Refined Core Relaxations for Core-Guided Maximum Satisfiability Algorithms

The so-called declarative approach has proven to be a viable paradigm for solving various real-world NP-hard optimization problems in practice. In the declarative approach, the problem at hand is encoded using a mathematical constraint language, and an algorithm for the specific language is employed to obtain optimal solutions to an instance of the problem. One of the most viable declarative optimization paradigms of the last years is maximum satisfiability (MaxSAT) with propositional logic as the constraint language. So-called core-guided MaxSAT algorithms are arguably one of the most effective MaxSAT-solving paradigms in practice today. Core-guided algorithms iteratively detect and rule out (relax) sources of inconsistencies (so-called unsatisfiable cores) in the instance being solved. Especially effective are recent algorithmic variants of the core-guided approach which employ so-called soft cardinality constraints for ruling out inconsistencies. In this thesis, we present a structure-sharing technique for the cardinality-based core relaxation steps performed by core-guided MaxSAT solvers. The technique aims at reducing the inherent growth in the size of the propositional formula resulting from the core relaxation steps. Additionally, it enables more efficient reasoning over the relationships between different cores. We empirically evaluate the proposed technique on two different core-guided algorithms and provide open-source implementations of our solvers employing the technique. Our results show that the proposed structure-sharing can improve the performance of the algorithms both in theory and in practice

Continuous-time Algorithms and Analog Integrated Circuits for Solving Partial Differential Equations

Analog computing (AC) was the predominant form of computing up to the end of World War II. The invention of digital computers (DCs) followed by developments in transistors and thereafter integrated circuits (IC), has led to exponential growth in DCs over the last few decades, making ACs a largely forgotten concept. However, as described by the impending slow-down of Moore’s law, the performance of DCs is no longer improving exponentially, as DCs are approaching clock speed, power dissipation, and transistor density limits. This research explores the possibility of employing AC concepts, albeit using modern IC technologies at radio frequency (RF) bandwidths, to obtain additional performance from existing IC platforms. Combining analog circuits with modern digital processors to perform arithmetic operations would make the computation potentially faster and more energy-efficient. Two AC techniques are explored for computing the approximate solutions of linear and nonlinear partial differential equations (PDEs), and they were verified by designing ACs for solving Maxwell\u27s and wave equations. The designs were simulated in Cadence Spectre for different boundary conditions. The accuracies of the ACs were compared with finite-deference time-domain (FDTD) reference techniques. The objective of this dissertation is to design software-defined ACs with complementary digital logic to perform approximate computations at speeds that are several orders of magnitude greater than competing methods. ACs trade accuracy of the computation for reduced power and increased throughput. Recent examples of ACs are accurate but have less than 25 kHz of analog bandwidth (Fcompute) for continuous-time (CT) operations. In this dissertation, a special-purpose AC, which has Fcompute = 30 MHz (an equivalent update rate of 625 MHz) at a power consumption of 200 mW, is presented. The proposed AC employes 180 nm CMOS technology and evaluates the approximate CT solution of the 1-D wave equation in space and time. The AC is 100x, 26x, 2.8x faster when compared to the MATLAB- and C-based FDTD solvers running on a computer, and systolic digital implementation of FDTD on a Xilinx RF-SoC ZCU1275 at 900 mW (x15 improvement in power-normalized performance compared to RF-SoC), respectively

Robust analysis of delaminating composites using adaptive isogeometric shell elements

Fibre reinforced composites are considered to be one of the material categories that offer the best possibilities to create efficient lightweight designs. Many companies in the transport sector therefore work towards increasing the amount of fibre composites in their products, in an attempt to lower the fuel consumption of their vehicles. However, from the perspective of simulation-driven design, an increased use of composite materials is accompanied with new modelling challenges. In this thesis, two such challenges have been considered.The first challenge concerns the often computationally demanding models needed to simulate delamination in fibre composites. The heterogeneous through-thickness nature of fibre composites necessitates a very fine through-thickness discretisation in order to capture the delamination process, which leads to very long (or even infeasible) simulation times. The second challenge addressed in this thesis is related to the difficulties arising when simulating the post-failure response of fibre composites. Specifically, in quasi-static simulations, the brittle material interfaces of layered fibre composites can lead to sudden failure, which standard incremental Newton-Raphson solvers are not able to trace.To address these problems, two new computational tools have been developed that can aid the design process of fibre reinforced composites. Firstly, in Paper A, an adaptive isogeometric shell element has been developed, that can refine its through-thickness kinematics as delamination propagates. Consequently, only the lowest level of detail needed to capture delamination is included in the model, which improves efficiency. To address the second issue, a dissipation based path-following solver has been developed in Paper B, which is able to robustly trace the equilibrium path of the post-peak response in quasi-static simulations.Both Paper A and Paper B shows that the developed adaptive isogeometric shell element and the dissipation based path-following solver can be combined to robustly and efficiently simulate composite structures with brittle delamination behaviour. Consequently, it is shown that the computational tools developed in this thesis can be used to aid the design process of fibre reinforced structures

Modification of stochastic ground motion models for matching target intensity measures

Stochastic ground motion models produce synthetic time‐histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation‐based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi‐objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives

Programming Wireless Security through Learning-Aided Spatiotemporal Digital Coding Metamaterial Antenna

