Stress myocardial perfusion imaging using computed tomography in stable coronary artery disease

Over the past decade, CT coronary angiography (CCTA) has emerged as a non-invasive diagnostic imaging modality that directly visualises the coronary anatomy with a reportedly high diagnostic accuracy when compared with invasive angiography. Given the high accuracy, it remains plausible that CCTA may serve as an effective gatekeeper for invasive angiography and revascularisation in patients with symptomatic stable coronary artery disease. However it is important to note that in its current form, CCTA is limited in assessing the functional significance of coronary stenoses. CT stress myocardial perfusion imaging is a novel method to assess myocardial ischemia and when used in combination with CCTA may allow cardiac CT to have the unique ability to assess coronary anatomy and myocardial perfusion in a single examination. The aim of the thesis is first to outline the current increasing role of cardiac CT and fractional flow reserve in the contemporary assessment and management of patients with stable coronary artery disease (chapter 2), to evaluate the use of CCTA as a gatekeeper for invasive angiography and revascularisation (chapter 3), to review the basics of CT stress myocardial perfusion imaging and the literature supporting its accuracy (chapter 4), to determine the accuracy of CT stress perfusion imaging when applied in patients considered for coronary revascularisation (chapter 5), to determine the accuracy of CT stress perfusion imaging when combined with CT coronary angiography when applied in a patients with suspected CAD (chapter 6), and to finally review the role of functional coronary assessment using CT in interventional cardiology (chapter 7).

The seagoing scientist's toolbox: integrated methods for quality control of marine geophysical data at sea

We announce a new and integrated system for planning and executing marine geophysical surveys and for scrutinizing and visualizing incoming shipboard data. The system incorporates free software designed for use by scientists and shipboard operators and pertains to underway geophysics and multibeam sonar surveys. Regarding underway data, a crucial first step in the approach is to reduce and merge incoming center beam depth, gravity, and towed magnetic data with navigation, then reformat to the standard exchange format. We are then able to apply established quality control methods including along-track and cross-track analyses to identify error sources and to incrementally build the candidate archive file as new data are acquired. Regarding multibeam data, these are subjected to both an automated error removal scheme for quick visualization and to subsequent ping editing in detail. The candidate archive file and sonar data are automatically and periodically updated and adapted for display in Google Earth, wherein survey planning is also carried out. Data layers are also updated automatically in Google Earth, allowing scientists to focus on visual inspection and interpretation of incoming data. By visualizing underway and sonar data together with reference gravity, magnetic, and bathymetry grids in Google Earth, data familiarity is enhanced and the likelihood of noticing extreme errors increased. We hope scientists will embrace these techniques so that each data set being submitted to a data repository is vetted by the seagoing science party.U.S. National Science FoundationNational Science Foundation (NSF) [1458964, 1558403]Korea Institute of Ocean Science and Technology [PM59941, PM60321

Eigentrigraphemes for under-resourced languages

Abstract Grapheme-based modeling has an advantage over phone-based modeling in automatic speech recognition for under-resourced languages when a good dictionary is not available. Recently we proposed a new method for parameter estimation of context-dependent hidden Markov model (HMM) called eigentriphone modeling. Eigentriphone modeling outperforms conventional tied-state HMM by eliminating the quantization errors among the tied states. The eigentriphone modeling framework is very flexible and can be applied to any group of modeling unit provided that they may be represented by vectors of the same dimension. In this paper, we would like to port the eigentriphone modeling method from a phone-based system to a grapheme-based system; the new method will be called eigentrigrapheme modeling. Experiments on four official South African under-resourced languages (Afrikaans, South African English, Sesotho, siSwati) show that the new eigentrigrapheme modeling method reduces the word error rates of conventional tied-state trigrapheme modeling by an average of 4.08% relative

Computing and Diagnosing Changes in Unit Test Energy Consumption

Many developers have reason to be concerned with with power consumption. For example, mobile app developers want to minimize how much power their applications draw, while still providing useful functionality. However, developers have few tools to get feedback about changes to their application\u27s power consumption behavior as they implement an application and make changes to it over time. We present a tool that, using a team\u27s existing test cases, performs repeated measurements of energy consumption based on instructions executed, objects generated, and blocking latency, generating a distribution of energy use estimates for each test run, recording these distributions in a time series of distributions over time. Then, when these distributions change substantially, we inform the developer of this change, and offer them diagnostic information about the elements of their code potentially responsible for the change and the inputs responsible. Through this information, we believe that developers will be better enabled to relate recent changes in their code to changes in energy consumption, enabling them to better incorporate changes in software energy consumption into their software evolution decisions

Visualization of the growth and production of grapes through analysis of sensory data

Grapes used in the wine industry have been one of the highest value crops in the United States. However, with unpredictable weather changes and recent drought in the Western United States, vineyard owners and grape growers have faced difficulties on producing good quality grapes suited for wine making. Therefore, a technology that would keep record of environmental data and incorporate the data to support agricultural decisions will help the growers to produce quality grapes even in extreme conditions. As such, this research focuses on developing an interactive system that uses sensory data and visual analytics to facilitate vineyard management and agricultural decisions (such as choosing irrigation strategy and deciding harvesting date) through predictive analysis and historical comparisons. The system visualizes the data gathered by data loggers at vineyard sites to aid growers in decision making. The current system incorporates a stack zooming graph of historical temperature data from different sites and depths with annotation of important dates like bud break and harvest. This stack zooming graph can also be used to check for any erroneous data and implement database cleaning to fix these errors. Some analysis of agricultural characteristics such as soil type and moisture relationship and collective effects of different weather components are currently being done. As this is an ongoing project, integrating new features with better predictive analysis and more visuals will be necessary for the growers to rely on this system

Visualization and Analysis of Sensory Data

Recently, California has suffered a severe drought, making water a scarce resource to its population. Many viticulturists are based in this area who rely on heavy irrigation to produce a better grape and a better wine. Not just in California, but throughout the nation, irrigation must be applied intelligently for efficient use of water and funding. By taking measurements of physical characteristics of a vineyard over time, one may be able to visualize trends in the data which lend itself to describing preferred growing methods. Wireless sensors can be used to take measurements including moisture, temperature, sunlight, and more. Sensors have been installed at multiple locations about a vineyard. A framework has been put in place to capture, adjust, and calibrate the data as well as store it for future retrieval. The data are visualized over time to see the effects of techniques in the long term. These are helpful for suggesting irrigation strategy that will lead to the best yield. Sensors are cheap and effective, but are prone to malfunction and transmission errors. When these problems occur, the faulty time-series data can be cleaned by correlating with similar time-series data in the same time span. The data system will be a necessity for competitive viticulturists, reducing cost of irrigation and improving quality of wine. In the future, the tool could be applied to other crops. Also, if any new important values must be derived or measured, the system can be extended to include them