Rapid advances in sensor technologies have fueled massive torrents of data streaming across networks. Such large volume of information, indeed, restricts the operational performance of data processing, causing inefficiency in sensing, computation, communication and control. Hence, classical data processing techniques need to be re-analyzed and re-designed prior to be applied to modern networked data systems. This thesis aims to understand and characterize fundamental principles and interactions in and among sensing, compression, communication, computation and control, involved in networked data systems. In this regard, the thesis investigates four problems. The common theme is the design and analysis of optimized low-delay transmission strategies with affordable complexity for reliable communication of acquired data over networks with the objective of providing high quality of service for users. In the first three problems considered in the thesis, an emerging framework for data acquisition, namely, compressed sensing, is used which performs acquisition and compression simultaneously. The first problem considers the design of iterative encoding schemes, based on scalar quantization, for transmission of compressed sensing measurements over rate-limited links. Our approach is based on an analysis-by-synthesis principle, where the motivation is to reflect non-linearity in reconstruction, raised by compressed sensing, via synthesis, on choosing the best quantized value for encoding, via analysis. Our design shows significant reconstruction performance compared to schemes that only consider direct quantization of compressed sensing measurements. In the second problem, we investigate the design and analysis of encoding--decoding schemes, based on vector quantization, for transmission of compressed sensing measurements over rate-limited noisy links. In so realizing, we take an approach adapted from joint source-channel coding framework. We show that the performance of the studied system can approach the fundamental theoretical bound by optimizing the encoder-decoder pair. The price, however, is increased complexity at the encoder. To address the encoding complexity of the vector quantizer, we propose to use low-complexity multi-stage vector quantizer whose optimized design shows promising performance. In the third problem considered in the thesis, we take one step forward, and study joint source-channel coding schemes, based on vector quantization, for distributed transmission of compressed sensing measurements over noisy rate-limited links. We design optimized distributed coding schemes, and analyze theoretical bounds for such topology. Under certain conditions, our results reveal that the performance of the optimized schemes approaches the analytical bounds. In the last problem and in the context of control under communication constraints, we bring the notion of system dynamicity into the picture. Particularly, we study relations among stability in dynamical networked control systems, performance of real-time coding schemes and the coding complexity. For this purpose, we take approaches adapted from separate source-channel coding, and derive theoretical bounds on the performance of two types of coding schemes: dynamic repetition codes, and dynamic Fountain codes. We analytically and numerically show that the dynamic Fountain codes, over binary-input symmetric channels, with belief propagation decoding, are able to provide system stability in a networked control system. The results in the thesis evidently demonstrate that impressive performance gain is feasible by employing tools from communication and information theory to control and sensing. The insights offered through the design and analysis will also reveal fundamental pieces for understanding real-world networked data puzzle. / <p>QC 20140407</p>

Joint source-channel coding (JSCC) has emerged to be a major field of research recently. Channel optimized vector quantization (COVQ) is a simple feasible JSCC scheme introduced for communication over practical channels. In this work, we propose an iterative design algorithm, referred to as the iterative maximum a posteriori (MAP) decoded (IMD) algorithm, to improve COVQ systems. Based on this algorithm, we design a COVQ based on symbol MAP hard-decision demodulation that exploits the non-uniformity of the quantization indices probability distribution. The IMD design algorithm consists of a loop which starts by designing a COVQ, obtaining the index source distribution, updating the discrete memoryless channel (DMC) according to the achieved index distribution, and redesigning the COVQ. This loop stops when the point-to-point distortion is minimized. We consider memoryless Gaussian and Gauss-Markov sources transmitted over binary phase-shift keying modulated additive white Gaussian noise (AWGN) and Rayleigh fading channels. Our scheme, which is shown to have less encoding complexity than conventional COVQ and less encoding complexity and storage requirements than soft-decision demodulated (SDD) COVQ systems, is also shown to provide a notable signal-to-distortion ratio (SDR) gain over the conventional COVQ designed for hard-decision demodulated channels while sometimes matching or exceeding the SDD COVQ performance, especially for higher quantization dimensions and/or rates. In addition to our main result, we also propose another iterative algorithm to design SDD COVQ based on the notion of the JSCC error exponent. This system is shown to have some gain over classical SDD COVQ both in terms of the SDR and the exponent itself. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2008-08-29 17:58:52.329

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We study the effects of loop quantum gravity motivated corrections in classical systems. Computational methods are used to simulate black hole formation from the gravitational collapse of a massless scalar field in Painleve-Gullstrand coordinates. Singularities present in the classical case are resolved by a radiation-like phase in the quantum collapse. The evaporation is not complete but leaves behind an outward moving shell of mass that disperses to infinity. We reproduce Choptuik scaling showing the usual behaviour for the curvature scaling, while observing previously unseen behaviour in the mass scaling. The quantum corrections are found to impose a lower limit on black hole mass and generate a new universal power law scaling relationship. In a parallel study, we quantize the Hamiltonian for a particle in the singular $1/r^2$ potential, a form that appears frequently in black hole physics. In addition to conventional Schrodinger methods, the quantization is performed using full and semiclassical polymerization. The various quantization schemes are in excellent agreement for the highly excited states but differ for the low-lying states, and the polymer spectrum is bounded below even when the Schrodinger spectrum is not.

