907 resultados para decoupling and matching network
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Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.
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Comunicación presentada en el XI Workshop of Physical Agents, Valencia, 9-10 septiembre 2010.
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A novel and selective electrochemical functionalization of a highly reactive superporous zeolite templated carbon (ZTC) with two different aminobenzene acids (2-aminobenzoic and 4-aminobenzoic acid) was achieved. The functionalization was done through potentiodynamic treatment in acid media under oxidative conditions, which were optimized to preserve the unique ZTC structure. Interestingly, it was possible to avoid the electrochemical oxidation of the highly reactive ZTC structure by controlling the potential limit of the potentiodynamic experiment in presence of aminobenzene acids. The electrochemical characterization demonstrated the formation of polymer chains along with covalently bonded functionalities to the ZTC surface. The functionalized ZTCs showed several redox processes, producing a capacitance increase in both basic and acid media. The rate performance showed that the capacitance increase is retained at scan rates as high as 100 mV s−1, indicating that there is a fast charge transfer between the polymer chains formed inside the ZTC porosity or the new surface functionalities and the ZTC itself. The success of the proposed approach was also confirmed by using other characterization techniques, which confirmed the presence of different nitrogen groups in the ZTC surface. This promising method could be used to achieve highly selective functionalization of highly porous carbon materials.
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Globalisation has led to new health challenges for the 21st Century. These challenges have transnational implications and involve a large range of actors and stakeholders. National governments no longer hold the sole responsibility for the health of their people. These changes in health trends have led to the rise of Global Health Governance as a theoretical notion for health policy-making. The Southeast Asian region is particularly prone to public health threats and it is for this reason that this brief looks at the potential of the Association of Southeast Asian Nations (ASEAN) as a regional organisation to take a lead in health cooperation. Through a comparative study between the regional mechanisms for health cooperation of the European Union (EU) and ASEAN, we look at how ASEAN could maximise its potential as a global health actor. Regional institutions and a network of civil society organisations are crucial in relaying global initiatives for health, and ensuring their effective implementation at the national level. While the EU benefits from higher degrees of integration and involvement in the sector of health policy making, ASEAN’s role as a regional body for health governance will depend both on greater horizontal and vertical regional integration through enhanced regional mechanisms and a wider matrix of cooperation.
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Member countries of the Economic and Monetary Union (EMU) initiated wide-ranging labour market reforms in the last decade. This process is ongoing as countries that are faced with serious labour market imbalances perceive reforms as the fastest way to restore competitiveness within a currency union. This fosters fears among observers about a beggar-thy-neighbour policy that leaves non-reforming countries with a loss in competitiveness and an increase in foreign debt. Using a two-country, two-sector search and matching DSGE model, we analyse the impact of labour market reforms on the transmission of macroeconomic shocks in both non-reforming and reforming countries. By analysing the impact of reforms on foreign debt, we contribute to the debate on whether labour market reforms increase or reduce current account imbalances.
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Is Europe's immigration policy attractive? One of the priorities driving current EU debates on labour immigration policies is the perceived need to boost Europe's attractiveness vis-á-vis 'talented' and 'highly skilled' immigrants. The EU sees itself playing a role in persuading immigrants to choose Europe over other competing destinations, such as the US or Canada. This book critically examines the determinants and challenges characterising discussions focused on the attractiveness of labour migration policies in the EU as well as other international settings. It calls for re-thinking some of the most commonly held premises and assumptions underlying the narratives of ‘attractiveness’ and ‘global competition for talent’ in migration policy debates. How can an immigration policy, in fact, be made to be ‘attractive’ and what are the incentives at play (if any)? A multidisciplinary team of leading scholars and experts in migration studies address the main issues and challenges related to the role played by rights and discrimination, qualifications and skills, and matching demand and supply in needs-based migration policies. The experiences in other jurisdictions such as South America, Canada and the United States are also covered: Are these countries indeed so ‘attractive’ and ‘competitive’, and if so what makes them more attractive than the EU? On the basis of the discussions and findings presented across the various contributions, the book identifies a number of priorities for policy formulation and design in the next generation of EU labour migration policies. In particular, it highlights important initiatives that the new European Commission should focus on in the years to come.
