There is an increasing needto design and implement technological solutions to span scientific advances, facilitate people’s life and increase the efficiency of daily tasks. This bringsinto the picture professionals with sufficient technical skills to bring to life these technological solutions. Considering the outreach and size of said solutions, technical knowledge is not enough to succeed, but softskills such as communication and teamwork. Engineering and technology professionals need to function effectively in teams to accomplish a common goal. Therefore, this study characterizes the strategies that teams use in order to accomplish their goals through successful team interactions.In addition, this study explores how these strategies vary during asemester-longproject and how these variations mayaffectteam interactions and different performance indicators

無處不在的無線業務以其不斷增長的需求促進了對稀缺無線資源的高效利用。多年來，優化技術被廣泛地運用在無線資源分配的方案設計上，從而達到改善系統性能之目的。在此領域的大部分工作中，用於定義優化問題的系統參數被假設為精確可知。然而，實際的系統參數往往是時變且隨機的。忽略系統參數的不確定性極易導致資源分配決策偏離最優狀態，或者甚至違反系統運行約束而使分配決策不可行。 / 本論文提出了一套用於無線通信的動態資源分配的隨機優化框架。具體而言，本文抓住了不確定系統參數的隨機本質，從而建立結合實際的問題模型，並且開發了高效的算法，以獲得最佳的分配決策。本文將提出的框架成功地應用於三個很有前景的無線通信系統中：自適應正交頻分多址接入（OFDMA）系統，多輸入多輸出（MIMO）天線系統，以及位置感知網絡。每一個應用系統中都存在與實踐相關的挑戰，而這些挑戰則源自於傳統基於靜態優化的設計在提供滿意的服務質量（QoS）中遇到的困難。結果表明，使用隨機優化的動態資源分配，可達到了更加穩健的QoS性能，並且顯著增強系統的實用性。 / 在自適應OFDMA系統中，本文提出了一套“慢適應“的最優子載波分配方案。該方案通過採用更新遠慢於無線信道波動的資源分配策略，從而使計算複雜度和控制信令大大降低。本文根據不同的應用背景，將慢適應子載波分配問題描述成為幾個不同的隨機規劃問題。其中，我們設計了一個高效的算法專門用以求解機會約束規劃類型的子載波分配問題。 / 在MIMO天線系統中，本文提出了一套天線和發射功率聯合分配的方案，使利用多天線支持單一移動終端上的多個無線電模塊同時運行成為可能。該方案最大化了長期系統吞吐量，同時以容許偶爾違反系統約束的方式滿足每個無線模塊的短期傳輸速率需求。結果表明，最優天線和發射功率分配顯著提高系統的吞吐量和滿足QoS的成功概率；而最優天線分配與最優功率分配相比，對提高系統吞吐量有更大的貢獻。 / 在位置感知網絡中，本文提出了一套魯棒功率分配方案，用以抵抗網絡參數的不確定性，這些參數包括用戶位置以及信道狀態。本文提出了一種新的魯棒優化方法，用以獲得最優功率分配，從而提高定位精度和網絡能效。結果表明，魯棒方案顯著優於非魯棒的功率分配和平均分配方案。 / 本論文著眼於縮短傳統基於靜態優化的設計與其現實針對性之間的差距。鑒於許多無線系統的參數在本質上都具有隨機性，本文所提出的採用隨機優化的資源分配方法，有望在未來無線通信中得到更多的應用。 / The growing demand of ubiquitous wireless services has prompted the efficient utilization of scarce radio resources. Over the years, optimization techniques have been widely employed to design optimal resource allocation schemes to achieve performance improvement. Most work in this area assumes that the system parameters defining the optimization problem are precisely known. In practical systems, however, these parameters are often time varying and random. Ignoring the parameter uncertainties would easily lead to suboptimality or even infeasible solutions that violate system operation constraints. / This thesis presents a stochastic optimization framework for the dynamic resource allocation in wireless communications. In particular, practice-relevant problem formulations are proposed to capture the stochastic nature of the uncertain system parameters, and efficient algorithms are developed to obtain the optimal allocation decisions. The proposed framework has been successfully applied in three promising wireless systems: adaptive orthogonal frequency division multiple access (OFDMA) systems, multiple-input and multiple-output (MIMO) antenna systems, and location-aware networks. Each application contains practice-relevant challenges, where the conventional designs using deterministic optimization fail to provide satisfactory quality of service (QoS). The results demonstrate that the dynamic resource allocation using stochastic optimization achieves more robust QoS performance and remarkably enhances the system practicality. / In adaptive OFDMA systems, a slow adaptation scheme is proposed for optimal subcarrier allocation. The proposed scheme updates the resource allocation decisions on a much slower timescale than that of channel fluctuation, which drastically reduces the computational complexity and control signaling overhead. The problems are formulated into several stochastic programs based on different application scenarios. An efficient algorithm is developed for solving the chance constrained subcarrier allocation problem. / In MIMO antenna systems, an antenna-and-power allocation scheme is proposed to enable the use of multiple antennas to support multiple radios co-operating on the same mobile device. The proposed scheme maximizes the long-term system throughput while satisfying the short-term data rate requirement of each radio transmission with occasional outage. The results show that both system throughput and success probability of QoS satisfaction are improved, and the optimal antenna allocation contributes to a larger portion of throughput increase comparing with the optimal power allocation. / In location-aware networks, robust power allocation schemes are proposed to combat the uncertainties in network parameters including user positions and channel conditions. A novel robust optimization method is developed to obtain the optimal power allocation, which improves both localization accuracy and network energy efficiency. The results show that the robust schemes remarkably outperform both non-robust power allocation and uniform allocation. / The goal of this thesis is to bridge the gap between the current designs under the deterministic optimization framework and their practical relevance. Given the fact that many wireless system parameters are stochastic in nature, the proposed resource allocation methods using stochastic optimization are expected to find further applications in wireless communications. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Li, Weiliang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 157-175). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / 摘要 / Abstract --- p.iii / Acknowledgement --- p.vi / Contents --- p.ix / List of Figures --- p.xiii / List of Tables --- p.xvii / List of Acronyms --- p.xviii / List of Notations --- p.xxi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Resource Allocation in Wireless Communications --- p.2 / Chapter 1.2 --- Stochastic Optimization and Its Applications --- p.4 / Chapter 1.2.1 --- Robust Optimization --- p.5 / Chapter 1.2.2 --- Chance Constrained Optimization --- p.8 / Chapter 1.3 --- Motivation and Research Focus --- p.10 / Chapter 1.3.1 --- Motivation --- p.10 / Chapter 1.3.2 --- OFDM and OFDMA Systems --- p.14 / Chapter 1.3.3 --- MIMO Antenna Systems --- p.16 / Chapter 1.3.4 --- Location-Aware Networks --- p.18 / Chapter 1.4 --- Contributions --- p.20 / Chapter 1.5 --- Organization --- p.23 / Chapter 2 --- Slow Subcarrier Allocation in Adaptive OFDMA Systems --- p.25 / Chapter 2.1 --- System and Channel Model --- p.29 / Chapter 2.1.1 --- Channel Model --- p.29 / Chapter 2.1.2 --- Slow Adaptive OFDMA --- p.30 / Chapter 2.2 --- Slow Adaptive OFDMA with Average Rate Constraints for Elastic Traffics --- p.32 / Chapter 2.2.1 --- Problem Formulation --- p.33 / Chapter 2.2.2 --- Computation of Expected Average Data Rate --- p.34 / Chapter 2.2.3 --- Numerical Results --- p.37 / Chapter 2.3 --- Slow Adaptive OFDMA with Average Rate Constraints for Inelastic Traffics --- p.40 / Chapter 2.3.1 --- Problem Formulation --- p.40 / Chapter 2.3.2 --- Numerical Results --- p.43 / Chapter 2.4 --- Slow Adaptive OFDMA with Probabilistic Rate Constraints --- p.46 / Chapter 2.4.1 --- Problem Formulation --- p.47 / Chapter 2.4.2 --- Safe Tractable Constraints --- p.48 / Chapter 2.4.3 --- Algorithm Design --- p.51 / Chapter 2.4.4 --- Problem Size Reduction --- p.59 / Chapter 2.4.5 --- Numerical Results --- p.61 / Chapter 2.5 --- Summary --- p.70 / Chapter 3 --- Dynamic Antenna-and-Power Allocation in Composite Radio MIMO Networks --- p.72 / Chapter 3.1 --- System Model --- p.76 / Chapter 3.1.1 --- Composite Radio System --- p.76 / Chapter 3.1.2 --- Channel Model --- p.77 / Chapter 3.1.3 --- Dynamic Antenna-and-Power Allocation --- p.78 / Chapter 3.2 --- Problem Formulation --- p.80 / Chapter 3.2.1 --- MIMO Channel Capacity --- p.80 / Chapter 3.2.2 --- Chance Constrained Formulation --- p.81 / Chapter 3.2.3 --- Safe Tractable Formulation --- p.82 / Chapter 3.3 --- Search for Feasible Solutions --- p.85 / Chapter 3.3.1 --- Algorithm Design --- p.87 / Chapter 3.4 --- Approach to Optimal Solution --- p.89 / Chapter 3.4.1 --- Cutting-Plane-Based Algorithm --- p.91 / Chapter 3.4.2 --- Optimal Antenna-and-Power Allocation --- p.95 / Chapter 3.5 --- Simulation Results --- p.96 / Chapter 3.6 --- Summary --- p.106 / Chapter 4 --- Robust Power Allocation for Energy-Efficient Location-Aware Networks --- p.107 / Chapter 4.1 --- System Model --- p.110 / Chapter 4.1.1 --- Network Settings --- p.110 / Chapter 4.1.2 --- Position Error Bound --- p.111 / Chapter 4.1.3 --- Directional Decoupling of SPEB --- p.113 / Chapter 4.2 --- Optimal Power Allocation via Conic Programming --- p.115 / Chapter 4.2.1 --- Problem Formulation Based on SPEB --- p.115 / Chapter 4.2.2 --- Problem Formulation Based on mDPEB --- p.117 / Chapter 4.2.3 --- Formulations with QoS Guarantee --- p.120 / Chapter 4.3 --- Robust Power Allocation under Imperfect Network Topology Parameters --- p.122 / Chapter 4.3.1 --- Robust Counterpart of SPEB Minimization --- p.123 / Chapter 4.3.2 --- Robust Counterpart of mDPEB Minimization --- p.131 / Chapter 4.4 --- Efficient Robust Algorithm Using Distributed Computations --- p.132 / Chapter 4.4.1 --- Algorithm for SPEB Minimization --- p.132 / Chapter 4.4.2 --- Algorithm for mDPEB Minimization --- p.136 / Chapter 4.5 --- Simulation Results --- p.137 / Chapter 4.5.1 --- Power Allocation with Perfect Network Topology Parameters --- p.137 / Chapter 4.5.2 --- Robust Power Allocation with Imperfect Network Topology Parameters --- p.140 / Chapter 4.6 --- Summary --- p.144 / Chapter 5 --- Conclusions and Future Work --- p.145 / Chapter 5.1 --- Conclusions --- p.145 / Chapter 5.1.1 --- Slow Adaptive OFDMA Systems --- p.146 / Chapter 5.1.2 --- Composite Radio MIMO Networks --- p.147 / Chapter 5.1.3 --- Energy-Efficient Location-Aware Networks --- p.148 / Chapter 5.2 --- Future Work --- p.150 / Chapter A --- Bernstein Approximation Theorem --- p.153 / Chapter B --- Ergodic MIMO Capacity and Moment Generating Function --- p.155 / Bibliography --- p.157