The advancement of future large-scale wireless networks necessitates the development of cost-effective and scalable security solutions. Conventional cryptographic methods, due to their computational and key management complexity, are unable to fulfill the low-latency and scalability requirements of these networks. Physical layer (PHY) security has been put forth as a cost-effective alternative to cryptographic mechanisms that can circumvent the need for explicit key exchange between communication devices, owing to the fact that PHY security relies on the physics of the signal transmission for providing security. In this work, a space-time-modulated digitally-coded metamaterial (MTM) leaky wave antenna (LWA) is proposed that can enable PHY security by achieving the functionalities of directional modulation (DM) using a machine learning-aided branch and bound (B&B) optimized coding sequence. From the theoretical perspective, it is first shown that the proposed space-time MTM antenna architecture can achieve DM through both the spatial and spectral manipulation of the orthogonal frequency division multiplexing (OFDM) signal received by a user equipment. Simulation results are then provided as proof-of-principle, demonstrating the applicability of our approach for achieving DM in various communication settings. To further validate our simulation results, a prototype of the proposed architecture controlled by a field-programmable gate array (FPGA) is realized, which achieves DM via an optimized coding sequence carried out by the learning-aided branch-and-bound algorithm corresponding to the states of the MTM LWA's unit cells. Experimental results confirm the theory behind the space-time-modulated MTM LWA in achieving DM, which is observed via both the spectral harmonic patterns and bit error rate (BER) measurements

Network planning for the future railway communications

Los Sistemas Inteligentes de Transporte están cambiando la forma en que concebimos el futuro de la movilidad. En particular, los ferrocarriles están experimentando un proceso de transformación para modernizar el transporte público y las operaciones ferroviarias. Tecnologías como el 5G, la fibra óptica y la nube han surgido como catalizadores para digitalizar el ferrocarril proporcionando comunicaciones de alta velocidad y baja latencia. Este TFG se centra en la exploración de redes que permitan el control del tren y la transmisión de datos a bordo. El objetivo es planificar la infraestructura de red (dimensionamiento y asignación de recursos) necesaria para las futuras comunicaciones del sistema ferroviario de larga distancia de la Deutsche Bahn en Alemania. En este trabajo, proponemos una arquitectura de red que puede satisfacer los requisitos de rendimiento de las aplicaciones para trenes y pasajeros. Presentamos un método para la colocación de estaciones base 5G a lo largo de las vías del tren para garantizar el rendimiento necesario en el borde de la celda. Por último, presentamos el problema de colocación y asignación de centros de datos. El objetivo es encontrar el número necesario de centros de datos y su ubicación en la red, y asignarlos a cada estación de tren. Realizamos simulaciones en cuatro escenarios diferentes, en los que modificamos parámetros de entrada como la latencia máxima tolerada y el número máximo de centros de datos. Los resultados obtenidos muestran el compromiso entre la latencia alcanzada y el coste de la infraestructura.Els Sistemes Intel·ligents de Transport estan canviant la manera en què concebem el futur de la mobilitat. En particular, els ferrocarrils estan experimentant un procés de transformació per modernitzar el transport públic i les operacions ferroviàries. Tecnologies com el 5G, la fibra òptica i el núvol han sorgit com a catalitzadors per digitalitzar el ferrocarril proporcionant comunicacions d'alta velocitat i baixa latència. Aquest TFG se centra en l'exploració de xarxes que permetin el control dels trens i la transmissió de dades a bord. L'objectiu és planificar la infraestructura de xarxa (dimensionament i assignació de recursos) necessària per a les futures comunicacions del sistema ferroviari de llarga distància de la Deutsche Bahn a Alemanya. En aquest treball, proposem una arquitectura de xarxa que pot satisfer els requisits de rendiment de les aplicacions per a trens i passatgers. Presentem un mètode per a la col·locació d'estacions base 5G al llarg de les vies del tren per garantir el rendiment necessari a la vora de la cel·la. Per últim, presentem el problema de col·locació i assignació de centres de dades. L'objectiu és trobar el nombre necessari de centres de dades i la seva ubicació a la xarxa, i assignar-los a cada estació de tren. Realitzem simulacions en quatre escenaris diferents, on modifiquem paràmetres d'entrada com la latència màxima tolerada i el nombre màxim de centres de dades. Els resultats obtinguts mostren el compromís entre la latència assolida i el cost de la infraestructura.Smart Transportation Systems are changing the way we conceive the future of mobility. In particular, railways are undergoing a transformation process to modernize public transportation and rail operation. Technologies like 5G, optical fiber and the cloud have emerged as catalysts to digitalize the railway by providing high-speed and low-latency communications. This bachelor's thesis focuses on the exploration of networks enabling train control and on-board data communications. The goal is to plan the network infrastructure (dimensioning and resource allocation) needed for the future communications in the train mobility scenario for Deutsche Bahn's long-distance railway system in Germany. In this work, we propose a network architecture that can meet the performance requirements of train and passenger applications. We present an approach for 5G base station placement along the rail tracks to guarantee the necessary throughput at the cell edge. Finally, we introduce the data center placement and assignment problem. The objective is to find the required number of data centers and their location in the network, and to assign them to each train station. We perform simulations in four different scenarios, in which we modify input parameters such as the maximum tolerated latency and the maximum number of data centers. The obtained results show the trade-off between the achieved latency and the infrastructure cost