Browsing by Author "Chong, Edwin K. P., advisor"
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Item Open Access Application of systems engineering to complex systems and system of systems(Colorado State University. Libraries, 2017) Sturdivant, Rick L., author; Chong, Edwin K. P., advisor; Sega, Ronald M., committee member; Jayasumana, Anura P., committee member; Atadero, Rebecca, committee memberThis dissertation is an investigation of system of systems (SoS). It begins with an analysis to define, with some rigor, the similarities and differences between complex systems and SoS. With this foundation, the baseline concept is development for several different types of systems and they are used as a practical approach to compare and contrast complex systems versus SoS. The method is to use a progression from simple to more complex systems. Specifically, a pico hydro electric power generation system, a hybrid renewable electric power generation system, a LEO satellites system, and Molniya orbit satellite system are investigated. In each of these examples, systems engineering methods are applied for the development of a baseline solution. While these examples are complex, they do not rise to the level of a SoS. In contrast, a multi-spectral drone detection system for protection of airports is investigated and a baseline concept for it is generated. The baseline is shown to meet the minimum requirements to be considered a SoS. The system combines multiple sensor types to distinguish drones as targets. The characteristics of the drone detection system which make it a SoS are discussed. Since emergence is considered by some to be a characteristic of a SoS, it is investigated. A solution to the problem of determining if system properties are emergent is presented and necessary and sufficient conditions for emergence are developed. Finally, this work concludes with a summary and suggestions for additional work.Item Open Access Autonomous UAV control and testing methods utilizing partially observable Markov decision processes(Colorado State University. Libraries, 2018) Eaton, Christopher M., author; Chong, Edwin K. P., advisor; Maciejewski, Anthony A., advisor; Bradley, Thomas, committee member; Young, Peter, committee memberThe explosion of Unmanned Aerial Vehicles (UAVs) and the rapid development of algorithms to support autonomous flight operations of UAVs has resulted in a diverse and complex set of requirements and capabilities. This dissertation provides an approach to effectively manage these autonomous UAVs, effectively and efficiently command these vehicles through their mission, and to verify and validate that the system meets requirements. A high level system architecture is proposed for implementation on any UAV. A Partially Observable Markov Decision Process algorithm for tracking moving targets is developed for fixed field of view sensors while providing an approach for more fuel efficient operations. Finally, an approach for testing autonomous algorithms and systems is proposed to enable efficient and effective test and evaluation to support verification and validation of autonomous system requirements.Item Open Access Compressive measurement design for detection and estimation of sparse signals(Colorado State University. Libraries, 2013) Zahedi, Ramin, author; Chong, Edwin K. P., advisor; Pezeshki, Ali, advisor; Estep, Donald, committee member; Young, Peter M., committee memberWe study the problem of designing compressive measurement matrices for two sets of problems. In the first set, we consider the problem of adaptively designing compressive measurement matrices for estimating time-varying sparse signals. We formulate this problem as a Partially Observable Markov Decision Process (POMDP). This formulation allows us to use Bellman's principle of optimality in the implementation of multi-step lookahead designs of compressive measurements. We introduce two variations of the compressive measurement design problem. In the first variation, we consider the problem of selecting a prespecified number of measurement vectors from a predefined library as entries of the compressive measurement matrix at each time step. In the second variation, the number of compressive measurements, i.e., the number of rows of the measurement matrix, is adaptively chosen. Once the number of measurements is determined, the matrix entries are chosen according to a prespecified adaptive scheme. Each of these two problems is judged by a separate performance criterion. The gauge of efficiency in the first problem is the conditional mutual information between the sparse signal support and the measurements. The second problem applies a linear combination of the number of measurements and the conditional mutual information as the performance measure. We present several simulations in which the primary focus is the application of a method known as rollout. The significant computational load for using rollout has also inspired us to adapt two data association heuristics in our simulations to the compressive sensing paradigm. These heuristics show promising decreases in the amount of computation for propagating distributions and searching for optimal solutions. In the second set of problems, we consider the problem of testing for the presence (or detection) of an unknown static