The development of electronics has been growing at a fast rate in recent years. More and more ideas have been searched and are increasing at a faster rate. However, there is more detail work in the nanolevel or nanostructure yet to be understood. Thus, more and more semiconductor physicists have move to the new field of study in nanostructures. Nanostructures are the future of electronic devices. By understanding nanostructure electronic devices, electronics is the key for the progress of any modern equipment and advancement. This comes about when electronic transport of a nanostructure is thoroughly understood. Thus, future electronic devices can utilize the development of conductance through components having dimensions on the nanometer scale.The objective of the proposed research project is to study electronic transport in a ring with an infinite potential barrier at the center and a modulated external potential in one of the arms. The relative phase between the two paths in this structure can be controlled by applying electrostatic potential in one of the arms. One can compare these types of systems with optical interferometers, where the phase difference between the two arms is controlled by changing the refractive index of one arm through the electro-optic effect. By modulating the potential in one arm of the ring, we will study the interference effect on conductance. The method of finding the conductance of a nanostructure will be using the recursive Green's function method. This includes finding transverse eigenvalues, eigenfunctions, and hopping integrals to determine Green's propagators. A FORTRAN 77 computer program is used for numerical calculations.These remarkable ultra-small and ultra-clean quantum systems are currently achieved due to significant technological advancement in fabrication. For ultra-small quantum devices, the theoretical understanding of device performance must be based on quantum carrier transport of confined electrons and holes in the channel. This theoretical research will lead to the understanding of the effects of geometry and impurities on transport of the carriers in the nanochannels. / Department of Physics and Astronomy

We study the effects of loop quantum gravity motivated corrections in classical systems. Computational methods are used to simulate black hole formation from the gravitational collapse of a massless scalar field in Painleve-Gullstrand coordinates. Singularities present in the classical case are resolved by a radiation-like phase in the quantum collapse. The evaporation is not complete but leaves behind an outward moving shell of mass that disperses to infinity. We reproduce Choptuik scaling showing the usual behaviour for the curvature scaling, while observing previously unseen behaviour in the mass scaling. The quantum corrections are found to impose a lower limit on black hole mass and generate a new universal power law scaling relationship. In a parallel study, we quantize the Hamiltonian for a particle in the singular $1/r^2$ potential, a form that appears frequently in black hole physics. In addition to conventional Schrodinger methods, the quantization is performed using full and semiclassical polymerization. The various quantization schemes are in excellent agreement for the highly excited states but differ for the low-lying states, and the polymer spectrum is bounded below even when the Schrodinger spectrum is not.

Research Doctorate - Doctor of Philosophy (PhD) / This thesis studies control systems with communication constraints. Such constraints arise due to the fact that practical control systems often use non-transparent communication links, i.e., links subject to data-rate constraints, random data-dropouts or random delays. Traditional control theory cannot deal with such constraints and the need for new tools and insights arises. We study two problems: control with average data-rate constraints and control over analog erasure channels with i.i.d. dropout profiles. When focusing on average data-rate constraints, it is natural to ask whether information theoretic ideas may assist the study of networked control systems. In this thesis we show that it is possible to use fundamental information theoretic concepts to arrive at a framework that allows one to tackle performance related control problems. In doing so, we show that there exists an exact link between control systems subject to average data-rate limits, and control systems which are closed over additive i.i.d. noise channels subject to a signal-to-noise ratio constraint. On the other hand, in the case of control systems subject to i.i.d. data-dropouts, we show that there exists a second-order moments equivalence between a linear feedback system which is interconnected over an analog erasure channel, and the same system when it is interconnected over an additive i.i.d. noise channel subject to a signal-to-noise ratio constraint. From the results foreshadowed above, it follows that the study of control systems closed over signal-to-noise ratio constrained additive i.i.d. noise channels is a task of relevance to many networked control problems. Moreover, the interplay between signal-to-noise ratio constraints and control objectives is an interesting issue in its own right. This thesis starts with such a study. Then, we use the resultant insights to address performance issues in control systems subject to either average data-rate constraints or i.i.d. data-dropouts. Our approach shows that, once key equivalences are exposed, standard control intuition and synthesis machinery can be used to tackle networked control problems in an exact manner. It also sheds light into fundamental results in the literature and gives (partial) answers to several previously open questions. We believe that the insights in this thesis are of fundamental importance and, to the best of the author's knowledge, novel.

