Software Framework

This paper presents a hardware and software framework for reliable fall detection in the home environment, with particular focus on the protection and assistance to the elderly. The integrated prototype includes three different sensors: a 3D time-of-flight range camera, a wearable MEMS accelerometer and a microphone. These devices are connected with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each of the three sensors, an optimized algorithm for fall-detection has been developed and benchmarked on a collected mulitimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system efficiency and reliability.

Purpose -Surgical simulations require haptic interactions and collaboration in a shared virtual environment. A software framework for decoupled surgical simulation based on a multi-controller and multi--viewer Model--View--Controller (MVC) pattern was developed and tested.

The finite element method is an important, widely used numerical technique for solving partial differential equations. This technique utilizes basis functions for approximating the geometry and the variation of the solution field over finite regions, or elements, of the domain. These basis functions are generally formed by combinations of polynomials. In the past, the polynomial order of the basis has been low-typically of linear and quadratic order. However, in recent years so-called p and hp methods have been developed, which may elevate the order of the basis to arbitrary levels with the aim of accelerating the convergence of the numerical solution. The increasing complexity of numerical basis functions poses a significant challenge to visualization systems. In the past, such systems have been loosely coupled to simulation packages, exchanging data via file transfer, and internally reimplementing the basis functions in order to perform interpolation and implement visualization algorithms. However, as the basis functions become more complex and, in some cases, proprietary in nature, it becomes increasingly difficult if not impossible to reimplement them within the visualization system. Further, most visualization systems typically process linear primitives, in part to take advantage of graphics hardware and, in part, due to the inherent simplicity of the resulting algorithms. Thus, visualization of higher-order finite elements requires tessellating the basis to produce data compatible with existing visualization systems. In this paper, we describe adaptive methods that automatically tessellate complex finite element basis functions using a flexible and extensible software framework. These methods employ a recursive, edge-based subdivision algorithm driven by a set of error metrics including geometric error, solution error, and error in image space. Further, we describe advanced pretessellation techniques that guarantees capture of the critical points of the polynomial basis. The framework has been designed using the adaptor design pattern, meaning that the visualization system need not reimplement basis functions, rather it communicates with the simulation package via simple programmatic queries. We demonstrate our method on several examples, and have implemented the framework in the open-source visualization system VTK.

Unified testing of multiple heterogeneous robotic software components is a challenging problem and many robotic systems rely on vendor-specific tools for testing and evaluation of individual subsystems. The consequence is often the unexpected interactions between components that arise during system integration.

This paper presents recent advances in response sensitivity, probabilistic response and reliability analyses of structural and geotechnical systems. These developments are integrated into general-purpose nonlinear finite element (FE) software frameworks and provide the structural engineers with analytical and computational tools to propagate uncertainties through advanced large-scale nonlinear simulations and obtain probabilistic estimates of the predicted system response performance. The Direct Differentiation Method (DDM) for accurate and efficient computation of FE response sensitivities is extended and applied to large-scale nonlinear Soil-Foundation-Structure-Interaction (SFSI) systems. Extensions include numerical algorithms for response sensitivity analysis of FE models with multi-point constraints, force-based and three-field mixed elements, as well as various nonlinear material constitutive models, including a pressure independent multi-yield-surface J 2 plasticity material model used to simulate the clay soil nonlinear behavior. Response sensitivity analysis results are shown for structural and SFSI systems. Examples of probabilistic response as well as time-invariant and time-variant reliability analyses are provided. Importance sampling and orthogonal plane sampling techniques are adopted and implemented into the considered FE software frameworks for accurate computation of failure probabilities. A new visualization technique, the Multidimensional Visualization in the Principal Planes (MVPP), is developed to visualize limit state surfaces in a neighborhood of the design points, giving insight into inaccuracy of the First-Order Reliability Method (FORM) for highly nonlinear systems. Based on the MVPP technique results, a novel hybrid method, the Design Point -Response Surface -Simulation (DP-RS-Sim) method is developed for both time-invariant and time-variant reliability analysis. Application examples are provided to show that the DP-RS-Sim method can provide failure probability estimates more accurate than FORM at a small increment of the computational cost.

In this paper, we overview the 2009 Simulated Car Racing Championship-an event comprising three competitions held in association with the 2009 IEEE Congress on Evolutionary Computation (CEC), the 2009 ACM Genetic and Evolutionary Computation Conference (GECCO), and the 2009 IEEE Symposium on Computational Intelligence and Games (CIG). First, we describe the competition regulations and the software framework. Then, the five best teams describe the methods of computational intelligence they used to develop their drivers and the lessons they learned from the participation in the championship. The organizers provide short summaries of the other competitors. Finally, we summarize the championship results, followed by a discussion about what the organizers learned about 1) the development of high-performing car racing controllers and 2) the organization of scientific competitions.