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Now showing 1 - 10 of 107
  • Publication
    Quantitative Mapping of Lung Ventilation Using Hyperpolarized Gas Magnetic Resonance Imaging
    (2011-05-16) Emami, Kiarash
    The main objective of this project was to develop and implement techniques for high-resolution quantitative imaging of ventilation in lungs using hyperpolarized gas magnetic resonance imaging (MRI). Pulmonary ventilation is an important aspect of lung function and is frequently compromised through several different mechanisms and at varying degrees in presence of certain lung conditions, such as chronic obstructive pulmonary diseases. The primary focus of this development is on large mammalian species as a steppingstone towards translation to human subjects. The key deliverables of this project are a device for real-time mixing and delivery of hyperpolarized gases such as 3He and 129Xe in combination with O2, an MRI acquisition scheme for practical imaging of ventilation signal build-up in the lungs, and a robust mathematical model for estimation of regional fractional ventilation values at a high resolution. A theoretical framework for fractional gas replacement in the lungs is presented to describe MRI signal dynamics during continuous breathing of a mixture of hyperpolarized gases in presence of several depolarization mechanisms. A hybrid ventilation and imaging acquisition scheme is proposed to acquire a series of images during short end-inspiratory breath-holds over several breaths. The sensitivity of the estimation algorithm is assessed with respect to noise, model uncertainty and acquisition parameters, and subsequently an optimal set of acquisition parameters is proposed to minimize the fractional ventilation estimation error. This framework is then augmented by an undersampled parallel MRI scheme to accelerate image acquisition to enable fractional ventilation imaging over the entire lung volume in a single pass. The image undersampling was also leveraged to minimize the coupling associated with signal buildup in the airways and the irreversible effect of RF pulses. The proposed technique was successfully implemented in pigs under mechanical ventilation, and preliminary measurements were performed in an adult human subject under voluntary breathing.
  • Publication
    Essays on Learning in Social Networks
    (2013-01-01) Molavi, Pooya
    Over the past few years, online social networks have become nearly ubiquitous, reshaping our social interactions as in no other point in history. The preeminent aspect of this social media revolution is arguably an almost complete transformation of the ways in which we acquire, process, store, and use information. In view of the evolving nature of social networks and their increasing complexity, development of formal models of social learning is imperative for a better understanding of the role of social networks in phenomena such as opinion formation, information aggregation, and coordination. This thesis takes a step in this direction by introducing and analyzing novel models of learning and coordination over networks. In particular, we provide answers to the following questions regarding a group of individuals who interact over a social network: 1) Do repeated communications between individuals with different subjective beliefs and pieces of information about a common true state lead them to eventually reach an agreement? 2) Do the individuals efficiently aggregate through their social interactions the information that is dispersed throughout the society? 3) And if so, how long does it take the individuals to aggregate the dispersed information and reach an agreement? This thesis provides answers to these questions given three different assumptions on the individuals' behavior in response to new information. We start by studying the behavior of a group of individuals who are fully rational and are only concerned with discovering the truth. We show that communications between rational individuals with access to complementary pieces of information eventually direct everyone to discover the truth. Yet in spite of its axiomatic appeal, fully rational agent behavior may not be a realistic assumption when dealing with large societies and complex networks due to the extreme computational complexity of Bayesian inference. Motivated by this observation, we next explore the implications of bounded rationality by introducing biases in the way agents interpret the opinions of others while at the same time maintaining the assumption that agents interpret their private observations rationally. Our analysis yields the result that when faced with overwhelming evidence in favor of the truth even biased agents will eventually learn to discover the truth. We further show that the rate of learning has a simple analytical characterization in terms of the relative entropy of agents' signal structures and their eigenvector centralities and use the characterization to perform comparative analysis. Finally, in the last chapter of the thesis, we introduce and analyze a novel model of opinion formation in which agents not only seek to discover the truth but also have the tendency to act in conformity with the rest of the population. Preference for conformity is relevant in scenarios ranging from participation in popular movements and following fads to trading in stock market. We argue that myopic agents who value conformity do not necessarily fully aggregate the dispersed information; nonetheless, we prove that examples of the failure of information aggregation are rare in a precise sense.
