCA method which is provably robust to both unstructured and structured outliers. Coherence Pursuit is remarkably simple and it notably outperforms the existing methods in dealing with structured outliers. In the proposed randomized designs, we leverage the low dimensional structure of the low rank component to apply the robust PCA algorithm to a random sketch of the data as opposed to the full scale data. Importantly, it is analytically shown that the presented randomized designs can make the computation or sample complexity of the low rank matrix recovery algorithm independent of the size of the data. At the end, we focus on the column sampling problem. A new sampling tool, dubbed Spatial Random Sampling, is presented which performs the random sampling in the spatial domain. The most compelling feature of Spatial Random Sampling is that it is the first unsupervised column sampling method which preserves the spatial distribution of the data.
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Date Issued: 2018
Identifier: CFE0007083, ucf:52010
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007083

Title: Development of an Adaptive Restoration Tool For a Self-Healing Smart Grid.
Creator: Golshani, Amir, Sun, Wei, Qu, Zhihua, Vosoughi, Azadeh, Zhou, Qun, Zheng, Qipeng, University of Central Florida
Abstract / Description: Large power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This...
Show moreLarge power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This dissertation is aimed to tackle the challenging task of developing an adaptive restoration decisionsupport system (RDSS). The RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. First, an efficient network partitioning approach is developed to provide initial conditions for RDSS by dividing large outage network into smaller islands. Then, the comprehensive formulation of RDSS integrates different recovery phases into one optimization problem, and encompasses practical constraints including AC powerflow, dynamic reserve, and dynamic behaviors of generators and load. Also, a frequency constrained load recovery module is proposed and integrated into the RDSS to determine the optimal location and amount of load pickup. Next, the proposed RDSS is applied to harness renewable energy sources and pumped-storage hydro (PSH) units by addressing the inherent variabilities and uncertainties of renewable and coordinating wind and PSH generators. A two-stage stochastic and robust optimization problem is formulated, and solved by the integer L-shaped and column-and-constraintsgeneration decomposition algorithms. The developed RDSS tool has been tested onthe modified IEEE 39-bus and IEEE 57-bus systems under different scenarios. Numerical results demonstrate the effectiveness and efficiency of the proposed RDSS. In case of contingencies or unexpected outages during the restoration process, RDSS can quickly update the restoration plan and adapt to changing system conditions. RDSS is an important step toward a self-healing power grid and its implementation will reduce the recovery time while maintaining system security.
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Date Issued: 2017
Identifier: CFE0007284, ucf:52169
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007284

Title: Reliability and Robustness Enhancement of Cooperative Vehicular Systems: A Bayesian Machine Learning Perspective.
Creator: Nourkhiz Mahjoub, Hossein, Pourmohammadi Fallah, Yaser, Vosoughi, Azadeh, Yuksel, Murat, Atia, George, Eluru, Naveen, University of Central Florida
Abstract / Description: Autonomous vehicles are expected to greatly transform the transportation domain in the near future. Some even envision that the human drivers may be fully replaced by automated systems. It is plausible to assume that at least a significant part of the driving task will be done by automated systems in not a distant future. Although we are observing a rapid advance towards this goal, which gradually pushes the traditional human-based driving toward more advanced autonomy levels, the full...
