Massive MU-MIMO (Multiuser-Multiple Input and Multple Output) is a promising technology for 5G wireless communications because of its spectrum and energy efficiency. To combat the distortion from multipath fading channel, the acquisition of channel state information is essential, which generally requires the training signal that lowers the data rate. In addition, coarse quantization can reduce the high computational energy and cost, yet results in the loss of information. In this thesis, an iterative decision feedback receiver, including iterative Channel Estimation (CE) and equalization, is constructed for a Massive MU-MIMO uplink system. The impact of multipath distortion and coarse quantization can be gradually reduced due to the iterative structure that exploits extrinsic feedback to improve the CE and data detection, so that the data rate is improved by reducing training signals for CE and by using low precision quantization. To observe and evaluate the convergence behaviour, an Extrinsic Information Transfer (EXIT) chart method is utilized to visualize the performance of the iterative receiver. / Graduate

Diese Dissertation befasst sich mit dem Verhalten eines generischen semiflexi- blen Polymermodells. Insbesondere untersucht es den Einfluss von Steifigkeit auf die unterschiedlichen thermodynamisch stabilen Konformationen. Es wird erläutert wie durch die Steifigkeit des Polymers verschiedene struk- turierte Phasen induziert werden. Insbesondere wird dabei auf die sta- bilen verknoteten Phasen eingegangen. Der zweite Teil der Dissertation beschäftigt sich dann mit dem Einfluss einer kugelförmigen Einsperrung auf das Phasendiagramm des selben Polymermodells. Es wird gezeigt wie in Abhängigkeit der Ordnung des Phasenüberganges die Einsperrung entweder zu einem stabilisierenden oder destabilisierenden Effekt führt. Im dritten Teil der Dissertation werden dann die komplexen Monte-Carlo Simulationen erläutert die für die Simulation der physikalischen Systeme genutzt wurde. Diese Algorithmen wurden in ein Framework integriert, so dass diese wieder verwendet werden können.

<div>Organic mixed ion-electron conducting (OMIEC) polymers are capable of transporting both electrons and ions. This unique functionality underpins many emerging applications, including biosensors, electrochemical transistors, and batteries. The fundamental operating principles and structure-function relationships of OMIECs are still being investigated. Computational tools such as coarse-grained molecular dynamics (CGMD), which use simpler representations than in atomistic modeling, are ideal to study OMIECs, as they can explore the slow dynamics and large length scale features of polymers. Nevertheless, methods development is still required for CGMD simulations to accurately describe OMIECs.</div><div><br></div><div>In this thesis, two CGMD simulation approaches have been adopted. One is a so-called "top-down" approach to develop a generic model of OMIECs. Top-down models are phenomenological but capable of exploring a broad space of materials variables, including backbone anisotropy, persistence length, side-chain density, and hydrophilicity. This newly developed model was used to interrogate the effect of side-chain polarity and patterning on OMIEC physics. These studies reproduce experimentally observed polymer swelling while for the first time clarifying several molecular factors affecting charge transport, including the role of trap sites, polaron delocalization, electrolyte percolation, and suggesting side-chain patterning as a potential tool to improve OMIEC performance.</div><div><br></div><div>The second strategy pursued in this thesis is bottom-up CGMD modeling of specific atomistic systems. The bottom-up approach enables CGMD simulations to be quantitatively related to specific materials; yet, the sources of error and methods for addressing them have yet to be systematically established. To address this gap, we have studied the effect of the CG mapping operator, an important CG variable, on the fidelity of atomistic and CGMD simulations. A major observation from this study is that prevailing CGMD methods are underdetermined with respect to atomistic training data. In a separate study, we have proposed a hybrid machine-learning and physics-based CGMD framework that utilizes information from multiple sources and improves on the accuracy of ML-only bottom-up CGMD approaches. </div>