  • Publication
    Distributed Algorithms for the Optimal Design of Wireless Networks
    (2013-01-01) Hu, Yichuan
    This thesis studies the problem of optimal design of wireless networks whose operating points such as powers, routes and channel capacities are solutions for an optimization problem. Different from existing work that rely on global channel state information (CSI), we focus on distributed algorithms for the optimal wireless networks where terminals only have access to locally available CSI. To begin with, we study random access channels where terminals acquire instantaneous local CSI but do not know the probability distribution of the channel. We develop adaptive scheduling and power control algorithms and show that the proposed algorithm almost surely maximizes a proportional fair utility while adhering to instantaneous and average power constraints. Then, these results are extended to random access multihop wireless networks. In this case, the associated optimization problem is neither convex nor amenable to distributed implementation, so a problem approximation is introduced which allows us to decompose it into local subproblems in the dual domain. The solution method based on stochastic subgradient descent leads to an architecture composed of layers and layer interfaces. With limited amount of message passing among terminals and small computational cost, the proposed algorithm converges almost surely in an ergodic sense. Next, we study the optimal transmission over wireless channels with imperfect CSI available at the transmitter side. To reduce the likelihood of packet losses due to the mismatch between channel estimates and actual channel values, a backoff function is introduced to enforce the selection of more conservative coding modes. Joint determination of optimal power allocations and backoff functions is a nonconvex stochastic optimization problem with infinitely many variables. Exploiting the resulting equivalence between primal and dual problems, we show that optimal power allocations and channel backoff functions are uniquely determined by optimal dual variables and develop algorithms to find the optimal solution. Finally, we study the optimal design of wireless network from a game theoretical perspective. In particular, we formulate the problem as a Bayesian game in which each terminal maximizes the expected utility based on its belief about the network state. We show that optimal solutions for two special cases, namely FDMA and RA, are equilibrium points of the game. Therefore, the proposed game theoretic formulation can be regarded as general framework for optimal design of wireless networks. Furthermore, cognitive access algorithms are developed to find solutions to the game approximately.
  • Publication
    Complex Networks: New Models and Distributed Algorithms
    (2009-12-22) Tahbaz Salehi, Alireza
    Over the past few years, a consensus has emerged among scientists and engineers that net-centric technology can provide unprecedented levels of performance, robustness, and efficiency. Success stories such as the Internet, distributed sensor networks, and multi-agent networks of mobile robots are only a few examples that support this view. The important role played by complex networks has been widely observed in various physical, natural, and social systems. Given the complexity of many of these systems, it is important to understand the fundamental rules that govern them and introduce appropriate models that capture such principles, while abstracting away the redundant details. The main goal of this thesis is to contribute to the emerging field of "network science'' in two ways. The first part of the thesis focuses on the question of information aggregation over complex networks. The problem under study is the asymptotic behavior of agents in a network when they are willing to share information with their neighbors. We start by focusing on conditions under which all agents in the network will asymptotically agree on some quantity of interest, what is known as the consensus problem. We present conditions that guarantee asymptotic agreement when inter-agent communication links change randomly over time. We then propose a distributed (non-Bayesian) algorithm that enables agents to not only agree, but also learn the true underlying state of the world. We prove that our proposed learning rule results in successful information aggregation, in the sense that all agents asymptotically learn the truth as if they were completely informed of all signals and updated their beliefs rationally. Moreover, the simplicity of our local update rule guarantees that agents eventually achieve full learning, while at the same time, avoiding highly complex computations that are essential for full Bayesian learning over networks. The second part of this thesis focuses on presenting a new modeling paradigm that greatly expands the tool set for mathematical modeling of networks, beyond graphs. The approach taken is based on using simplicial complexes, which are objects of study in algebraic topology, as generalizations of graphs to higher dimensions. We show how simplicial complexes serve as more faithful models of the network and are able to capture many of its global topological properties. Furthermore, we develop distributed algorithms for computing various topological invariants of the network. These concepts and algorithms are further explored in the context of a specific application: coverage verification in coordinate-free sensor networks, where sensor nodes have no access to location, distance, or orientation information. We propose real-time, scalable, and decentralized schemes for detection of coverage holes, as well as computation of a minimal set of sensors required to monitor a given region of interest. The presented algorithms clarify the benefits of using simplicial complexes and simplicial homology, instead of applying tools from graph theory, in modeling and analyzing complex networks.