This thesis contains contributions in three areas: the theory of generalized calculus, numerical algorithms for operations research, and applications of optimization to problems in modern electric power systems. A geometric approach is used to advance the theory and tools used for studying generalized notions of derivatives for nonsmooth functions. These advances specifically pertain to methods for calculating subdifferentials and to expanding our understanding of a certain notion of derivative of set-valued maps, called the coderivative, in infinite dimensions. A strong understanding of the subdifferential is essential for numerical optimization algorithms, which are developed and applied to nonsmooth problems in operations research, including non-convex problems. Finally, an optimization framework is applied to solve a problem in electric power systems involving a smart solar inverter and battery storage system providing energy and ancillary services to the grid.

acase@tulane.edu

Introduction: Previous literature indicates that foreign-born women have lower rates of cervical cancer testing and higher mortality rates when compared to U.S.-born women. Factors that influence receipt of cervical cancer screening among foreign-born women include acculturation and human papillomavirus (HPV) knowledge. Methods: In this cross-sectional study, the 2007 California Health Interview Survey (CHIS) was used to examine the impact of acculturation on cervical cancer screening and HPV knowledge and the 2000-2008 Surveillance Epidemiology and End Results (SEER) database was used to determine differences in stage of diagnosis and survival time. The study population included a total of 3,603,412 foreign-born and 6,749,557 U.S-born women in the CHIS between the age of 18 to 65 and a total of 10,733 U.S.-born and 5,069 foreign-born women in the SEER database. Logistic regression was used to examine the predictors for cervical cancer screening and Cox’s proportional hazards ratios were used to determine the effect of covariates on survival time. Kaplan-Meier survival analysis generated survival curves. Results: Acculturation levels were positively associated with ever having a Pap test, ever hearing about HPV, knowledge that HPV causes cancer and HPV does not cause AIDS, but not with current receipt of a Pap test, knowledge that HPV can be sexually transmitted and that HPV can go away without treatment. Women with low (0.38, (CI, 0.22, 0.66)) and medium (0.50, (CI, 0.39, 0.81) levels of acculturation were less likely to ever receive a Pap test and less likely to ever hear of HPV compared to highly acculturated women. Foreign-born women had a lower risk of death than U.S.-born women. Conclusions: Despite a reported lower risk of death, foreign-born women, particularly those less acculturated, may benefit from targeted interventions to increase cervical cancer screening utilization and general HPV awareness. / acase@tulane.edu