sparse signal in additive white noise. Given a fixed measurement budget, much smaller than the dimension of the signal, we consider the general problem of designing compressive measurements to maximize the measurement signal-to-noise ratio (SNR), as increasing SNR improves the detection performance in a large class of detectors. We use a lexicographic optimization approach, where the optimal measurement design for sparsity level k is sought only among the set of measurement matrices that satisfy the optimality conditions for sparsity level k-1. We consider optimizing two different SNR criteria, namely a worst-case SNR measure, over all possible realizations of a k-sparse signal, and an average SNR measure with respect to a uniform distribution on the locations of the up to k nonzero entries in the signal. We establish connections between these two criteria and certain classes of tight frames. We constrain our measurement matrices to the class of tight frames to avoid coloring the noise covariance matrix. For the worst-case problem, we show that the optimal measurement matrix is a Grassmannian line packing for most—and a uniform tight frame for all—sparse signals. For the average SNR problem, we prove that the optimal measurement matrix is a uniform tight frame with minimum sum-coherence for most—and a tight frame for all—sparse signals.Item Open Access Continuum limits of Markov chains with application to wireless network modeling and control(Colorado State University. Libraries, 2014) Zhang, Yang, author; Chong, Edwin K. P., advisor; Estep, Donald, committee member; Luo, J. Rockey, committee member; Pezeshki, Ali, committee memberWe investigate the continuum limits of a class of Markov chains. The investigation of such limits is motivated by the desire to model networks with a very large number of nodes. We show that a sequence of such Markov chains indexed by N , the number of components in the system that they model, converges in a certain sense to its continuum limit, which is the solution of a partial differential equation (PDE), as N goes to infinity. We provide sufficient conditions for the convergence and characterize the rate of convergence. As an application we approximate Markov chains modeling large wireless networks by PDEs. We first describe PDE models for networks with uniformly located nodes, and then generalize to networks with nonuniformly located, and possibly mobile, nodes. While traditional Monte Carlo simulation for very large networks is practically infeasible, PDEs can be solved with reasonable computation overhead using well-established mathematical tools. Based on the PDE models, we develop a method to control the transmissions in nonuniform networks so that the continuum limit is invariant under perturbations in node locations. This enables the networks to maintain stable global characteristics in the presence of varying node locations.Item Open Access Cooperative control of mobile sensor platforms in dynamic environments(Colorado State University. Libraries, 2014) Ragi, Shankarachary, author; Chong, Edwin K. P., advisor; Krapf, Diego, committee member; Luo, J. Rockey, committee member; Oprea, Iuliana, committee memberWe develop guidance algorithms to control mobile sensor platforms, for both centralized and decentralized settings, in dynamic environments for various applications. More precisely, we develop control algorithms for the following mobile sensor platforms: unmanned aerial vehicles (UAVs) with on-board sensors for multitarget tracking, autonomous amphibious vehicles for flood-rescue operations, and directional sensors (e.g., surveillance cameras) for maximizing an information-gain-based objective function. The following is a brief description of each of the above-mentioned guidance control algorithms. We develop both centralized and decentralized control algorithms for UAVs based on the theories of partially observable Markov decision process (POMDP) and decentralized POMDP (Dec-POMDP) respectively. Both POMDPs and Dec-POMDPs are intractable to solve exactly; therefore we adopt an approximation method called nominal belief-state optimization (NBO) to solve (approximately) the control problems posed as a POMDP or a Dec-POMDP. We then address an amphibious vehicle guidance problem for a flood rescue application. Here, the goal is to control multiple autonomous amphibious vehicles while minimizing the average rescue time of multiple human targets stranded in a flood situation. We again pose this problem as a POMDP, and extend the above-mentioned NBO approximation method to solve the guidance problem. In the final phase, we study the problem of controlling multiple 2-D directional sensors while maximizing an objective function based on the information gain corresponding to multiple target locations. This problem is found to be a combinatorial optimization problem, so we develop heuristic methods to solve the problem approximately, and provide analytical results on performance guarantees. We then improve the performance of our heuristics by applying an approximate dynamic programming approach called rollout.Item Open Access Decision and learning in large networks(Colorado State University. Libraries, 2013) Zhang, Zhenliang, author; Pezeshki, Ali, advisor; Chong, Edwin K. P., advisor; Wang, Haonan, committee member; Luo, Rockey