Developing a new SVPWM control strategy for open-winding brushless doubly fed reluctance generators

In this paper, a new open-winding control strategy is proposed for a brushless doubly fed reluctance generator (BDFRG) used for stand-alone wind turbine or ship generators. The BDFRG is characterized with two windings on the stator: a power winding and a control winding. The control winding is fed with dual two-level three-phase converters, and a vector control scheme based on space vector pulsewidth modulation is designed. Compared with traditional three-level inverter systems, the dc-link voltage and the voltage rating of power devices in the proposed system are reduced by 50% while still greatly improving the reliability, redundancy, and fault tolerance of the proposed system by increasing the switching modes. Its performance is evaluated by simulation in MATLAB/Simulink and an experimental study on a 42-kW prototype machine.

Optimizing the radiation pattern of smart antennas using genetic algorithms

This work is directed towards optimizing the radiation pattern of smart antennas using genetic algorithms. The structure of the smart antennas based on Space Division Multiple Access (SDMA) is proposed. It is composed of adaptive antennas, each of which has adjustable weight elements for amplitudes and phases of signals. The corresponding radiation pattern formula available for the utilization of numerical optimization techniques is deduced. Genetic algorithms are applied to search the best phase-amplitude weights or phase-only weights with which the optimal radiation pattern can be achieved. ^ One highlight of this work is the proposed optimal radiation pattern concept and its implementation by genetic algorithms. The results show that genetic algorithms are effective for the true Signal-Interference-Ratio (SIR) design of smart antennas. This means that not only nulls can be put in the directions of the interfering signals but also simultaneously main lobes can be formed in the directions of the desired signals. The optimal radiation pattern of a smart antenna possessing SDMA ability has been achieved. ^ The second highlight is on the weight search by genetic algorithms for the optimal radiation pattern design of antennas having more than one interfering signal. The regular criterion for determining which chromosome should be kept for the next step iteration is modified so as to improve the performance of the genetic algorithm iteration. The results show that the modified criterion can speed up and guarantee the iteration to be convergent. ^ In addition, the comparison between phase-amplitude perturbations and phase-only perturbations for the radiation pattern design of smart antennas are carried out. The effects of parameters used by the genetic algorithm on the optimal radiation pattern design are investigated. Valuable results are obtained. ^

A novel signal processing method for intraoperative neurophysiological monitoring in spinal surgeries

Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded. An algorithm was developed using Chebychev time windowing for preconditioning of SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. A unique Walsh transform operation was then used to identify the position of the SSEP event. An alarm is raised when there is a 10% time in latency deviation and/or 50% peak-to-peak amplitude deviation, as per the clinical requirements. The algorithm shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials. In this study, the analysis was performed on the data recorded in 29 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm takes 4sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.

Espaço de todos ou de ninguém: análise morfológica de transformações espaciais dos Conjuntos Residenciais Parque Serrambi em Natal/ RN

This work analyses the effects of form over the emergence of new patterns of use and occupation in public spaces of housing estates designed in the 1980s and 1990s in Natal’s municipality (Brazil). We start from the premise that form acts on social process (HILLIER; HANSON, 1984), and verify how much the original spatial configuration of Parque Serrambi housing states contributed to the creation of new spatial patterns following the interventions of the people living there. The Serrambi states were built in Natal’s south based on an urbanization model following modernist’s principals and aimed to supply demands for popular dwellings. They were one of the last estates financed by the former National Dwelling Bank (BNH) and supervised by the Dwelling Cooperatives Orientation Institute (INOCOOP), materialized a spatial form different from the highrise dwelling experiences from the same period. The results were obtained through configurational analysis based on Space Syntax conceptual and methodological framework, in which space and society are viewed as interrelated. The analyses was based on represanting and quantifying spatial properties and identificatying social patterns related to the interventions. We identified that the original spatial configurations, associated to subtle changes in the states social patterns, analysed independently of non-morphological categories, gave way to the occupation patterns verrified in both Serrambi cases.

Principal Component Analysis of Results Obtained from Finite-Difference Time-Domain Algorithms

Finite-Differences Time-Domain (FDTD) algorithms are well established tools of computational electromagnetism. Because of their practical implementation as computer codes, they are affected by many numerical artefact and noise. In order to obtain better results we propose using Principal Component Analysis (PCA) based on multivariate statistical techniques. The PCA has been successfully used for the analysis of noise and spatial temporal structure in a sequence of images. It allows a straightforward discrimination between the numerical noise and the actual electromagnetic variables, and the quantitative estimation of their respective contributions. Besides, The GDTD results can be filtered to clean the effect of the noise. In this contribution we will show how the method can be applied to several FDTD simulations: the propagation of a pulse in vacuum, the analysis of two-dimensional photonic crystals. In this last case, PCA has revealed hidden electromagnetic structures related to actual modes of the photonic crystal.

Caractérisation de l’activité Nur77 et de ses complexes en essais BRET par complémentation

Dans le système nerveux central, la dopamine joue un rôle crucial dans de nombreuses fonctions physiologiques telles que : l’apprentissage, le mouvement volontaire, la motivation, la cognition et la production hormonale. Il a été aussi démontré que le système de signalisation dopaminergique est altéré dans plusieurs maladies neurologiques et psychiatriques comme la maladie de Parkinson et la schizophrénie. Des études, effectuées dans le laboratoire du Dr.Daniel Lévesque (laboratoire d’accueil), ont montré que les récepteurs nucléaires Nur77 (NR4A1, NGFI-B) et RXRγ (retinoid X receptors γ) sont impliqués dans la régulation des effets de la dopamine dans le système nerveux central. De plus, ces données suggèrent que le complexe Nur77 et RXR joueraient un rôle crucial dans l’effet des médicaments antipsychotiques et antiparkinsoniens. Toutefois, très peu de médicaments ciblant Nur77 ont été identifiés à ce jour et les médicaments agissant sur RXRγ restent mal caractérisés. En outre, les analyses actuellement disponibles ne peuvent pas résumer la complexité des activités des NRs et génèrent des mesures indirectes des activités des drogues. Afin de mieux comprendre comment est régulée l’interaction Nur77/RXRγ dans ces processus, mon projet a été de mettre au point un essai BRET (Bioluminescence Resonance Energy Transfer) et PCA-BRET (Protein Complementation Assay-BRET) basé sur le recrutement d'un motif mimant un co-activateur fusionné avec la YFP. Nos différents essais ont été validés par courbes dose-réponse en utilisant différents composés RXR . Les EC50 (concentration efficace médiane, qui permet de mesurer l'efficacité d'un composé) obtenues étaient très semblables aux valeurs précédemment rapportées dans la littérature. Nous avons aussi pu identifier un composé le SR11237 (BMS649) qui semble posséder une sélectivité pour le complexe Nur77/RXRγ par rapport aux complexes Nurr1/RXRγ et RXRγ /RXRγ. Nos résultats indiquent que ces essais de BRET peuvent être utilisés pour évaluer la sélectivité de nouveaux composés pour les complexes Nur77/RXRγ, Nurr1/RXRγ et RXRγ /RXRγ. Un autre aspect de mon projet de doctorat a été de mettre en évidence par BRET l’importance de la SUMOylation dans la régulation de l'activité de Nur77 dans sa forme monomèrique, homodimèrique et hétérodimèrique. Nous avons ainsi identifié que Nur77 recrute principalement SUMO2 sur sa lysine 577. Il est intéressant de noté que le recrutement de la SUMO2 à Nur77 est potentialisé en présence de la SUMO E3 Ligase PIASγ. Aussi, la perte de la SUMOylation sur la lysine 577 entraîne l'incapacité de Nur77 de recruter divers motifs de co-activation mais pas pour ses formes homo- et hétérodimèrique. Cependant, la présence de PIASγ ne potentialise pas le recrutement du co-activateur, suggérant que cette SUMO E3 Ligase est seulement impliqué dans le processus de recrutement de la SUMO mais pas dans celui du co-activateur. Nous avons ainsi déterminé une nouvelle modification post-traductionnelle sur Nur77 régulant spécifiquement son activité monomérique Ces projets pourraient donc apporter de nouvelles données cruciales pour l’amélioration du traitement de la maladie de Parkinson ou de la schizophrénie, ainsi que d'obtenir une meilleure compréhension sur les mécanismes permettant la régulation de la fonction de Nur77

Industry Linkages and Audit Firms' Industry Portfolio Choice: Evidence from Product Language

Audit firms are organized along industry lines and industry specialization is a prominent feature of the audit market. Yet, we know little about how audit firms make their industry portfolio decisions, i.e., how audit firms decide which set of industries to specialize in. In this study, I examine how the linkages between industries in the product space affect audit firms’ industry portfolio choice. Using text-based product space measures to capture these industry linkages, I find that both Big 4 and small audit firms tend to specialize in industry-pairs that 1) are close to each other in the product space (i.e., have more similar product language) and 2) have a greater number of “between-industries” in the product space (i.e., have a greater number of industries with product language that is similar to both industries in the pair). Consistent with the basic tradeoff between specialization and coordination, these results suggest that specializing in industries that have more similar product language and more linkages to other industries in the product space allow audit firms greater flexibility to transfer industry-specific expertise across industries as well as greater mobility in the product space, hence enhancing its competitive advantage. Additional analysis using the collapse of Arthur Andersen as an exogenous supply shock in the audit market finds consistent results. Taken together, the findings suggest that industry linkages in the product space play an important role in shaping the audit market structure.

