A high resolution workstation prototype for diagnosis of digital mammograms

The development of a total digital high resolution mammography display system must meet a number of requirements that remain a challenge nowadays, most probably because of the special nature of breast imaging. In this paper, we discuss our particular approach to address some problems concerning the complexity of soft-copy diagnosis in digital mammography, such as image quality and user interface evaluation. Based on the experience obtained in the previous implementation of a medical image browser, a more ambitious project is being developed at the Department of Radiology of the University of Santiago de Compostela (Spain) in collaboration with the Department of Medical Informatics of INTELSIS, an emerging software company in our country. This new system will provide complete support to display, store and analyze mammographic studies in digital format.     

Topology optimization for crashworthiness of thin-walled structures under axial impact using hybrid cellular automata

Although topology optimization is well established in most engineering fields, it is still in its infancy concerning highly non-linear structural applications like vehicular crashworthiness. One of the approaches recently proposed and based on Hybrid Cellular Automata is modified here such that it can be applied for the first time to thin-walled structures. Classical methods based on voxel techniques, i.e., on solid three-dimensional volume elements, cannot derive structures made from thin metal sheets where the main energy absorption mode is related to plastic buckling, folding and failure. Because the main components of car structures are made from such thin-walled beams and panels, a special approach using SFE CONCEPT was developed, which is presented in this paper.     

Characterization of a miniaturized liquid bridge for nL sample infusion: a comparative study of sample flush-out behavior using flow simulations and direct ESI-MS analysis

In this work we shed light on the microfluidics of a miniaturized liquid bridge that forms the central part of a so-called “capillary gap sampler,” a novel device for rapid and seamless injection of nanoliter sample volumes into an electrospray ionization mass spectrometer (ESI-MS). Parameters relevant for sample flush-out at the liquid bridge and in the spray capillary were identified by systematic variation of the capillary dimensions, the linear buffer flow rate (2.1–34 mm/s) and molecular weight of the analytes (0.5–30 kDa). We found that a reduction in capillary wall thickness by a factor of 1.6 significantly influences analyte peak shapes, leads to an inversion of the relationship between peak width and analyte molecular weight, and allows a fivefold decrease in peak width for large molecules down to 5 s. The results could be verified and explained by simulations, in which the presence of diffusion-controlled “dead zones” at the liquid bridge and dispersion in the spray tip that depend on analyte molecular weight were identified as key factors relevant for the sample flush-out process. The merging of simulations and experimental data gives useful hints toward the re-design of a spray tip as built-in ESI-MS interface for an optimized gap sampler performance.     

Compressed Multiresolution Hierarchies for High‐Quality Precomputed Shadows

The quality of shadow mapping is traditionally limited by texture resolution. We present a novel lossless compression scheme for high‐resolution shadow maps based on precomputed multiresolution hierarchies. Traditional multiresolution trees can compactly represent homogeneous regions of shadow maps at coarser levels, but require many nodes for fine details. By conservatively adapting the depth map, we can significantly reduce the tree complexity. Our proposed method offers high compression rates, avoids quantization errors, exploits coherency along all data dimensions, and is well‐suited for GPU architectures. Our approach can be applied for coherent shadow maps as well, enabling several applications, including high‐quality soft shadows and dynamic lights moving on fixed‐trajectories.     

A robot learning from demonstration framework to perform force-based manipulation tasks

This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.     

Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.     

Event-Based Techniques to Debug an Object Request Broker

This work presents a debugging system built for the Object Request Broker (ORB) used in the construction of Solaris MC, a multicomputer OS. Even though it has been built and tested on a particular ORB, we believe similar ideas could be employed on other ORBs with similar structure and goals. The goal of this system is to provide a means to stress the ORB behavior in a controlled manner while logging the events occurred during its execution. The tool, called the Fault Injection and Event Logging Tool (FIELT) helps system programmers to find possible inconsistencies in the code by means of a post-mortem analysis of the collected trace data. The approach taken to design the event logging follows the event-driven techniques to monitorize distributed systems. Failures in the ORB are injected by software instrumentation and these injected failures are considered as special events. This allows us to reason about the correctness of the ORB in a broad sense, where its expected behavior includes to gracefully cope with failures. The number of potentially relevant events produced during the ORB execution is unmanageably high. There is, thus, a need to find a minimum subset of those events which, without losing relevant system behavior, allows us to infer its correctness (or lack of). We address this problem using a new model for ORB computations, assigning each event produced by the ORB to one of the high level objects it manages.     

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high‐performance applications, such as local clusters, high‐performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads; hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications.     

Spectral Gradient Sampling for Path Tracing

Spectral Monte‐Carlo methods are currently the most powerful techniques for simulating light transport with wavelength‐dependent phenomena (e.g., dispersion, colored particle scattering, or diffraction gratings). Compared to trichromatic rendering, sampling the spectral domain requires significantly more samples for noise‐free images. Inspired by gradient‐domain rendering, which estimates image gradients, we propose spectral gradient sampling to estimate the gradients of the spectral distribution inside a pixel. These gradients can be sampled with a significantly lower variance by carefully correlating the path samples of a pixel in the spectral domain, and we introduce a mapping function that shifts paths with wavelength‐dependent interactions. We compute the result of each pixel by integrating the estimated gradients over the spectral domain using a one‐dimensional screened Poisson reconstruction. Our method improves convergence and reduces chromatic noise from spectral sampling, as demonstrated by our implementation within a conventional path tracer.