Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

A Genetic Algorithm to associate optical measurements and estimate orbits of geocentric objects

Cataloging geocentric objects can be put in the framework of Multiple Target Tracking (MTT). Current work tends to focus on the S = 2 MTT problem because of its favorable computational complexity of O(n²). The MTT problem becomes NP-hard for a dimension of S˃3. The challenge is to find an approximation to the solution within a reasonable computation time. To effciently approximate this solution a Genetic Algorithm is used. The algorithm is applied to a simulated test case. These results represent the first steps towards a method that can treat the S˃3 problem effciently and with minimal manual intervention.

High Performance Algorithms and Implementations Using Sparse and Parallelization Techniques on MBS

In this paper we will see how the efficiency of the MBS simulations can be improved in two different ways, by considering both an explicit and implicit semi-recursive formulation. The explicit method is based on a double velocity transformation that involves the solution of a redundant but compatible system of equations. The high computational cost of this operation has been drastically reduced by taking into account the sparsity pattern of the system. Regarding this, the goal of this method is the introduction of MA48, a high performance mathematical library provided by Harwell Subroutine Library. The second method proposed in this paper has the particularity that, depending on the case, between 70 and 85% of the computation time is devoted to the evaluation of forces derivatives with respect to the relative position and velocity vectors. Keeping in mind that evaluating these derivatives can be decomposed into concurrent tasks, the main goal of this paper lies on a successful and straightforward parallel implementation that have led to a substantial improvement with a speedup of 3.2 by keeping all the cores busy in a quad-core processor and distributing the workload between them, achieving on this way a huge time reduction by doing an ideal CPU usage

FPGA Acceleration of Monte Carlo-based Financial Simulation: Design Challenges and Lessons Learnt

The simulation of interest rate derivatives is a powerful tool to face the current market fluctuations. However, the complexity of the financial models and the way they are processed require exorbitant computation times, what is in clear conflict with the need of a processing time as short as possible to operate in the financial market. To shorten the computation time of financial derivatives the use of hardware accelerators becomes a must.

Facing informatics via three level complexity views

In informatics there is one kind of complexity that is perceived by everyone. It is the complexity of a concrete, isolated object, normally situated completely within one of the branches universally recognized by the scientific and technical community. Examples of this are the complexity of integrated electronic circuits, the complexity of lgorithms and the complexity of software. The first complexity deals with the number of circuit components, the second with computation time and the third with the number of necessary mental discriminations. In arder to illustrate my point, I will take up the last complexity, which, m o reo ver, is the least well-known.

Herramientas de distribución de datos en un sistema Publish/Subscribe

El objetivo del presente trabajo es el desarrollo de una infraestructura de enrutamiento distribuida para un sistema publish/subscribe (en adelante pub/sub) que se incluirá en WireCloud, una plataforma para la composición de aplicaciones. Actualmente existen numerosas implementaciones de protocolos pub/sub en el mercado [3-6], y en este trabajo se hará un recorrido por aquellas con mayor relevancia, explicando sus características, ventajas y desventajas. De esta forma se podrán seleccionar aquellas cuyas prestaciones se adapten de la mejor forma posible a las necesidades de la plataforma. De entre todas las implementaciones existentes, algunas han sido simuladas para comprender mejor su funcionamiento. Esto permitió analizar las diferentes estrategias de enrutamiento de mensajes y qué estructuras de datos son las más óptimas para que el procesamiento de mensajes entrantes sea lo más rápido posible. Una vez que se determinó cuales son las estrategias y las estructuras de almacenamiento que mejor se adaptan a nuestro sistema, se ha procedido a la implementación real del sistema pub/sub en Java. Para cada uno de los módulos desarrollados se ha generado una serie de pruebas que permitirán comprobar el correcto funcionamiento de los componentes cuando se produzcan cambios o ampliaciones. Además, este trabajo también incluye el análisis e implementación de un nuevo protocolo de comunicación entre los diferentes componentes debido a que el existente era lento y pesado al consumir gran cantidad de recursos. El protocolo de comunicación analizado está basado en RMI (Remote Method Invocation), y aunque no mejoró la eficiencia con que los mensajes eran enviados si que permitió reducir en gran medida el consumo de recursos (CPU y memoria RAM). ------------------------------------------------------------ The goal of the current project is the development of a distributed pub/sub system to include it on WireCloud which is a platform to build compositional applications. Several implementations of pub/sub systems [3-6] have been developed until now and this project explains some of them, their characteristic, advantages and disadvantages. Thus, the implementations which better adapt to the project needs have been selected. Some of these implementations have been simulated to get a better knowledge of their behavior. The aim of these simulations was to select the best strategies to route messages and the optimal structures to reduce the computation time of a received message. After defining the better strategies and structures, the real pub/sub system have been finally developed in Java. For each developed module, a set of tests have been built to check its correct behavior in case of changes or extensions. Besides, the project also includes the analysis and the implementation of a new communication protocol among the different components because the existing one was slow and consumed a lot of computer resources. The new communication protocol is based in RMI (Remote Method Invocation) and has improved the consumption of resources. However, the efficiency of this new protocol was worse than the one of the existing protocol.

