Search

The paper presents a dynamic data-driven mixed reality environment to complement a full-scale bio-terror preparedness exercise. The environment consists of a simulation of the virtual geographic locations involved in the exercise scenario, along with an artificially intelligent agent-based population. The crisis scenario, like the epidemiology of a disease or the plume of a chemical spill or radiological explosion, is then simulated in the virtual environment. The public health impact, the economic impact and the public approval rating impact is then calculated based on the sequence of events defined in the scenario, and the actions and decisions made during the full-scale exercise. The decisions made in the live exercise influence the outcome of the simulation, and the outcomes of the simulation influence the decisions being made during the exercise. The mixed reality environment provides the long-term and large-scale impact of the decisions made during the full-scale exercise. [ABSTRACT FROM AUTHOR]

Dynamic reconfigurable simulation based on Discrete Event System Specification (DEVS) requires efficient verification of simulation models. Traditional verification method of DEVS model is based on I/O test in which a DEVS model is regarded as a black box or a grey box. This method is low efficient and insufficient because input samples are often limited. This paper proposes a formal method which can translate Parallel DEVS model into a restrict kind of Timed Automata (TA) with equivalent behaviors. By this translation, a formal verification problem of Parallel DEVS model can be changed into the formal verification of according timed automata. [ABSTRACT FROM AUTHOR]

This paper describes a signal recognition system that is jointly optimized from mathematical representation, algorithm dand final implementation. The goal is to exploit signal properties to jointly optimize a computation, beginning with first principles (mathematical representation) and completed with implementation. We use a BestBasis algorithm to search a large collection of orthogonal transforms derived from the Walsh-Hadamard transform to find a series of transforms which best discriminate among signal classes.The implementation exploits the structure of these matrices to compress the matrix representation, and in the process of multiplying the signal by the transform, reuse the results of prior computation and parallelize the implementation in hardware. Through this joint optimization, this dynamic, data-driven system is able to yield much more highly optimized results than if the optimizations were performed statically and in isolation. We provide results taken from applying this system to real input signals of spoken digits, and perform the initial analyses to demonstrate the properties of the transform matrices lead to optimized solutions. [ABSTRACT FROM AUTHOR]

We seek to increase user confidence in simulations as they are adapted to meet new requirements. Our approach includes formal representation of uncertainty, lightweight validation, and novel techniques for exploring emergent behavior. Uncertainty representation, using formalisms such as Dempster-Shafer theory, can capture designer insight about uncertainty, enabling formal analysis and improving communication with decision and policy makers. Lightweight validation employs targeted program analysis and automated regression testing to maintain user confidence as adaptations occur. Emergent behavior validation exploits the semi-automatic adaptation capability of COERCE to make exploration of such behavior efficient and productive. We describe our research on these three technologies and their impact on validating dynamically evolving simulations. [ABSTRACT FROM AUTHOR]

We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security including malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. MINDS has shown great operational success in detecting network intrusions in several real deployments. In sophisticated distributed cyber attacks using a multitude of wide-area nodes, combining the results of several MINDS instances can enable additional early-alert cyber security. We also describe a Grid service system that can deploy and manage multiple MINDS instances across a wide-area network. [ABSTRACT FROM AUTHOR]

This paper reviews recent developments in our project that are focused on dynamic data-driven methods for efficient and reliable simulation based optimization, which may be suitable for a wide range of different application problems. The emphasis in this paper is on the coupling of parallel multiblock predictive models with optimization, the development of autonomic execution engines for distributing the associated computations, and deployment of systems capable of handling large datasets. The integration of these components results in a powerful framework for developing large-scale and complex decision-making systems for dynamic data-driven applications. [ABSTRACT FROM AUTHOR]