  • Publication
    High Performance Optical Transmitter Ffr Next Generation Supercomputing and Data Communication
    (2013-01-01) Wu, Xiaotie
    High speed optical interconnects consuming low power at affordable prices are always a major area of research focus. For the backbone network infrastructure, the need for more bandwidth driven by streaming video and other data intensive applications such as cloud computing has been steadily pushing the link speed to the 40Gb/s and 100Gb/s domain. However, high power consumption, low link density and high cost seriously prevent traditional optical transceiver from being the next generation of optical link technology. For short reach communications, such as interconnects in supercomputers, the issues related to the existing electrical links become a major bottleneck for the next generation of High Performance Computing (HPC). Both applications are seeking for an innovative solution of optical links to tackle those current issues. In order to target the next generation of supercomputers and data communication, we propose to develop a high performance optical transmitter by utilizing CISCO SystemsĀ®'s proprietary CMOS photonic technology. The research seeks to achieve the following outcomes: 1. Reduction of power consumption due to optical interconnects to less than 5pJ/bit without the need for Ring Resonators or DWDM and less than 300fJ/bit for short distance data bus applications. 2. Enable the increase in performance (computing speed) from Peta-Flop to Exa-Flops without the proportional increase in cost or power consumption that would be prohibitive to next generation system architectures by means of increasing the maximum data transmission rate over a single fiber. 3. Explore advanced modulation schemes such as PAM-16 (Pulse-Amplitude-Modulation with 16 levels) to increase the spectrum efficiency while keeping the same or less power figure. This research will focus on the improvement of both the electrical IC and optical IC for the optical transmitter. An accurate circuit model of the optical device is created to speed up the performance optimization and enable co-simulation of electrical driver. Circuit architectures are chosen to minimize the power consumption without sacrificing the speed and noise immunity. As a result, a silicon photonic based optical transmitter employing 1V supply, featuring 20Gb/s data rate is fabricated. The system consists of an electrical driver in 40nm CMOS and an optical MZI modulator with an RF length of less than 0.5mm in 0.13&mu m SOI CMOS. Two modulation schemes are successfully demonstrated: On-Off Keying (OOK) and Pulse-Amplitude-Modulation-N (PAM-N N=4, 16). Both versions demonstrate signal integrity, interface density, and scalability that fit into the next generation data communication and exa-scale computing. Modulation power at 20Gb/s data rate for OOK and PAM-16 of 4pJ/bit and 0.25pJ/bit are achieved for the first time of an MZI type optical modulator, respectively.
  • Publication
    Engineering Tunable Plasmonic Nanostructures To Enhance Upconversion Luminescence
    (2013-01-01) Saboktakin, Marjan
    Plasmonic nanostructures, which can confine and manipulate light below the diffraction limit, are becoming increasingly important in many areas of optical physics and devices. One of the areas that can greatly benefit from surface-plasmon mediated confinement of optical fields is the enhancement of emission in low quantum yield materials. The resonant wavelength for plasmonic structures used for emission enhancement is either the excitation or emission wavelengths of the luminescent material. Therefore, a key component in designing plasmonic structures used in luminescent enhancement applications is the ability to engineer and tune plasmonic building blocks to create structures resonant at the desired wavelength. In this thesis, we have used two approaches to build tunable structures for luminescent enhancement: 1) using already synthesized metallic nanocrystals resonant at the desired wavelengths as building blocks, we designed structures that would result in maximum emission enhancement. 2) Designing arrays of plasmonic nanostructures with the help of simulation software to be resonant at the desired wavelength and then fabricating them with top-down nanoscale fabrication techniques. In either approach, the resulting large area structures were macroscopically studied by steady state and time-resolved photoluminescence measurements to quantify the plasmonic effects on enhancement. We were able to achieve high enhancement factors in almost all of the structures and designs. Furthermore, we were able to identify and study various effects that play a role in plasmonic enhancement processes.