Show moreAutonomous vehicles are expected to greatly transform the transportation domain in the near future. Some even envision that the human drivers may be fully replaced by automated systems. It is plausible to assume that at least a significant part of the driving task will be done by automated systems in not a distant future. Although we are observing a rapid advance towards this goal, which gradually pushes the traditional human-based driving toward more advanced autonomy levels, the full autonomy concept still has a long way before being completely fulfilled and realized due to numerous technical and societal challenges. During this long transition phase, blended driving scenarios, composed of agents with different levels of autonomy, seems to be inevitable. Therefore, it is critical to design appropriate driving systems with different levels of intelligence in order to benefit all participants. Vehicular safety systems and their more advanced successors, i.e., Cooperative Vehicular Systems (CVS), have originated from this perspective. These systems aim to enhance the overall quality and performance of the current driving situation by incorporating the most advanced available technologies, ranging from on-board sensors such as radars, LiDARs, and cameras to other promising solutions e.g. Vehicle-to-Everything (V2X) communications. However, it is still challenging to attain the ideal anticipated benefits out of the cooperative vehicular systems, due to the inherent issues and challenges of their different components, such as sensors' failures in severe weather conditions or the poor performance of V2X technologies under dense communication channel loads. In this research we aim to address some of these challenges from a Bayesian Machine- Learning perspective, by proposing several novel ideas and solutions which facilitate the realization of more robust, reliable, and agile cooperative vehicular systems. More precisely, we have a two-fold contribution here. In one aspect, we have investigated the notion of Model-Based Communications (MBC) and demonstrated its effectiveness for V2X communication performance enhancement. This improvement is achieved due to the more intelligent communication strategy of MBC in comparison with the current state-of-the-art V2X technologies. Essentially, MBC proposes a conceptual change in the nature of the disseminated and shared information over the communication channel compared to what is being disseminated in current technologies. In the MBC framework, instead of sharing the raw dynamic information among the network agents, each agent shares the parameters of a stochastic forecasting model which represents its current and future behavior and updates these parameters as needed. This model sharing strategy enables the receivers to precisely predict the future behaviors of the transmitter even when the update frequency is very low. On the other hand, we have also proposed receiver-side solutions in order to enhance the CVS performance and reliability and mitigate the issues caused by imperfect communication and detection processes. The core concept for these solutions is incorporating other informative elements in the system to compensate for the lack of information which is lost during the imperfect communication or detection phases. For proof of concept, we have designed an adaptive FCW framework which considers the driver's feedbacks to the CVS system. This adaptive framework mitigates the negative impact of imperfectly received or detected information on system performance, using the inherent information of these feedbacks and responses. The effectiveness and superiority of this adaptive framework over traditional design has been demonstrated in this research.
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Date Issued: 2019
Identifier: CFE0007845, ucf:52807
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007845

Title: Game-Theoretic Frameworks and Strategies for Defense Against Network Jamming and Collocation Attacks.
Creator: Hemida, Ahmed, Atia, George, Simaan, Marwan, Vosoughi, Azadeh, Sukthankar, Gita, Guirguis, Mina, University of Central Florida
Abstract / Description: Modern networks are becoming increasingly more complex, heterogeneous, and densely connected. While more diverse services are enabled to an ever-increasing number of users through ubiquitous networking and pervasive computing, several important challenges have emerged. For example, densely connected networks are prone to higher levels of interference, which makes them more vulnerable to jamming attacks. Also, the utilization of software-based protocols to perform routing, load balancing and...
Show moreModern networks are becoming increasingly more complex, heterogeneous, and densely connected. While more diverse services are enabled to an ever-increasing number of users through ubiquitous networking and pervasive computing, several important challenges have emerged. For example, densely connected networks are prone to higher levels of interference, which makes them more vulnerable to jamming attacks. Also, the utilization of software-based protocols to perform routing, load balancing and power management functions in Software-Defined Networks gives rise to more vulnerabilities that could be exploited by malicious users and adversaries. Moreover, the increased reliance on cloud computing services due to a growing demand for communication and computation resources poses formidable security challenges due to the shared nature and virtualization of cloud computing. In this thesis, we study two types of attacks: jamming attacks on wireless networks and side-channel attacks on cloud computing servers. The former attacks disrupt the natural network operation by exploiting the static topology and dynamic channel assignment in wireless networks, while the latter attacks seek to gain access to unauthorized data by co-residing with target virtual machines (VMs) on the same physical node in a cloud server. In both attacks, the adversary faces a static attack surface and achieves her illegitimate goal by exploiting a stationary aspect of the network functionality. Hence, this dissertation proposes and develops counter approaches to both attacks using