Hardware-Based Particle Filter with Evolutionary Resampling Stage

Autonomous systems require, in most of the cases, reasoning and decision-making capabilities. Moreover, the decision process has to occur in real time. Real-time computing means that every situation or event has to have an answer before a temporal deadline. In complex applications, these deadlines are usually in the order of milliseconds or even microseconds if the application is very demanding. In order to comply with these timing requirements, computing tasks have to be performed as fast as possible. The problem arises when computations are no longer simple, but very time-consuming operations. A good example can be found in autonomous navigation systems with visual-tracking submodules where Kalman filtering is the most extended solution. However, in recent years, some interesting new approaches have been developed. Particle filtering, given its more general problem-solving features, has reached an important position in the field. The aim of this thesis is to design, implement and validate a hardware platform that constitutes itself an embedded intelligent system. The proposed system would combine particle filtering and evolutionary computation algorithms to generate intelligent behavior. Traditional approaches to particle filtering or evolutionary computation have been developed in software platforms, including parallel capabilities to some extent. In this work, an additional goal is fully exploiting hardware implementation advantages. By using the computational resources available in a FPGA device, better performance results in terms of computation time are expected. These hardware resources will be in charge of extensive repetitive computations. With this hardware-based implementation, real-time features are also expected.

Business Process Optimization in Madrid City Council.

While designing systems and products requires a deep understanding of influences that achieve desirable performance, the need for an efficient and systematic decision-making approach drives the need for optimization strategies. This paper provides the motivation for this topic as well as a description of applications in Computing Center of Madrid city Council. Optimization applications can be found in almost all areas of engineering. Typical problems in process, working with a database, arise in query design, entity model design and concurrent processes. This paper proposes a solution to optimize a night process dealing with millions of records with an overall performance of about eight times in computation time.

Medidas experimentales de la complejidad computacional de un código autoadaptativo HP para problemas abiertos acelerado mediante ACA

The aim of the novel experimental measures presented in this paper is to show the improvement achieved in the computation time for a 2D self-adaptive hp finite element method (FEM) software accelerated through the Adaptive Cross Approximation (ACA) method. This algebraic method (ACA) was presented in an previous paper in the hp context for the analysis of open region problems, where the robust behaviour, good accuracy and high compression levels of ACA were demonstrated. The truncation of the infinite domain is settled through an iterative computation of the Integral Equation (IE) over a ficticious boundary, which, regardless its accuracy and efficiency, turns out to be the bottelneck of the code. It will be shown that in this context ACA reduces drastically the computational effort of the problem.

System failure prediction through rare-events elastic-net logistic regression

Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to overfit on this situations; while manually selecting a subset of variables to create the model is error- prone. On this paper, we solve an industrial research case that presented this situation with a combination of elastic net logistic regression, a method that allows us to automatically select useful variables, a process of cross-validation on top of it and the application of a rare events prediction technique to reduce computation time. This process provides two layers of cross- validation that automatically obtain the optimal model complexity and the optimal mode l parameters values, while ensuring even rare events will be correctly predicted with a low amount of training instances. We tested this method against real industrial data, obtaining a total of 60 out of 80 possible models with a 90% average model accuracy.