  • Publication
    Transformation Optics Using Graphene: One-Atom-Thick Optical Devices Based on Graphene
    (2012-01-01) Vakil, Ashkan
    Metamaterials and transformation optics have received considerable attention in the recent years, as they have found an immense role in many areas of optical science and engineering by offering schemes to control electromagnetic fields. Another area of science that has been under the spotlight for the last few years relates to exploration of graphene, which is formed of carbon atoms densely packed into a honey-comb lattice. This material exhibits unconventional electronic and optical properties, intriguing many research groups across the world including us. But our interest is mostly in studying interaction of electromagnetic waves with graphene and applications that might follow. Our group as well as few others pioneered investigating prospect of graphene for plasmonic devices and in particular plasmonic metamaterial structures and transformation optical devices. In this thesis, relying on theoretical models and numerical simulations, we show that by designing and manipulating spatially inhomogeneous, nonuniform conductivity patterns across a flake of graphene, one can have this material as a one-atom-thick platform for infrared metamaterials and transformation optical devices. Varying the graphene chemical potential by using static electric field allows for tuning the graphene conductivity in the terahertz and infrared frequencies. Such design flexibility can be exploited to create "patches" with differing conductivities within a single flake of graphene. Numerous photonic functions and metamaterial concepts are expected to follow from such platform. This work presents several numerical examples demonstrating these functions. Our findings show that it is possible to design one-atom-thick variant of several optical elements analogous to those in classic optics. Here we theoretically study one-atom-thick metamaterials, one-atom-thick waveguide elements, cavities, mirrors, lenses, Fourier optics and finally a few case studies illustrating transformation optics on a single sheet of graphene in mid-infrared wavelengths.
  • Publication
    Green Scheduling of Control Systems
    (2012-01-01) Nghiem, Truong Xuan
    Electricity usage under peak load conditions can cause issues such as reduced power quality and power outages. For this reason, commercial electricity customers are often subject to demand-based pricing, which charges very high prices for peak electricity demand. Consequently, reducing peaks in electricity demand is desirable for both economic and reliability reasons. In this thesis, we investigate the peak demand reduction problem from the perspective of safe scheduling of control systems under resource constraint. To this end, we propose Green Scheduling as an approach to schedule multiple interacting control systems within a constrained peak demand envelope while ensuring that safety and operational conditions are facilitated. The peak demand envelope is formulated as a constraint on the number of binary control inputs that can be activated simultaneously. Using two different approaches, we establish a range of sufficient and necessary schedulability conditions for various classes of affine dynamical systems. The schedulability analysis methods are shown to be scalable for large-scale systems consisting of up to 1000 subsystems. We then develop several scheduling algorithms for the Green Scheduling problem. First, we develop a periodic scheduling synthesis method, which is simple and scalable in computation but does not take into account the influence of disturbances. We then improve the method to be robust to small disturbances while preserving the simplicity and scalability of periodic scheduling. However the improved algorithm usually result in fast switching of the control inputs. Therefore, event-triggered and self-triggered techniques are used to alleviate this issue. Next, using a feedback control approach based on attracting sets and robust control Lyapunov functions, we develop event-triggered and self-triggered scheduling algorithms that can handle large disturbances affecting the system. These algorithms can also exploit prediction of the disturbances to improve their performance. Finally, a scheduling method for discrete-time systems is developed based on backward reachability analysis. The effectiveness of the proposed approach is demonstrated by an application to scheduling of radiant heating and cooling systems in buildings. Green Scheduling is able to significantly reduce the peak electricity demand and the total electricity consumption of the radiant systems, while maintaining thermal comfort for occupants.
