Guided Filtering based Pyramidal Stereo Matching for Unrectified Images

Stereo matching deals with recovering quantitative depth information from a set of input images, based on the visual disparity between corresponding points. Generally most of the algorithms assume that the processed images are rectified. As robotics becomes popular, conducting stereo matching in the context of cloth manipulation, such as obtaining the disparity map of the garments from the two cameras of the cloth folding robot, is useful and challenging. This is resulted from the fact of the high efficiency, accuracy and low memory requirement under the usage of high resolution images in order to capture the details (e.g. cloth wrinkles) for the given application (e.g. cloth folding). Meanwhile, the images can be unrectified. Therefore, we propose to adapt guided filtering algorithm into the pyramidical stereo matching framework that works directly for unrectified images. To evaluate the proposed unrectified stereo matching in terms of accuracy, we present three datasets that are suited to especially the characteristics of the task of cloth manipulations. By com- paring the proposed algorithm with two baseline algorithms on those three datasets, we demonstrate that our proposed approach is accurate, efficient and requires low memory. This also shows that rather than relying on image rectification, directly applying stereo matching through the unrectified images can be also quite effective and meanwhile efficien

A New Monte Carlo Algorithm for Protein Folding

We demonstrate that the recently proposed pruned-enriched Rosenbluth method (P. Grassberger, Phys. Rev. E 56 (1997) 3682) leads to extremely efficient algorithms for the folding of simple model proteins. We test them on several models for lattice heteropolymers, and compare to published Monte Carlo studies. In all cases our algorithms are faster than all previous ones, and in several cases we find new minimal energy states. In addition to ground states, our algorithms give estimates for the partition sum at finite temperatures.Comment: 4 pages, Latex incl. 3 eps-figs., submitted to Phys. Rev. Lett., revised version with changes in the tex

SLAM : an automated structure to layout synthesis system

SLAM is a structure to layout synthesis system. It incorporates parameterisable bit-sliced and glue-logic generators to produce high density layout. In this paper, we describe a sliced layout architecture and SLAM system. In addition, we present partitioning algorithms for generating the floorplan for such an architecture. The algorithms partition the netlist into component sets best suited for different layout styles such as bit-sliced or strip-oriented logic. Each group is partitioned further into clusters to achieve better area utilization. Several experiments demonstrate that highly dense layouts can be achieved by using these algorithms with the sliced layout architecture

A new partitioning approach for layout synthesis from register-transfer netlists

Most of the IC today are described and documented using heiarchical netlists. In addition to gates, latches, and flip-flops, these netlists include sliceable register-transfer components such as registers, counters, adders, ALUs, shifters, register files, and multiplexers. Usually, these components are decomposed into basic gates, latches, and flip-flops, and are laid out using standard cells. The standard cell architecture requires excessive routing area, and does not exploit the bit-sliced nature of register-transfer components. In this paper, we present a new sliced-layout architecture to alleviate the preceding problems. We also describe partitioning algorithms that are used to generate the floorplan for this layout architecture. The partitioning algorithms not only select the best suited layout style for each component, but also consider critical paths, I/O pin locations, and connections between blocks. This approach improves the overall area utilization and minimizes the total wire length

Monte Carlo Update for Chain Molecules: Biased Gaussian Steps in Torsional Space

We develop a new elementary move for simulations of polymer chains in torsion angle space. The method is flexible and easy to implement. Tentative updates are drawn from a (conformation-dependent) Gaussian distribution that favors approximately local deformations of the chain. The degree of bias is controlled by a parameter b. The method is tested on a reduced model protein with 54 amino acids and the Ramachandran torsion angles as its only degrees of freedom, for different b. Without excessive fine tuning, we find that the effective step size can be increased by a factor of three compared to the unbiased b=0 case. The method may be useful for kinetic studies, too.Comment: 14 pages, 4 figure