A power system control scheme based on security visualisation in parameter space

Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20 minutes’ time to meet the real time requirement. The recent California black out again highlighted the importance of system security. This paper proposed an approach for power system security assessment and enhancement based on the information provided from the pre-defined system parameter space. The proposed scheme opens up an efficient way for real time security assessment and enhancement in a competitive electricity market for single contingency case

Design of microchannel free-space optical interconnects based on vertical-cavity surface-emitting laser arrays

We investigate the design of free-space optical interconnects (FSOIs) based on arrays of vertical-cavity surface-emitting lasers (VCSELs), microlenses, and photodetectors. We explain the effect of the modal structure of a multimodeVCSEL beam on the performance of a FSOI with microchannel architecture. A Gaussian-beam diffraction model is used in combination with the experimentally obtained spectrally resolved VCSEL beam profiles to determine the optical channel crosstalk and the signal-to-noise ratio (SNR) in the system. The dependence of the SNR on the feature parameters of a FSOI is investigated. We found that the presence of higher-order modes reduces the SNR and the maximum feasible interconnect distance. We also found that the positioning of a VCSEL array relative to the transmitter microlens has a significant impact on the SNR and the maximum feasible interconnect distance. Our analysis shows that the departure from the traditional confocal system yields several advantages including the extended interconnect distance and/or improved SNR. The results show that FSOIs based on multimode VCSELs can be efficiently utilized in both chip-level and board-level interconnects. (C) 2002 Optical Society of America.

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.

On-line system for faults detection in induction motors based on PCA

Dissertation to obtain the degree of Master in Electrical and Computer Engineering

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization.

The goal of the present work was assess the feasibility of using a pseudo-inverse and null-space optimization approach in the modeling of the shoulder biomechanics. The method was applied to a simplified musculoskeletal shoulder model. The mechanical system consisted in the arm, and the external forces were the arm weight, 6 scapulo-humeral muscles and the reaction at the glenohumeral joint, which was considered as a spherical joint. The muscle wrapping was considered around the humeral head assumed spherical. The dynamical equations were solved in a Lagrangian approach. The mathematical redundancy of the mechanical system was solved in two steps: a pseudo-inverse optimization to minimize the square of the muscle stress and a null-space optimization to restrict the muscle force to physiological limits. Several movements were simulated. The mathematical and numerical aspects of the constrained redundancy problem were efficiently solved by the proposed method. The prediction of muscle moment arms was consistent with cadaveric measurements and the joint reaction force was consistent with in vivo measurements. This preliminary work demonstrated that the developed algorithm has a great potential for more complex musculoskeletal modeling of the shoulder joint. In particular it could be further applied to a non-spherical joint model, allowing for the natural translation of the humeral head in the glenoid fossa.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Improved earthquake response via simulation and integrated space- and ground-based technologies: the TREMOR proposal

Earthquakes occurring around the world each year cause thousands ofdeaths, millions of dollars in damage to infrastructure, and incalculablehuman suffering. In recent years, satellite technology has been asignificant boon to response efforts following an earthquake and itsafter-effects by providing mobile communications between response teamsand remote sensing of damaged areas to disaster management organizations.In 2007, an international team of students and professionals assembledduring theInternational Space University’s Summer Session Program in Beijing, Chinato examine how satellite and ground-based technology could be betterintegrated to provide an optimised response in the event of an earthquake.The resulting Technology Resources for Earthquake MOnitoring and Response(TREMOR) proposal describes an integrative prototype response system thatwill implement mobile satellite communication hubs providing telephone anddata links between response teams, onsite telemedicine consultation foremergency first-responders, and satellite navigation systems that willlocate and track emergency vehicles and guide search-and-rescue crews. Aprototype earthquake simulation system is also proposed, integratinghistorical data, earthquake precursor data, and local geomatics andinfrastructure information to predict the damage that could occur in theevent of an earthquake. The backbone of these proposals is a comprehensiveeducation and training program to help individuals, communities andgovernments prepare in advance. The TREMOR team recommends thecoordination of these efforts through a centralised, non-governmentalorganization.