No description available.

No description available.

<p>In this thesis we study radio resource management (RRM) in two types of CDMA-based wireless networks, cognitive radio networks (CRNs) and cooperative communication networks. In the networks, all simultaneous transmissions share the same spectrum and interfere with one another. Therefore, managing the transmission power is very important as it determines other aspects of the network resource allocations, such as transmission time and rate allocations. The main objective of the RRM is to efficiently utilize the available network resources for providing the mobile users with satisfactory quality of service (QoS).</p> / Doctor of Philosophy (PhD)

<p>In this thesis we study a wireless mesh network (WMN), where a number of access points (APs) form a wireless infrastructure and provide communications to the mobile stations (MSs). Different APs share the same frequency channel. We study how to provide fair throughput for the MSs while efficiently utilizing the channel resources through effective handoff management and channel timeline allocations.</p> <p>In the first part of the thesis, we assume that the channel time allocations at the AP level are given, and jointly consider the handoff management of the MSs and the channel time allocations at the MS level. An optimization problem is formulated based on long-term proportional fairness (PF) and solved. A heuristic distributed scheme is then proposed, which can be easily implemented in a practical WMN.</p> <p>In the second part, we jointly study the channel time allocations at the AP level and the MS level together with the MS handoff management. An optimization problem is first formulated and solved as a benchmark. Two distributed schemes are proposed by decoupling the handoff management and time allocations. The HO-CA scheme performs heuristic handoff decisions for the MSs and then optimizes the channel time allocations. The CA-HO scheme allocates the channel time to individual APs based on the neighboring relationship of the APs, and then makes handoff.</p> <p>Numerical results indicate that our proposed distributed schemes can achieve close-to-optimum fairness, improve the network utilization and balance the traffic load under uneven MSs geographical distributions.</p> / Master of Applied Science (MASc)

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes, providing insight into the disease's process. With the rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of thousands of genes and proteins resulting in enormous data sets where the number of genomic variables (covariates) is far greater than the number of subjects. It is also typical for such data sets to have a high proportion of censored observations. Methods based on univariate Cox regression are often used to select genes related to survival outcome. However, the Cox model assumes proportional hazards (PH), which is unlikely to hold for each gene. When applied to genes exhibiting some form of non-proportional hazards (NPH), these methods could lead to an under- or over-estimation of the effects. In this thesis, we develop methods that will directly address t / Statistics

Indiana University-Purdue University Indianapolis (IUPUI) / The counting process is the fundamental of many real-world problems with event data. Poisson process, used as the background intensity of Hawkes process, is the most commonly used point process. The Hawkes process, a self-exciting point process fits to temporal event data, spatial-temporal event data, and event data with covariates. We study the Hawkes process that fits to heterogeneous drug overdose data via a novel semi-parametric approach. The counting process is also related to survival data based on the fact that they both study the occurrences of events over time. We fit a Cox model to temporal event data with a large corpus that is processed into high dimensional covariates. We study the significant features that influence the intensity of events.

One of the most common measures of clinical outcomes is the survival time. Accurately linking cancer molecular profiling with survival outcome advances clinical management of cancer. However, existing survival analysis relies intensively on statistical evidence from a single level of data, without paying much attention to the integration of interacting multi-level data and the underlying biology. Advances in genomic techniques provide unprecedented power of characterizing the cancer tissue in a more complete manner than before, opening the opportunity of designing biologically informed and integrative approaches for survival analysis. Many cancer tissues have been profiled for gene expression levels and genomic variants (such as copy number alterations, sequence mutations, DNA methylation, and histone modification). However, it is not clear how to integrate the gene expression and genetic variants to achieve a better prediction and understanding of the cancer survival. To address this challenge, we propose two approaches for data integration in order to both biologically and statistically boost the features selection process for proper detection of the true predictive players of survival. The first approach is data-driven yet biologically informed. Consistent with the biological hierarchy from DNA to RNA, we prioritize each survival-relevant feature with two separate scores, predictive and mechanistic. With mRNA expression levels in concern, predictive features are those mRNAs whose variation in expression levels are associated with the survival outcome, and mechanistic features are those mRNAs whose variation in expression levels are associated with genomic variants (copy number alterations (CNAs) in this study). Further, we propose simultaneously integrating information from both the predictive model and the mechanistic model through our new approach GEMPS (Gene Expression as a Mediator for Predicting Survival). Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power than peer methods. Gene set enrichment analysis confirms that the genes utilized for the final survival analysis are biologically important and relevant. The second approach is a generic mathematical framework to biologically regularize the Cox's proportional hazards model that is widely used in survival analysis. We propose a penalty function that both links the mechanistic model to the clinical model and reflects the biological downstream regulatory effect of the genomic variants on the mRNA expression levels of the target genes. Fast and efficient optimization principles like the coordinate descent and majorization-minimization are adopted in the inference process of the coefficients of the Cox model predictors. Through this model, we develop the regulator-target gene relationship to a new one: regulator-target-outcome relationship of a disease. Assessed via a simulation study and analysis of two real cancer data sets, the proposed method showed better performance in terms of selecting the true predictors and achieving better survival prediction. The proposed method gives insightful and meaningful interpretability to the selected model due to the biological linking of the mechanistic model and the clinical model. Other important forms of clinical outcomes are monitoring angiogenesis (formation of new blood vessels necessary for tumor to nourish itself and sustain its existence) and assessing therapeutic response. This can be done through dynamic imaging, in which a series of images at different time instances are acquired for a specific tumor site after injection of a contrast agent. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive tool to examine tumor vasculature patterns based on accumulation and washout of the contrast agent. DCE-MRI gives indication about tumor vasculature permeability, which in turn indicates the tumor angiogenic activity. Observing this activity over time can reflect the tumor drug responsiveness and efficacy of the treatment plan. However, due to the limited resolution of the imaging scanners, a partial-volume effect (PVE) problem occurs, which is the result of signals from two or more tissues combining together to produce a single image concentration value within a pixel, with the effect of inaccurate estimation to the values of the pharmacokinetic parameters. A multi-tissue compartmental modeling (CM) technique supported by convex analysis of mixtures is used to mitigate the PVE by clustering pixels and constructing a simplex whose vertices are of a single compartment type. CAM uses the identified pure-volume pixels to estimate the kinetics of the tissues under investigation. We propose an enhanced version of CAM-CM to identify pure-volume pixels more accurately. This includes the consideration of the neighborhood effect on each pixel and the use of a barycentric coordinate system to identify more pure-volume pixels and to test those identified by CAM-CM. Tested on simulated DCE-MRI data, the enhanced CAM-CM achieved better performance in terms of accuracy and reproducibility. / Ph. D.

