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Micro-electro-mechanical systems (MEMS) technology ignites major breakthroughs in several research areas. Optics/photonics is one of these research fields impacted by MEMS techniques. Generally, microoptical elements with sizes ranging from a few microns to a few millimeters belong to the category of optical MEMS. They are inherently suited for cost effective wafer scale manufacturing as the processes are derived from the semiconductor industry. The advantages of applying microelectronics technology to silicon micromechanical devices were presented by Petersen in his classic paper, ‘Silicon as a Mechanical Material’[1].

We survey some recent work on interactive modeling, simulation, and control of large-scale crowds. Our primary focus is on interactive algorithms that can handle a large number of autonomous agents. This includes techniques for automatically computing collision-free trajectories for each agent as well as generating emergent crowd behaviors including lane formation, edge effects, vortices, congestion avoidance, swirling and modeling varying crowd density. Some of these methods map well to current multi-core and many-core processors and we highlight their performance in different urban scenarios.

In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace. In our formulation, each robot acts fully independently, and does not communicate with other robots. Based on the definition of velocity obstacles [5], we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program. We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few milliseconds. To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace.

We present a novel, biomechanically-inspired, kinematic- based, example-driven walking synthesis model. Our model is ideally suited towards interactive applications such as games. It synthesizes motion interactively without a priori knowledge of the trajectory. The model is very efficient, producing foot-skate free, smooth motion over a large, continuous range of speeds and while turning, in as little as 5 μs. We've formulated our model so that an artist has extensive control over how the walking gait manifests itself at all speeds.

The demand for data center solutions with lower total cost of ownership and lower complexity of management is driving the creation of next generation datacenters. The information technology industry is in the midst of a transformation to lower the cost of operation through consolidation and better utilization of critical data center resources. Successful consolidation necessitates increasing utilization of capital intensive “always-on” data center infrastructure, reduction in the recurring cost of power and management of physical resources. In this paper, we describe a tool that allows the data center facility managers and administrators to view and analyze the Key Performance Indicators (KPIs) associated with their data centers using pixel cell-based [10,11] visual analytics. The basic idea of our technique is to use the smallest element in the display to present the detailed information of the poser and thermal data records. Administrators can quickly recognize the patterns, trends, and anomalies. Furthermore, we discuss case studies of mobile visual analytics for energy and thermal state monitoring utilizing data from a rich sensor network.

We survey some of our recent work on interactive modeling, simulation, and control of large-scale crowds and traffic for urban scenes. The driving applications of our work include real-time simulation for computer games, virtual environments, and avatar-based online 3D social networks. We also present some preliminary results and proof-of-concept demonstrations.

In this paper we study the problem of path planning among movable obstacles, in which a robot is allowed to move the obstacles if they block the robot’s way from a start to a goal position. We make the observation that we can decouple the computations of the robot motions and the obstacle movements, and present a probabilistically complete algorithm, something which to date has not been achieved for this problem. Our algorithm maintains an explicit representation of the robot’s configuration space. We present an efficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios.

We propose a new approach for validating deformable image registration algorithms. Since difference images do not necessarily reflect the 3D correspondence of organs, we propose to use the deformation fields generated in our FEM-based simulations to assess the displacement resulted from other registration methods. Unlike traditional FEM-based registration methods, the boundary condition for the target organ is not given explicitly. Instead it is driven by inter-organ contact forces generated by boundary conditions on surrounding organs to reduce the uncertainty induced by geometry-based surface matching. To validate our system, real CT images of the male pelvis are analyzed, and the prostate can be reasonably registered without matching its surface to the image. Several registration methods are then evaluated using our system.

We survey some of our recent work on real-time path planning and navigation of multiple autonomous agents in dynamic environments. The driving application of our work are real-time crowd simulation for computer games, virtual environments, and avatar-based online 3D social networks. We also present extensions to these methods for accelerating the overall simulation and for modeling more complex behaviors. Finally, we present some preliminary results from our simulations.

We present an efficient algorithm for haptic rendering of deformable bodies with highly detailed surface geometry using a fast contact handling algorithm. We exploit a layered deformable representation to augment the physically based deformation simulation with efficient collision detection, contact handling and interactive haptic force feedback.

A Human-Computer Interaction (HCI) system has been developed with an Artificial Neural Network (ANN) using a motion tracker and a data glove. The HCI system is able to recognize American Sign Language letter and number gestures. The finger joint angle data obtained from the strain gauges in the sensory glove define the hand shape while the data from the motion tracker describe the hand position and orientation. The data flow from the sensory glove is controlled by a software trigger using the data from the motion tracker during signing. Then, the glove data is processed by a recognition neural network.

An American Sign Language (ASL) finger spelling and an alphabet gesture recognition system was designed with ANN and constructed in order to translate the ASL alphabet into the corresponding printed and sounded English letters. The system uses a sensory Cyberglove and a Flock of Birds 3-D motion tracker to extract the gestures. The finger joint angle data obtained from strain gauges in the sensory glove define the hand shape while the data from the tracker describes the trajectory and orientation. The data flow from these devices is controlled by a motion trigger. Then, data is processed by an alphabet recognition network to generate the words and names. Our goal is to establish an ASL finger spelling system using these devices in real time. We trained and tested our system for ASL alphabet, names and word spelling. Our test results show that the accuracy of recognition is 96%.

We present a novel algorithm for planning the motion of rigid and articulated robots in complex, dynamic, 3D environments. Our approach is to reformulate the motion planning problem as a simulation of a constrained dynamical system, and guide this system using generalized Voronoi diagrams (GVDs). In our framework, each rigid robot is subject to virtual forces induced by geometric and mechanical constraints. These may include constraints to have a robot follow an estimated path computed using a GVD, constraints to link rigid objects together to represent an articulated robot, or constraints to enforce a spatial relationship between multiple collaborative robots. The resulting algorithm uses all constraint forces to move the robot along an estimated path through the environment, while avoiding collisions with obstacles and enforcing joint and positional constraints. Our algorithm works well in dynamic environments with moving obstacles and is applicable to planning scenarios where multiple robots must move simultaneously to achieve a collision free path.