J., committee memberTo view the abstract, please see the full text of the document.Item Open Access Design of a gait acquisition and analysis system for assessing the recovery in a classical murine model of Parkinson's disease(Colorado State University. Libraries, 2015) Damale, Pranav, author; Chong, Edwin K. P., advisor; Tjalkens, Ronald, committee member; Ebert-Uphoff, Imme, committee memberGait deficits are important clinical symptoms of Parkinson's disease (PD). Data focusing on gait can be used to measure recovery of motor impairments in rodents with systemic dopamine depletion. This thesis presents a design for a gait acquisition and analysis system able to capture paw strikes of a mouse, extract their positions and timing data, and report quantitative gait metrics to the operator. These metrics can then be used to evaluate the gait changes in mice. This work presents the design evaluation of the system, from initial cellphone captured video concepts through prototyping and testing to the final implementation. The system utilizes a GoPro camera, optimally lit walkway design, image processing techniques to capture footfalls, and algorithms for their quantitative assessment. The results gained from live animal study with methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced murine model of PD and treated with 1,1-bis(3'-indolyl)-1-(p-chlorophenyl)methane (C-DIM12) are presented, and it is shown how the quantitative measurements can be used to determine healthy, injured, and recovering gait.Item Open Access Empirical evaluation of a dimension-reduction method for time-series prediction(Colorado State University. Libraries, 2020) Ghorbani, Mahsa, author; Chong, Edwin K. P., advisor; Pezeshki, Ali, committee member; Young, Peter, committee member; Bradley, Thomas, committee memberStock price prediction is one of the most challenging problems in finance. The multivariate conditional mean is a point estimator to minimize the mean square error of prediction giver past data. However, the calculation of the condition mean and covariance involves the numerical inverse of a typically ill-conditioned matrix, leading to numerical issues. To overcome this problem, we develop a method based on filtering the data using principle components. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This method is often used for dimensionality reduction and analysis of the data. Our method bears some similarities with subspace filtering methods. Projecting the noisy observation onto a principle subspace leads to significantly better numerical conditioning. Our method accounts for time-varying covariance information. We first introduce our method for predicting future price values over a short period of time using just historical price values. The literature provides strong evidence that stock price values can be predicted from past price data. Different economic variables have also been used in the literature to estimate stock-price values with high accuracy. To accommodate using historical data for such economic variables, we build on our method to include multiple predictors. We use multichannel cross-correlation coefficient as a measure for selecting the most correlated set of variables for each stock. Then we apply our filtering operation based on the local covariance of the data. Our method is easily implemented and can be configured to include an arbitrary number of predictors, subject to computational constraints. Time-series prediction can be posed as a matrix completion problem. Matrix completion is an important problem in many fields and has been receiving considerable attention in recent years. Different approaches and algorithms have been proposed to solve this problem. We investigate the effectiveness of an iterative rank minimizing matrix completion algorithm for predicting financial time series. As a key performance to compare different schemes, we use computational complexity, which focuses on the computational burden of these schemes. We compare the prediction results from the iterative matrix completion method to our method in terms of asymptotic and empirical computational complexity. Both methods show similar performance for forecasting future stock price values in terms of different performance metrics, but our proposed method has lower computational complexity.Item Open Access Green communication and security in wireless networks based on Markov decision process and semivariance optimization(Colorado State University. Libraries, 2020) Elsherif, Fateh, author; Chong, Edwin K. P., advisor; Jayasumana, Anura P., committee member; Luo, J. Rockey, committee member; Atadero, Rebecca, committee memberWireless networking has become an integral part of our everyday life. Certainly, wireless technologies have improved many aspects of the way people communicate, interact, and perform tasks, in addition to enabling new use cases, such as massive machine-type communications and industry verticals, among others. While convenient, these technologies impose new challenges and introduce new design problems. In this dissertation, we consider three problems in wireless networking. Specifically, we formulate optimization problems in green communication and security, and develop computationally efficient solutions to these optimization problems. First, we study