Motivated by the fact that anti-ship missiles present a serious threat to today's Navy, a tracking filter which will give superior tracking and trajectory extrapolation when tracking anti-ship missiles is desired. Because most anti-ship missiles use proportional navigation in their guidance systems, it is best to model their motion using the proportional navigation guidance law. An unbiased narrowband filter is required because the state estimate is used to extrapolate the trajectory over the long time of flight of the gun projectile used to intercept the anti-ship missile. Using the proportional navigation guidance law, a tracking filter is developed which meets the stated requirements. An advantage in using the proportional navigation model, which is not found in previous target models, is the end goal or destination constraint inherent in the proportional navigation guidance law: the anti-ship missile's goal is to strike ownership; the proportional navigation trajectory always passes through the origin. Because of model mismatch when tracking missiles using proportional navigation guidance, previous tracking filters, which use constant velocity, exponentially correlated acceleration, or constant acceleration models of target motion, must use a wide bandwidth or else develop significant bias errors. / M.S.

This last decade experienced a sudden increase of interest in the control of thermo-acoustic instabilities, in particular through the use of fuel modulation techniques. The primary goal of this research was to design, construct and characterize a high bandwidth proportional fuel injection system, which could be used to study the effect of specific levels of fuel modulation on the combustion process and the reduction of thermo-acoustic instabilities. A fuel injection system, incorporating the use of a closed loop piston and check valve, was designed to modulate the primary fuel supply of an atmospheric liquid-fueled swirl stabilized combustor operating at a mean volumetric fuel flow rate of 0.4 GPH. The ability of the fuel injection system to modulate the fuel was examined by measuring the fuel line pressure and the flow rate produced during operation. The authority of this modulation over the combustion process was investigated by examining the effect of fuel modulation on the combustor pressure and the heat release of the flame. Sinusoidal operation of the fuel injection system demonstrated: a bandwidth greater that 800 Hz, significant open loop authority (averaging 12 dB) with regards to the combustor pressure, significant open loop authority (averaging 33 dB) with regards to the unsteady heat release rate and an approximate 8 dB reduction of the combustor pressure oscillation present at 100 Hz, using a phase shift controller. It is possible to scale the closed loop piston and check valve configuration used to create the fuel injection system discussed in this work to realistic combustor operating conditions for further active combustion control studies. / Master of Science

High-bandwidth fuel modulation is currently one of the most promising methods for active combustion control. To attenuate the large pressure oscillations in the combustion chamber, the fuel is pulsed so that the heat release rate fluctuations damp the pressure oscillations in the combustor. This thesis focuses on the development and implementation of a high-bandwidth, proportional modulation system for liquid-fuel active combustion control. The throttle valve modulation system, discussed in this thesis, uses a 500-um piezoelectric stack coupled with an off-the-shelf valve. After comparing three other types of actuators, the piezoelectric stack was selected because of its compact size, bandwidth capabilities, and relatively low cost. Using the acoustic resonance of the fuel line, the system is able to achieve 128% pressure modulation, relative to the mean pressure, and is capable of producing more than 75% flow modulation at 115 Hz. Additionally, at 760 Hz the system produces 40% pressure modulation and 21% flow modulation with flow rates between 0.4 and 10 gph. Control authority was demonstrated on a single-nozzle kerosene combustor which exhibits a well-pronounced instability at ~115 Hz. Using the modulation system, the fundamental peak of the combustion instability was reduced by 30 dB, and the broadband sound pressure levels inside the combustor were reduced by 12 dB. However, the most important conclusion from the combustion control experiments was not the system?s accomplishments, but rather its inability to control the combustor at high global equivalence ratios. Our work indicates that having the ability to modulate a large percentage of the primary fuel is not always sufficient for active combustion control. / Master of Science