Sensing is at the core of virtually every DDDAS application. Sensing applications typically involve distributed communication and coordination over large self-organized networks of heterogeneous devices with severe resource constraints. As a consequence, developers must explicitly deal with low-level details, making programming time-consuming and error-prone. To reduce this burden, current sensor network programming languages espouse a model that relies on packaged reusable components to implement relevant pieces of a distributed communication infrastructure. Unfortunately, programmers are often forced to understand the mechanisms used by these implementations in order to optimize resource utilization and performance, and to ensure application requirements are met. To address these issues, we propose a novel and high-level programming model that directly exposes control over sensor network behavior using temporal logic specifications, in conjunction with a set of system state abstractions to specify, generate, and automatically validate resource and communication behavior for sensor network applications. TLA+ (the temporal logic of actions) is used as the underlying specification language to express global state abstractions as well as user invariants. We develop a synthesis engine that utilizes TLC (a temporal logic model-checker) to generate detailed actions so that user-provided behavioral properties can be satisfied, guaranteeing program correctness. The synthesis engine generates specifications in TLA+, which are compiled down to sensor node primitive actions. We illustrate our model using a detailed experimental evaluation on our structural sensing and control testbed. The proposed framework is integrated into the COSMOS macroprogramming environment, which is extensively used to develop sensing and control applications at the Bowen Lab for Structural Engineering at Purdue. [ABSTRACT FROM AUTHOR]

In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. The new methodology consists of robust data pre-processing modules, interrelated, multi-level models that describe different details of the system behaviors, and a dynamic strategy that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. This paper summarizes the latest progresses in the research. [ABSTRACT FROM AUTHOR]

We describe a dynamic data-driven methodology that is capable of simultaneously determining both the parameters of a constitutive model associated with the response of a composite material, and the optimum experimental design that leads to the corresponding material characterization. The optimum design of experiments may contain two parts. One involving the identification of the parameters that are tunable prior to performing the experiment such as specimen characteristics and a priori loading path. The other is involving the parameters characterizing the experiment during the experiment itself such as the directionality of the loading path for the case of multi-axial loading machine. A multi-level coupled design optimization methodology is developed and applied to demonstrate the concept. Essential to the process is the development of objective functions that express the quality of the experimental procedure in terms of the uniqueness and distinguishability associated with the inverse solution of the constitutive model determination. The examples provided are based on the determination of the linear constitutive response of a laminate composite material. [ABSTRACT FROM AUTHOR]

A Dynamic Data Driven Application Systems (DDDAS) me-thodology is developed for evaluation of fluid-thermal systems wherein a complete specification of the boundary conditions is not known a priori and experimental diagnostics are restricted to a limited region of the flowfield. The Closed Loop formulation of DDDAS is used whereby experiment and simulation are synergized in an iterative manner to determine the unknown boundary conditions, thereby enabling a full simulation (and hence, evaluation) of the fluid-thermal system. In this DDDAS methodology, the experiment directs the simulation and vice-versa. The DDDAS methodology is applied to a heated jet injected into a laminar boundary layer where the jet temperature and velocity are not known a priori for the simulations. The DDDAS methodology accurately determines the unknown jet temperature and velocity. [ABSTRACT FROM AUTHOR]

This paper discusses recent progress achieved in two areas related to the development of a Dynamic Data Driven Applications System (DDDAS) for structural and material health monitoring and critical event prediction. The first area concerns the development and demonstration of a sensor data compression algorithm and its application to the detection of structural damage. The second area concerns the prediction in near real-time of the transient dynamics of a structural system using a nonlinear reduced-order model and a time-parallel ODE (Ordinary Differential Equation) solver. [ABSTRACT FROM AUTHOR]

