The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular to that group. Moreover, the types of data and metadata collected are quite diverse, including image, physiological, and behavioral data, as well as descriptions of experimental design, and preprocessing and analysis methods. Each of these types of data utilizes a variety of software tools for collection, storage, and processing. Furthermore sites are reluctant to release control over the distribution and access to the data and the tools. To address these needs, the Biomedical Informatics Research Network (BIRN) has developed a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The infrastructure consists of distributed data collections hosted on dedicated storage and computational resources located at each participating site, a federated data management system and data integration environment, an Extensible Markup Language (XML) schema for data exchange, and analysis pipelines, designed to leverage both the distributed data management environment and the available grid computing resources.

The use of visualization and computational steering can often assist scientists in analyzing large-scale scientific applications. Fault-tolerance to failures is of great importance when running on a distributed system. However, the details of implementing these features are complex and tedious, leaving many scientists with inadequate development tools. CUMULVS is a library that enables programmers to easily incorporate interactive visualization and computational steering into existing parallel programs. The library is divided into two pieces: one for the application program and one for the, possibly commercial, visualization and steering front-end. Together these two libraries encompass all the connection and data protocols needed to dynamically attach multiple independent viewer front-ends to a running parallel application. Viewer programs can also steer one or more user-defined parameters to "close the loop" for computational experiments and analyses. CUMULVS allows the programmer to specify user-directed checkpoints for saving important program state in case of failures, and also provides a mechanism to migrate tasks across heterogeneous machine architectures to achieve improved performance. Details of the CUMULVS design goals and compromises as well as future directi?s are gken, . .

A technique is presented and experimentally validated for solving the inverse dynamics and kinematics of multi-link flexible robots. The proposed method finds the joint torques necessary to produce a specified end-effector motion. Since the inverse dynamic problem in elastic manipulators is closely coupled to the inverse kinematic problem, the solution of the first also renders the displacements and rotations at any point of the manipulator, including the joints. Furthermore the formulation is complete in the sense that it includes all the nonlinear terms due to the large rotation of the links. The Timoshenko beam theory is used to model the elastic characteristics, and the resulting equations of motion are discretized using the finite element method. An iterative solution scheme is proposed that relies on local linearization of the problem. The solution of each linearization is carried out in the frequency domain.The performance and capabilities of this technique are tested, first through simulation analysis, and second through experimental validation using feed-forward control. Results show the potential use of this method not only for open-loop control, but also for incorporation in feedback control strategies.

This architecture/infrastructure of parallel optical networks couples data exploration, visualizationy and ' •• collaboration technologies through IP at multi-gigabit speeds. he OptlPuter exploits a new world of distributed Grid infrastructure in which the ' central architectural element is optical networking, not computers, creating "supernetworks," or networks faster than te the computers attached to them. As in supercomputing a decade ago, parallelism makes this transition possible. But this time, parallelism takes the form of multiple wavelengths of light, or lambdas, capable of traversing individual strands of optical fiber.

SUMMARYMany production Grid and e-Science infrastructures have begun to offer services to end-users during the past several years with an increasing number of scientific applications that require access to a wide variety of resources and services in multiple Grids. Therefore, the Grid Interoperation Now-Community Group of the Open Grid Forum-organizes and manages interoperation efforts among those production Grid infrastructures to reach the goal of a world-wide Grid vision on a technical level in the near future. This contribution highlights fundamental approaches of the group and discusses open standards in the context of production e-Science infrastructures.