moving target defense strategies. We study the strategic interactions between the adversary and the network administrator within a game-theoretic framework.First, in the context of jamming attacks, we present and analyze a game-theoretic formulation between the adversary and the network defender. In this problem, the attack surface is the network connectivity (the static topology) as the adversary jams a subset of nodes to increase the level of interference in the network. On the other side, the defender makes judicious adjustments of the transmission footprint of the various nodes, thereby continuously adapting the underlying network topology to reduce the impact of the attack. The defender's strategy is based on playing Nash equilibrium strategies securing a worst-case network utility. Moreover, scalable decomposition-based approaches are developed yielding a scalable defense strategy whose performance closely approaches that of the non-decomposed game for large-scale and dense networks. We study a class of games considering discrete as well as continuous power levels.In the second problem, we consider multi-tenant clouds, where a number of VMs are typically collocated on the same physical machine to optimize performance and power consumption and maximize profit. This increases the risk of a malicious virtual machine performing side-channel attacks and leaking sensitive information from neighboring VMs. The attack surface, in this case, is the static residency of VMs on a set of physical nodes, hence we develop a timed migration defense approach. Specifically, we analyze a timing game in which the cloud provider decides when to migrate a VM to a different physical machine to mitigate the risk of being compromised by a collocated malicious VM. The adversary decides the rate at which she launches new VMs to collocate with the victim VMs. Our formulation captures a data leakage model in which the cost incurred by the cloud provider depends on the duration of collocation with malicious VMs. It also captures costs incurred by the adversary in launching new VMs and by the defender in migrating VMs. We establish sufficient conditions for the existence of Nash equilibria for general cost functions, as well as for specific instantiations, and characterize the best response for both players. Furthermore, we extend our model to characterize its impact on the attacker's payoff when the cloud utilizes intrusion detection systems that detect side-channel attacks. Our theoretical findings are corroborated with extensive numerical results in various settings as well as a proof-of-concept implementation in a realistic cloud setting.
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Date Issued: 2019
Identifier: CFE0007468, ucf:52677
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007468

Title: Scalable Map Information Dissemination for Connected and Automated Vehicle Systems.
Creator: Gani, S M Osman, Pourmohammadi Fallah, Yaser, Vosoughi, Azadeh, Yuksel, Murat, Chatterjee, Mainak, Hasan, Samiul, University of Central Florida
Abstract / Description: Situational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle...
Show moreSituational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle this challenge, the inter-vehicle communication system is envisioned that allows vehicles to exchange self-status updates aiming to extend their effective field of view and thus compensate for the limitations of the vehicle tracking subsystem that relies substantially on onboard sensing devices. Tracking capability in such systems can be further improved through the cooperative sharing of locally created map data instead of transmitting only self-update messages containing core basic safety message (BSM) data. In the cooperative sharing of safety messages, it is imperative to have a scalable communication protocol to ensure optimal use of the communication channel. This dissertation contributes to the analysis of the scalability issue in vehicle-to-everything (V2X) communication and then addresses the range issue of situational awareness in CAV systems by proposing a content-adaptive V2X communication architecture. To that end, we first analyze the BSM scheduling protocol standardized in the SAE J2945/1 and present large-scale scalability results obtained from a high-fidelity simulation platform to demonstrate the protocol's efficacy to address the scalability issues in V2X communication. By employing a distributed opportunistic approach, the SAE J2945/1 congestion control algorithm keeps the overall offered channel load within an optimal operating range, while meeting the minimum tracking requirements set forth by upper-layer applications. This scheduling protocol allows event-triggered and vehicle-dynamics driven message transmits that further the situational awareness in a cooperative V2X context. Presented validation results of the congestion control algorithm include position tracking errors as the performance measure, with the age of communicated information as the evaluation measure. In addition, we examine the optimality of the default settings of the congestion control parameters. Comprehensive analysis and trade-off study of the control parameters reveal some areas of improvement to further the algorithm's efficacy. Motivated by the effectiveness of channel congestion control mechanism, we further investigate message content and length adaptations, together with transmit rate control. Reasonably, the content of the exchanged information has a significant impact on the map accuracy in cooperative driving systems. We investigate different content control schemes for a communication architecture aimed at map sharing and evaluate their performance in terms of position tracking error. This dissertation determines that message content should be concentrated to mapped objects that are located farther away from the sender to the edge of the local sensor range. This dissertation also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system.
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Date Issued: 2019
Identifier: CFE0007634, ucf:52470
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007634

Title: Sparse signal recovery under sensing and physical hardware constraints.