The most critical element of the nation's energy infrastructure is our electricity generation, transmission, and distribution system known as the "power grid." Computer simulation is an effective tool that can be used to identify vulnerabilities and predict the system response for various contingencies. However, because the power grid is a very large-scale nonlinear system, such studies are presently conducted "open loop" using predicted loading conditions months in advance and, due to uncertainties in model parameters, the results do not provide grid operators with accurate "real time" information that can be used to avoid major blackouts such as were experienced on the East Coast in August of 2003. However, the paradigm of Dynamic Data-Driven Applications Systems (DDDAS) provides a fundamentally new framework to rethink the problem of power grid simulation. In DDDAS, simulations and field data become a symbiotic feedback control system and this is refreshingly different from conventional power grid simulation approaches in which data inputs are generally fixed when the simulation is launched. The objective of the research described herein was to utilize the paradigm of DDDAS to develop a marriage between sensing, visualization, and modelling for large-scale simulation with an immediate impact on the power grid. Our research has focused on methodological innovations and advances in sensor systems, mathematical algorithms, and power grid simulation, security, and visualization approaches necessary to achieve a meaningful large-scale real-time simulation that can have a significant impact on reducing the likelihood of major blackouts. [ABSTRACT FROM AUTHOR]

We describe a prototype emergency and disaster information system designed and implemented using DDDAS concepts. The system is designed to use real-time cell phone calling data from a geographical region, including calling activity - who calls whom, call duration, services in use, and cell phone location information - to provide enhanced situational awareness for managers in emergency operations centers (EOCs) during disaster events. Powered-on cell phones maintain contact with one or more within-range cell towers so as to receive incoming calls. Thus, location data about all phones in an area are available, either directly from GPS equipped phones, or by cell tower, cell sector, distance from tower and triangulation methods. This permits the cell phones of a geographical region to serve as an ad hoc mobile sensor net, measuring the movement and calling patterns of the population. A prototype system, WIPER, serves as a test bed to research open DDDAS design issues, including dynamic validation of simulations, algorithms to interpret high volume data streams, ensembles of simulations, runtime execution, middleware services, and experimentation frameworks [1]. [ABSTRACT FROM AUTHOR]

Electric power transmission systems are comprised of a large number of physical assets, including transmission lines, power transformers, and circuit breakers, that are capital-intensive, highly distributed, and may fail. Managing these assets under resource constraints requires equipment health monitoring integrated with system level decision-making to optimize a number of various operational, maintenance, and investment-related objectives. Industry processes to these ends have evolved ad-hoc over the years, and no systematic structures exist to coordinate the various decision problems. In this paper, we describe our progress in building a prototype structure for this purpose together with a software-hardware environment to deploy and test it. We particularly focus on the decision algorithms and the Benders approach we have taken to solve them in an integrated fashion. [ABSTRACT FROM AUTHOR]

Humanity's understanding of the Earth's weather and climate depends critically on accurate forecasting and state-estimation technology. It is not clear how to build an effective dynamic data-driven application system (DDDAS) in which computer models of the planet and observations of the actual conditions interact, however. We are designing and building a laboratory-scale dynamic data-driven application system (DDDAS), called Planet-in-a-Bottle, as a practical and inexpensive step toward a planet-scale DDDAS for weather forecasting and climate model. The Planet-in-a-Bottle DDDAS consists of two interacting parts: a fluid lab experiment and a numerical simulator. The system employs data assimilation in which actual observations are fed into the simulator to keep the models on track with reality, and employs sensitivity-driven observations in which the simulator targets the real-time deployment of sensors to particular geographical regions and times for maximal effect, and refines the mesh to better predict the future course of the fluid experiment. In addition, the feedback loop between targeting of both the observational system and mesh refinement will be mediated, if desired, by human control. [ABSTRACT FROM AUTHOR]

We present a physical, laboratory-scale analog of large-scale atmos- pheric circulation and develop an observatory for it. By combining observations of a hydro-dynamically unstable flow with a 3D numerical fluid model, we obtain a real-time estimate of the state of the evolving fluid which is better than either model or observations alone. To the best of our knowledge this is the first such observatory for laboratory simulations of planetary flows that functions in real time. New algorithms in modeling, parameter and state estimation, and observation targeting can be rapidly validated, thus making weather and climate application accessible to computational scientists. Properties of the fluid that cannot be directly observed can be effectively studied by a constrained model, thus facilitating scientific inquiry. [ABSTRACT FROM AUTHOR]

Classical formulations of data-assimilation perform poorly when forecast locations of weather systems are displaced from their observations. They compensate position errors by adjusting amplitudes, which can produce unacceptably "distorted" states. Motivated by cyclones, in earlier work we show a new method for handling position and amplitude errors using a single variational objective. The solution could be used with either ensemble or deterministic methods. In this paper, extension of this work in two directions is reported. First, the methodology is extended to multivariate fields commonly used in models, thus making this method readily applicable. Second, an application of this methodology to rainfall modeling is presented. [ABSTRACT FROM AUTHOR]

Using a scenario of multiple mobile observing platforms (UAVs) measuring weather variables in distributed regions of the Pacific, we are developing algorithms that will lead to improved forecasting of high-impact weather events. We combine technologies from the nonlinear weather prediction and planning/control communities to create a close link between model predictions and observed measurements, choosing future measurements that minimize the expected forecast error under time-varying conditions. We have approached the problem on three fronts. We have developed an information-theoretic algorithm for selecting environment measurements in a computationally effective way. This algorithm determines the best discrete locations and times to take additional measurement for reducing the forecast uncertainty in the region of interest while considering the mobility of the sensor platforms. Our second algorithm learns to use past experience in predicting good routes to travel between measurements. Experiments show that these approaches work well on idealized models of weather patterns. [ABSTRACT FROM AUTHOR]

In this paper, active learning with support vector machines (SVMs) is applied to the problem of tornado prediction. This method is used to predict which storm-scale circulations yield tornadoes based on the radar derived Mesocyclone Detection Algorithm (MDA) and near-storm environment (NSE) attributes. The main goal of active learning is to choose the instances or data points that are important or have influence to our model to be labeled and included in the training set. We compare this method to passive learning with SVMs where the next instances to be included to the training set are randomly selected. The preliminary results show that active learning can achieve high performance and significantly reduce the size of training set. [ABSTRACT FROM AUTHOR]

Linked Environments for Atmospheric Discovery (LEAD) is a large-scale cyberinfrastructure effort in support of mesoscale meteorology. One of the primary goals of the infrastructure is support for real-time dynamic, adaptive response to severe weather. In this paper we revisit the conception of dynamic adaptivity as appeared in our 2005 DDDAS workshop paper, and discuss changes since the original conceptualization, and lessons learned in working with a complex service oriented architecture in support of data driven science. [ABSTRACT FROM AUTHOR]

We present a Dynamic Data Driven Application System (DDDAS) to track 2D shapes across large pose variations by learning non-linear shape manifold as overlapping, piecewise linear subspaces. The learned subspaces adaptively adjust to the subject by tracking the shapes independently using Kanade Lucas Tomasi(KLT) point tracker. The novelty of our approach is that the tracking of feature points is used to generate independent training examples for updating the learned shape manifold and the appearance model. We use landmark based shape analysis to train a Gaussian mixture model over the aligned shapes and learn a Point Distribution Model(PDM) for each of the mixture components. The target 2D shape is searched by first maximizing the mixture probability density for the local feature intensity profiles along the normal followed by constraining the global shape using the most probable PDM cluster. The feature shapes are robustly tracked across multiple frames by dynamically switching between the PDMs. The tracked 2D facial features are used deform the 3D face mask.The main advantage of the 3D deformable face models is the reduced dimensionality. The smaller number of degree of freedom makes the system more robust and enables capturing subtle facial expressions as change of only a few parameters. We demonstrate the results on tracking facial features and provide several empirical results to validate our approach. Our framework runs close to real time at 25 frames per second. [ABSTRACT FROM AUTHOR]

Complex Automata were recently proposed as a paradigm to model multi-scale complex systems. The concept is formalized and the scale separation map is further investigated in relation with its capability to specify the components of Complex Automata. Five classes of scale separation are identified, each potentially giving rise to a specific multi-scale modeling paradigm. A number of canonical examples are briefly discussed. [ABSTRACT FROM AUTHOR]

This paper presents a prototype implementation of an intelligent assistance architecture for data-driven simulation specialising in qualitative data in the social sciences. The assistant architecture semi-automates an iterative sequence in which an initial simulation is interpreted and compared with real-world observations. The simulation is then adapted so that it more closely fits the observations, while at the same time the data collection may be adjusted to reduce uncertainty. For our prototype, we have developed a simplified agent-based simulation as part of a social science case study involving decisions about housing. Real-world data on the behaviour of actual households is also available. The automation of the data-driven modelling process requires content interpretation of both the simulation and the corresponding real-world data. The paper discusses the use of Association Rule Mining to produce general logical statements about the simulation and data content and the applicability of logical consistency checking to detect observations that refute the simulation predictions. [ABSTRACT FROM AUTHOR]

The electric power grid is a vital network for every aspect of our life. The lack of buffer between generation and consumption makes the power grid unstable and fragile. While large scale power storage is not technically and economically feasible at present stage, we argue that a virtual buffer could be effectively implemented through a demand side management strategy built upon the concept of dynamic data driven paradigm. An intelligent scheduling module implemented inside a smart meter enables customers to access electricity safely and economically. The managed use of electricity acts effectively as a buffer to provide the much needed stability for the power grid. Pioneering efforts intending to implement these concepts have been conducted by groups such as the Consortium for the Intelligent Management of Electric Grid. Progresses have been made and reported in this paper. [ABSTRACT FROM AUTHOR]

This paper describes a preliminary cyberinfrastructure for contaminant characterization in water distribution systems and its deployment on the grid. The cyberinfrastructure consists of the application, middleware and hardware resources. The application core consists of various optimization modules and a simulation module. This paper focuses on the development of specific middleware components of the cyberinfrastructure that enables efficient seamless execution of the application core in a grid environment. The components developed in this research include: (i) a coarse-grained parallel wrapper for the simulation module that includes additional features for persistent execution, (ii) a seamless job submission interface, and (iii) a graphical real time application monitoring tool. The performance of the cyberinfrastructure is evaluated on a local cluster and the TeraGrid. [ABSTRACT FROM AUTHOR]

We consider the linear system which arises from discretization of the pressure Poisson equation with Neumann boundary conditions, coming from bubbly flow problems. In literature, preconditioned Krylov iterative solvers are proposed, but these show slow convergence for relatively large and complex problems. We extend these traditional solvers with the so-called deflation technique, which accelerates the convergence substantially. Several numerical aspects are considered, like the singularity of the coefficient matrix and the varying density field at each time step. We demonstrate theoretically that the resulting deflation method accelerates the convergence of the iterative process. Thereafter, this is also demonstrated numerically for 3-D bubbly flow applications, both with respect to the number of iterations and the computational time. [ABSTRACT FROM AUTHOR]

We present an overview of an ongoing project to build DDDAS to use all available data for a short term wildfire prediction. The project involves new data assimilation methods to inject data into a running simulation, a physics based model coupled with weather prediction, on-site data acquisition using sensors that can survive a passing fire, and on-line visualization using Google Earth. [ABSTRACT FROM AUTHOR]

Current research in applying the Dynamic Data Driven Application Systems (DDDAS) concept to monitor and manage surface transportation systems in day-to-day and emergency scenarios is described. This work is focused in four, tightly coupled areas. First, a novel approach to predicting future system states termed ad hoc distributed simulations has been developed and is under investigation. Second, on-line simulation models that can incorporate real-time data and perform rollback operations for optimistic ad hoc distributed simulations are being developed and configured with data corresponding to the Atlanta metropolitan area. Third, research in the analysis of real-time data is being used to define approaches for transportation system data collection that can drive distributed on-line simulations. Finally, research in data dissemination approaches is examining effective means to distribute information in mobile distributed systems to support the ad hoc distributed simulation concept. [ABSTRACT FROM AUTHOR]

