Channel Coding in Molecular Communication

This dissertation establishes and analyzes a complete molecular transmission system from a communication engineering perspective. Its focus is on diffusion-based molecular communication in an unbounded three-dimensional fluid medium. As a basis for the investigation of transmission algorithms, an equivalent discrete-time channel model (EDTCM) is developed and the characterization of the channel is described by an analytical derivation, a random walk based simulation, a trained artificial neural network (ANN), and a proof of concept testbed setup. The investigated transmission algorithms cover modulation schemes at the transmitter side, as well as channel equalizers and detectors at the receiver side. In addition to the evaluation of state-of-the-art techniques and the introduction of orthogonal frequency-division multiplexing (OFDM), the novel variable concentration shift keying (VCSK) modulation adapted to the diffusion-based transmission channel, the lowcomplex adaptive threshold detector (ATD) working without explicit channel knowledge, the low-complex soft-output piecewise linear detector (PLD), and the optimal a posteriori probability (APP) detector are of particular importance and treated. To improve the error-prone information transmission, block codes, convolutional codes, line codes, spreading codes and spatial codes are investigated. The analysis is carried out under various approaches of normalization and gains or losses compared to the uncoded transmission are highlighted. In addition to state-of-the-art forward error correction (FEC) codes, novel line codes adapted to the error statistics of the diffusion-based channel are proposed. Moreover, the turbo principle is introduced into the field of molecular communication, where extrinsic information is exchanged iteratively between detector and decoder. By means of an extrinsic information transfer (EXIT) chart analysis, the potential of the iterative processing is shown and the communication channel capacity is computed, which represents the theoretical performance limit for the system under investigation. In addition, the construction of an irregular convolutional code (IRCC) using the EXIT chart is presented and its performance capability is demonstrated. For the evaluation of all considered transmission algorithms the bit error rate (BER) performance is chosen. The BER is determined by means of Monte Carlo simulations and for some algorithms by theoretical derivation

The influence of pH on the binding of immunoglobulin G to staphylococcal protein A

The interaction between protein A and immunoglobulin G (IgG) was studied at a variety of pH values using a surface plasmon resonance (SPR) device, which provides real time kinetic data without labelling or molecular alteration. This study was carried out due to the large scale use of Protein A affinity chromatography for the purification of IgG for pharmaceutical purposes, and is one of the most costly steps in the purification process. The results produced were largely in line with those produced in previous literature with binding remaining strong between pH 7.4 and 5.0, although the association rate decreased as pH decreased. Below pH 5.0, the rate of IgG elution markedly increased, with pH 3.5 showing near full elution seconds after the association phase of the SPR interaction finished. Problems were encountered with non-specific binding between the SPR sensor chip and IgG occurring under a variety of conditions, requiring various remedies. However, no complete interactions were successfully carried out under pH 5.0, so the results obtained below this value were obtained by binding at pH 7.4 and then elution at the desired pH. The data showed binding behaviour that was most successfully explained by a three-site model, each with a binding ratio of 1:1. The binding ratio is questionable given that Protein A and IgG typically bind at a ratio of 1:2 but may be explained by the sites being independent of one another and thus no secondary attachment is observed. A variety of models were fitted to the data but only two- and three-site models fitted the experimental data, with the three-site model being a more accurate and robust fit across pH changes. A multiple site model seems intuitively correct given the six different binding sites that Protein A has for interaction with IgG. The models produced have potential applications in a larger model of Protein A affinity chromatography, although a number of additional factors would need to be taken into account, such as mass transfer effects and the IgG concentration gradient

Enriched Event Streams: A General Platform For Empirical Studies On In-IDE Activities Of Software Developers

Current studies on software development either focus on the change history of source code from version-control systems or on an analysis of simplistic in-IDE events without context information. Each of these approaches contains valuable information that is unavailable in the other case. This work proposes enriched event streams, a solution that combines the best of both worlds and provides a holistic view on the in-IDE software development process. Enriched event streams not only capture developer activities in the IDE, but also specialized context information, such as source-code snapshots for change events. To enable the storage of such code snapshots in an analyzable format, we introduce a new intermediate representation called Simplified Syntax Trees (SSTs) and build CARET, a platform that offers reusable components to conveniently work with enriched event streams. We implement FeedBaG++, an instrumentation for Visual Studio that collects enriched event streams with code snapshots in the form of SSTs and share a dataset of enriched event streams captured in an ongoing field study from 81 users and representing 15K hours of active development. We complement this with a dataset of 69M lines of released source code extracted from 360 GitHub repositories. To demonstrate the usefulness of our platform, we use it to conduct studies on the in-IDE development process that are both concerned with source-code evolution and the analysis of developer interactions. In addition, we build recommendation systems for software engineering and analyze and improve current evaluation techniques

Investigation of coding and equalization for the digital HDTV terrestrial broadcast channel

Includes bibliographical references (p. 241-248).Supported by the Advanced Telecommunications Research Program.Julien J. Nicolas