In soil, where trees and crops grow, heavy vehicles shear and compact the soil, leading to reduced plant growth and diminished nutrient recycling. Computer simulations offer the possibility to improve the understanding of these undesired phenomena. In this thesis, soils were modelled as large collections of contacting spherical particles using the Discrete Element Method (DEM) and the physics engine AGX Dynamics, and these entities were analyzed. In the first part of the thesis, soils, which were considered to be continua, were subjected to various controlled deformations and fields for quantities such as stress and strain were visualized using coarse graining (CG). These fields were then compared against analytical solutions. The main goal of the thesis was to evaluate the usefulness, accuracy, and precision of this plotting technique when applied to DEM-soils. The general behaviour of most fields agreed well with analytical or expected behaviour. Moreover, the fields presented valuable information about phenomena in the soils. Relative errors varied from 1.2 to 27 %. The errors were believed to arise chiefly from non-uniform displacement (due to the inherent granularity in the technique), and unintended uneven particle distribution. The most prominent drawback with the technique was found to be the unreliability of the plots near the boundaries. This is significant, since the behaviour of a soil at the surface where it is in contact with e.g. a vehicle tyre is of interest. In the second part of the thesis, a vehicle traversed a soil and fields were visualized using the same technique. Following a limited analysis, it was found that the stress in the soil can be crudely approximated as the stress in a linear elastic solid.

This study uses a spatial model to visualize LWD mobility areas in an approximate 1km reach of Cummins Creek, a fourth-order stream flowing through an old-growth Sitka spruce-western hemlock forest in the Oregon Coast Range. The model solves a LWD incipient motion equation for nine wood size combinations (0.1m, 0.4m, 1.7m diameters by 1.0m, 6.87m, 47.2m lengths) during the 2-year, 10-year, and 100-year discharge events. Model input variables were derived from a combination of field survey, remotely sensed, and modeled data collected or derived between June 2010 and July 2011. LWD mobility map results indicate the 2-year discharge mobilizes all modeled diameters, but mobile piece lengths are shorter than the bankfull channel boundary. Mobility areas for each wood size combination increases with discharge; 10-year and 100-year discharge events mobilize wood longer than average bankfull width within a confined section of the main stem channel, and mobilize LWD shorter than bankfull width within the main stem channel, side channels, and floodplain. No discharge event mobilizes the largest LWD size combination (1.7m / 47.2). Recruitment process was recorded for all LWD during June 2010, revealing that all mobile wood in the study reach was shorter than bankfull width. Based on these conflicting results, I hypothesize the distribution of wood in Cummins Creek can be described in terms of discharge frequency and magnitude, instead of as a binary mobile/stable classification. Mobility maps could be a useful tool for land managers using LWD as part of a stream restoration or conservation plan, but will require additional calibration.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第23405号 / 理博第4740号 / 新制||理||1679(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 高田 彰二, 教授 川口 真也, 准教授 立川 正志 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

No description available.

One of the major goal of the researchers is to reduce energy loss including nanoscale to the structural level. For instance, around 65% of fuel energy is lost during the propulsion of the automobiles, where 11% of the loss happens at tires due to rolling friction. Out of that tire loss, 90 to 95% loss happens due to hysteresis of tire materials. This dissertation focuses on multiscale modeling techniques in order to facilitate the discovery new rubber materials. Enhanced coarse-grained models of elastomers (thermoplastic polyurethane elastomer and natural rubber) are constructed from full-atomic models with reasonable repeat units/beads associated with pressure-correction for non-bonded interactions of the beads using inverse Boltzmann method (IBM). Equivalent continuum modeling is performed with volumetric/isochoric loading to predict macroscopic mechanical properties using molecular mechanics (MM) and molecular dynamics (MD). Glass-transition and rate-dependent mechanical properties along with hysteresis loss under uniaxial deformation is predicted with varying composition of the material. A statistical non-Gaussian treatment of a rubber chain is performed and linked with molecular dynamics in order predict hyperelastic material constants without fitting with any experimental data.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18107号 / 理博第3985号 / 新制||理||1575(附属図書館) / 30965 / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 朝倉 彰, 講師 宮崎 勝己, 教授 疋田 努 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18117号 / 理博第3995号 / 新制||理||1576(附属図書館) / 30975 / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 高田 彰二, 教授 大野 睦人, 准教授 土井 知子 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM