International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Due to the rapid advancement of technology in GUI design tools within Microsoft Windows™, a sophisticated human-machine interface can be developed for telemetry systems. A PC Windows™-based telemetry system would effectively provide a "bridge" between the telemetry world and the Windows™ world, bringing many low cost off-the-shelf software and hardware tools into the telemetry realm that has been unprecedented. This paper describes the results of such an approach in the development of Loral's Visual Telemetry System (VTS) software.

Approved for public release; distribution is unlimited / This work adds new functionality to an existing visual programming environment. It applies software maintenance techniques for use with the Java Language in a Microsoft Windows operating system environment. The previously undocumented application is intended to support programming with executable diagrams. This application has the potential to expand programming access to non-programmers, provide better software documentation and improve software maintainability. It is currently capable of supporting meta-programming tasks such as parsing and compiler building. The 11,184 legacy lines of code(LOC) were corrected, extended and documented to support future maintenance using an additional 957 LOC and changes to 45 LOC. / Major, United States Marine Corps

Musik-applikationen c3n play används på surfplattor och mobiltelefoner som har den kapacitiva pekskärmstypen. I dagsläget kan musik-applikationen enbart styras med fingrarna. Spelpjäser, utvecklades för att målgruppen skulle kunna kombinera fingerinmatning med något fysiskt och för att underlätta spelandet. En konvergerande produktutvecklingsprocess innefattande faser och aktiviteter tillämpades. Inledningsvis genomfördes en detaljerad informationsinsamling som grund för det fortsatta arbetet. Analys av konkurrenternas produkter och en benchmarking implementerades, för att både identifiera kraven som ställts på produkten och upprätta en kravspecifikation. Spelpjäsen delades in i tre delar: formen på hela spelpjäsen, mönster som gör att användarna kan identifiera varje spelpjäs och mönster på undersidan som programvaran kunde identifiera. Därefter genererades flera idéer på formen av spelpjäsen, vilka utvärderades med hänsyn till de uppsatta kraven. Idéer som ansågs uppfylla kraven bäst gick vidare till konceptgenereringsfasen, där fem olika förslag kunde visualiseras i skisser och CAD-modeller (Computer Aidid Design). Koncepten utvärderades och förbättrades för att tillfredsställa användarnas behov och förväntningar. Det slutliga konceptet på formen gjordes i fyra olika varianter, vilka framställdes som prototyper för vidare test av målgruppen. Konceptlösningar på spelpjäsens identifieringsmönster av användaren analyserades för att passa in i c3n play-användargränssnitten. Tekniken för att identifiera spelpjäsen mot den kapacitiva pekskärmstypen utvecklades via tester, där avstånd och vinklar på kontaktpunkterna bestämdes. Viktiga produktutvecklingsverktyg implementerades för att säkerställa såväl spelpjäsernas hållbarhet som miljövänlighet, med hänsyn till produktlivscykel, tillverkning, montering och tillförlitlighet samt kostnadsmedvetenhet. Därefter valdes lämpliga material samt en lämplig tillverkningsmetod. Detta resulterade i en produktfamilj bestående av åtta stycken spelpjäser som ändrar på var sin funktion i c3n play-applikationen. En spelpjäs som förändrar amplitud, resonans, högtpassfilter, lågt-passfilter, delay, zoom + pan, kompression och feedback. / The application c3n play can be used on tablets and mobile phones that have capacitive multi touchscreens. The application can only be controlled by using the finger input. Tangible user interface was developed so the target group can use something physically “checkers” in a combination with the finger input. A product development process comprising phases and activities was implemented. Initially a detailed information collection were conducted and used as underlying to the continued work. Competitors’ products were analyzed and a benchmarking was implemented to identify user needs and to be able to make a target specification. Checkers was divided in three parts, shape of the whole checker, pattern so users can identify every checker and a pattern on the bottom so the software can identify the checkers on the screen. After that ideas were generated and the shape of the checker was evaluated. Ideas that was considered to meet the user needs went on to concept generation phase, where five different proposals was visualized in sketches and CAD (Computer Aided Design) - models. Concepts was evaluated and improved to satisfy customer needs and expectations. Final concept of the shape was developed into four different variants that was prepared as prototypes for further testing by target group. Concept solutions on identification pattern was analyzed to fit c3n play user interface. Technology to identify the checker against capacitive screen was developed by tests. Where distance and angles of contact points was determined. Important product development tools was implemented to ensure the checkers sustainability. With respect to the product life cycle, environment, manufacturing, assembly, reliability and cost consciousness. Suitable materials and manufacturing method was then chosen. Resulted in a product family consisted of eight different checkers. Each checker can change a different function in the c3n play-application. Amplitude, Resonance, Highpassfilter, Low-passfilter, Delay, Zoom + Pan, Compression and Feedback.

This thesis is concerned with graphical Gaussian models with equality constraints on the concentration or partial correlation matrix introduced by Højsgaard and Lauritzen (2008) as RCON and RCOR models. The models can be represented by vertex and edge coloured graphs G = (V,ε), where parameters associated with equally coloured vertices or edges are restricted to being identical. In the first part of this thesis we study the problem of estimability of a non-zero model mean μ if the covariance structure Σ is restricted to satisfy the constraints of an RCON or RCOR model but is otherwise unknown. Exploiting results in Kruskal (1968), we obtain a characterisation of suitable linear spaces Ω such that if Σ is restricted as above, the maximum likelihood estimator μ(with circumflex) and the least squares estimator μ* of μ coincide for μ ∈ Ω, thus allowing μ and Σ to be estimated independently. For the special case of Ω being specified by equality relations among the entries of μ according to a partition M of the model variables V, our characterisation translates into a necessary and sufficient regularity condition on M and (V,ε). In the second part we address model selection of RCON and RCOR models. Due to the large number of models, we study the structure of four model classes lying strictly within the sets of RCON and RCOR models, each of which is defined by desirable statistical properties corresponding to colouring regularity conditions. Two of these appear in Højsgaard and Lauritzen (2008), while the other two arise from the regularity condition ensuring equality of estimators μ(with circumflex) = μ* we find in the first part. We show each of the colouring classes to form complete lattices, which qualifies the corresponding model spaces for an Edwards-Havránek model selection procedure (Edwards and Havránek, 1987). We develop a coresponding algorithm for one of the model classes and give an algorithm for a systematic search in accordance with the Edwards-Havránek principles for a second class. Both are applied to data sets previously analysed in the literature, with very encouraging performances.

To improve efficacy in the field of drug discovery simpler in vivo, non invasive methods such as PET and SPECT are used. Pharmacokinetic analysis is the underlying method for analyzing the PET data. Imaging of tracer distribution is used to study a metabolic process. Using Matlab as the programming language, a software tool is developed to analyze the quantitative information from PET and to obtain an estimate of pharmacokinetic parameters, representing the bio- distribution of the radiotracer. A Graphical User Interface developed allows two types of analysis, depending upon the nature of the radiotracer: Compartmental Modeling and Logan Plot Analysis. Compartmental analysis gives us rate constants and blood volume where as Logan analysis gives us the distribution volume as the parameter of interest. Code validation is done for two radiotracers, 15O-water (Single compartment model) and FPAC (Logan Plot). Results from the code were compared with those obtained during a research work done to study MDR.

In practice, we often meet data in the form of observations of several variables at various points in time. These data are called time series. We present various approaches in time series analysis; graphical models, vector autoregres- sive models and vector moving-average models. We try to get information about mutual relationship of the variables and then to model their behavior. The used techniques are illustrated on log returns of monthly average exchange rates. The programs are processed in the software Mathematica 7 and can be found on the CD. 1

The goal of this Thesis was to evaluate a possibility to solve systems of linear algebraic equations with the help of graphical processing units (GPUs). While such solvers for generally dense systems seem to be more or less a part of standard production libraries, the Thesis concentrates on this low-level parallelization of equations with a sparse system that still presents a challenge. In particular, the Thesis considers a specific algorithm of an approximate inverse decomposition of symmetric and positive definite systems combined with the conjugate gradient method. An important part of this work is an innovative parallel implementation. The presented experimental results for systems of various sizes and sparsity structures point out that the approach is rather promising and should be further developed. Summarizing our results, efficient preconditioning of sparse systems by approximate inverses on GPUs seems to be worth of consideration. Powered by TCPDF (www.tcpdf.org)

Master of Science / Department of Computing and Information Sciences / Gurdip Singh / A Health Care System is targeted to provide well-monitored patient care. ‘A Visualization Framework for Patient data and its environment’ offers a broad portfolio of patient monitoring to help and improve patient care. ‘A Visualization Framework for Patient data and its environment’ is an application to monitor and analyze any patient’s activity visually through a period of time in conjunction with the surroundings aspects like the room temperature, status of lights in the room, etc. in a healthcare system. A set of sensors equipped with each patient record data, pertaining to the patient’s movement and location in addition to a few other sensor values like temperature and light sensors for recording the room temperature, status of lights in the room respectively. On accepting the activity time bounds as the input, the application retrieves the appropriate values from the database and displays the patient position as a continuous stream of images in association with a slider along with the temperature values. A floor map of the hospital, similar to a blueprint model, is portrayed along with graphical display of lights, in conjunction with the slider. The patient locations are depicted on the map by minute icons with a patient id associated with the each of the icons for identification purposes. Individual window frames for each patient, displaying a patient’s position, enable the user to customize monitoring to specific patients at any instance of time and thus keep track of every move of the patient over a considerate period of time. The locations of the patients on the map, the lights in the rooms depicted on the floor map, the patient position in the individual windows and the temperature are all in synchronization with the slider whose movement is a function of time. The application allows monitoring of values that correspond closely to real-time data values thus maximizing the scope of improvements in the patient’s progress. The application is implemented on a Java Platform using Swings and is expected to handle considerable amounts of data up to two days.

Master of Science / Department of Electrical & Computer Engineering / Steve Warren / Activity recognition can be an important part of human health awareness. Many benefits can be generated from the recognition results, including knowledge of activity intensity as it relates to wellness over time. Various activity-recognition techniques have been presented in the literature, though most address simple activity-data collection and off-line analysis. More sophisticated real-time identification is less often addressed. Therefore, it is promising to consider the combination of current off-line, activity-detection methods with wearable, embedded tools in order to create a real-time wireless human activity recognition system with improved accuracy. Different from previous work on activity recognition, the goal of this effort is to focus on specific activities that an astronaut may encounter during a mission. Planetary navigation field test (PNFT) tasks are designed to meet this need. The approach used by the KSU team is to pre-record data on the ground in normal earth gravity and seek signal features that can be used to identify, and even predict, fatigue associated with these activities. The eventual goal is to then assess/predict the condition of an astronaut in a reduced-gravity environment using these predetermined rules. Several classic machine learning algorithms, including the k-Nearest Neighbor, Naïve Bayes, C4.5 Decision Tree, and Support Vector Machine approaches, were applied to these data to identify recognition algorithms suitable for real-time application. Graphical user interfaces (GUIs) were designed for both MATLAB and LabVIEW environments to facilitate recording and data analysis. Training data for the machine learning algorithms were recorded while subjects performed each activity, and then these identification approaches were applied to new data sets with an identification accuracy of around 86%. Early results indicate that a single three-axis accelerometer is sufficient to identify the occurrence of a given PNFT activity. A custom, embedded acceleration monitoring system employing ZigBee transmission is under development for future real-time activity recognition studies. A different GUI has been implemented for this system, which uses an on-line algorithm that will seek to identify activity at a refresh rate of 1 Hz.

Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex solvers have not yet reached sufficient maturity to fully decouple the convex programming model from the numerical algorithms required for implementation. Especially as datasets grow in size, there is a significant gap in speed and scalability between general solvers and specialized algorithms. This thesis addresses this gap with a new model for convex programming based on an intermediate representation of convex problems as a sum of functions with efficient proximal operators. This representation serves two purposes: 1) many problems can be expressed in terms of functions with simple proximal operators, and 2) the proximal operator form serves as a general interface to any specialized algorithm that can incorporate additional `2-regularization. On a single CPU core, numerical results demonstrate that the prox-affine form results in significantly faster algorithms than existing general solvers based on conic forms. In addition, splitting problems into separable sums is attractive from the perspective of distributing solver work amongst multiple cores and machines. We apply large-scale convex programming to several problems arising from building the next-generation, information-enabled electrical grid. In these problems (as is common in many domains) large, high-dimensional datasets present opportunities for novel data-driven solutions. We present approaches based on convex models for several problems: probabilistic forecasting of electricity generation and demand, preventing failures in microgrids and source separation for whole-home energy disaggregation.