In these days, many dynamically reconfigurable architectures have been introduced to fill the gap between ASICs and software-programmed processors such as GPPs and DSPs. These reconfigurable architectures have shown to achieve higher performance compared to software-programmed processors. However, reconfigurable architectures suffer from a significant reconfiguration overhead and a speedup limitation. By reducing the reconfiguration overhead, the overall performance of reconfigurable architectures can be improved. Therefore, we will describe temporal partitioning, which are able to amortize the reconfiguration overhead at synthesis phase or compilation time. Our temporal partitioning methodology splits a configuration context into temporal partitions to amortize reconfiguration overhead. And then, we will present benchmark results to demonstrate the effectiveness of our methodology.

In the adaptive lattice estimation process, it is well known that the convergence speed of the successive stage is affected by the estimation errors of reflection coefficients in its preceding stages. In this paper, we propose block estimation methods of two-dimensional (2-D) adaptive lattice filter. The convergence speed of the proposed algorithm is significantly enhanced by improving the adaptive performance of preceding stages. Furthermore, this process can be simply realized. The modeling of 2-D AR field and texture image are demonstrated through computer simulations.

This paper proposes a novel method for determining a three-dimensional (3D) bounding box to estimate pose (position and orientation) and size of a 3D object corresponding to a segmented object region in an image acquired by a single calibrated camera. The method is designed to work upon an object on the ground and to determine a bounding box aligned to the direction of the object, thereby reducing the number of degrees of freedom in localizing the bounding box to 5 from 9. Observations associated with the structural properties of back-projected object regions on the ground are suggested, which are useful for determining the object points expected to be on the ground. A suitable base is then estimated from the expected on-ground object points by applying to them an assumption of bilateral symmetry. A bounding box with this base is finally constructed by determining its height, such that back-projection of the constructed box onto the ground minimally encloses back-projection of the given object region. Through experiments with some 3D-modelled objects and real objects, we found that a bounding box aligned to the dominant direction estimated from edges with common direction looks natural, and the accuracy of the pose and size is enough for localizing actual on-ground objects in an industrial working space. The proposed method is expected to be used effectively in the fields of smart surveillance and autonomous navigation.