<p dir="ltr">Gestational Diabetes Mellitus (GDM) is defined as glucose intolerance with first onset during pregnancy in women without previous history of diabetes. The global prevalence of GDM oscillates between 2% and 17%, varying across countries and ethnicities. In the United States (U.S.), every year up to 13% of pregnancies are affected by this disease. Several risk factors for GDM are well established, such as race, age and BMI, while additional factors have been proposed that could affect the risk of developing the disease; some of them are modifiable, such as diet, while others are not, such as environmental factors.</p><p dir="ltr">Taking effective preventive actions against GDM require the early identification of women at highest risk. A crucial task to this end is the establishment of factors that increase the probabilities of developing the disease. These factors are both individual characteristics and choices and likely include environmental conditions.</p><p dir="ltr">The first part of the dissertation focuses on examining the relationship between food insecurity and GDM by using the National Health and Nutrition Examination Survey (NHANES), which has a representative sample of the U.S. population. The aim of this analysis is to determine a national estimate of the impact of food environment on the likelihood of developing GDM stratified by race and ethnicity. A survey weighted logistic regression model is used to assess these relationships which are described using odds ratios.</p><p dir="ltr">The goal of the second part of this research is to determine whether a woman’s risk of developing GDM is affected by her environment, also referred to in this work as level 2 variables. For that purpose, Medicaid claims information from Indiana was analyzed using a multilevel logistic regression model with sample balancing to improve the class imbalance ratio.</p><p dir="ltr">Finally, for the third part of this dissertation, a simulation study was performed to examine the impact of balancing on the prediction quality and inference of model parameters when using multilevel logistic regression models. Data structure and generating model for the data were informed by the findings from the second project using the Medicaid data. This is particularly relevant for medical data that contains measurements at the individual level combined with other data sources measured at the regional level and both prediction and model interpretation are of interest.</p>

Research Doctorate - Doctor of Philosophy (PhD) / This thesis presents results on optimal coding and decoding of discrete-time stochastic signals, in the sense of minimizing a distortion metric subject to a constraint on the bit-rate and on the signal transfer function from source to reconstruction. The first (preliminary) contribution of this thesis is the introduction of new distortion metric that extends the mean squared error (MSE) criterion. We give this extension the name Weighted-Correlation MSE (WCMSE), and use it as the distortion metric throughout the thesis. The WCMSE is a weighted sum of two components of the MSE: the variance of the error component uncorrelated to the source, on the one hand, and the remainder of the MSE, on the other. The WCMSE can take account of signal transfer function constraints by assigning a larger weight to deviations from a target signal transfer function than to source-uncorrelated distortion. Within this framework, the second contribution is the solution of a family of feedback quantizer design problems for wide sense stationary sources using an additive noise model for quantization errors. These associated problems consist of finding the frequency response of the filters deployed around a scalar quantizer that minimize the WCMSE for a fixed quantizer signal-to-(granular)-noise ratio (SNR). This general structure, which incorporates pre-, post-, and feedback filters, includes as special cases well known source coding schemes such as pulse coded modulation (PCM), Differential Pulse-Coded Modulation (DPCM), Sigma Delta converters, and noise-shaping coders. The optimal frequency response of each of the filters in this architecture is found for each possible subset of the remaining filters being given and fixed. These results are then applied to oversampled feedback quantization. In particular, it is shown that, within the linear model used, and for a fixed quantizer SNR, the MSE decays exponentially with oversampling ratio, provided