acase@tulane.edu

acase@tulane.edu

acase@tulane.edu

Efficient organization requires rigorous and systematic information management, which encompasses information processing and decision making. Within the efforts in management science and informatics invested towards advancing the knowledge on, and providing assistance to decision making, this thesis focuses on the conceptualizations and techniques intended to facilitate the identification, evaluation, and selection of decisions during the earliest stages of information systems engineering, whereby the systems of interest are deployed to partly or fully automate various organizational processes, including information processing ones. The overall motivating problem that drove to, and that unites the various contributions presented in this thesis is how to better inform decision making and guide it towards decisions that will increase the quality (as evaluated both by the engineer and the stakeholders) of the information system being engineered. Topics in two key related areas are therefore addressed. First, boundedly rational individuals cannot take engineering decisions by accounting for all information that may be, or actually is available to them. As their information processing abilities are limited and their perception biased, it is necessary to filter the available information to a manageable level, and to bring it to a format that facilitates the rigorous reasoning invested in decision making. Second, it is necessary to provide guidance on how to use the given information in decision making. The first part of this thesis therefore focuses on conceptualizations that facilitate the identification of relevant information and its organization for subsequent analysis, all in the aim of achieving high quality of the system being engineered. In particular, Part I discusses, shows deficiencies, and accordingly revises the conceptual foundations of requirements engineering, a field of information systems engineering that focuses on the identification and analysis of requirements communicated by the stakeholders to the engineer of the system. The novelty of the suggested revision lies primarily in (i) the separation between functional and nonfunctional (i.e., quality) requirements grounded in a foundational ontology, (ii) the introduction of stakeholders' communicated attitudes as important sources of information for the evaluation of alternative requirements engineering decisions, (iii) the reformulation of the so-called ``requirements problem' -- which precisely defines when the requirements engineering effort is successfully completed -- to account for attitudes and nonfunctional requirements, and (iv) the recognition of the importance of defeasible reasoning in the search for a solution to the requirements problem. Acknowledging the importance of defeasible reasoning leads -- in Part II -- to the study of how defeasible reasoning can be incorporated into established decision making processes involved in the identification and analysis of requirements. Novelty in Part II lies mainly in (i) the use of argumentation and justification processes in the modeling and analysis of requirements, (ii) the combined use of design rationale approaches with argumentation and justification, (iii) the recognition that the clarity of arguments is variable (due to ambiguity, vagueness, synonymy, and overgenerality of information going into premises and conclusions in arguments), (iv) the definition of a number of techniques for the detection of unclear information and its clarification, and (v) the use of ``clarity' as a criterion for the discrimination among arguments. Part III shows how the conceptualizations and techniques introduced in Parts I and II are applied within and are relevant to the engineering of information systems, including those that rely on heterogenous and distributed components, as in service-oriented and agent-oriented computing.

An analytical framework is developed for distributed management of large networks where each node makes locally its decisions. Two issues remain open. One is whether a distributed algorithm would result in a near-optimal management. The other is the complexity, i.e., whether a distributed algorithm would scale gracefully with a network size. We study these issues through modeling, approximation, and randomized distributed algorithms. For near-optimality issue, we first derive a global probabilistic model of network management variables which characterizes the complex spatial dependence of the variables. The spatial dependence results from externally imposed management constraints and internal properties of communication environments. We then apply probabilistic graphical models in machine learning to show when and whether the global model can be approximated by a local model. This study results in a sufficient condition for distributed management to be nearly optimal. We then show how to obtain a near-optimal configuration through decentralized adaptation of local configurations. We next derive a near-optimal distributed inference algorithm based on the derived local model. We characterize the trade-off between near-optimality and complexity of distributed and statistical management. We validate our formulation and theory through simulations.