991 resultados para soft-commutation techniques


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En la investigació de la complexació de metalls mitjançant eines electroanalítiques són emprades dues aproximacions generals. La primera, anomenada de modelatge dur (hardmodelling), es basa en la formulació d'un model fisicoquímic conjunt per als processos electròdic i de complexació i en la resolució analítica o numèrica del model. Posteriorment, l'ajust dels paràmetres del model a les dades experimentals donarà la informació desitjada sobre el procés de complexació. La segona aproximació, anomenada de modelatge tou (soft-modelling), es basa en la identificació d'un model de complexació a partir de l'anàlisi numèrica i estadística de les dades, sense cap assumpció prèvia d'un model. Aquesta aproximació, que ha estat extensivament emprada amb dades espectroscòpiques, ho ha estat poquíssim amb dades electroquímiques. En aquest article tractem de la formulació d'un model (hard-modelling) per a la complexació de metalls en sistemes amb mescles de lligands, incloent-hi lligands macromoleculars, i de l'aplicació d

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Multi-input multi-output (MIMO) technology is an emerging solution for high data rate wireless communications. We develop soft-decision based equalization techniques for frequency selective MIMO channels in the quest for low-complexity equalizers with BER performance competitive to that of ML sequence detection. We first propose soft decision equalization (SDE), and demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered, and linear in the signal constellation size. Building upon the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Motivated by the need for low-complexity receiver processing, we further present an alternative low-complexity soft-decision equalization approach for frequency selective MIMO communication systems. With the help of iterative processing, two detection and estimation schemes based on second-order statistics are harmoniously put together to yield a two-part receiver structure: local multiuser detection (MUD) using soft-decision Probabilistic Data Association (PDA) detection, and dynamic noise-interference tracking using Kalman filtering. The proposed Kalman-PDA detector performs local MUD within a sub-block of the received data instead of over the entire data set, to reduce the computational load. At the same time, all the inter-ference affecting the local sub-block, including both multiple access and inter-symbol interference, is properly modeled as the state vector of a linear system, and dynamically tracked by Kalman filtering. Two types of Kalman filters are designed, both of which are able to track an finite impulse response (FIR) MIMO channel of any memory length. The overall algorithms enjoy low complexity that is only polynomial in the number of information-bearing bits to be detected, regardless of the data block size. Furthermore, we introduce two optional performance-enhancing techniques: cross- layer automatic repeat request (ARQ) for uncoded systems and code-aided method for coded systems. We take Kalman-PDA as an example, and show via simulations that both techniques can render error performance that is better than Kalman-PDA alone and competitive to sphere decoding. At last, we consider the case that channel state information (CSI) is not perfectly known to the receiver, and present an iterative channel estimation algorithm. Simulations show that the performance of SDE with channel estimation approaches that of SDE with perfect CSI.

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AIM: The aim of the present review was to systematically assess the dental literature in terms of soft tissue grafting techniques. The focused question was: is one method superior over others for augmentation and stability of the augmented soft tissue in terms of increasing the width of keratinized tissue (part 1) and gain in soft tissue volume (part 2). METHODS: A Medline search was performed for human studies focusing on augmentation of keratinized tissue and/or soft tissue volume, and complemented by additional hand searching. Relevant studies were identified and statistical results were reported for meta-analyses including the test minus control weighted mean differences with 95% confidence intervals, the I-squared statistic for tests of heterogeneity, and the number of significant studies. RESULTS: Twenty-five (part 1) and three (part 2) studies met the inclusion criteria; 14 studies (part 1) were eligible for comparison using meta-analyses. An apically positioned flap/vestibuloplasty (APF/V) procedure resulted in a statistically significantly greater gain in keratinized tissue than untreated controls. APF/V plus autogenous tissue revealed statistically significantly more attached gingiva compared with untreated controls and a borderline statistical significance compared with APF/V plus allogenic tissue. Statistically significantly more shrinkage was observed for the APF/V plus allogenic graft compared with the APF/V plus autogenous tissue. Patient-centered outcomes did not reveal any of the treatment methods to be superior regarding postoperative complications. The three studies reporting on soft tissue volume augmentation could not be compared due to lack of homogeneity. The use of subepithelial connective tissue grafts (SCTGs) resulted in statistically significantly more soft tissue volume gain compared with free gingival grafts (FGGs). CONCLUSIONS: APF/V is a successful treatment concept to increase the width of keratinized tissue or attached gingiva around teeth. The addition of autogenous tissue statistically significantly increases the width of attached gingiva. For soft tissue volume augmentation, only limited data are available favoring SCTGs over FGG.

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Soft contact lens wear has become a common phenomenon in recent times. The contact lens when placed in the eye rapidly undergoes change. A film of biological material builds up on and in the lens matrix. The long term wear characteristics of the lens ultimately depend on this process. With time distinct structures made up of biological material have been found to build up on the lens. A fuller understanding of this process and how it relates to the lens chemistry could lead to contact lenses that are better tolerated by the eye. The tear film is a complex biological fluid, it is this fluid that bathes the lens during wear. It is reasonable to suppose that it is material derived from this source that accumulates on the lens. To understand this phenomenon it was decided to investigate the make up and conformation of the protein species that are found on and in the lens. As inter individual variations in tear fluid composition have been found it is important to be able to study the proteins on a single lens. Many of the analytical techniques used in bio research are not suitable for this study because of the lack of sensitivity. Work with poly acrylamide electrophoresis showed the possibility of analyzing the proteins extracted from a single lens. The development of a biotin avidin electro-blot and an enzyme linked aniibody electro-blot, lead to the high sensitivity detection and identification of the proteins present. The extraction of proteins from a lens is always incomplete. A method that analyses the proteins in situ would be a great advancement. Fourier transform infra red microscopy was developed to a point where a thin section of a contact lens could yield information about the proteins present and their conformation. The three dimensional structure of the gross macroscopic structures termed white spots was investigated using confocal laser microscopy.