Creator: Mardaninajafabadi, Davood, Atia, George, Mikhael, Wasfy, Vosoughi, Azadeh, Rahnavard, Nazanin, Abouraddy, Ayman, University of Central Florida
Abstract / Description: This dissertation focuses on information recovery under two general types of sensing constraints and hardware limitations that arise in practical data acquisition systems. We study the effects of these practical limitations in the context of signal recovery problems from interferometric measurements such as for optical mode analysis.The first constraint stems from the limited number of degrees of freedom of an information gathering system, which gives rise to highly constrained sensing...
Show moreThis dissertation focuses on information recovery under two general types of sensing constraints and hardware limitations that arise in practical data acquisition systems. We study the effects of these practical limitations in the context of signal recovery problems from interferometric measurements such as for optical mode analysis.The first constraint stems from the limited number of degrees of freedom of an information gathering system, which gives rise to highly constrained sensing structures. In contrast to prior work on compressive signal recovery which relies for the most part on introducing additional hardware components to emulate randomization, we establish performance guarantees for successful signal recovery from a reduced number of measurements even with the constrained interferometer structure obviating the need for non-native components. Also, we propose control policies to guide the collection of informative measurements given prior knowledge about the constrained sensing structure. In addition, we devise a sequential implementation with a stopping rule, shown to reduce the sample complexity for a target performance in reconstruction.The second limitation considered is due to physical hardware constraints, such as the finite spatial resolution of the used components and their finite aperture size. Such limitations introduce non-linearities in the underlying measurement model. We first develop a more accurate measurement model with structured noise representing a known non-linear function of the input signal, obtained by leveraging side information about the sampling structure. Then, we devise iterative denoising algorithms shown to enhance the quality of sparse recovery in the presence of physical constraints by iteratively estimating and eliminating the non-linear term from the measurements. We also develop a class of clipping-cognizant reconstruction algorithms for modal reconstruction from interferometric measurements that compensate for clipping effects due to the finite aperture size of the used components and show they yield significant gains over schemes oblivious to such effects.
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Date Issued: 2019
Identifier: CFE0007675, ucf:52467
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007675

Title: Frequency-Reconfigurable Microstrip Patch and Cavity-Backed Slot ESPARs.
Creator: Ouyang, Wei, Gong, Xun, Vosoughi, Azadeh, Wahid, Parveen, Abdolvand, Reza, Kuebler, Stephen, University of Central Florida
Abstract / Description: Wireless communication systems have rapidly evolved over the past decade which has led to an explosion of mobile data traffic. Since more and more wireless devices and sensors are being connected, the transition from the current 4G/LTE mobile network to 5G is expected to happen within the next decade. In order to improve signal-to-noise ratio (SNR), system capacity, and link budget, beam steerable antenna arrays are desirable due to their advantage in spatial selectivity and high directivity....
Show moreWireless communication systems have rapidly evolved over the past decade which has led to an explosion of mobile data traffic. Since more and more wireless devices and sensors are being connected, the transition from the current 4G/LTE mobile network to 5G is expected to happen within the next decade. In order to improve signal-to-noise ratio (SNR), system capacity, and link budget, beam steerable antenna arrays are desirable due to their advantage in spatial selectivity and high directivity. Electronically steerable parasitic array radiator (ESPAR) that can achieve low-cost continuously beamsteering using varactor diodes have attracted a lot of attention. This dissertation explores bandwidth enhancement of the ESPAR using frequency-reconfigurable microstrip patch and cavity-backed slot (CBS) antennas. In chapter 2, an ESPAR of three closely-coupled rectangular patch elements that do not use phase shifters is presented; the beamsteering is realized by tunable reactive loads which are used to control the mutual coupling between the elements. Additional loading varactors are strategically placed on the radiating edge of all the antenna elements to achieve a 15% continuous frequency tuning range while simultaneously preserving the beamsteering capability at each operating frequency. Therefore, this frequency-reconfigurable ESPAR is able to provide spectrum diversity in addition to the spatial diversity inherent in a frequency-fixed ESPAR. A prototype of the patch ESPAR is fabricated and demonstrated to operate from 0.87 to 1.02 GHz with an instantaneous fractional bandwidth (FBW) of ~1%. At each operating frequency, this ESPAR is able to scan from -20 to +20 degrees in the H plane. However, the beamsteering of the patch ESPAR is limited in the H-plane and its instantaneous S11 fractional bandwidth (FBW) is very narrow. This dissertation also explores how to achieve 2-D beamsteering with enhanced FBW using CBS antennas. A 20-element cavity-backed slot antenna array is designed and fabricated based on a CBS ESPAR cross subarray in chapter 5. This ESPAR array is able to steer the main beam from +45 degrees to -45 degrees in the E plane and from +40 degrees to -40 degrees in the H plane, respectively, without grating lobes in either plane. The impedance matching is maintained below -10 dB from 6.0 to 6.4 GHz (6.4% fractional bandwidth) at all scan angles. In addition, the CBS ESPAR exhibits minimum beam squint at all scan angles within the impedance matching bandwidth. This array successfully demonstrates the cost savings and associated reduction in the required number of phase shifters in the RF front end by employing ESPAR technology.