The Louisiana Coastal Area presents an array of rich and urgent scientific problems that require new computational approaches. These problems are interconnected with common components: hurricane activity is aggravated by ongoing wetland erosion; water circulation models are used in hurricane forecasts, ecological planning and emergency response; environmental sensors provide information for models of different processes with varying spatial and time scales. This has prompted programs to build an integrated, comprehensive, computational framework for meteorological, coastal, and ecological models. Dynamic and adaptive capabilities are crucially important for such a framework, providing the ability to integrate coupled models with real-time sensor information, or to enable deadline based scenarios and emergency decision control systems. This paper describes the ongoing development of a Dynamic Data Driven Application System for coastal and environmental applications (DynaCode), highlighting the challenges of providing accurate and timely forecasts for hurricane events. [ABSTRACT FROM AUTHOR]

In this paper we discuss variational data assimilation using the STEM atmospheric Chemical Transport Model. STEM is a multiscale model and can perform air quality simulations and predictions over spatial and temporal scales of different orders of magnitude. To improve the accuracy of model predictions we construct a dynamic data driven application system (DDDAS) by integrating data assimilation techniques with STEM. We illustrate the improvements in STEM field predictions before and after data assimilation. We also compare three popular optimization methods for data assimilation and conclude that L-BFGS method is the best for our model because it requires fewer model runs to recover optimal initial conditions. [ABSTRACT FROM AUTHOR]

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (DDDAS) that efficiently integrate the observational data and the models. Data assimilation, the dynamic incorporation of additional data into an executing application, is an essential DDDAS concept with wide applicability. In this paper we discuss practical aspects of nonlinear ensemble Kalman data assimilation applied to atmospheric chemical transport models. We highlight the challenges encountered in this approach such as filter divergence and spurious corrections, and propose solutions to overcome them, such as background covariance inflation and filter localization. The predictability is further improved by including model parameters in the assimilation process. Results for a large scale simulation of air pollution in North-East United States illustrate the potential of nonlinear ensemble techniques to assimilate chemical observations. [ABSTRACT FROM AUTHOR]

Assimilation of spatially- and temporally-distributed state observations into simulations of dynamical systems stemming from discretized PDEs leads to inverse problems with high-dimensional control spaces in the form of discretized initial conditions. Solution of such inverse problems in "real-time" is often intractable. This motivates the construction of reduced-order models that can be used as surrogates of the high-fidelity simulations during inverse solution. For the surrogates to be useful, they must be able to approximate the observable quantities over a wide range of initial conditions. Construction of the reduced models entails sampling the initial condition space to generate an appropriate training set, which is an intractable proposition for high dimensional initial condition spaces unless the problem structure can be exploited. Here, we present a method that extracts the dominant spectrum of the input-output map (i.e. the Hessian of the least squares optimization problem) at low cost, and uses the principal eigenvectors as sample points. We demonstrate the efficacy of the reduction methodology on a large-scale contaminant transport problem. [ABSTRACT FROM AUTHOR]

We present an overview of an ongoing project to build a DDDAS for identifying and tracking chemicals in water. The project involves a new class of intelligent sensor, building a library to optically identify molecules, communication techniques for moving objects, and a problem solving environment. We are developing an innovative environment so that we can create a symbiotic relationship between computational models for contaminant identification and tracking in water bodies and a new instrument, the Solid-State Spectral Imager (SSSI), to gather hydrological and geological data and to perform chemical analyses. The SSSI is both small and light and can scan ranges of up to about 10 meters. It can easily be used with remote sensing applications. [ABSTRACT FROM AUTHOR]

Observing systems facilitate scientific studies by instrumenting the real world and collecting corresponding measurements, with the aim of detecting and tracking phenomena of interest. Our AMBROSia project focuses on a class of observing systems which are embedded into the environment, consist of stationary and mobile sensors, and react to collected observations by reconfiguring the system and adapting which observations are collected next. In this paper, we report on recent research directions and corresponding results in the context of AMBROSia. [ABSTRACT FROM AUTHOR]

