The interaction region (IR) quadrupoles [1] for the Relativistic Heavy Ion Collider (RHIC)[2] are the best field quality superconducting magnets ever built for any major accelerator. This field quality is primarily achieved with the help of eight tuning shims [3] that remove the residual errors from a magnet after it is built and tested. These shims overcome the limitations from the typical tolerances in parts and manufacturing. This paper describes the tuning shims and discusses the evolution of a flexible approach that allowed changes in the design parameters and facilitated using parts with significant dimensional variations while controlling cost and maintaining schedule and field quality. The RHIC magnet program also discovered that quench and thermal cycles cause small changes [4] in magnet geometry. The ultimate field quality performance is now understood to be determined by these changes rather than the manufacturing tolerances or the measurement errors.

We present a method for learning feature descriptors using multiple images, motivated by the problems of mobile robot navigation and localization. The technique uses the relative simplicity of small baseline tracking in image sequences to develop descriptors suitable for the more challenging task of wide baseline matching across significant viewpoint changes. The variations in the appearance of each feature are learned using kernel principal component analysis (KPCA) over the course of image sequences. An approximate version of KPCA is applied to reduce the computational complexity of the algorithms and yield a compact representation. Our experiments demonstrate robustness to wide appearance variations on non-planar surfaces, including changes in illumination, viewpoint, scale, and geometry of the scene.

We present a high-level synthesis methodology that applies a coordinated set of coarse-grain and fine-grain parallelizing transformations. The transformations are applied both during a presynthesis phase and during scheduling, with the objective of optimizing the results of synthesis and reducing the impact of control flow constructs on the quality of results. We first apply a set of source level presynthesis transformations that include common sub-expression elimination (CSE), copy propagation, dead code elimination and loop-invariant code motion, along with more coarse-level code restructuring transformations such as loop unrolling. We then explore scheduling techniques that use a set of aggressive speculative code motions to maximally parallelize the design by re-ordering, speculating and sometimes even duplicating operations in the design. In particular, we present a new technique called "Dynamic CSE" that dynamically coordinates CSE and code motions such as speculation and conditional speculation during scheduling. We implemented our parallelizing high-level synthesis in the SPARK framework. This framework takes a behavioral description in ANSI-C as input and generates synthesizable register-transfer level VHDL. Our results from computationally expensive portions of three moderately complex design targets, namely, MPEG-1, MPEG-2 and the GIMP image processing too], validate the utility of our approach to the behavioral synthesis of designs with complex control flows.