  • Publication
    Iron-Based Microstructures For Biodegradable And Non-Biodegradable Applications
    (2022-01-01) Zhang, Tao
    Traditional microfabrication techniques for microelectromechanical systems (MEMS) have emphasized silicon and silicon-related materials. As more MEMS applications emerge, including the Internet of Things (IoT) and biodegradable applications, widely expanded materials sets are being considered. Since iron (Fe) has good electrical and magnetic properties, it undergoes degradation in moist or oxygenated environments, and since it is prevalent in both biological and environmental systems, it is being seriously considered as a candidate material to satisfy these new needs. In this dissertation, we developed iron-based microstructures for use in biodegradable and non-biodegradable applications. Specifically, electrical interconnects were developed with biodegradable iron/polymer composites. From the materials perspective, electrical, mechanical, and electrochemical properties of the composite material under physiological degradation were investigated. Stable electrical resistivity of the composite was shown over 20 days in degradation, even if under mechanical straining. Good adhesion to similarly biodegradable substrates of the composite was also shown in degradation. Further, the biodegradability of the composite was demonstrated by characterizing its degradation behavior. From the application perspective, a functional lifetime of over 4 days of the electrical interconnects with the packaging was achieved under physiological degradation to prove the capability of iron-based materials in biodegradable applications. In addition, miniaturized step-up transformers with laminated electrodeposited iron-alloy cores were developed. From the materials perspective, the magnetic permeability of the iron-alloy thin films at micro-scale thicknesses was characterized to demonstrate the retained magnetic properties of these materials at smaller scales. From the application perspective, the performance of the transformers was characterized at the device and circuit level to demonstrate the utility of iron-based bulk material in non-biodegradable magnetic applications. Finally, electrostatically tunable inductors fabricated from iron/ceramic composites were developed. From the materials perspective, the magnetic permeability tunability of the iron/ceramic composites was demonstrated. From the application perspective, the reasonable tuning ability of the device with minimum power consumption was shown to prove the utility of iron-based composite materials in non-biodegradable magnetic applications.
  • Publication
    Extracting Generalizable Hierarchical Patterns Of Functional Connectivity In The Brain
    (2022-01-01) Sahoo, Dushyant
    The study of the functional organization of the human brain using resting-state functional MRI (rsfMRI) has been of significant interest in cognitive neuroscience for over two decades. The functional organization is characterized by patterns that are believed to be hierarchical in nature. From a clinical context, studying these patterns has become important for understanding various disorders such as Major Depressive Disorder, Autism, Schizophrenia, etc. However, extraction of these interpretable patterns might face challenges in multi-site rsfMRI studies due to variability introduced due to confounding variability introduced by different sites and scanners. This can reduce the predictive power and reproducibility of the patterns, affecting the confidence in using these patterns as biomarkers for assessing and predicting disease. In this thesis, we focus on the problem of robustly extracting hierarchical patterns that can be used as biomarkers for diseases. We propose a matrix factorization based method to extract interpretable hierarchical decomposition of the rsfRMI data. We couple the method with adversarial learning to improve inter-site robustness in multi-site studies, removing non-biological variability that can result in less interpretable and discriminative biomarkers. Finally, a generative-discriminative model is built on top of the proposed framework to extract robust patterns/biomarkers characterizing Major Depressive Disorder. Results on large multi-site rsfMRI studies show the effectiveness of our method in uncovering reproducible connectivity patterns across individuals with high predictive power while maintaining clinical interpretability. Our framework robustly identiļ¬es brain patterns characterizing MDD and provides an understanding of the manifestation of the disorder from a functional networks perspective which can be crucial for effective diagnosis, treatment and prevention. The results demonstrate the method's utility and facilitate a broader understanding of the human brain from a functional perspective.