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Date Issued: 2019
Identifier: CFE0007699, ucf:52426
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007699

Title: Stability and Control in Complex Networks of Dynamical Systems.
Creator: Manaffam, Saeed, Vosoughi, Azadeh, Behal, Aman, Atia, George, Rahnavard, Nazanin, Javidi, Tara, Das, Tuhin, University of Central Florida
Abstract / Description: Stability analysis of networked dynamical systems has been of interest in many disciplines such as biology and physics and chemistry with applications such as LASER cooling and plasma stability. These large networks are often modeled to have a completely random (Erd\"os-R\'enyi) or semi-random (Small-World) topologies. The former model is often used due to mathematical tractability while the latter has been shown to be a better model for most real life networks.The recent emergence of cyber...
Show moreStability analysis of networked dynamical systems has been of interest in many disciplines such as biology and physics and chemistry with applications such as LASER cooling and plasma stability. These large networks are often modeled to have a completely random (Erd\"os-R\'enyi) or semi-random (Small-World) topologies. The former model is often used due to mathematical tractability while the latter has been shown to be a better model for most real life networks.The recent emergence of cyber physical systems, and in particular the smart grid, has given rise to a number of engineering questions regarding the control and optimization of such networks. Some of the these questions are: \emph{How can the stability of a random network be characterized in probabilistic terms? Can the effects of network topology and system dynamics be separated? What does it take to control a large random network? Can decentralized (pinning) control be effective? If not, how large does the control network needs to be? How can decentralized or distributed controllers be designed? How the size of control network would scale with the size of networked system?}Motivated by these questions, we began by studying the probability of stability of synchronization in random networks of oscillators. We developed a stability condition separating the effects of topology and node dynamics and evaluated bounds on the probability of stability for both Erd\"os-R\'enyi (ER) and Small-World (SW) network topology models. We then turned our attention to the more realistic scenario where the dynamics of the nodes and couplings are mismatched. Utilizing the concept of $\varepsilon$-synchronization, we have studied the probability of synchronization and showed that the synchronization error, $\varepsilon$, can be arbitrarily reduced using linear controllers.We have also considered the decentralized approach of pinning control to ensure stability in such complex networks. In the pinning method, decentralized controllers are used to control a fraction of the nodes in the network. This is different from traditional decentralized approaches where all the nodes have their own controllers. While the problem of selecting the minimum number of pinning nodes is known to be NP-hard and grows exponentially with the number of nodes in the network we have devised a suboptimal algorithm to select the pinning nodes which converges linearly with network size. We have also analyzed the effectiveness of the pinning approach for the synchronization of oscillators in the networks with fast switching, where the network links disconnect and reconnect quickly relative to the node dynamics.To address the scaling problem in the design of distributed control networks, we have employed a random control network to stabilize a random plant network. Our results show that for an ER plant network, the control network needs to grow linearly with the size of the plant network.
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Date Issued: 2015
Identifier: CFE0005834, ucf:50902
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005834

Title: Distributed Extremum Seeking and Cooperative Control for Mobile Cooperative Communication Systems.