optimal filters are used at each oversampling ratio. If a subtractively dithered quantizer is utilized, then the noise model is exact, and the SNR constraint can be directly related to the bit-rate if entropy coding is used, regardless of the number of quantization levels. On the other hand, in the case of fixed-rate quantization, the SNR is related to the number of quantization levels, and hence to the bit-rate, when overload errors are negligible. It is shown that, for sources with unbounded support, the latter condition is violated for sufficiently large oversampling ratios. By deriving an upper bound on the contribution of overload errors to the total WCMSE, a lower bound for the decay rate of the WCMSE as a function of the oversampling ratio is found for fixed-rate quantization of sources with finite or infinite support. The third main contribution of the thesis is the introduction of the rate-distortion function (RDF) when WCMSE is the distortion metric, denoted by WCMSE-RDF. We provide a complete characterization for Gaussian sources. The resulting WCMSE-RDF yields, as special cases, Shannon's RDF, as well as the recently introduced RDF for source-uncorrelated distortions (RDF-SUD). For cases where only source-uncorrelated distortion is allowed, the RDF-SUD is extended to include the possibility of linear-time invariant feedback between reconstructed signal and coder input. It is also shown that feedback quantization schemes can achieve a bit-rate only 0.254 bits/sample above this RDF by using the same filters that minimize the reconstruction MSE for a quantizer-SNR constraint. The fourth main contribution of this thesis is to provide a set of conditions under which knowledge of a realization of the RDF can be used directly to solve encoder-decoder design optimization problems. This result has direct implications in the design of subband coders with feedback, as well as in the design of encoder-decoder pairs for applications such as networked control. As the fifth main contribution of this thesis, the RDF-SUD is utilized to show that, for Gaussian sta-tionary sources with memory and MSE distortion criterion, an upper bound on the information-theoretic causal RDF can be obtained by means of an iterative numerical procedure, at all rates. This bound is tighter than 0:5 bits/sample. Moreover, if there exists a realization of the causal RDF in which the re-construction error is jointly stationary with the source, then the bound obtained coincides with the causal RDF. The iterative procedure proposed here to obtain Ritc(D) also yields a characterization of the filters in a scalar feedback quantizer having an operational rate that exceeds the bound by less than 0:254 bits/sample. This constitutes an upper bound on the optimal performance theoretically attainable by any causal source coder for stationary Gaussian sources under the MSE distortion criterion.

This thesis proposes a decision-feedback equalizer (DFE) scheme for blind ADC-based receivers to overcome the challenges introduced by blind sampling. It presents the design, simulation, and implementation of a 5Gb/s speculative DFE for a 2x blind ADC-based receiver. The complete receiver, including the ADC, the DFE, and a 2x blind clock and data recovery (CDR) circuit, is implemented in Fujitsu’s 65-nm CMOS process. Measurements of the fabricated test-chip confirm 5Gb/s data recovery with bit error rate (BER) less than 1e−12 in the presence of a test channel introducing 13.3dB of attenuation at the Nyquist frequency of 2.5GHz. The receiver tolerates 0.24UIpp of high-frequency sinusoidal jitter (SJ) in this case. Without the DFE, the BER exceeds 1e−8 even when no SJ is applied.

This thesis proposes a decision-feedback equalizer (DFE) scheme for blind ADC-based receivers to overcome the challenges introduced by blind sampling. It presents the design, simulation, and implementation of a 5Gb/s speculative DFE for a 2x blind ADC-based receiver. The complete receiver, including the ADC, the DFE, and a 2x blind clock and data recovery (CDR) circuit, is implemented in Fujitsu’s 65-nm CMOS process. Measurements of the fabricated test-chip confirm 5Gb/s data recovery with bit error rate (BER) less than 1e−12 in the presence of a test channel introducing 13.3dB of attenuation at the Nyquist frequency of 2.5GHz. The receiver tolerates 0.24UIpp of high-frequency sinusoidal jitter (SJ) in this case. Without the DFE, the BER exceeds 1e−8 even when no SJ is applied.

