An Overview of Integral Quadratic Constraints for Delayed Nonlinear and Parameter-Varying Systems

A general framework is presented for analyzing the stability and performance of nonlinear and linear parameter varying (LPV) time delayed systems. First, the input/output behavior of the time delay operator is bounded in the frequency domain by integral quadratic constraints (IQCs). A constant delay is a linear, time-invariant system and this leads to a simple, intuitive interpretation for these frequency domain constraints. This simple interpretation is used to derive new IQCs for both constant and varying delays. Second, the performance of nonlinear and LPV delayed systems is bounded using dissipation inequalities that incorporate IQCs. This step makes use of recent results that show, under mild technical conditions, that an IQC has an equivalent representation as a finite-horizon time-domain constraint. Numerical examples are provided to demonstrate the effectiveness of the method for both class of systems

Small Polarons in Transition Metal Oxides

The formation of polarons is a pervasive phenomenon in transition metal oxide compounds, with a strong impact on the physical properties and functionalities of the hosting materials. In its original formulation the polaron problem considers a single charge carrier in a polar crystal interacting with its surrounding lattice. Depending on the spatial extension of the polaron quasiparticle, originating from the coupling between the excess charge and the phonon field, one speaks of small or large polarons. This chapter discusses the modeling of small polarons in real materials, with a particular focus on the archetypal polaron material TiO2. After an introductory part, surveying the fundamental theoretical and experimental aspects of the physics of polarons, the chapter examines how to model small polarons using first principles schemes in order to predict, understand and interpret a variety of polaron properties in bulk phases and surfaces. Following the spirit of this handbook, different types of computational procedures and prescriptions are presented with specific instructions on the setup required to model polaron effects.Comment: 36 pages, 12 figure

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

Collaborative beamforming schemes for wireless sensor networks with energy harvesting capabilities

In recent years, wireless sensor networks have attracted considerable attention in the research community. Their development, induced by technological advances in microelectronics, wireless networking and battery fabrication, is mainly motivated by a large number of possible applications such as environmental monitoring, industrial process control, goods tracking, healthcare applications, to name a few. Due to the unattended nature of wireless sensor networks, battery replacement can be either too costly or simply not feasible. In order to cope with this problem and prolong the network lifetime, energy efficient data transmission protocols have to be designed. Motivated by this ultimate goal, this PhD dissertation focuses on the design of collaborative beamforming schemes for wireless sensor networks with energy harvesting capabilities. On the one hand, by resorting to collaborative beamforming, sensors are able to convey a common message to a distant base station, in an energy efficient fashion. On the other, sensor nodes with energy harvesting capabilities promise virtually infinite network lifetime. Nevertheless, in order to realize collaborative beamforming, it is necessary that sensors align their transmitted signals so that they are coherently combined at the destination. Moreover, sensor nodes have to adapt their transmissions according to the amounts of harvested energy over time. First, this dissertation addresses the scenario where two sensor nodes (one of them capable of harvesting ambient energy) collaboratively transmit a common message to a distant base station. In this setting, we show that the optimal power allocation policy at the energy harvesting sensor can be computed independently (i.e., without the knowledge of the optimal policy at the battery operated one). Furthermore, we propose an iterative algorithm that allows us to compute the optimal policy at the battery operated sensor, as well. The insights gained by the aforementioned scenario allow us to generalize the analysis to a system with multiple energy harvesting sensors. In particular, we develop an iterative algorithm which sequentially optimizes the policies for all the sensors until some convergence criterion is satisfied. For the previous scenarios, this PhD dissertation evaluates the impact of total energy harvested, number of sensors and limited energy storage capacity on the system performance. Finally, we consider some practical schemes for carrier synchronization, required in order to implement collaborative beamforming in wireless sensor networks. To that end, we analyze two algorithms for decentralized phase synchronization: (i) the one bit of feedback algorithm previously proposed in the literature; and (ii) a decentralized phase synchronization algorithm that we propose. As for the former, we analyze the impact of additive noise on the beamforming gain and algorithm’s convergence properties, and, subsequently, we propose a variation that performs sidelobe control. As for the latter, the sensors are allowed to choose their respective training timeslots randomly, relieving the base station of the burden associated with centralized coordination. In this context, this PhD dissertation addresses the impact of number of timeslots and additive noise on the achieved received signal strength and throughputEn los últimos años, las redes de sensores inalámbricas han atraído considerable atención en la comunidad investigadora. Su desarrollo, impulsado por recientes avances tecnológicos en microelectrónica y radio comunicaciones, está motivado principalmente por un gran abanico de aplicaciones, tales como: Monitorización ambiental, control de procesos industriales, seguimiento de mercancías, telemedicina, entre otras. En las redes de sensores inalámbricas, es primordial el diseño de protocolos de transmisión energéticamente eficientes ya que no se contempla el reemplazo de baterías debido a su coste y/o complejidad. Motivados por esta problemática, esta tesis doctoral se centra en el diseño de esquemas de conformación de haz distribuidos para redes de sensores, en el que los nodos son capaces de almacenar energía del entorno, lo que en inglés se denomina energy harvesting. En primer lugar, esta tesis doctoral aborda el escenario en el que dos sensores (uno de ellos capaz de almacenar energía del ambiente) transmiten conjuntamente un mensaje a una estación base. En este contexto, se demuestra que la política de asignación de potencia óptima en el sensor con energy harvesting puede ser calculada de forma independiente (es decir, sin el conocimiento de la política óptima del otro sensor). A continuación, se propone un algoritmo iterativo que permite calcular la política óptima en el sensor que funciona con baterías. Este esquema es posteriormente generalizado para el caso de múltiples sensores. En particular, se desarrolla un algoritmo iterativo que optimiza las políticas de todos los sensores secuencialmente. Para los escenarios anteriormente mencionados, esta tesis evalúa el impacto de la energía total cosechada, número de sensores y la capacidad de la batería. Por último, se aborda el problema de sincronización de fase en los sensores con el fin de poder realizar la conformación de haz de forma distribuida. Para ello, se analizan dos algoritmos para la sincronización de fase descentralizados: (i) el algoritmo "one bit of feedback" previamente propuesto en la literatura, y (ii) un algoritmo de sincronización de fase descentralizado que se propone en esta tesis. En el primer caso, se analiza el impacto del ruido aditivo en la ganancia y la convergencia del algoritmo. Además, se propone una variación que realiza el control de lóbulos secundarios. En el segundo esquema, los sensores eligen intervalos de tiempo de forma aleatoria para transmitir y posteriormente reciben información de la estación base para ajustar sus osciladores. En este escenario, esta tesis doctoral aborda el impacto del número de intervalos de tiempo y el ruido aditivo sobre la ganancia de conformación