Wireless sensor networks can be viewed as the integration of three subsystems: a low-impact in situ data acquisition and collection system, a system for inference of process models from observed data and a priori information, and a system that controls the observation and collection. Each of these systems is connected by feedforward and feedback signals from the others; moreover, each subsystem is formed from behavioral components that are distributed among the sensors and out-of-network computational resources. Crucially, the overall performance of the system is constrained by the costs of energy, time, and computational complexity. We are addressing these design issues in the context of monitoring forest environments with the objective of inferring ecosystem process models. We describe here our framework of treating data and models jointly, and its application to soil moisture processes. [ABSTRACT FROM AUTHOR]

In this paper we present our experience with an Image Guided Neurosurgery Grid-enabled Software Environment (IGNS-GSE) which integrates real-time acquisition of intraoperative Magnetic Resonance Imaging (IMRI) with the preoperative MRI, fMRI, and DT-MRI data. We describe our distributed implementation of a non-rigid image registration method which can be executed over the Grid. Previously, non-rigid registration algorithms which use landmark tracking across the entire brain volume were considered not practical because of the high computational demands. The IGNS-GSE, for the first time ever in clinical practice, alleviated this restriction. We show that we can compute and present enhanced MR images to neurosurgeons during the tumor resection within minutes after IMRI acquisition. For the last 12 months this software system is used routinely (on average once a month) for clinical studies at Brigham and Women's Hospital in Boston, MA. Based on the analysis of the registration results, we also present future directions which will take advantage of the vast resources of the Grid to improve the accuracy of the method in places of the brain where precision is critical for the neurosurgeons. [ABSTRACT FROM AUTHOR]

Hyperthermia based cancer treatments are used to increase the susceptibility of cancerous tissue to subsequent radiation or chemotherapy treatments, and in the case in which a tumor exists as a well-defined region, higher intensity heat sources may be used to ablate the tissue. Utilizing the guidance of real-time treatment data while applying a laser heat source has the potential to provide unprecedented control over the outcome of the treatment process [6,12]. The goals of this work are to provide a working snapshot of the current system architecture developed to provide a real-time finite element solution of the problems of calibration, optimal heat source control, and goal-oriented error estimation applied the equations of bioheat transfer and demonstrate that current finite element technology, parallel computer architecture, peer-to-peer data transfer infrastructure, and thermal imaging modalities are capable of inducing a precise computer controlled temperature field within the biological domain. [ABSTRACT FROM AUTHOR]

New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test a dynamic data-driven (BMI) Brain-Machine Interface architecture that relies on multiple pairs of forward-inverse models to predict, control, and learn the trajectories of a robotic arm in a real-time closed-loop system. A method of window-RLS was used to compute the forward-inverse model pairs in real-time and a model switching mechanism based on reinforcement learning was used to test the ability to map neural activity to elementary behaviors. The architectures were tested with in vivo data and implemented using remote computing resources. [ABSTRACT FROM AUTHOR]

This is the 5th International Workshop on Dynamic Data Driven Applications Systems (DDDAS), organized in conjunction with ICCS. The DDDAS concept entails the ability to dynamically incorporate data into an executing application simulation, and in reverse, the ability of applications to dynamically steer measurement processes. Such dynamic data inputs can be acquired in real-time on-line or they can be archival data. DDDAS is leading to new capabilities by improving applications modeling and systems management methods, augmenting the analysis and prediction capabilities of simulations, improving the efficiency of simulations and the effectiveness of measurement systems. The scope of the present workshop provides examples of research and technology advances enabled through DDDAS and driven by DDDAS. The papers presented in this workshop represent ongoing multidisciplinary research efforts by an international set of researchers from academe, industry, national and research laboratories. [ABSTRACT FROM AUTHOR]