Research Doctorate - Doctor of Philosophy (PhD) / This thesis presents results on optimal coding and decoding of discrete-time stochastic signals, in the sense of minimizing a distortion metric subject to a constraint on the bit-rate and on the signal transfer function from source to reconstruction. The first (preliminary) contribution of this thesis is the introduction of new distortion metric that extends the mean squared error (MSE) criterion. We give this extension the name Weighted-Correlation MSE (WCMSE), and use it as the distortion metric throughout the thesis. The WCMSE is a weighted sum of two components of the MSE: the variance of the error component uncorrelated to the source, on the one hand, and the remainder of the MSE, on the other. The WCMSE can take account of signal transfer function constraints by assigning a larger weight to deviations from a target signal transfer function than to source-uncorrelated distortion. Within this framework, the second contribution is the solution of a family of feedback quantizer design problems for wide sense stationary sources using an additive noise model for quantization errors. These associated problems consist of finding the frequency response of the filters deployed around a scalar quantizer that minimize the WCMSE for a fixed quantizer signal-to-(granular)-noise ratio (SNR). This general structure, which incorporates pre-, post-, and feedback filters, includes as special cases well known source coding schemes such as pulse coded modulation (PCM), Differential Pulse-Coded Modulation (DPCM), Sigma Delta converters, and noise-shaping coders. The optimal frequency response of each of the filters in this architecture is found for each possible subset of the remaining filters being given and fixed. These results are then applied to oversampled feedback quantization. In particular, it is shown that, within the linear model used, and for a fixed quantizer SNR, the MSE decays exponentially with oversampling ratio, provided optimal filters are used at each oversampling ratio. If a subtractively dithered quantizer is utilized, then the noise model is exact, and the SNR constraint can be directly related to the bit-rate if entropy coding is used, regardless of the number of quantization levels. On the other hand, in the case of fixed-rate quantization, the SNR is related to the number of quantization levels, and hence to the bit-rate, when overload errors are negligible. It is shown that, for sources with unbounded support, the latter condition is violated for sufficiently large oversampling ratios. By deriving an upper bound on the contribution of overload errors to the total WCMSE, a lower bound for the decay rate of the WCMSE as a function of the oversampling ratio is found for fixed-rate quantization of sources with finite or infinite support. The third main contribution of the thesis is the introduction of the rate-distortion function (RDF) when WCMSE is the distortion metric, denoted by WCMSE-RDF. We provide a complete characterization for Gaussian sources. The resulting WCMSE-RDF yields, as special cases, Shannon's RDF, as well as the recently introduced RDF for source-uncorrelated distortions (RDF-SUD). For cases where only source-uncorrelated distortion is allowed, the RDF-SUD is extended to include the possibility of linear-time invariant feedback between reconstructed signal and coder input. It is also shown that feedback quantization schemes can achieve a bit-rate only 0.254 bits/sample above this RDF by using the same filters that minimize the reconstruction MSE for a quantizer-SNR constraint. The fourth main contribution of this thesis is to provide a set of conditions under which knowledge of a realization of the RDF can be used directly to solve encoder-decoder design optimization problems. This result has direct implications in the design of subband coders with feedback, as well as in the design of encoder-decoder pairs for applications such as networked control. As the fifth main contribution of this thesis, the RDF-SUD is utilized to show that, for Gaussian sta-tionary sources with memory and MSE distortion criterion, an upper bound on the information-theoretic causal RDF can be obtained by means of an iterative numerical procedure, at all rates. This bound is tighter than 0:5 bits/sample. Moreover, if there exists a realization of the causal RDF in which the re-construction error is jointly stationary with the source, then the bound obtained coincides with the causal RDF. The iterative procedure proposed here to obtain Ritc(D) also yields a characterization of the filters in a scalar feedback quantizer having an operational rate that exceeds the bound by less than 0:254 bits/sample. This constitutes an upper bound on the optimal performance theoretically attainable by any causal source coder for stationary Gaussian sources under the MSE distortion criterion.