Vector Quantization importance has been increasing and it is becoming a vital element in the process of classification and clustering of different types of information to help in the development of machines learning and decisions making, however the different techniques that implements Vector Quantization have always come short in some aspects. A lot of researchers have turned their heads towards the idea of creating a Vector Quantization mechanism that is fast and can be used to classify data that is rapidly being generated from some source, most of the mechanisms depend on a specific style of neural networks, this research is one of those attempts. One of the dilemmas that this technology faces is the compromise that has to be made between the accuracy of the results and the speed of the classification or quantization process, also the complexity of the suggested algorithms makes it very hard to implement and realize any of them on a hardware that can be used as a fast-online classifier which can keep up with the speed of the information being presented to the system, an example for such information sources would be high speed processors, and computer networks intrusion detection systems. This research focuses on creating a Vector Quantizer using neural networks, the neural network that is used in this study is a novel one and has a unique feature that comes from the fact that it is based solely on a set of ordinary differential equations. The input data will be injected in those equations and the classification would be based on finding the equilibrium points of the system with the presence of those input patterns. The elimination of conditional statements in this neural network would mean that the implementation and the execution of the classification process of this technique would have one single path that can accommodate any value. A single execution path will provide easier algorithm analysis and open the possibility to realizing it on a pure analog circuit that can have an operation speed able to match the speed of incoming information and classify the data in a real time fashion. The details of this dynamical system will be provided in this research, also the shortcomings that we have faced and how we overcame them will be explained in particulars. Also, a drastic change in the way of looking at the speed vs. accuracy compromise has been made and presented in this research, aiming towards creating a technique that can produce accurate results with high speeds.

La presente tesis, tuvo como objetivo mostrar una estrategia a través de redes neuronales, para los vehículos del Cuerpo General de Bomberos Voluntarios del Perú (CGBVP) durante una emergencia en el distrito de Surco, contribuyendo a la fluidez vehicular de las unidades en situaciones de emergencia. A nivel mundial se puede apreciar que se han desarrollado diferentes estrategias o sistemas que apoyan a las unidades de emergencia. El desarrollo del sistema propuesto consiste en preparar los semáforos con anticipación al paso de una unidad. Para ello se consideraron dos tipos de datos, ubicación y dirección, con el fin de activar los semáforos tiempo antes que el vehículo llegue a la intersección. El presente estudio analizó la red Neuronal LVQ (Learning Vector Quantization) y 2 tipos de red Backpropagation con el fin de determinar cuál de ellas es la más adecuada para el caso propuesto. Finalmente a través de simulaciones se determinó la red Backpropagation [100 85 10] obtuvo mejores resultados, siendo el de regresión igual a 0.99 y presentando valores de error en un rango de 10^-5 o menores. El algoritmo por Backpropagation [100 85 10] demostró durante sus 3 simulaciones responder correctamente a los 3 escenarios planteados. Demostrando únicamente variaciones pequeñas durante las simulaciones pero ninguna superando valores aceptables de 0 o 1 lógico. The following thesis had as objective to show a strategy using neural networks to help vehicles of the fire fighter brigade in Peru (CGBVP) during emergencies on the district of Surco, helping with the response times of the unit on emergency situations. Worldwide can be seen that strategies or systems are being used to help lower the problems of traffic. The development of the proposed system consist on preparing the traffic lights previous the arrival of the unit to the intersection. For this 2 type of data is being considered, location and direction, in order to activate the lights time before the vehicle arrives to the intersection. The present study analyzed the LVQ (Learning Vector Quantization) and 2 types of backpropagation networks in order to determine which of them is the most fitting for the situation to handle. Finally, going through the simulations it was determined that the [100 85 10] backpropagation network had the best response, being the regression 0.99 and showing error on the range of 10^-5 or lowers. The algorithm by backpropagation [100 85 10] showed during the 3 simulations that works property on all 3 situations. It showed small variations on some of the simulations but nothing out of the acceptable values of a logic 1 or 0.

Submitted by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-09T14:01:45Z No. of bitstreams: 2 Dissertacao Telmo Filho_DEFINITIVA.pdf: 781380 bytes, checksum: fb398deff6f8aa856428277eb3236020 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-09T14:01:45Z (GMT). No. of bitstreams: 2 Dissertacao Telmo Filho_DEFINITIVA.pdf: 781380 bytes, checksum: fb398deff6f8aa856428277eb3236020 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-27 / A Análise de Dados Simbólicos lida com tipos de dados complexos, capazes de modelar a variabilidade interna dos dados e dados imprecisos. Dados simbólicos intervalares surgem naturalmente de valores como variação de temperatura diária, pressão sanguínea, entre outros. Esta dissertação introduz um algoritmo de Learning Vector Quantization para dados simbólicos intervalares, que usa uma distância Euclidiana intervalar ponderada e generalizada para medir a distância entre instâncias de dados e protótipos. A distância proposta tem quatro casos especiais. O primeiro caso é a distância Euclidiana intervalar e tende a modelar classes e clusters com formas esféricas. O segundo caso é uma distância intervalar baseada em protótipos que modela subregiões não-esféricas e de tamanhos similares dentro das classes. O terceiro caso permite à distância lidar com subregiões não-esféricas e de tamanhos variados dentro das classes. O último caso permite à distância modelar classes desbalanceadas, compostas de subregiões de várias formas e tamanhos. Experimentos são feitos para avaliar os desempenhos do Learning Vector Quantization intervalar proposto, usando todos os quatro casos da distância proposta. Três conjuntos de dados intervalares sintéticos e um conjunto de dados intervalares reais são usados nesses experimentos e seus resultados mostram a utilidade de uma distância localmente ponderada.