FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Previsão de vento baseado em técnicas de Data Mining

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

A biosignal embedded system for physiological computing

Thesis submitted in the fulfilment of the requirements for the Degree of Master in Electronic and Telecomunications Engineering

Efeito da fortificação alimentar com ácido fólico na prevalência de defeitos do tubo neural

OBJETIVO:Analisar o efeito de alimentos fortificados com ácido fólico na prevalência de defeitos de fechamento do tubo neural entre nascidos vivos. MÉTODOS: Estudo longitudinal de nascidos vivos do município de Recife (PE) entre 2000 e 2006. Os dados pesquisados foram obtidos do Sistema Nacional de Informações de Nascidos Vivos. Os defeitos de fechamento do tubo neural foram definidos de acordo com o Código Internacional de Doenças-10ª Revisão: anencefalia, encefalocele e espinha bífida. Compararam-se as prevalências nos períodos anterior (2000-2004) e posterior (2005-2006) ao período mandatório à fortificação. Analisou-se a tendência temporal das prevalências trimestrais de defeitos do fechamento do tubo neural pelos testes de Mann-Kendall e Sen's Slope. RESULTADOS: Não se identificou tendência de redução na ocorrência do desfecho (Teste de Mann-Kendall; p= 0,270; Sen's Slope =-0,008) no período estudado. Não houve diferença estatisticamente significativa entre as prevalências de defeitos do fechamento do tubo neural nos períodos anterior e posterior à fortificação dos alimentos com acido fólico de acordo com as características maternas. CONCLUSÕES: Embora não tenha sido observada redução dos defeitos do fechamento do tubo neural após o período mandatório de fortificação de alimentos com ácido fólico, os resultados encontrados não permitem descartar o seu benefício na prevenção desta malformação. São necessários estudos avaliando maior período e considerando o nível de consumo dos produtos fortificados pelas mulheres em idade fértil.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Private cloud computing platforms. Analysis and implementation in a Higher Education Institution

The constant evolution of the Internet and its increasing use and subsequent entailing to private and public activities, resulting in a strong impact on their survival, originates an emerging technology. Through cloud computing, it is possible to abstract users from the lower layers to the business, focusing only on what is most important to manage and with the advantage of being able to grow (or degrades) resources as needed. The paradigm of cloud arises from the necessity of optimization of IT resources evolving in an emergent and rapidly expanding and technology. In this regard, after a study of the most common cloud platforms and the tactic of the current implementation of the technologies applied at the Institute of Biomedical Sciences of Abel Salazar and Faculty of Pharmacy of Oporto University a proposed evolution is suggested in order adorn certain requirements in the context of cloud computing.

Cloud computing sobre a plataforma Windows Azure

A utilização massiva da internet e dos serviços que oferece por parte do utilizador final potencia a evolução dos mesmos, motivando as empresas a apostarem no desenvolvimento deste tipo de soluções. Requisitos como o poder de computação, flexibilidade e escalabilidade tornam-se cada vez mais indissociáveis do desenvolvimento aplicacional, o que leva ao surgimento de paradigmas como o de Cloud Computing. É neste âmbito que surge o presente trabalho. Com o objetivo de estudar o paradigma de Cloud Computing inicia-se um estudo sobre esta temática, onde é detalhado o seu conceito, a sua evolução histórica e comparados os diferentes tipos de implementações que suporta. O estudo detalha posteriormente a plataforma Azure, sendo analisada a sua topologia e arquitetura, detalhando-se os seus componentes e a forma como esta mitiga alguns dos problemas mencionados. Com o conhecimento teórico é desenvolvido um protótipo prático sobre esta plataforma, em que se exploram algumas das particularidades da topologia e se interage com as principais redes sociais. O estudo culmina com uma análise sobre os benefícios e desvantagens do Azure e através de um levantamento das necessidades da empresa, determinam-se as oportunidades que a utilização da plataforma poderá proporcionar.

A study on security grade assignment model for mobile users in urban computing

The relation between the information/knowledge expression and the physical expression can be involved as one of items for an ambient intelligent computing [2],[3]. Moreover, because there are so many contexts around user/spaces during a user movement, all appplcation which are using AmI for users are based on the relation between user devices and environments. In these situations, it is possible that the AmI may output the wrong result from unreliable contexts by attackers. Recently, establishing a server have been utilizes, so finding secure contexts and make contexts of higher security level for save communication have been given importance. Attackers try to put their devices on the expected path of all users in order to obtain users informationillegally or they may try to broadcast their SPAMS to users. This paper is an extensionof [11] which studies the Security Grade Assignment Model (SGAM) to set Cyber-Society Organization (CSO).

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Distributed computing for the factory-floor: a real-time approach using WorldFIP networks

Field communication systems (fieldbuses) are widely used as the communication support for distributed computer-controlled systems (DCCS) within all sort of process control and manufacturing applications. There are several advantages in the use of fieldbuses as a replacement for the traditional point-to-point links between sensors/actuators and computer-based control systems, within which the most relevant is the decentralisation and distribution of the processing power over the field. A widely used fieldbus is the WorldFIP, which is normalised as European standard EN 50170. Using WorldFIP to support DCCS, an important issue is “how to guarantee the timing requirements of the real-time traffic?” WorldFIP has very interesting mechanisms to schedule data transfers, since it explicitly distinguishes periodic and aperiodic traffic. In this paper, we describe how WorldFIP handles these two types of traffic, and more importantly, we provide a comprehensive analysis on how to guarantee the timing requirements of the real-time traffic.

Neural networks for condition monitoring of wind turbines gearbox

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

Mobile collaborative cloudless computing

Although the computational power of mobile devices has been increasing, it is still not enough for some classes of applications. In the present, these applications delegate the computing power burden on servers located on the Internet. This model assumes an always-on Internet connectivity and implies a non-negligible latency. The thesis addresses the challenges and contributions posed to the application of a mobile collaborative computing environment concept to wireless networks. The goal is to define a reference architecture for high performance mobile applications. Current work is focused on efficient data dissemination on a highly transitive environment, suitable to many mobile applications and also to the reputation and incentive system available on this mobile collaborative computing environment. For this we are improving our already published reputation/incentive algorithm with knowledge from the usage pattern from the eduroam wireless network in the Lisbon area.

BITalino: a novel hardware framework for physiological computing

Physical computing has spun a true global revolution in the way in which the digital interfaces with the real world. From bicycle jackets with turn signal lights to twitter-controlled christmas trees, the Do-it-Yourself (DiY) hardware movement has been driving endless innovations and stimulating an age of creative engineering. This ongoing (r)evolution has been led by popular electronics platforms such as the Arduino, the Lilypad, or the Raspberry Pi, however, these are not designed taking into account the specific requirements of biosignal acquisition. To date, the physiological computing community has been severely lacking a parallel to that found in the DiY electronics realm, especially in what concerns suitable hardware frameworks. In this paper, we build on previous work developed within our group, focusing on an all-in-one, low-cost, and modular biosignal acquisition hardware platform, that makes it quicker and easier to build biomedical devices. We describe the main design considerations, experimental evaluation and circuit characterization results, together with the results from a usability study performed with volunteers from multiple target user groups, namely health sciences and electrical, biomedical, and computer engineering. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

Trends Of CPU, GPU and FPGA for high-performance computing

Floating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems. © 2014 Technical University of Munich (TUM).