Compile and Run-Time Support for the Parallelization of Sparse Matrix Updating Algorithms

This work presents a survey of the capabilities that the sparse computation offers for improving performance when parallelized, either automatically or through a data-parallel compiler. The characterization of a sparse code gets more complicated as code length increases: Access patterns change from loop to loop, thus making necessary to redefine the parallelization strategy. While dense computation solely offers the possibility of redistributing data structures, several other factors influence the performance of a code excerpt in the sparse field, like source data representation on file, compressed data storage in memory, the creation of new nonzeroes at run-time (fill-in) or the number of processors available. We analize the alternatives that arise from each issue, providing a guideline for the underlying compilation work and illustrating our techniques with examples on the Cray T3E.     

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

The paper presents a formulation for multidisciplinary design optimization of vessels, subject to uncertain operating conditions. The formulation couples the multidisciplinary design analysis with the Bayesian approach to decision problems affected by uncertainty. In the present context, the design specifications are no longer given in terms of a single operating design point, but in terms of probability density function of the operating scenario. The optimal configuration is that which maximizes the performance expectation over the uncertain parameters variation. In this sense, the optimal solution is “robust” within the stochastic scenario assumed. Theoretical and numerical issues are addressed and numerical results in the hydroelastic optimization of a keel fin of a sailing yacht are presented.     

Development of the design of car bodywork from first concept to prototype

This paper deals with the design of car bodies from the first sketches to the master model, ie to the last stage before mass manufacture. Close scrutiny of the various steps needed in drawing, control and construction make it possible to determine which of them can be replaced by modern automatic procedures.     

The roles of textural images in improving land-cover classification in the Brazilian Amazon

Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l’Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving land-cover classification. The classification accuracy can be improved by 5.2–13.4% as the pixel size changes from 30 to 0.6 m.     

Vessel Inspection: A Micro-Aerial Vehicle-based Approach

Vessel maintenance entails periodic visual inspections of the internal and external parts of the hull in order to detect the typical defective situations affecting metallic structures, such as coating breakdown, corrosion, cracks, etc. The main goal of project MINOAS is the automation of the inspection process, currently undertaken by human surveyors, by means of a fleet of robotic agents. This paper overviews an approach to the inspection problem based on an autonomous Micro Aerial Vehicle (MAV) which, as part of this fleet, is in charge of regularly supplying images that can teleport the surveyor from a base station to the areas of the hull to be inspected. The control software approach adopted for the MAV is fully described, with a special emphasis on the self-localization capabilities of the vehicle. Experimental results showing the suitability of the platform to the application are as well reported and discussed.     

A fast procedure for manipulator inverse kinematics computation and singularities prevention

In this article, a fast procedure for numerical manipulator inverse kinematics computation and singularities prevention is presented. The approach is based upon solving a linear system and automatically calculating some parameters. These parameters are properly used in either one of two original schemes that are also proposed to induce robustness to the pseudoinverse. Furthermore, here it is also shown how to properly implement one of these schemes in conjunction with a recently developed approach for the singularities prevention of redundant manipulators. The resultant algorithms are tested on the simulation of a planar redundant manipulator. From the results obtained, it is observed that the proposed approach compares favorably with the approaches using a Gaussian elimination procedure and with pseudoinverse robustness based on a manipulability measure.     

Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit

In this paper, we review an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function. We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas we have previously (Lai et al., 2000; Fyfe and MacDonald, 2002) viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit. We illustrate this on both artificial and real data sets.     

Nonlinear Versus Linear Learning Devices: A Procedural Perspective

We provide a discussion of bounded rationality learning behind traditional learning mechanisms, i.e., Recursive Ordinary Least Squares and Bayesian Learning . These mechanisms lack for many reasons a behavioral interpretation and, following the Simon criticism, they appear to be substantively rational. In this paper, analyzing the Cagan model, we explore two learning mechanisms which appear to be more plausible from a behavioral point of view and somehow procedurally rational: Least Mean Squares learning for linear models and Back Propagation for Artificial Neural Networks . The two algorithms look for a minimum of the variance of the error forecasting by means of a steepest descent gradient procedure. The analysis of the Cagan model shows an interesting result: non-convergence of learning to the Rational Expectations Equilibrium is not due to the restriction to linear learning devices; also Back Propagation learning for Artificial Neural Networks may fail to converge to the Rational Expectations Equilibrium of the model.     

HumanTop: a multi-object tracking tabletop

In this paper, a computer vision based interactive multi-touch tabletop system called HumanTop is introduced. HumanTop implements a stereo camera vision subsystem which allows not only an accurate fingertip tracking algorithm but also a precise touch-over-the-working surface detection method. Based on a pair of visible spectra cameras, a novel synchronization circuit makes the camera caption and the image projection independent from each other, providing the minimum basis for the development of computer vision analysis based on visible spectrum cameras without any interference coming from the projector. The assembly of both cameras and the synchronization circuit is not only capable of performing an ad-hoc version of a depth camera, but it also introduces the recognition and tracking of textured planar objects, even when contents are projected over them. On the other hand HumanTop supports the tracking of sheets of paper and ID-code markers. This set of features makes the HumanTop a comprehensive, intuitive and versatile augmented tabletop that provides multitouch interaction with projective augmented reality on any flat surface. As an example to exploit all the capabilities of HumanTop, an educational application has been developed using an augmented book as a launcher to different didactic contents. A pilot study in which 28 fifth graders participated is presented. Results about efficiency, usability/satisfaction and motivation are provided. These results suggest that HumanTop is an interesting platform for the development of educational contents.