Creator: Alabri, Said, Qu, Zhihua, Wei, Lei, Vosoughi, Azadeh, Atia, George, University of Central Florida
Abstract / Description: In this thesis, a distributed extremum seeking and cooperative control algorithm is designed for mobile agents to dispersethemselves optimally in maintaining communication quality and maximizing their coverage. The networked mobile agentslocally form a virtual multiple-input multiple-output (MIMO) communication system, and they cooperatively communicateamong them by using the decode and forward cooperative communication technique. The outage probability is usedas the measure of communication...
Show moreIn this thesis, a distributed extremum seeking and cooperative control algorithm is designed for mobile agents to dispersethemselves optimally in maintaining communication quality and maximizing their coverage. The networked mobile agentslocally form a virtual multiple-input multiple-output (MIMO) communication system, and they cooperatively communicateamong them by using the decode and forward cooperative communication technique. The outage probability is usedas the measure of communication quality, and it can be estimated real-time. A general performance index balancing outageprobability and spatial dispersion is chosen for the overall system. The extremum seeking control approachis used to estimate and optimize the value of the performance index, and the cooperative formation control is applied tomove the mobile agents to achieve the optimal solution by using only the locally-available information. Through theintegration of cooperative communication and cooperative control, network connectivity and coverage of the mobile agentsare much improved when compared to either non-cooperative communication approaches or other existing control results.Analytical analysis is carried out to demonstrate the performance and robustness of the proposal methodology, andsimulation is done to illustrate its effectiveness.
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Date Issued: 2013
Identifier: CFE0005082, ucf:50744
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005082

Title: Compressive Sensing and Recovery of Structured Sparse Signals.
Creator: Shahrasbi, Behzad, Rahnavard, Nazanin, Vosoughi, Azadeh, Wei, Lei, Atia, George, Pensky, Marianna, University of Central Florida
Abstract / Description: In the recent years, numerous disciplines including telecommunications, medical imaging, computational biology, and neuroscience benefited from increasing applications of high dimensional datasets. This calls for efficient ways of data capturing and data processing. Compressive sensing (CS), which is introduced as an efficient sampling (data capturing) method, is addressing this need. It is well-known that the signals, which belong to an ambient high-dimensional space, have much smaller...
Show moreIn the recent years, numerous disciplines including telecommunications, medical imaging, computational biology, and neuroscience benefited from increasing applications of high dimensional datasets. This calls for efficient ways of data capturing and data processing. Compressive sensing (CS), which is introduced as an efficient sampling (data capturing) method, is addressing this need. It is well-known that the signals, which belong to an ambient high-dimensional space, have much smaller dimensionality in an appropriate domain. CS taps into this principle and dramatically reduces the number of samples that is required to be captured to avoid any distortion in the information content of the data. This reduction in the required number of samples enables many new applications that were previously infeasible using classical sampling techniques.Most CS-based approaches take advantage of the inherent low-dimensionality in many datasets. They try to determine a sparse representation of the data, in an appropriately chosen basis using only a few significant elements. These approaches make no extra assumptions regarding possible relationships among the significant elements of that basis. In this dissertation, different ways of incorporating the knowledge about such relationships are integrated into the data sampling and the processing schemes.We first consider the recovery of temporally correlated sparse signals and show that using the time correlation model. The recovery performance can be significantly improved. Next, we modify the sampling process of sparse signals to incorporate the signal structure in a more efficient way. In the image processing application, we show that exploiting the structure information in both signal sampling and signal recovery improves the efficiency of the algorithm. In addition, we show that region-of-interest information can be included in the CS sampling and recovery steps to provide a much better quality for the region-of-interest area compared the rest of the image or video. In spectrum sensing applications, CS can dramatically improve the sensing efficiency by facilitating the coordination among spectrum sensors. A cluster-based spectrum sensing with coordination among spectrum sensors is proposed for geographically disperse cognitive radio networks. Further, CS has been exploited in this problem for simultaneous sensing and localization. Having access to this information dramatically facilitates the implementation of advanced communication technologies as required by 5G communication networks.
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Date Issued: 2015
Identifier: CFE0006392, ucf:51509
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006392

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