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Eleven commercial nuclear reactors used to generate electricity are currently operating at six sites in Illinois; no other state has as many nuclear reactors. In addition, there are two major research facilities in Illinois operated by the US Department of Energy (Argonne National Laboratory and FermiLab), uranium processing facilities at Metropolis and in nearby Paducah, Kentucky, several manufacturers of radiopharmaceuticals and other radioactive materials, thousands of radiation-producing machines used in medicine and industry, and a network of major arterial highways and rail lines over which radioactive material shipments move on a regular basis. Protecting the health and safety of Illinois citizens and the environment from the potentially harmful effects of ionizing radiation is a key function of IEMA'S Division of Nuclear Safety (DNS). That role is fulfilled through programs that monitor nuclear facilities around the clock, ensure the proper operation of radiation-producing equipment and the use of radioactive materials, and measure radioactivity in the environment to ensure no threats to public health exist.
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Recent developments in workplace learning have focused on relational and social network views of learning that introduce practitioners to the norms, values and assumptions of the workplace as well as the learning processes through which knowledge is acquired. This article reports on a qualitative study of a mentoring programme designed to assist women education managers gain promotion by broadening their networks and stimulating insights into the senior management positions for which they were being prepared. The findings are that members reflexively assess and reassess goals and values to demystify knowledge and resolved cognitive dissonance in these processes. Moreover, this article shows that women participants learn from the networks, and that the networks learn from the participant in a reciprocal and informal way. The article concludes that organizational learning programmes must focus on enabling such networks to flourish.
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A set of DCT domain properties for shifting and scaling by real amounts, and taking linear operations such as differentiation is described. The DCT coefficients of a sampled signal are subjected to a linear transform, which returns the DCT coefficients of the shifted, scaled and/or differentiated signal. The properties are derived by considering the inverse discrete transform as a cosine series expansion of the original continuous signal, assuming sampling in accordance with the Nyquist criterion. This approach can be applied in the signal domain, to give, for example, DCT based interpolation or derivatives. The same approach can be taken in decoding from the DCT to give, for example, derivatives in the signal domain. The techniques may prove useful in compressed domain processing applications, and are interesting because they allow operations from the continuous domain such as differentiation to be implemented in the discrete domain. An image matching algorithm illustrates the use of the properties, with improvements in computation time and matching quality.
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We used magnetoencephalography (MEG) to examine the nature of oscillatory brain rhythms when passively viewing both illusory and real visual contours. Three stimuli were employed: a Kanizsa triangle; a Kanizsa triangle with a real triangular contour superimposed; and a control figure in which the corner elements used to form the Kanizsa triangle were rotated to negate the formation of illusory contours. The MEG data were analysed using synthetic aperture magnetometry (SAM) to enable the spatial localisation of task-related oscillatory power changes within specific frequency bands, and the time-course of activity within given locations-of-interest was determined by calculating time-frequency plots using a Morlet wavelet transform. In contrast to earlier studies, we did not find increases in gamma activity (> 30 Hz) to illusory shapes, but instead a decrease in 10–30 Hz activity approximately 200 ms after stimulus presentation. The reduction in oscillatory activity was primarily evident within extrastriate areas, including the lateral occipital complex (LOC). Importantly, this same pattern of results was evident for each stimulus type. Our results further highlight the importance of the LOC and a network of posterior brain regions in processing visual contours, be they illusory or real in nature. The similarity of the results for both real and illusory contours, however, leads us to conclude that the broadband (< 30 Hz) decrease in power we observed is more likely to reflect general changes in visual attention than neural computations specific to processing visual contours.
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The key to the correct application of ANOVA is careful experimental design and matching the correct analysis to that design. The following points should therefore, be considered before designing any experiment: 1. In a single factor design, ensure that the factor is identified as a 'fixed' or 'random effect' factor. 2. In more complex designs, with more than one factor, there may be a mixture of fixed and random effect factors present, so ensure that each factor is clearly identified. 3. Where replicates can be grouped or blocked, the advantages of a randomised blocks design should be considered. There should be evidence, however, that blocking can sufficiently reduce the error variation to counter the loss of DF compared with a randomised design. 4. Where different treatments are applied sequentially to a patient, the advantages of a three-way design in which the different orders of the treatments are included as an 'effect' should be considered. 5. Combining different factors to make a more efficient experiment and to measure possible factor interactions should always be considered. 6. The effect of 'internal replication' should be taken into account in a factorial design in deciding the number of replications to be used. Where possible, each error term of the ANOVA should have at least 15 DF. 7. Consider carefully whether a particular factorial design can be considered to be a split-plot or a repeated measures design. If such a design is appropriate, consider how to continue the analysis bearing in mind the problem of using post hoc tests in this situation.