In the first part of this article, we derive a New Isothermal Quantum Euler model. Starting from the quantum Liouville equation, the system of moments is closed by a density operator which minimizes the quantum free energy. Several simplifications of the model are then written for the special case of irrotational flows. The second part of the paper is devoted to a formal analysis of the asymptotic behavior of the quantum Euler system in two situations: at the semiclassical limit and at the zero-temperature limit. The remarkable fact is that in each case we recover a known model: respectively the isothermal Euler system and the Madelung equations. Finally, we give in the third part some preliminary numerical simulations. [ABSTRACT FROM AUTHOR]

The reduction of NOx-formation in biomass fired grate furnaces requires the development of numerical models. To represent the variety in scales and physical processes playing a role in the conversion, newly developed submodels are required. Here, a submodel for the reverse combustion process in the solid fuel layer on the grate is described. The submodel is shown to give good predictions for the velocity of the combustion front as well as for the spatial profiles of porosity, oxygen mass fraction and temperature. These predictions are essential input for NOx-calculations. [ABSTRACT FROM AUTHOR]

Multiphysics and multiscale simulation systems are emerging as a new grand challenge in computational science, largely because of increased computing power provided by the distributed-memory parallel programming model on commodity clusters. These systems often present a parallel coupling problem in their intercomponent data exchanges. Another potential problem in these coupled systems is language interoperability between their various constituent codes. In anticipation of combined parallel coupling/language interoperability challenges, we have created a set of interlanguage bindings for a successful parallel coupling library, the Model Coupling Toolkit. We describe the method used for automatically generating the bindings using the Babel language interoperability tool, and illustrate with short examples how MCT can be used from the C++ and Python languages. We report preliminary performance reports for the MCT interpolation benchmark. We conclude with a discussion of the significance of this work to the rapid prototyping of large parallel coupled systems. [ABSTRACT FROM AUTHOR]

A new methodology for the simulation of multiscale pro-cesses, called Macro-Micro Interlocked (MMI) Simulation, is introduced. The MMI simulation is carried out by the two-way connection of different numerical models, which may handle macroscopic and microscopic dynamics, respectively. The MMI simulation are applied to several multiscale phenomena, for instance, cloud formation, gas detonation, and plasma dynamics. The results indicate that the MMI simulation provide us an effective and prospective framework for multiscale simulation. [ABSTRACT FROM AUTHOR]

Manual surface reconstruction is still an everyday practice in applications involving complex irregular domains, necessary for modeling biological systems. Rapid development of biomedical imaging and simulation, however, requires automatic computations involving frequent re-meshing of (r)evolving domains that human-driven generation can simply no longer deliver. This bottleneck hinders the development of many applications of high social importance, like computational physiology or computer aided medicine. While many commercial packages offer mesh generation options, these depend on high quality input, which is rarely available when depending on image segmentation results. We propose a simple approach to automatically recover a high quality surface mesh from low-quality, oversampled and possibly non-consistent inputs that are often obtained via 3-D acquisition systems. As opposed to the majority of the established meshing techniques, our procedure is easy to implement and very robust against damaged or partially incomplete, inconsistent or discontinuous inputs. [ABSTRACT FROM AUTHOR]

In this study we use a fast Fourier spectral technique to simulate the Navier-Stokes equations with no-slip boundary conditions. This is enforced by an immersed boundary technique called volume-penalization. The approach has been justified by analytical proofs of the convergence with respect to the penalization parameter. However, the solution of the penalized Navier-Stokes equations is not smooth on the surface of the penalized volume. Therefore, it is not a priori known whether it is possible to actually perform accurate fast Fourier spectral computations. Convergence checks are reported using a recently revived, and unexpectedly difficult dipole-wall collision as a test case. It is found that Gibbs oscillations have a negligible effect on the flow evolution. This allows higher-order recovery of the accuracy on a Fourier basis by means of a post-processing procedure. [ABSTRACT FROM AUTHOR]