Building intuition of iron evolution during solar cell processing through analysis of different process models

An important aspect of Process Simulators for photovoltaics is prediction of defect evolution during device fabrication. Over the last twenty years, these tools have accelerated process optimization, and several Process Simulators for iron, a ubiquitous and deleterious impurity in silicon, have been developed. The diversity of these tools can make it difficult to build intuition about the physics governing iron behavior during processing. Thus, in one unified software environment and using self-consistent terminology, we combine and describe three of these Simulators. We vary structural defect distribution and iron precipitation equations to create eight distinct Models, which we then use to simulate different stages of processing. We find that the structural defect distribution influences the final interstitial iron concentration ([Fe-i]) more strongly than the iron precipitation equations. We identify two regimes of iron behavior: (1) diffusivity-limited, in which iron evolution is kinetically limited and bulk [Fe-i] predictions can vary by an order of magnitude or more, and (2) solubility-limited, in which iron evolution is near thermodynamic equilibrium and the Models yield similar results. This rigorous analysis provides new intuition that can inform Process Simulation, material, and process development, and it enables scientists and engineers to choose an appropriate level of Model complexity based on wafer type and quality, processing conditions, and available computation time.

Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis

Abstract. Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial–temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow-varying biological and industrial processes.

Metodologia para execução de aplicações paralelas baseadas no modelo BSP com tarefas heterogêneas.

A computação paralela permite uma série de vantagens para a execução de aplicações de grande porte, sendo que o uso efetivo dos recursos computacionais paralelos é um aspecto relevante da computação de alto desempenho. Este trabalho apresenta uma metodologia que provê a execução, de forma automatizada, de aplicações paralelas baseadas no modelo BSP com tarefas heterogêneas. É considerado no modelo adotado, que o tempo de computação de cada tarefa secundária não possui uma alta variância entre uma iteração e outra. A metodologia é denominada de ASE e é composta por três etapas: Aquisição (Acquisition), Escalonamento (Scheduling) e Execução (Execution). Na etapa de Aquisição, os tempos de processamento das tarefas são obtidos; na etapa de Escalonamento a metodologia busca encontrar a distribuição de tarefas que maximize a velocidade de execução da aplicação paralela, mas minimizando o uso de recursos, por meio de um algoritmo desenvolvido neste trabalho; e por fim a etapa de Execução executa a aplicação paralela com a distribuição definida na etapa anterior. Ferramentas que são aplicadas na metodologia foram implementadas. Um conjunto de testes aplicando a metodologia foi realizado e os resultados apresentados mostram que os objetivos da proposta foram alcançados.

Propiedad, control y gestión del agua en regadíos deficitarios del Sureste español: la huerta de Alicante durante la Edad Moderna

La Huerta de Alicante, prototipo de los denominados regadíos deficitarios o secanos mejorados, ofrece uno de los mejores ejemplos de cómo la escasez hídrica activó la búsqueda de soluciones técnicas, propició la inversión en infraestructuras hidráulicas y desarrolló unas peculiares relaciones entre los labradores regantes y los propietarios del agua. La gestión de los recursos hídricos y su distribución por estricto cómputo horario por parte del municipio alicantino hasta 1739 permitió, pese a los condicionantes físicos y climáticos, el desarrollo de una actividad agrícola significativa durante los siglos modernos. La temprana separación del agua de la tierra a la que estaba adscrita en los primeros repartos medievales provocó numerosos conflictos a lo largo de la historia e intentos de unificar ambas propiedades, algo que nunca se consiguió.

Integration of robotic inverse kinematic routines in an algorithmic 3D modeling software

An industrial manipulator equipped with an automatic clay extruder is used to realize a machine that can manufacture additively clay objects. The desired geometries are designed by means of a 3D modeling software and then sliced in a sequence of layers with the same thickness of the extruded clay section. The profiles of each layer are transformed in trajectories for the extruder and therefore for the end-effector of the manipulator. The goal of this thesis is to improve the algorithm for the inverse kinematic resolution and the integration of the routine within the development software that controls the machine (Rhino/Grasshopper). The kinematic model is described by homogeneous transformations, adopting the Denavit-Hartenberg standard convention. The function is implemented in C# and it has been preliminarily tested in Matlab. The outcome of this work is a substantial reduction of the computation time relative to the execution of the algorithm, which is halved.