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Molecularly imprinted polymers (MIPs) are crosslinked polymers containing bespoke functionalised cavities arising from the inclusion of template molecules in the polymerisation mixture and their later extraction. When the polymers are prepared functional polymerisable monomers are included which become part of the polymer matrix and serve to decorate the cavities with functionality appropriate to the template molecules. Overall, binding sites are created which have a memory for the template both in terms of shape and matching functionality. Fluorescent molecularly imprinted polymers have the benefit of a fluorophore in their cavities that may respond to the presence of bound test compound by a change in their fluorescence output. The work presented falls into three main areas. A series of fluorescent MIPs was prepared with a view to generating material capable of mimicking the binding characteristics of the metabolically important cytochrome isoform CYP2D6. The MIPs re-bound their templates and various cross-reactivities were encountered for test compound/drug recognition. One MIP in particular exhibited a rational discrimination amongst the related synthetic templates and was reasonably successful in recognising CYP2D6 substrates from the drug set tested. In order to give some insights into binding modes in MIPs, attempts were made to produce functional monomers containing two or more fluorophores that could be interrogated independently. A model compound was prepared which fitted the dual-fluorophore criteria and which will be the basis for future incorporation into MIPs. A further strand to this thesis is the deliberate incorporation of hydrophobic moieties into fluorescent functional monomers so that the resulting imprinted cavities might be biomimetic in their impersonation of enzyme active sites. Thus the imprinted cavities had specific hydrophobic regions as well as the usual polar functionality with which to interact with binding test compounds.
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The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.
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Satellite information, in combination with conventional point source measurements, can be a valuable source of information. This thesis is devoted to the spatial estimation of areal rainfall over a region using both the measurements from a dense and sparse network of rain-gauges and images from the meteorological satellites. A primary concern is to study the effects of such satellite assisted rainfall estimates on the performance of rainfall-runoff models. Low-cost image processing systems and peripherals are used to process and manipulate the data. Both secondary as well as primary satellite images were used for analysis. The secondary data was obtained from the in-house satellite receiver and the primary data was obtained from an outside source. Ground truth data was obtained from the local Water Authority. A number of algorithms are presented that combine the satellite and conventional data sources to produce areal rainfall estimates and the results are compared with some of the more traditional methodologies. The results indicate that the satellite cloud information is valuable in the assessment of the spatial distribution of areal rainfall, for both half-hourly as well as daily estimates of rainfall. It is also demonstrated how the performance of the simple multiple regression rainfall-runoff model is improved when satellite cloud information is used as a separate input in addition to rainfall estimates from conventional means. The use of low-cost equipment, from image processing systems to satellite imagery, makes it possible for developing countries to introduce such systems in areas where the benefits are greatest.
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Fifteen Miscanthus genotypes grown in five locations across Europe were analysed to investigate the influence of genetic and environmental factors on cell wall composition. Chemometric techniques combining near infrared reflectance spectroscopy (NIRS) and conventional chemical analyses were used to construct calibration models for determination of acid detergent lignin (ADL), acid detergent fibre (ADF), and neutral detergent fibre (NDF) from sample spectra. Results generated were subsequently converted to lignin, cellulose and hemicellulose content and used to assess the genetic and environmental variation in cell wall composition of Miscanthus and to identify genotypes which display quality traits suitable for exploitation in a range of energy conversion systems. The NIRS calibration models developed were found to predict concentrations with a good degree of accuracy based on the coefficient of determination (R2), standard error of calibration (SEC), and standard error of cross-validation (SECV) values. Across all sites mean lignin, cellulose and hemicellulose values in the winter harvest ranged from 76–115 g kg-1, 412–529 g kg-1, and 235–338 g kg-1 respectively. Overall, of the 15 genotypes Miscanthus x giganteus and Miscanthus sacchariflorus contained higher lignin and cellulose concentrations in the winter harvest. The degree of observed genotypic variation in cell wall composition indicates good potential for plant breeding and matching feedstocks to be optimised to different energy conversion processes.