the problem of base station (BS) dynamic switching for energy efficient design of fifth generation (5G) cellular networks and beyond. We formulate this problem as a Markov decision process (MDP) and use an approximation method known as policy rollout to solve it. This method employs Monte Carlo sampling to approximate the Q-value. In this work, we introduce a novel approach to design an energy-efficient algorithm based on MDP to control the ON/OFF switching of BSs; we exploit user mobility and location information in the selection of the optimal control actions. We start our formulation with the simple case of one-user one-ON. We then gradually and systematically extend this formulation to the multi-user multi-ON scenario. Simulation results show the potential of our novel approach of exploiting user mobility information within the MDP framework to achieve significant energy savings while providing quality-of-service guarantees. Second, we study the problem of jamming-aware-multi-path routing in wireless networks. Multipath routing is a technique for transmitting data from one or more source node(s) to one or more destination node(s) over multiple routing paths. We study the problem of wireless jamming-mitigation multipath routing. To address this problem, we propose a new framework for mitigating jamming risk based on semivariance optimization. Semivariance is a mathematical quantity used originally in finance and economics to measure the dispersion of a portfolio return below a risk-aversion benchmark. We map the problem of jamming-mitigation multipath routing to that of portfolio selection within the semivariance risk framework. Then we use this framework to design a new, and computationally feasible, RF-jamming mitigation algorithm. We use simulation to study the properties of our method and demonstrate its efficacy relative to a competing scheme that optimizes the jamming risk in terms of variance instead of semivariance. To the best of our knowledge, our work is the first to use semivariance as a measure of jamming risk. Directly optimizing objective functions that involve exact semivariance introduces certain computational issues. However, there are approximations to the semivariance that overcome these issues. We study semivariance problems—from the literature of finance and economics—and survey their solutions. Based on one of these solutions, we develop an efficient algorithm for solving semivariance optimization problems. Efficiency is imperative for many telecommunication applications such as tactile Internet and Internet of Things (recall that these types of applications have stringent constraints on latency and computing power). Our algorithm provides a general approach to solving semivariance optimization problems, and can be used in other applications. Last, we consider the problem of multiple--radio-access technology (multi-RAT) connectivity in heterogeneous networks (HetNets). Recently, multi-RAT connectivity has received significant attention—both from industry and academia—because of its potential as a method to increase throughput, to enhance communication reliability, and to minimize communication latency. We introduce a new approach to the problem of multi-RAT traffic allocation in HetNets. We propose a new risk-averse multi-RAT connectivity (RAM) algorithm. Our RAM algorithm allows trading off expected throughput for risk measured in throughput semivariance. Here we also adopt semivariance as a measure of throughput dispersion below a risk-aversion--throughput benchmark. We then formulate the multi-RAT connectivity problem as a semivariance-optimization problem. However, we tackle a different optimization problem in this part of the research. The objective function of the optimization problem considered here is different from the objective function of the optimization problem above that also uses semivariance to quantify risk (because the underlying standard form of portfolio selection is different). In addition, the set of constraints is different in this optimization problem: We introduce new capacity constraints to account for the stochastic capacity of the involved wireless links. We also introduce a new performance metric, the risk-adjusted throughput; risk-adjusted throughput is the ratio between the expected throughput and the throughput semideviation, where semideviation is the square root of semivariance. We evaluate the performance of our algorithm through simulation of a system with three radio-access technologies: 4G LTE, 5G NR, and WiFi. Simulation results show the potential gains of using our algorithm.Item Open Access Modeling, simulation, and control of soft robots using Koopman operator theory(Colorado State University. Libraries, 2023) Singh, Ajai, author; Chong, Edwin K. P., advisor; Zhao, Jianguo, committee member; Pasricha, Sudeep, committee memberIn nature, animals with soft body parts can control their parts to different shapes, e.g., an elephant trunk can wrap on a tree branch to pick it up. But most research on manipulators only focuses on how to control the end effector, partly because the arm of the manipulator is rigidly articulated. With recent advances in soft robotics research, controlling a soft manipulator into many different shapes will significantly improve the robot's functionality, such as medical