Studies of two-dimensional materials beyond graphene: from first-principles to machine learning approaches

Monolayers and heterostructures of two-dimensional (2D) electronic materials with spin-orbit interactions offer the promise of observing many novel physical effects. While theoretical predictions of 2D layered materials based on density functional theory (DFT) are many, the DFT approach is limited to small simulation sizes (several nanometers), and thus inhomogeneous strain and boundary effects that are often observed experimentally cannot be simulated within a reasonable time. The aim of this thesis is (i) to study effects of strain on 2D materials beyond graphene using first-principles and tight-binding methods and (ii) to investigate the effects of cuts--"kirigami"-- on 2D materials using molecular dynamics and machine learning approach. The first half of this thesis focuses on the effects of strain on manipulating spin and valley degrees of freedom for two classes of 2D materials--monochalcogenide and lead chalcogenide monolayers--using DFT. A tight-binding (TB) approach is developed to describe the electronic changes in lead chalcogenide monolayers due to strains that often persist in real devices. The strain-dependent TB model allows one to establish a relationship between the Rashba field and the out-of-plane strain or electric polarization from a microscopic view, a connection that is not well understood in the ferroelectric Rashba materials. This framework connecting strain fields and electronic changes is important to overcome the size and computational limitations associated with DFT. The second part of the thesis focuses on defect engineering and design of 2D materials via the "kirigami" technique of introducing different patterns of cuts. A machine learning (ML) approach is presented to provide physical insights and an effective model to describe the physical system. We demonstrate that a machine learning model based on a convolutional neural network is able to find the optimal design from a training data set that is much smaller than the design space

