This thesis examines the problem of scheduling with incomplete and/or local information in wireless systems. With large numbers of users and limited feedback resources, wireless systems require good scheduling algorithms to attain their performance limits. Classical studies on wireless scheduling investigate in much detail settings where the full state of the system is available when scheduling users. In contrast, this thesis focuses on the case where valuable network state information is lacking at the scheduler, and studies its resulting effect on system performance. The insights gained from the analysis are used to develop efficient wireless scheduling algorithms that operate with limited state information, and that guarantee high throughput and delay performance. The first part of the thesis considers scheduling for stability in a wireless downlink system, where a base station or server schedules transmissions to users, while acquiring channel state information from only subsets of users. It is shown that the system’s throughput region is completely characterized by the marginal channel statistics over observable channel subsets. Effective, queue-length based joint sampling and scheduling algorithms are developed that observe appropriate subsets of channels and schedule users, and the algorithms are shown to be optimal in the sense of throughput. Next, the thesis studies the queue-length performance of wireless scheduling algorithms that use only partial, subset-based channel state information. The chief objective here is to design partial information-based scheduling algorithms that keep the packet queues in the system short, and in this regard, the contributions of this thesis are twofold. First, from the algorithmic perspective, wireless scheduling algorithms using partial channel state information are designed that minimize the likelihood of queue overflow, in a suitable sense, across all partial information scheduling algorithms. The second key contribution is technical, by the development of novel analytical techniques to study the stochastic dynamics of partial state information-based algorithms. These techniques are not only instrumental in showing the optimality results above, but are also of independent interest in understanding the behavior of algorithms which rely on partially sampled system state. The second part of the thesis investigates coordinated inter-cell wireless scheduling across multiple base stations, each possessing only local and partial channel state information for its own users. Coordinated scheduling is necessary to mitigate interference between users in adjacent cells, but information sharing between the base stations is limited by high latencies in the backhauls that interconnect them. A class of distributed scheduling algorithms is developed in which the base stations share only delayed queue length information with each other, and locally acquire partial channel state information, to schedule users. These algorithms are shown to be throughput-optimal, and their average backlog performance in terms of the inter-base station latency is quantified. / text

Thesis (M. Sc.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (leaves 108-110). Also available via the World Wide Web.

Technogenic pollution level with heavy metals (Cr, Pb, Ni, Cu, Zn) of different pedogenesis Luvisols by various physical chemical methods estimated. Their sorption capacity in soils clay fraction (< 0,005 mm) analyzed. The results obtained permitted to carry out the theoretical and experimental modeling of heavy metals sorption in clay fraction of Luvisols. There was determined that it’s heavy metals sorption capacity makes up to 2000 mg kg-1.

The Holstein beef sector is a fascinating and integral part of the United States beefsystem; however, it has been largely overlooked in academic research. Holstein beef has longsuffered from perceptions that it is of poor quality. Recent changes in slaughter industrystructure, marketing systems, and production models have made the Holstein systemunbelievably complex. Coupled with econometric modeling, this sector analysis uses a semistructuredinterview approach to evaluate the reality of these perceptions, the impact of thesechanges, and to determine what truly drives the Holstein beef market. Results suggest that manyof the perceptions of Holstein beef are inaccurate; the market for Holstein steers was found to bequite similar to the market for native steers. Recent changes in production systems appear tohave been driven by changes in market preferences. Finally, the driving forces behind theHolstein market are not that different from the driving factors in the native cattle market,although some of the impacts were found to be different.

Cattle producers are faced with difficult decisions on how they market theircalves. This study examines the different characteristics that play a role in determiningthe price of a group of animals. Identifying characteristics that determine pricedifferentials relative to the price premium given to producers participating in CPH salesis important information when producers are making a marketing decision. The modeldeveloped in this study provides producers with evidence of what characteristics generatethe highest price, as well as relative differences between sales locations and types ofsales. The more information available to producers, the better equipped they are to makedecisions.

This thesis is focused on solving specific problems in NGO organization AIESEC Praha, caused by big fluctuation, by process analysis and process management implementation. The aim of this paper is provide theoretical introduction to process management and its associated areas. In the beginning of practical part organization is introduced, also with its culture and legal environment that affects its main processes. Following strategic analysis is base for identification of single processes in the organization. Process analysis itself takes major part of thesis and involves described several driving processes through the organization. At the end the knowledge database Tiki is introduced with final conclusions and evaluation.

The study of social network dynamics has become an increasingly important component of many disciplines in the social sciences. In the past decade, statistical models and methods have been proposed which permit researchers to draw statistical inference on these dynamics. This thesis builds on one such family of models, the stochastic actor oriented model (SAOM) proposed by Snijders [2001]. Goodness of fit for SAOMs is an area that is only just beginning to be filled in with appropriate methods. This thesis proposes a Mahalanobis distance based, Monte Carlo goodness of fit test that can depend on arbitrary features of the observed network data and covariates. As remediating poor fit can be a difficult process, a modified model distance (MMD) estimator is devised that can help researchers to choose among a set of model elaborations. In practice, panel data is typically used to draw SAOM-based inference. This thesis also proposes a score-type test for time heterogeneity between the waves in the panel that is computationally cheap and fits into a convenient, forward model selecting workflow. Next, this thesis proposes a rigorous method for aggregating so-called relational event data (e.g. emails and phone calls) by extending the SAOM family to a family of hidden Markov models that suppose a latent social network is driving the observed relational events. Finally, this thesis proposes a measurement model for SAOMs inspired by error-in-variables (EiV) models employed in an array of disciplines. Like the relational event aggregation model, the measurement model is a hidden Markov model extension to the SAOM family. These models allow the researcher to specify the form of the mesurement error and buffer against potential attenuating biases and other problems that can arise if the errors are ignored.

abstract The parameters required for network monitoring are not directly measurable and could be estimated indirectly by network tomography. Some important research issues, related to network tomography, motivated the research in this dissertation. The research work in this dissertation makes four significant novel contributions to the field of network tomography. These research contributions were focused on the blind techniques for performing network tomography, the modeling of errors in network tomography, improving estimates with multi-metric-based network tomography, and distributed network tomography. All of these four research problems, related to network tomography, were solved by various blind techniques including NNMF, SCS, and NTF. These contributions have been verified by processing the data obtained from laboratory experiments and by examining the correlation between the estimated and measured link delays. Evaluation of these contributions was based on the data obtained from various test beds that consisted of networking devices.

abstract: Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity. / Dissertation/Thesis / M.A. Geography 2010

Mechanical structure-property relations have been quantified for AISI 4140 steel under different strain rates and temperatures. The structure-property relations were used to calibrate a microstructure-based internal state variable plasticity-damage model for monotonic tension, compression and torsion plasticity, as well as damage evolution. Strong stress state and temperature dependences were observed for the AISI 4140 steel. Tension tests on three different notched Bridgman specimens were undertaken to study the damage-triaxiality dependence for model validation purposes. Fracture surface analysis was performed using Scanning Electron Microscopy (SEM) to quantify the void nucleation and void sizes in the different specimens. The stress-strain behavior exhibited a fairly large applied stress state (tension, compression dependence, and torsion), a moderate temperature dependence, and a relatively small strain rate dependence.