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Using synchrotron radiation, we combined simultaneously wide angle X-ray scattering (WAXS) and small angle X-ray scattering (SAXS) techniques to obtain the scattering profiles of normal and neoplastic breast tissu-es samples at the momentum transfer range 6.28 nm(-1) <= Q(=4 pi.sin(theta/2)lambda) <= 50.26 nm(-1) and 0.15 nm(-1) <= Q <= 1.90 nm(-1), respectively. The results obtained show considerable differences between the scattering profiles of these tissues. We verified that the combination of some parameters (ratio between glandular and adipose peak intensity and third-order axial peak intensity) extracted from scattering profiles can be used for identifying breast cancer. (c) 2009 Elsevier Ltd. All rights reserved.

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Objective. The objective of this study was to investigate the prevalence of tooth abnormalities and soft tissue changes in patients with velocardiofacial syndrome. Study design. Twenty-six patients with velocardiofacial syndrome were examined to investigate the presence of tooth abnormalities and soft tissue alterations. The occurrence of tooth agenesis and supernumerary teeth was compared to patients without morphofunctional alterations, matched for gender and age. Results. Of all patients, 76.92% exhibited at least one tooth abnormality, with predominance of hypoplastic alterations, especially represented by hypodevelopment of the lingual cusp of mandibular first premolars and enamel opacities. The occurrence of tooth agenesis and supernumerary teeth was similar in both study and control groups. Conclusion. the present results suggest an association between hypodevelopment of the lingual cusp of mandibular first premolars and enamel opacities, yet these findings still require corroboration. Future studies should further investigate these aspects in larger samples compared to control groups, as well as employing molecular genetics techniques.

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In distributed soft real-time systems, maximizing the aggregate quality-of-service (QoS) is a typical system-wide goal, and addressing the problem through distributed optimization is challenging. Subtasks are subject to unpredictable failures in many practical environments, and this makes the problem much harder. In this paper, we present a robust optimization framework for maximizing the aggregate QoS in the presence of random failures. We introduce the notion of K-failure to bound the effect of random failures on schedulability. Using this notion we define the concept of K-robustness that quantifies the degree of robustness on QoS guarantee in a probabilistic sense. The parameter K helps to tradeoff achievable QoS versus robustness. The proposed robust framework produces optimal solutions through distributed computations on the basis of Lagrangian duality, and we present some implementation techniques. Our simulation results show that the proposed framework can probabilistically guarantee sub-optimal QoS which remains feasible even in the presence of random failures.

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Liquid crystalline cellulosic-based solutions described by distinctive properties are at the origin of different kinds of multifunctional materials with unique characteristics. These solutions can form chiral nematic phases at rest, with tuneable photonic behavior, and exhibit a complex behavior associated with the onset of a network of director field defects under shear. Techniques, such as Nuclear Magnetic Resonance (NMR), Rheology coupled with NMR (Rheo-NMR), rheology, optical methods, Magnetic Resonance Imaging (MRI), Wide Angle X-rays Scattering (WAXS), were extensively used to enlighten the liquid crystalline characteristics of these cellulosic solutions. Cellulosic films produced by shear casting and fibers by electrospinning, from these liquid crystalline solutions, have regained wider attention due to recognition of their innovative properties associated to their biocompatibility. Electrospun membranes composed by helical and spiral shape fibers allow the achievement of large surface areas, leading to the improvement of the performance of this kind of systems. The moisture response, light modulated, wettability and the capability of orienting protein and cellulose crystals, opened a wide range of new applications to the shear casted films. Characterization by NMR, X-rays, tensile tests, AFM, and optical methods allowed detailed characterization of those soft cellulosic materials. In this work, special attention will be given to recent developments, including, among others, a moisture driven cellulosic motor and electro-optical devices.

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

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Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.

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Neuroimaging techniques provide valuable tools for diagnosing Alzheimer's disease (AD), monitoring disease progression and evaluating responses to treatment. There is currently a wide array of techniques available including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and, for recording electrical brain activity, electroencephalography (EEG). The choice of technique depends on the contrast between tissues of interest, spatial resolution, temporal resolution, requirements for functional data and the probable number of scans required. For example, while PET, CT and MRI can be used to differentiate between AD and other dementias, MRI is safer and provides better contrast of soft tissues. Neuroimaging is a technique spanning many disciplines and requires effective communication between doctors requesting a scan of a patient or group of patients and those with technical expertise. Consideration and discussion of the most suitable type of scan and the necessary settings to achieve the best results will help ensure appropriate techniques are chosen and used effectively. Neuroimaging techniques are currently expanding understanding of the structural and functional changes that occur in dementia. Further research may allow identification of early neurological signs ofAD, before clinical symptoms are evident, providing the opportunity to test preventative therapies. CombiningMRI and machine learning techniques may be a powerful approach to improve diagnosis ofAD and to predict clinical outcomes.

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We present the uncommon case of a subcutaneous fascia-based extramedullary plasmacytoma in the leg, which was confirmed by the pathology report and followed up until its remission. We report the differential diagnosis with other more common soft tissue masses. Imaging findings are nonspecific but are important to determine the tumour extension and to plan the biopsy.

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The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.