Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.

Optimization of breast tomosynthesis image reconstruction using parallel computing

Breast cancer is the most common cancer among women, being a major public health problem. Worldwide, X-ray mammography is the current gold-standard for medical imaging of breast cancer. However, it has associated some well-known limitations. The false-negative rates, up to 66% in symptomatic women, and the false-positive rates, up to 60%, are a continued source of concern and debate. These drawbacks prompt the development of other imaging techniques for breast cancer detection, in which Digital Breast Tomosynthesis (DBT) is included. DBT is a 3D radiographic technique that reduces the obscuring effect of tissue overlap and appears to address both issues of false-negative and false-positive rates. The 3D images in DBT are only achieved through image reconstruction methods. These methods play an important role in a clinical setting since there is a need to implement a reconstruction process that is both accurate and fast. This dissertation deals with the optimization of iterative algorithms, with parallel computing through an implementation on Graphics Processing Units (GPUs) to make the 3D reconstruction faster using Compute Unified Device Architecture (CUDA). Iterative algorithms have shown to produce the highest quality DBT images, but since they are computationally intensive, their clinical use is currently rejected. These algorithms have the potential to reduce patient dose in DBT scans. A method of integrating CUDA in Interactive Data Language (IDL) is proposed in order to accelerate the DBT image reconstructions. This method has never been attempted before for DBT. In this work the system matrix calculation, the most computationally expensive part of iterative algorithms, is accelerated. A speedup of 1.6 is achieved proving the fact that GPUs can accelerate the IDL implementation.

Synchronization properties of multi-oscillator circadian systems

Dissertation presented to obtain the Ph.D degree in Computational Biology

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

Diffusional kurtosis imaging using a fast heuristic constrained linear least squares algorithm: a plugin for OsiriX

Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis. This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE). The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed, after which, and with some few minor adjust-ments the plugin shall become a valid option for DKI computation

Towards printed carbon nanotube transistors on paper substrates

This thesis reports the work performed in the optimization of deposition parameters of Multi – Walled Carbon Nanotubes (MWCNT) targeting the development of a Field Effect Transistors (FET) on paper substrates. The CNTs were dispersed in a water solution with sodium dodecyl sulphate (SDS) through ultrasonication, ultrasonic bath and a centrifugation to remove the supernatant and have a homogeneous solution. Several deposition tests were performed using different types of CNTs, dis-persants, papers substrates and deposition techniques, such as spray coating and inkjet printing. The characterization of CNTs was made by Scanning Electron Microscopy (SEM) and Hall Effect. The most suitable CNT coatings able to be used as semiconductor in FETs were deposited by spray coat-ing on a paper substrate with hydrophilic nanoporous surface (FS2) at 100 ºC, 4 bar, 10 cm height, 5 second of deposition time and 90 seconds of drying between steps (4 layers of CNTs were deposited). Planar electrolyte gated FETs were produced with these layers using gold-nickel gate, source and drain electrodes. Despite the small current modulation (Ion/Ioff ratio of 1.8) one of these devices have p-type conduction with a field effect mobility of 1.07 cm2/V.s.

Estudo do motor em disco com o rotor em alumínio e em supercondutor multi-semente

Nesta dissertação é realizado o estudo de um motor em disco polifásico com armadura convencional e rotor que pode ser convencional, em alumínio, ou composto por material supercondutor de alta temperatura multi‐semente. O motor com o rotor em alumínio apresenta um comportamento assíncrono bem definido, baseado na lei geral de indução de eletromotrizes no induzido. Contudo, ao ser mergulhado em azoto líquido, evidencia melhores características eléctricas, tanto do induzido como do indutor, mesmo que o núcleo ferromagnético convencional possa exibir degradação magnética. O motor com o rotor SAT exibe um comportamento distinto, evidenciando ambos os regimes síncrono e assíncrono estáveis. O regime síncrono fica caracterizado pelo fenómeno de aprisionamento de campo, que garante que o rotor gire síncrono com o campo girante do estator. Em regime assíncrono, para um determinado valor de binário resistente aplicado superior ao electromagnético máximo desenvolvido pelo motor, o fenómeno de escoamento de campo está presente, sendo o binário electromagnético motor reforçado por um binário extra de perdas de carácter resistivo desenvolvidas no rotor devido à interacção da densidade de corrente a campos eléctricos que se estabelecem no rotor. Neste caso, o fenómeno de escoamento de campo não é devido a forças de Lorentz que se definem na matriz de Abrikosov, mas sim devido a esta ser “puxada” pelo campo girante do estator, ficando a matriz a deslizar sobre o rotor. De modo a obter as características dos sistemas electromecânicos em estudo é realizada uma análise teórica, baseada na teoria convencional das máquinas eléctricas, com o objectivo de compreender alguns dos fenómenos do motor de fluxo axial, e uma análise baseada num programa comercial de elementos finitos onde o fenómeno da supercondutividade é simulado com base na relação entre o campo elétrico e a densidade de corrente pela lei da potenciação (E‐J power law). O ensaio dos motores referidos é realizado para comparar ambos os sistemas e de realçar o princípio de funcionamento e as características mais relevantes de cada um. Da análise realizada é desenvolvido um modelo que caracteriza o funcionamento da máquina com o rotor com SAT.

Integration of a mobile autonomous robot in a surveillance multi-agent system

This dissertation aims to guarantee the integration of a mobile autonomous robot equipped with many sensors in a multi-agent distributed and georeferenced surveillance system. The integration of a mobile autonomous robot in this system leads to new features that will be available to clients of surveillance system may use. These features may be of two types: using the robot as an agent that will act in the environment or by using the robot as a mobile set of sensors. As an agent in the system, the robot can move to certain locations when alerts are received, in order to acknowledge the underlying events or take to action in order to assist in resolving this event. As a sensor platform in the system, it is possible to access information that is read from the sensors of the robot and access complementary measurements to the ones taken by other sensors in the multi-agent system. To integrate this mobile robot in an effective way it is necessary to extend the current multi-agent system architecture to make the connection between the two systems and to integrate the functionalities provided by the robot into the multi-agent system.

Use of multi-sensory marketing techniques and its influence on brand experience in retail and HORECA sector

Double degree. A Work Project presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA- School of Business and Economics and Warsaw School of Economics

Multi-Enzymatic Systems for Cleaning-up Synthetic Dyes from the Environment

Environmental pollution is one of the major and most important problems of the modern world. In order to fulfill the needs and demands of the overgrowing human population, developments in agriculture, medicine, energy sources, and all chemical industries are necessary (Ali 2010). Over the last century, the increased industrialization and continued population growth led to an augmented production of environmental pollutants that are released into air, water, and soil, with significant impact in the degradation of various ecosystems (Ali 2010, Khan et al. 2013).(...)