robots morphing their shape to navigate the digestive system and then delivering drugs to the required location. However, controlling the shape of soft robots is challenging since the dynamics of soft robots are highly nonlinear and computationally intensive. In this research, we leverage a data-driven method using the Koopman operator to realize the shape control of soft robots. The dynamics of a soft manipulator are simulated using a physics-based simulator (PyElastica) to generate the input-output data. The data is used to identify an approximated linear model based on the Koopman operator. We then formulate the shape-control problem as a convex optimization problem that is computationally efficient. We demonstrated the linear model is over 12 times faster than the physics-based model in simulating the manipulator's motion. Further, we can control a soft manipulator into different shapes using model predictive control (MPC), and then in the subsequent chapters, we build a soft grid consisting of 40 such soft manipulators. We then address the issues related to the Extended Dynamic Mode Decomposition (EDMD) algorithm used for approximating the Koopman operator by developing a deep learning-based framework to learn the Koopman embeddings. On comparing the EDMD and deep learning framework it was found that the deep learning framework was far more accurate than the EDMD framework We then show that the proposed methods can be effectively used to control the shapes of soft robots by having the single soft manipulator morph into "C", "S", and "U" shapes and then extend the shape control method to the soft grid by morphing it into 3 different shapes. We envision that shape control will allow the soft robots to interact with uncertain environments or the shapes of shape-morphing robots to fulfill different tasks.Item Open Access Optimal path planning for detection and classification of underwater targets using sonar(Colorado State University. Libraries, 2021) Robbiano, Christopher P., author; Chong, Edwin K. P., advisor; Azimi-Sadjadi, Mahmood R., advisor; Pezeshki, Ali, committee member; Oprea, Iuliana, committee memberThe work presented in this dissertation focuses on choosing an optimal path for performing sequential detection and classification state estimation to identify potential underwater targets using sonar imagery. The detection state estimation falls under the occupancy grid framework, modeling the relationship between occupancy state of grid cells and sensor measurements, and allows for the consideration of statistical dependence between the occupancy state of each grid cell in the map. This is in direct contrast to the classical formulations of occupancy grid frameworks, in which the occupancy state of each grid cell is considered statistically independent. The new method provides more accurate estimates, and occupancy grids estimated with this method typically converge with fewer measurements. The classification state estimation utilises a Dirichlet-Categorical model and a one-step classifier to perform efficient updating of the classification state estimate for each grid cell. To show the performance capabilities of the developed sequential state estimation methods, they are applied to sonar systems in littoral areas in which targets lay on the seafloor, could be proud, partially or fully buried. Additionally, a new approach to the active perception problem, which seeks to select a series of sensing actions that provide the maximal amount of information to the system, is developed. This new approach leverages the aforementioned sequential state estimation techniques to develop a set of information-theoretic cost functions that can be used for optimal sensing action selection. A path planning cost function is developed, defined as the mutual information between the aforementioned state variables before and after a measurement. The cost function is expressed in closed form by considering the prior and posterior distributions of the state variables. Choice of the optimal sensing actions is performed by modeling the path planning as a Markov decision problem, and solving it with the rollout algorithm. This work, supported by the Office of Naval Research (ONR), is intended to develop a suite of interactive sensing algorithms to autonomously command an autonomous underwater vehicle (AUV) for the task of detection and classification of underwater mines, while choosing an optimal navigation route that increases the quality of the detection and classification state estimates.Item Open Access Path planning for autonomous aerial vehicles using Monte Carlo tree search(Colorado State University. Libraries, 2024) Vasutapituks, Apichart, author; Chong, Edwin K. P., advisor; Azimi-Sadjadi, Mahmood, committee member; Pinaud, Olivier, committee member; Pezeshki, Ali, committee memberUnmanned aerial vehicles (UAVs), or drones, are widely used in civilian and defense applications, such as search and rescue operations, monitoring and surveillance, and aerial photography. This dissertation focuses on autonomous UAVs for tracking mobile ground targets. Our approach builds on optimization-based artificial intelligence for path planning by calculating approximately optimal trajectories. This approach poses a number of challenges, including the need to search over large