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-10-23T16:35:51Z No. of bitstreams: 1 marceloladeiramarques.pdf: 721811 bytes, checksum: 747d74835ffaa88383924f9f6783667b (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-11-09T13:52:02Z (GMT) No. of bitstreams: 1 marceloladeiramarques.pdf: 721811 bytes, checksum: 747d74835ffaa88383924f9f6783667b (MD5) / Made available in DSpace on 2017-11-09T13:52:02Z (GMT). No. of bitstreams: 1 marceloladeiramarques.pdf: 721811 bytes, checksum: 747d74835ffaa88383924f9f6783667b (MD5) Previous issue date: 2017-09-01 / O presente trabalho tem como proposta o desenvolvimento de uma abordagem capaz de melhorar os resultados obtidos por algoritmos de classificação quando aplicados em bases desbalanceadas. O método, denominado Algoritmo de Balanceamento Sintético In-cremental (Incremental Synthetic Balancing Algorithm – ISBA), realiza um procedimento iterativo baseado em classificadores de larga margem, visando gerar amostras sintéticas com o intuito de reduzir o nível de desbalanceamento. No processo são utilizados vetores suporte como referência para a geração das novas instâncias, permitindo posicioná-las em regiões com uma maior representatividade. Além disso, a estratégia permite que as novas amostras ultrapassem os limites das amostras utilizadas como referência para sua geração, o que possibilita uma extrapolação dos limites da classe minoritária, objetivando, assim, alcançar um maior reconhecimento dessa classe de interesse. São apresentados experimentos comparativos com demais técnicas, entre elas o Synthetic Minority Over-sampling Technique (SMOTE), os quais fornecem fortes evidências da aplicabilidade da abordagem proposta. / In this work we propose the development of an approach capable of improving the results obtained by classification algorithms when applied to unbalanced datasets. The method, called Incremental Synthetic Balancing Algorithm (ISBA), performs an iterative procedure based on large margin classifiers, aiming to generate synthetic samples in order to reduce the level of unbalance. In the process, we use the support vectors as reference for the generation of new instances, allowing them to be positioned in regions with greater representativeness. Furthermore, the strategy allows the new samples to exceed the limits of the samples used as reference for their generation, which allows an extrapolation of the limits of the minority class, in order to achieve greater recognition of this class of interest. We present comparative experiments with other techniques, among them the Synthetic Minority Over-sampling Technique (SMOTE), which provide strong evidence of the applicability of the proposed approach.

Sustainably realizing applications of the future with high performance demands requires that energy efficiency becomes a central design criterion for the entire system. For example, the power consumption of the analog-to-digital converter (ADC) can become a major factor when transmitting at large bandwidths and carrier frequencies, e.g., for ultra-short range high data rate communication. The consumed energy per conversion step increases with the sampling rate such that high resolution ADCs become unfeasible in the sub-THz regime at the very high sampling rates required. This makes signaling schemes adapted to 1-bit quantizers a promising alternative. We therefore quantify the performance of bandlimited 1-bit quantized wireless communication channels using techniques like oversampling and faster-than-Nyquist (FTN) signaling to compensate for the loss of achievable rate. As a limiting case, we provide bounds on the mutual information rate of the hard bandlimited 1-bit quantized continuous-time – i.e., infinitely oversampled – additive white Gaussian noise channel in the mid-to-high signal-to-noise ratio (SNR) regime. We derive analytic expressions using runlength encoded input signals. For real signals the maximum value of the lower bound on the spectral efficiency in the high-SNR limit was found to be approximately 1.63 bit/s/Hz. Since in practical scenarios the oversampling ratio remains finite, we derive bounds on the achievable rate of the bandlimited oversampled discrete-time channel. These bounds match the results of the continuous-time channel remarkably well. We observe spectral efficiencies up to 1.53 bit/s/Hz in the high-SNR limit given hard bandlimitation. When excess bandwidth is tolerable, spectral efficiencies above 2 bit/s/Hz