Green Supply Chain Design: A Lagrangian Approach

The expansion of supply chains into global networks has drastically increased the distance travelled along shipping lanes in a logistics system. Inherently, the increase in travel distances produces increased carbon emissions from transport vehicles. When increased emissions are combined with a carbon tax or emissions trading system, the result is a supply chain with increased costs attributable to the emission generated on the transportation routes. Most traditional supply chain design models do not take emissions and carbon costs into account. Hence, there is a need to incorporate emission costs into a supply chain optimization model to see how the optimal supply chain configuration may be affected by the additional expenses. This thesis presents a mathematical programming model for the design of green supply chains. The costs of carbon dioxide (CO2) emissions were incorporated in the objective function, along with the fixed and transportation costs that are typically modeled in traditional facility location models. The model also determined the unit flows between the various nodes of the supply chain, with the objective of minimizing the total cost of the system by strategically locating warehouses throughout the network. The literature shows that CO2 emissions produced by a truck are dependent on the weight of the vehicle and can be modeled using a concave function. Hence, the carbon emissions produced along a shipping lane are dependent upon the number of units and the weight of each unit travelling between the two nodes. Due to the concave nature of the emissions, the addition of the emission costs to the problem formulation created a nonlinear mixed integer programming (MIP) model. A solution algorithm was developed to evaluate the new problem formulation. Lagrangian relaxation was used to decompose the problem by echelon and by potential warehouse site, resulting in a problem that required less computational effort to solve and allowed for much larger problems to be evaluated. A method was then suggested to exploit a property of the relaxed formulation and transform the problem into a linear MIP problem. The solution method computed the minimum cost for a complete network that would satisfy all the needs of the customers. A primal heuristic was introduced into the Lagrangian algorithm to generate feasible solutions. The heuristic utilized data from the Lagrangian subproblems to produce good feasible solutions. Due to the many characteristics of the original problem that were carried through to the subproblems, the heuristic produced very good feasible solutions that were typically within 1% of the Lagrangian bound. The proposed algorithm was evaluated through a number of tests. The rigidity of the problem and cost breakdown were varied to assess the performance of the solution method in many situations. The test results indicated that the addition of emission costs to a network can change the optimal configuration of the supply chain. As such, this study concluded that emission costs should be considered when designing supply chains in jurisdictions with carbon costs. Furthermore, the tests revealed that in regions without carbon costs it may be possible to significantly reduce the emissions produced by the supply chain with only a small increase in the cost to operate the system

Electronic Structure of Novel Two-dimensional Materials and Graphene Heterostructures

Today a well-equipped library of two-dimensional materials can be synthesized or exfoliated, ranging from insulating hexagonal boron nitride, to semi-metallic graphene, and metallic as well as superconducting transition metal dichalcogenides and many others. Due to strong intra-layer covalent bondings, but weak inter-layer Van-der-Waals interactions, these layered materials can be stacked in a Lego-like fashion to artificial heterostructures which do not occur in nature. Thereby, these novel systems offer the possibility to combine specific properties of each of its constituents to tailor the heterostructure's properties on demand which might allow for completely new device classes. In fact, these kind of systems are already constructed and studied in labs around the world. In order to guide these efforts, we need an in-depth understanding of these complex heterostructures starting with its smallest components, namely the different two-dimensional materials and their mutual interactions. To this end, we study electronic and optical properties of novel two-dimensional materials in this thesis. In more detail, we here aim to investigate functionalized graphene, graphene heterostructures and doped or optically excited molybdenum disulfide (MoS$_2$) monolayers for which we combine \abinitio based models with many-body or multi-scale approaches. The first part is devoted to functionalized graphene and is subdivided into the investigation of disorder-induced optical effects of fluorographene and into a detailed study of the Coulomb interaction in graphene heterostructures in form of multilayer graphene, intercalated graphite and few-layer graphene within a dielectric environment. In the case of fluorographene we use a multi-scale approach to study the effects of realistic disorder patterns to the optical conductivity. Thereby, we provide important insights into the role of non-perfect fluorination of graphene. Regarding the graphene heterostructures we present a novel approach to easily and reliably derive Coulomb-interaction matrix elements in these structures. This method is used to study the robustness of bilayer graphene's ground state to changes in its dielectric surrounding. In the second part of the thesis we study a variety of many-body effects that arise in doped and optically excited MoS$_2$ monolayers. Once again, by deriving simplified yet accurate models from first-principles we are able to investigate many-body excitations like plasmons or excitons as well as many-body instabilities like superconductivity or charge-density wave phases. Regarding the latter, we are able to extend the electron-doping phase diagram of MoS$_2$ by the formation of a charge-density-wave phase and reveal its potential coexistence with the superconducting state. In the field of many-body excitations we study in detail excitonic line shifts upon optical excitations and we precisely describe different types of plasmonic excitations under electron or hole doping in MoS$_2$. Finally, we make use of the fundamental properties of the many-body interactions in layered materials in order to externally induce heterojunctions within homogeneous semiconducting monolayers by non-local manipulations of the Coulomb interaction