solution spaces in real-time. To address these challenges, we adopt a technique involving a rapidly-exploring random tree (RRT) and Monte Carlo tree search (MCTS). The RRT technique increases in computational cost as we increase the number of mobile targets and the complexity of the dynamics. Our MCTS approach executes a tree search based on random sampling to generate trajectories in real time. We develop a variant of MCTS for online path-planning to track ground targets together with an associated algorithm called P-UAV. Our algorithm is based on the framework of partially observable Monte Carlo planning, originally developed in the context of MCTS for Markov decision processes. Our real-time approach exploits a parallel-computing strategy with a heuristic random-sampling process. In our framework, We explicitly incorporate threat evasion, obstacle collision avoidance, and resilience to wind. The approach embodies an exploration-exploitation tradeoff in seeking a near-optimal solution in spite of the huge search space. We provide simulation results to demonstrate the effectiveness of our path-planning method.Item Open Access Performance bounds for greedy strategies in submodular optimization problems(Colorado State University. Libraries, 2018) Liu, Yajing, author; Chong, Edwin K. P., advisor; Pezeshki, Ali, advisor; Luo, J. Rockey, committee member; Bates, Dan, committee memberTo view the abstract, please see the full text of the document.Item Open Access Performance-computation tradeoffs in detection and estimation(Colorado State University. Libraries, 2023) Damale, Pranav U., author; Chong, Edwin K. P., advisor; Pezeshki, Ali, committee member; Tjalkens, Ronald B., committee member; Cavalieri, Renzo, committee memberDetection and estimation problems involve challenging tasks that often demand real-time, accurate results. Algorithms able to produce highly accurate results are often computationally expensive or inefficient. Naturally, we need to tailor algorithms to the specific needs of problems to optimally trade off between computation and accuracy. To explore this ever-present tradeoff, this dissertation describes three distinct problems in detection and estimation and our contribution to the decision-making process for choosing the best algorithms for solving these problems. First, we look at tradeoffs involved in designing a low-cost, camera-based autonomous gait acquisition and analysis system for inspecting gait impairments in mice. Specifically, we give a detailed description of our detection and classification algorithms for gait-event detection and gait-parameter extraction. Using the videos acquired in a live-animal study, we validate the performance of our system for assessing recovery in a mouse model of Parkinson's disease. Next, we analyze the tradeoffs involved in designing a modified data association algorithm for tracking multiple objects using measurements of uncertain origins, such as radar detection with false alarms and missed detection. Specifically, we explore the performance of the distance-weighting probabilistic data association approach in conjunction with the loopy-sum product algorithm and, using simulation data, we analyze its performance in terms of tracking accuracy and computation against other state-of-the-art data association methods for tracking multiple targets in clutter. Finally, to address the ill-conditioning of linear minimum mean square error estimation, we develop four approximate Wiener filter formulas that do not directly involve the inverse of the observation covariance matrix. Using real data, we evaluate the performance-complexity tradeoff for our approximated filters. The common underlying theme that connects our solutions to these distinct problems is that our decisions for selecting various parameters in each solution are based on the performance-computation tradeoff. Throughout this dissertation, we employ various methods to handle this tradeoff, such as receiver operating characteristics analysis and line-search procedure. Our analysis is beneficial for choosing the best algorithm to optimally trade off between performance and computation.Item Open Access Problems on decision making under uncertainty(Colorado State University. Libraries, 2019) Sarkale, Yugandhar, author; Chong, Edwin K. P., advisor; Young, Peter, committee member; Luo, J. Rockey, committee member; Zhou, Yongcheng, committee memberHumans and machines must often make rational choices in the face of uncertainty. Determining decisions, actions, choices, or alternatives that optimize objectives for real-world problems is computationally difficult. This dissertation proposes novel solutions to such optimization problems for both deterministic and stochastic cases; the proposed methods maintain near-optimal solution quality. Even though the applicability of the techniques developed in our work cannot be limited to a few examples, the applications addressed in our work include post-hazard large-scale real-world community recovery management, path planning of UAVs by incorporating feedback from intelligence assets, and closed-loop, urban target tracking in challenging environments. As an illustration of the properties shared by the solutions developed in this dissertation, we will describe the example of community recovery in depth. In the