The Architecture of a Software Library for String Processing

We present our project to develop a software library of basic tools and data structures for string processing. Our goal is to provide an environment for testing new algorithms as well as for prototyping. The library has a natural hierarchy comprising basic objects such as the alphabet and strings, data structures to manipulate these objects, and powerful algorithmic techniques driving these data structures. Furthermore, it has the natural taxonomy imposed by the underlying string processing tasks (such as static/dynamic, off-line/on-line, exact/approximate). We believe that our architecture presents a unified view of string processing encompassing recently developed techniques and insights -- this may be of independent interest to those who seek an introduction to this field. Our design is preliminary and we hope to refine it based on feedback. 1. Introduction String problems have attracted a lot of interest throughout the history of Computer Science. Many areas and applications have ..

Peer group and fuzzy metric to remove noise in images using heterogeneous computing

In this paper, we report a study on the parallelization of an algorithm for removing impulsive noise in images. The algorithm is based on the concept of peer group and fuzzy metric. We have developed implementations using Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) for Graphics Processing Unit (GPU). Many sequential algorithms have been proposed to remove noise, but their computational cost is excessive for real-time processing of large images. We developed implementations for a multi- core CPU, for a multi-GPU (several GPUs) and for a combination of both. These implementations were compared also with different sizes of the image in order to find out the settings with the best performance. A study is made using the shared memory and texture memory to minimize access time to data in GPU global memory. The result shows that when the image is distributed in multicore and multi-GPU a greater number of Mpixels/second are processed.This work was funded by the Spanish Ministry of Science and Innovation (Project TIN2008-06570-C04-04) and M. Guadalupe would also like to acknowledge DGEST ITCG for the scholarship awarded through the PROMEP program (Mexico)Sanchez, G.; Vidal Gimeno, VE.; Bataller Mascarell, J. (2012). Peer group and fuzzy metric to remove noise in images using heterogeneous computing. En Euro-Par 2011: Parallel Processing Workshops. Springer Verlag (Germany). 7155:502-510. https://doi.org/10.1007/978-3-642-29737-3S502510715

Looking for a pattern: Error analysis as a diagnostic assessment for making instructional decisions to promote academic success

We examined the type of errors on multiplication and division computation problems of 326 rising fifth graders enrolled in four elementary schools in Northern Portugal. We further examined whether there was a difference in the number of errors across age and whether there was an association between students' performance on number knowledge and multiplication and division computation problems. Error analysis of students' responses indicated that miscalculation and no attempt to solve the problem were the two most frequent error types. We found that older students made more errors compared to younger students. We argue that knowledge of individual student error types is critical to making sound instructional decisions. Based on the results of the present study, we discuss implications for future research and classroom practice.This research was sponsored by a research award to the second author from the Fulbright U.S. Scholar Program