Design of diesel engine's optimal control maps for high efficiency and emission reduction

The objective of this thesis is to create static optimal control maps of diesel engines for high efficiency and emission reduction. The calibration tool to be used to create the control maps, named "Off-line parameterization tool", was designed based on the Design of Experiments method. The optimization goal is to minimize the Brake Specific Fuel Consumption (BSFC) of the engine by the engine's input parameters and under some emission constraints. The tool was designed to be able to work both fully automatically and semi-automatically. Though most of the previous researches on engine calibration have used the Design of Experiments approach, their implementations in choosing experimental design types and optimization process are different compared to this thesis. The unique aspect of this research lies on the significant properties of the Off-line parameterization tool. Firstly, this tool is flexible, it is able to work with multiple inputs and multiple outputs. Secondly, it can reduce the calibration time as the engine running time is kept as small as possible and all the data processing work is done automatically

Modulation, Coding, and Receiver Design for Gigabit mmWave Communication

While wireless communication has become an ubiquitous part of our daily life and the world around us, it has not been able yet to deliver the multi-gigabit throughput required for applications like high-definition video transmission or cellular backhaul communication. The throughput limitation of current wireless systems is mainly the result of a shortage of spectrum and the problem of congestion. Recent advancements in circuit design allow the realization of analog frontends for mmWave frequencies between 30GHz and 300GHz, making abundant unused spectrum accessible. However, the transition to mmWave carrier frequencies and GHz bandwidths comes with new challenges for wireless receiver design. Large variations of the channel conditions and high symbol rates require flexible but power-efficient receiver designs. This thesis investigates receiver algorithms and architectures that enable multi-gigabit mmWave communication. Using a system-level approach, the design options between low-power time-domain and power-hungry frequency-domain signal processing are explored. The system discussion is started with an analysis of the problem of parameter synchronization in mmWave systems and its impact on system design. The proposed synchronization architecture extends known synchronization techniques to provide greater flexibility regarding the operating environments and for system efficiency optimization. For frequency-selective environments, versatile single-carrier frequency domain equalization (SC-FDE) offers not only excellent channel equalization, but also the possibility to integrate additional baseband tasks without overhead. Hence, the high initial complexity of SC-FDE needs to be put in perspective to the complexity savings in the other parts of the baseband. Furthermore, an extension to the SC-FDE architecture is proposed that allows an adaptation of the equalization complexity by switching between a cyclic-prefix mode and a reduced block length overlap-save mode based on the delay spread. Approaching the problem of complexity adaptation from time-domain, a high-speed hardware architecture for the delayed decision feedback sequence estimation (DDFSE) algorithm is presented. DDFSE uses decision feedback to reduce the complexity of the sequence estimation and allows to set the system performance between the performance of full maximum-likelihood detection and pure decision feedback equalization. An implementation of the DDFSE architecture is demonstrated as part of an all-digital IEEE802.11ad baseband ASIC manufactured in 40nm CMOS. A flexible architecture for wideband mmWave receivers based on complex sub-sampling is presented. Complex sub-sampling combines the design advantages of sub-sampling receivers with the flexibility of direct-conversion receivers using a single passive component and a digital compensation scheme. Feasibility of the architecture is proven with a 16Gb/s hardware demonstrator. The demonstrator is used to explore the potential gain of non-equidistant constellations for high-throughput mmWave links. Specifically crafted amplitude phase-shift keying (APSK) modulation achieve 1dB average mutual information (AMI) advantage over quadrature amplitude modulation (QAM) in simulation and on the testbed hardware. The AMI advantage of APSK can be leveraged for a practical transmission using Polar codes which are trained specifically for the constellation

Adaptive equalisation for fading digital communication channels

This thesis considers the design of new adaptive equalisers for fading digital communication channels. The role of equalisation is discussed in the context of the functions of a digital radio communication system and both conventional and more recent novel equaliser designs are described. The application of recurrent neural networks to the problem of equalisation is developed from a theoretical study of a single node structure to the design of multinode structures. These neural networks are shown to cancel intersymbol interference in a manner mimicking conventional techniques and simulations demonstrate their sensitivity to symbol estimation errors. In addition the error mechanisms of conventional maximum likelihood equalisers operating on rapidly time-varying channels are investigated and highlight the problems of channel estimation using delayed and often incorrect symbol estimates. The relative sensitivity of Bayesian equalisation techniques to errors in the channel estimate is studied and demonstrates that the structure's equalisation capability is also susceptible to such errors. Applications of multiple channel estimator methods are developed, leading to reduced complexity structures which trade performance for a smaller computational load. These novel structures are shown to provide an improvement over the conventional techniques, especially for rapidly time-varying channels, by reducing the time delay in the channel estimation process. Finally, the use of confidence measures of the equaliser's symbol estimates in order to improve channel estimation is studied and isolates the critical areas in the development of the technique — the production of reliable confidence measures by the equalisers and the statistics of symbol estimation error bursts