per domain are achievable w.r.t. the 95 %-power containment bandwidth. Applying the obtained bounds to a bandlimited oversampled 1-bit quantized multiple-input multiple-output channel, we show the benefits when using appropriate power allocation schemes. As a constant envelope modulation scheme, continuous phase modulation is considered in order to relieve linearity requirements on the power amplifier. Noise-free performance limits are investigated for phase shift keying (PSK) and continuous phase frequency shift keying (CPFSK) using higher-order modulation alphabets and intermediate frequencies. Adapted waveforms are designed that can be described as FTN-CPFSK. With the same spectral efficiency in the high-SNR limit as PSK and CPFSK, these waveforms provide a significantly improved bit error rate (BER) performance. The gain in SNR required for achieving a certain BER can be up to 20 dB. / Die nachhaltige Realisierung von zukünftigen Übertragungssystemen mit hohen Leistungsanforderungen erfordert, dass die Energieeffizienz zu einem zentralen Designkriterium für das gesamte System wird. Zum Beispiel kann die Leistungsaufnahme des Analog-Digital-Wandlers (ADC) zu einem wichtigen Faktor bei der Übertragung mit großen Bandbreiten und Trägerfrequenzen werden, z. B. für die Kommunikation mit hohen Datenraten über sehr kurze Entfernungen. Die verbrauchte Energie des ADCs steigt mit der Abtastrate, so dass hochauflösende ADCs im Sub-THz-Bereich bei den erforderlichen sehr hohen Abtastraten schwer einsetzbar sind. Dies macht Signalisierungsschemata, die an 1-Bit-Quantisierer angepasst sind, zu einer vielversprechenden Alternative. Wir quantifizieren daher die Leistungsfähigkeit von bandbegrenzten 1-Bit-quantisierten drahtlosen Kommunikationssystemen, wobei Techniken wie Oversampling und Faster-than-Nyquist (FTN) Signalisierung eingesetzt werden, um den durch Quantisierung verursachten Verlust der erreichbaren Rate auszugleichen. Wir geben Grenzen für die Transinformationsrate des Extremfalls eines strikt bandbegrenzten 1-Bit quantisierten zeitkontinuierlichen – d.h. unendlich überabgetasteten – Kanals mit additivem weißen Gauß’schen Rauschen bei mittlerem bis hohem Signal-Rausch-Verhältnis (SNR) an. Wir leiten analytische Ausdrücke basierend auf lauflängencodierten Eingangssignalen ab. Für reelle Signale ist der maximale Wert der unteren Grenze der spektralen Effizienz im Hoch-SNR-Bereich etwa 1,63 Bit/s/Hz. Da die Überabtastrate in praktischen Szenarien endlich bleibt, geben wir Grenzen für die erreichbare Rate eines bandbegrenzten, überabgetasteten zeitdiskreten Kanals an. Diese Grenzen stimmen mit den Ergebnissen des zeitkontinuierlichen Kanals bemerkenswert gut überein. Im Hoch-SNR-Bereich sind spektrale Effizienzen bis zu 1,53 Bit/s/Hz bei strikter Bandbegrenzung möglich. Wenn Energieanteile außerhalb des Frequenzbandes tolerierbar sind, können spektrale Effizienzen über 2 Bit/s/Hz pro Domäne – bezogen auf die Bandbreite, die 95 % der Energie enthält – erreichbar sein. Durch die Anwendung der erhaltenen Grenzen auf einen bandbegrenzten überabgetasteten 1-Bit quantisierten Multiple-Input Multiple-Output-Kanal zeigen wir Vorteile durch die Verwendung geeigneter Leistungsverteilungsschemata. Als Modulationsverfahren mit konstanter Hüllkurve betrachten wir kontinuierliche Phasenmodulation, um die Anforderungen an die Linearität des Leistungsverstärkers zu verringern. Beschränkungen für die erreichbare Datenrate bei rauschfreier Übertragung auf Zwischenfrequenzen mit Modulationsalphabeten höherer Ordnung werden für Phase-shift keying (PSK) and Continuous-phase frequency-shift keying (CPFSK) untersucht. Weiterhin werden angepasste Signalformen entworfen, die als FTN-CPFSK beschrieben werden können. Mit der gleichen spektralen Effizienz im Hoch-SNR-Bereich wie PSK und CPFSK bieten diese Signalformen eine deutlich verbesserte Bitfehlerrate (BER). Die Verringerung des erforderlichen SNRs zur Erreichung einer bestimmten BER kann bis zu 20 dB betragen.

Communications receivers that rely on 1-bit analogto-digital conversion are advantageous in terms of hardware complexity and power dissipation. Performance limitations due to the 1-bit quantization can be tackled with oversampling. This paper considers the oversampling gain from an information-theoretic perspective by analyzing the channel capacity with 1-bit quantization and oversampling at the receiver for the particular case of AWGN channels. This includes a numerical computation of the capacity and optimal transmit symbol constellations, as well as the derivation of closed-form expressions for large oversampling ratios and for high signal-to-noise ratios of the channel.