work associated with community recovery, we handle both deterministic and stochastic recovery decisions. For the deterministic problems (outcome of recovery actions is deterministic but we handle the uncertainty in the underlying models), we develop a sequential discrete-time decision-making framework and compute the near-optimal decisions for a community modeled after Gilroy, California. We have designed stochastic models to calculate the damage to the infrastructures systems within the community after an occurrence of an earthquake. Our optimization framework to compute the recovery decisions, which is hazard agnostic (the hazard could be a nuclear explosion or a disruptive social event), is based on an approximate dynamic programming paradigm of rollout; we have modeled the recovery decisions as string of actions. We design several base heuristics pertaining to the problem of community recovery to be used as a base heuristic in our framework; in addition, we also explore the performance of random heuristics. In addition to modeling the interdependence between several networks and the cascading effect of a single recovery action on these networks, we also fuse the traditional optimization approaches, such as simulated annealing, to compute efficient decisions, which mitigates the simultaneous spatial and temporal evolution of the recovery problem. For the stochastic problems, in addition to the previous complexities, the outcome of the decisions is stochastic. Inclusion of this single complexity in the problem statement necessitates an entirely novel way of developing solutions. We formulate the recovery problem in the powerful framework of Markov Decision Processes (MDPs). In contrast to the conventional matrix-based representation, we have formulated our problem as a simulation-based MDP. Classical solutions to solve an MDP are inadequate; therefore, approximation to compute the Q-values (based on Bellman's equation) is necessary. In our framework, we have employed Approximate Policy Improvement to circumvent the limitation with the classical techniques. We have also addressed the risk-attitudes of the policymakers and the decision-makers, who are a key stakeholder in the recovery process. Despite the use of a state-of-the-art computational platform, additional optimization must be made to the resultant stochastic simulation optimization problem owing to the massive size of the recovery problem. Our solutions are calculated using one of the best performing simulation optimization method of Optimal Computing Budget Allocation. Further, in the stochastic setting, scheduling of decisions for the building portfolio recovery is even more computationally difficult than some of the other coarsely-modeled networks like Electric Power Networks (EPN). Our work proposes a stochastic non-preemptive scheduling framework to address this challenging problem at scale. For the stochastic problems, one of the major highlights of this dissertation is the decision-automation framework for EPN recovery. The novel decision-making-under-uncertainty algorithms developed to plan sequential decisions for EPN recovery demonstrate a state-of-the-art performance; our algorithms should be of interest to practitioners in several fields—those that deal with real-world large-scale problem of selecting a single choice given a massive number of alternatives. The quality of recovery decisions calculated using the decision-automation framework does not deteriorate despite a massive increase in the size of the recovery problem. Even though the focus of this dissertation is primarily on application to recovery of communities affected by hazards, our algorithms contributes to the general problem of MDPs with massive action spaces. The primary objective of our work in the community recovery problem is to address the issue of food security. Particularly, we address the objective of making the community food secure to the pre-hazard levels in minimum amount of time or schedule the recovery actions so that maximum number of people are food secure after a sequence of decisions. In essence, our framework accommodates the stochastic hazard models, handles the stochastic nature of outcome of human or machine repair actions, has lookahead, does not suffer from decision fatigue, and incorporates the current policies of the decision makers. The decisions calculated using our framework have been aided by the free availability of a powerful supercomputer.Item Open Access Resource management in QoS-aware wireless cellular networks(Colorado State University. Libraries, 2011) Zhang, Zhi, author; Chong, Edwin K. P., advisor; Azimi-Sadjadi, Mahmood R., committee member; Young, Peter M., committee member; Duff, William S., committee memberEmerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost.Item Open Access Spanning sensor resource management(Colorado State University. Libraries, 2018) Krakow, Lucas W., author; Chong, Edwin K. P., advisor; Burns, Patrick, committee member; Pezeshki, Ali, committee member; Luo, Jie, committee memberThis paper presents multiple applications of sensor resource management. The general focus entails two chapters on adaptive estimation of time-varying sparse signals and three chapters exploring autonomous control of unmanned aerial vehicles (UAVs) sensor platforms employed