With the advent of telecommunication technology, the need to transport multimedia content is increasing day by day. Successful video transmission over the wireless network faces a lot of challenges because of the limited resource and error prone nature of the wireless environment. To deal with these two challenges, not only the video needs to be compressed very efficiently but also the compression scheme needs to provide some error resilient features to deal with the high packet loss probability. In this thesis, we have worked with the H.264/ Advanced Video Coding (AVC) video compression standard since this is the most recent and most efficient video compression scheme. Also H.264 provides novel error resilient features e.g. slicing of the frame, Flexible Macroblock Ordering (FMO), data partitioning etc. In this thesis, we investigate how to utilize the error resilient schemes of H.264 to ensure a good quality picture at the receiving end. In the first part of the thesis, we find the optimum slice size that will enhance the quality of video transmission in a 3G environment. In the second part, we jointly optimize the data partitioning property and partial reliability extension property of the new transport layer protocol, Stream Control Transmission Protocol (SCTP). In the third and last part, we focus more on the network layer issues. We obtain the optimum point of application layer Forward Error Correction (FEC) and Medium Access Control (MAC) layer retransmission in a capacity constrained network. We assume that the bit rate assigned for the video application is more than the video bit rate so that the extra capacity available can be used for error correction.

Quantum information processing devices promise to solve some problems more efficiently than their classical counterparts. The source of the speedup is the structure of quantum theory itself. In that sense, the physical units that are the building blocks of such devices are its power. The quest then is to find or manufacture a system that behaves according to quantum theory, and yet is controllable in such a way that the desired algorithms can be implemented. Candidate systems are benchmarked against general criteria to evaluate their success. In this thesis, I advance a particular system and present the progress made towards each of these criteria. The system is a three-qubit 13C solid-state nuclear magnetic resonance (NMR) based quantum processor. I report results concerning system characterization and control, pseudopure state preparation, and quantum error correction. I also report on using the system to test a central question in the foundation of quantum mechanics.

‘What was I thinking ?!?’ – No matter age, intelligence or social status, we all experience moments like these. Perhaps it is walking into a room and forgetting what you went there to do or maybe failing to add sugar to your coffee due to an interruption. Regardless, even though many of our daily activities are accomplished through routines that require very little conscious effort, errors of attention or slips of action do occur. This collection of studies was designed with three main questions in mind: 1) can action slips be induced in a laboratory-based task (Slip Induction Task; SIT), 2) how well do currently established theories of action slips explain the errors that are induced within the SIT, and 3) what insight can be gained about preventing such errors? The first experiment was developed to replicate previous findings regarding the effectiveness of the SIT, as well as to determine the extent to which SIT performance correlates with other measures of attention failure. The study discussed in Chapter 3 expands on those results by investigating the effects of healthy aging on slip induction and finds that while older adults were better able to avoid action slips, they appear to sacrifice speed for accurate performance. The goal of the subsequent study was to determine whether young adult participants would also enjoy increased accuracy if they completed the task at a slower pace. Finally, the study discussed in Chapter 5 looks at whether changing the goal of the SIT would alter participants’ ability to inhibit unexpected cue information.

Using both single and vector processes, we fitted the Box-Jenkin’s ARIMA model and the Vector Autoregressive model following the Johansen approach, to forecast soy bean and green bean prices on the Chinese futures markets. The results are encouraging and provide empirical evidence that the vector processes perform better than the single series. The co-integration test indicated that the null hypothesis of no co-integration among the relevant variables could be rejected. This is one of the most important findings in this paper. The purposes for analyzing and modeling the series jointly are to understand the dynamic relationships over time among the series and improve the accuracy of forecasts for individuals series by utilizing the additional information available from the related series in the forecasts for each series.

Šiame darbe detaliai išanalizuotas klaidos skleidimo atgal algoritmas, atlikti tyrimai. Išsamiai analizuota neuroninių tinklų teorija. Algoritmui taikyti ir analizuoti sistemoje Visual Studio Web Developer 2008 sukurta programa su įvairiais tyrimo metodais, padedančiais ištirti algoritmo daromą klaidą. Taip pat naudotasi Matlab 7.1 sistemos įrankiais neuroniniams tinklams apmokyti. Tyrimo metu analizuotas daugiasluoksnis dirbtinis neuroninis tinklas su vienu paslėptu sluoksniu. Tyrimams naudoti gėlių irisų ir oro taršos duomenys. Atlikti gautų rezultatų palyginimai. / The present work provides an in-depth analysis of the error back-propagation algorithm, as well as information on the investigation carried out. A neural network theory has been analysed in detail. For the application and analysis of the algorithm in the system Visual Studio Web Developer 2008, a program has been developed with various investigation methods, which help to research into the error of the algorithm. For training neural networks, Matlab 7.1 tools have been used. In the course of the investigation, a multilayer artificial neural network with one hidden layer has been analysed. For the purpose of the investigation, data on irises (plants) and air pollution have been used. Comparisons of the results obtained have been made.