Most modern clock and data recovery circuits (CDR) are based on analog blocks that need to be redesigned whenever the technology process is to be changed. On the other hand, CDR based blind oversampling architecture (BO-CDR) can be completely designed in a digital process which makes its migration very simple. The main disadvantages of the BO-CDR that are usually mentioned in a literature are complexity of its digital circuitry and finite phase resolution resulting in larger jitter sensitivity and higher error rate. This thesis will show that those problems can be solved by designing a new algorithm of BO-CDR and subsequent optimization. For this task an FPGA was selected as simulation and verification platform. This enables to change parameters of the optimized circuit in real time while measuring on real links (unlike a simulation using inaccurate link models). The output of this optimization is a new BO-CDR algorithm with heavily reduced complexity and very low error rate. A new FPGA-based method of jitter measurement was developed (primary for CDR analysis), which enables a quick link characterization without using probing or additional equipment. The new method requires only a minimum usage of FPGA resources. Finally, new measurement equipment was developed to measure bit error distribution on FSO links to be able to develop a suitable error correction scheme based on ARQ protocol.

[pt] Sistemas de múltiplas antenas de larga escala são técnicas fundamentais para sistemas de comunicação sem fio do futuro, que deverão servir dezenas de usuários por estação rádio-base. Neste contexto, um problema chave é o aumento do consumo de energia à medida que o número de antenas cresce. Recentemente, CADs de baixa resolução têm atraído grande interesse de pesquisa. Em particular, CADs de 1 bit são adequados para sistemas de larga escala devido ao seu baixo custo e consumo de energia. Nesta tese, CADs de 1 bit são usados em três diferentes abordagens de projeto, que operam a taxa de Nyquist e a taxas superiores a taxa de Nyquist com estratégias de amostragem uniforme e dinâmica. Nos sistemas operando a taxa de Nyquist, algoritmos de estimação de canal que exploram o conhecimento da baixa resolução e um novo esquema de detecção e decodificação iterativas são propostos, em que códigos low-density paritycheck de bloco curto são considerados para evitar alta latência. Nos sistemas operando a taxas superiores a taxa de Nyquist com sobreamostragem uniforme, algoritmos eficientes de estimação de canal e de detecção com janela deslizante com exploração da baixa resolução são propostos. Além disso, são deduzidas expressões analíticas associadas aos limitantes de Cramér-Rao para os sistemas com sobreamostragem. Resultados numéricos ilustram o desempenho dos algoritmos de estimação de canal propostos e existentes e os limitantes teóricos deduzidos. Nos sistemas operando com sobreamostragem dinâmica, duas abordagens de projeto são desenvolvidas: uma técnica baseada na maximização da soma das taxas e uma técnica baseada na minimização do erro médio quadrático. Em seguida, três algoritmos de redução de dimensão são apresentados e investigados. Resultados de simulações mostram que os sistemas com sobreamostragem dinâmica têm melhor desempenho do que os sistemas com sobreamostragem uniforme em termos de soma das taxas alcançáveis e de taxa de erro de símbolos, enquanto o custo computacional das técnicas examinadas é comparável. / [en] Large-scale multiple-antenna systems are a key technique for future wireless communications, which will serve tens of users per base station (BS). In this scenario, one problem faced is the large energy consumption as the number of receive antennas scales up. Recently, low-resolution analogto-digital converters (ADCs) have attracted much attention. Specifically, 1-bit ADCs in the front-end are suitable for such systems due to their low cost and low energy consumption. In this thesis, 1-bit ADCs are applied in three different system designs, which operate at the Nyquist rate and faster than Nyquist rates along with uniform and dynamic strategies. In the Nyquist-sampling system, low-resolution-aware channel estimation algorithms and a novel iterative detection and decoding scheme are proposed, where short block length low-density parity-check codes are considered for avoiding high latency. In the faster than Nyquist rates with uniform oversampling system, lowresolution-aware channel estimation and sliding window based detection algorithms are proposed due to their low computational cost and high detection accuracy. Particularly, analytical expressions associated with the Bayesian Cramér-Rao bounds for the oversampled systems are presented. Numerical results are provided to illustrate the performance of the proposed channel estimation algorithms and the derived theoretical bounds. In the dynamic-oversampling system, two different system designs are devised, namely, sum rate and mean square error based. Three different dimension reduction algorithms are presented and thoroughly investigated. Simulation results show that the systems with the proposed dynamic oversampling outperform the uniformly oversampled system in terms of the computational cost, achievable sum rate and symbol error rate performance.