for target tracking. All of the included applications are posed as decision control problems formulated in the rigorous framework of a partially observable Markov decision process (POMDP) and solution methods based on Bellman's equation are exercised, generating adaptive control policies for action selections in the given scenarios. Specifically, the rollout optimization method is administered in the cases of signal estimation under the objective of maximizing the information gain about the unknown sparse signal. For the UAV sensor platform control, nominal belief-state optimization (NBO) is employed for control selection for optimizing objectives including target-tracking error, surveillance performance and fuel efficiency. The empirical studies in each investigation present evidence that non-myopic solution methods, accounting for both the immediate and future costs of the current action choices, provide performance gains for these scenarios.Item Open Access Target tracking with distributed sensing: information-theoretic bounds and closed-loop scheduling for urban terrain(Colorado State University. Libraries, 2010) de Rezende Barbosa, Patricia, author; Chong, Edwin K. P., advisor; Scharf, Louis L., committee member; Luo, J. Rockey, committee member; Lee, Chihoon, committee memberWe address both theoretical and practical aspects of target tracking in a distributed sensing environment. First, we consider the problem of tracking a target that moves according to a Markov chain in a sensor network. We provide necessary and sufficient conditions on the number of (queries per time step to track a target in three different scenarios: (1) the tracker is required to know the exact location of the target at each time step; (2) the tracker may lose track of the target at a given time step, but it is able to “catch-up”, regaining up-to-date information about the target’s track; and (3) tracking information is only known by the tracker after a delay of d time steps. We then address the problem of target tracking in urban terrain. Specifically, we investigate' the integration of detection, signal processing, tracking, and scheduling, by simultaneously exploiting three diversity modes: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. A closed-loop active sensing system is presented, and Monte Carlo simulations demonstrate its effectiveness in urban terrain. Finally, we propose a scheduling scheme that adaptively selects the sequence of transmitters and waveforms that maximizes the overall tracking accuracy, while maintaining the sensing system’s covertness in a hostile environment. We formulate this problem as a POMDP and use two distinct schedulers: (1) a myopic scheduler that updates waveforms at every radar scan; and (2) a non-myopic scheduler that activates a new set of transmitters if the overall tracking accuracy falls below a threshold or if a detection risk is exceeded. By simultaneously exploiting myopic and non-myopic scheduling schemes, with benefit from trading off short-term for long-term performance, while maintaining low computational costs. Monte Carlo simulations are used to evaluate the proposed scheduling scheme in a multitarget tracking setting.Item Open Access Using locally observed swarm behaviors to infer global features of harsh environments(Colorado State University. Libraries, 2021) Emmons, Megan R., author; Maciejewski, Anthony A., advisor; Chong, Edwin K. P., advisor; Anderson, Charles W., committee member; Young, Peter M., committee memberRobots in a swarm are programmed with individual behaviors but then interactions with the environment and other robots produce more complex, emergent swarm behaviors. A partial differential equation (PDE) can be used to accurately quantify the distribution of robots throughout the environment at any given time if the robots have simple individual behaviors and there are a finite number of potential environments. A least mean square algorithm can then be used to compare a given observation of the swarm distribution to the potential models to accurately identify the environment being explored. This technique affirms that there is a correlation between the individual robot behaviors, robot distribution, and the environment being explored. For more complex behaviors and environments, there is no closed-form model for the emergent behavior but there is still a correlation which can be used to infer one property if the other two are known. A simple, single-layer neural network can replace the PDE and be trained to correlate local observations of the robot distribution to the environment being explored. The neural network approach allows for more sophisticated robot behaviors, more varied environments, and is robust to variations in environment type and number of robots. By replacing the neural network with a simulated human rescuer who uses only locally observed velocity information to navigate a disaster scenario, the impact of fundamental swarm properties can be systematically explored. Further, the baseline swarm resilience can be quantified. Collectively, this development lays a foundation for using minimalist swarms, where robots have simple motions and no communication, to achieve collective sensing which can be leveraged in a variety of applications where no other robotic solutions currently exist.