Cooper and Shallice (2000) implemented a computational version of the Norman and Shallice’s (1986) Contention Scheduling Model (CSM). The CSM is a hierarchically organized network of action schemas and goals. Botvinick and Plaut (2004) instead took a connectionist approach to modeling routine procedural behavior. They argued in favor of holistic, distributed representation of learned step co-occurrence associations. Two experiments found that people can adapt routine procedural behavior to changing circumstances quite readily and that other factors besides statistical co-occurrence can have influence on action selection. A CSM-inspired ACT-R model of the two experiments is the first to postdict differential error rates across multiple between-subjects conditions and trial types. Results from the behavioral and modeling studies favor a CSM-like theory of human routine procedural memory that uses discrete, hierarchically-organized goal and action representations that are adaptable to new but similar procedures. / Office of Naval Research grants #N00014-03-1-0094 and #N00014-06-1-0056

Effective use of bioassessments requires an understanding of their performance. This study evaluated the performance of Skeena Reference Condition Approach (RCA) bioassessments by calculating Type I and Type II error rates and power using a data set of artificially-impacted test sites. Results from this study demonstrated that there are trade-offs between the two error types. Type I error rates – the chance of concluding that a site is impacted when it isn’t – were higher than expected based on decision points set in the assessments. Type II error rates – the chance of concluding that a site is not impacted when it is – were often even greater. To achieve sustainable development, resource managers who use Skeena bioassessments must carefully consider the risks associated with making errors, and may wish to set decision points that result in more Type I errors in order to reduce the likelihood of making costly Type II errors.

Research into adverse events in home care is at a very early stage worldwide. Adverse event research in other health care sectors has demonstrated that patients can and do suffer harm, much of which is preventable, during the receipt of health care services. A stratified, random sample of patients who had received home care nursing service and were discharged in 2004/05 from three Ontario home care programs was studied to develop basic exploratory and descriptive evidence to advance the understanding of AEs in home care. The outcome is an estimate of the incidence of adverse events among patients, description of adverse event types and factors associated with adverse events, and the development of models predictive of home care patients with higher and lower potential for adverse events, and of the location of patients with adverse events. Positive critical indicators were identified in 66.5% of 430 cases. Sixty-one adverse events were identified in 55 (19.2%) of these 286 cases. When adjusted for sampling methodology, the adverse event rate was 13.2 per 100 patients (95%, CI 10.4% - 16.6%, SE 1.6%). Thirty-three percent of the adverse events were rated as having more than a 50% probability of preventability; 1.4% of all patients experienced an adverse event related death. Eight of the 45 factors significantly associated with adverse events formed a single factor model predictive of adverse events. Six two-factor interactions and the absence of one factor were also predictive of the occurrence of adverse events. Five of the 12 critical indicators significantly related to adverse events, as well as 7 critical indicator combinations formed models that reliably located about two-thirds of patients who had, and almost all patients who had not, experienced an adverse event. This study suggests that a significant number of home care patients experience adverse events, two-thirds of which are preventable. Use of adverse event sensitive factors as a screening tool for patients that may benefit from enhanced case management and clinical vigilance, and those unlikely to be placed at increased adverse event risk by maintaining current levels of vigilance, presents an opportunity to improve patient safety. Retrospective critical indicator models identifying home care patients who have experienced an adverse event can be used to estimate adverse event incidence rates and changes in rates over time.

Aircraft operators demand an ever increasing availability of their fleets with constant reduction of their operational costs. With the age of many fleets measured in decades, the options to face these challenges are limited. Integrated Vehicle Health Management (IVHM) uses data gathered through sensors in the aircraft to assess the condition of components to detect and isolate faults or even estimate their Remaining Useful Life (RUL). This information can then be used to improve the planning of maintenance operations and even logistics and operational planning, resulting in shorter maintenance stops and lower cost. Retrofitting health monitoring technology onto legacy aircraft has the capability to deliver what operators and maintainers demand, but working on aging platforms presents numerous challenges. This thesis presents a novel methodology to select the combination of diagnostic and prognostic tools for legacy aircraft that best suits the stakeholders’ needs based on economic return and financial risk. The methodology is comprised of different steps in which a series of quantitative analyses are carried out to reach an objective solution. Beginning with the identification of which components could bring higher reduction of maintenance cost and time if monitored, the methodology also provides a method to define the requirements for diagnostic and prognostic tools capable of monitoring these components. It then continues to analyse how combining these tools affects the economic return and financial risk. Each possible combination is analysed to identify which of them should be retrofitted. Whilst computer models of maintenance operations can be used to analyse the effect of retrofitting IVHM technology on a legacy fleet, the number of possible combinations of diagnostic and prognostic tools is too big for this approach to be practicable. Nevertheless, computer models can go beyond the economic analysis performed thus far and simulations are used as part of the methodology to get an insight of other effects or retrofitting the chosen toolset.

It has been shown in the literature that the oil and gas industry deals with a substantial number of biases that impact project evaluation and portfolio performance. Previous studies concluded that properly estimating uncertainties will significantly impact the success of risk takers and their profits. Although a considerable number of publications investigated the impact of cognitive biases, few of these publications tackled the problem from a quantitative point of view. The objective of this work is to demonstrate the value of quantifying uncertainty and evaluate its impact on the optimization of oil and gas portfolios, taking into consideration the risk of each project. A model has been developed to perform portfolio optimization using Markowitz theory. In this study, portfolio optimization has been performed in the presence of different levels of overconfidence and directional bias to determine the impact of these biases on portfolio performance. The results show that disappointment in performance occurs not only because the realized portfolio net present value (NPV) is lower than estimated, but also because the realized portfolio risk is higher than estimated. This disappointment is due to both incorrect estimation of value and risk (estimation error) and incorrect project selection (decision error). The results of the cases analyzed show that, in a high-risk-tolerance environment, moderate overconfidence and moderate optimism result in an expected decision error of about 19% and an expected disappointment of about 50% of the estimated portfolio. In a low-risk-tolerance environment, the same amounts of moderate overconfidence and optimism result in an expected decision error up to 103% and an expected disappointment up to 78% of the estimated portfolio. Reliably quantifying uncertainty has the value of reducing the expected disappointment and the expected decision error. This can be achieved by eliminating overconfidence in the process of project evaluation and portfolio optimization. Consequently, overall industry performance can be improved because accurate estimates enable identification of superior portfolios, with optimum reward and risk levels, and increase the probability of meeting expectations.