A class of serial concatenated ordinary (non-punctured) convolutional (SCOC) codes is introduced. Simulation results over additive white Guassian noise channels show that the SCOC codes have comparable performance as ...
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A class of serial concatenated ordinary (non-punctured) convolutional (SCOC) codes is introduced. Simulation results over additive white Guassian noise channels show that the SCOC codes have comparable performance as 3GPP turbo codes and LDPC codes, while with overall decoding complexity smaller than that of LDPC codes and 3GPP turbo codes.
This special issue of Recherche Operationnelle is devoted to recent researchwhich significantly enhances our ability to model, either mathematically or algo-rithmically,increasingly complex service systems. All the pa...
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This special issue of Recherche Operationnelle is devoted to recent researchwhich significantly enhances our ability to model, either mathematically or algo-rithmically,increasingly complex service systems. All the papers in this issue are methodological in nature,even though the first paper addresses a very practical approach. This collection of papers beginswith a survey by Dr Khaled Hussain and Mr Varol Kaptan of recent advances which allow modelling andsimulation to be conducted in a very realistic visual setting. We all know how frustrating it can beto deal with the need to determine whether the objects we model are represented in a realisticmanner, and whether the simulation conditions are indeed realistic. The approach described in thisfirst paper is based on the premise that the system being simulated has a realistic visualcounterpart, e.g. some natural scenery (as in a military tactical simulation), a room (as whenpeople''s behaviour may be simulated), some roads or an airport (as when a transportation system hasto be simulated), a hospital operating room (as when a surgical procedure needs to be simulated),and so on. In all of these cases, the real world being simulated can be based on an actual videosequence of the real world setting. It can be even more realistic and the simulation can beconducted within a video viewing of the real world itself. This paper shows how this can actually bedone and describes a system that has been designed and developed at the University of CentralFlorida and at Imperial College to achieve this. In such a simulator, the simulated conditions arethe real world itself, while the behaviour of synthetic objects or synthetic changes to real objectsis what is being simulated. The value of such simulations can be tremendous, and can allow testingof complex behaviours in a fully realistic setting with the possibility of the human viewer actuallyobserving different operating conditions. In addition to the visual aspects, the authors a
A modified linearly constrained constant modulus algorithm for blind adaptive multiuser detection in DS/CDMA systems is presented. By selecting a suitable linearly constrained cost function, the performance of the pro...
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A modified linearly constrained constant modulus algorithm for blind adaptive multiuser detection in DS/CDMA systems is presented. By selecting a suitable linearly constrained cost function, the performance of the proposed algorithm is improved in terms of the capability of suppressing multiple access interference. Simulation results indicate that the proposed algorithm provides a faster convergence and steady-state performance.
In this letter, we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunc...
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In this letter, we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunctions of an operator defined from a kernel and the unknown data-generating density. Whereas spectral embedding methods provided only coordinates for the training points, the analysis justifies a simple extension to out-of-sample examples (the Nystrom formula) for multidimensional scaling (MDS), spectral clustering, Laplacian eigenmaps, locally linear embedding (LLE), and Isomap. The analysis provides, for all such spectral embedding methods, the definition of a loss function, whose empirical average is minimized by the traditional algorithms. The asymptotic expected value of that loss defines a generalization performance and clarifies what these algorithms are trying to learn. Experiments with LLE, Isomap, spectral clustering, and MDS show that this out-of-sample embedding formula generalizes well, with a level of error comparable to the effect of small perturbations of the training set on the embedding.
Rate models are often used to study the behavior of large networks of spiking neurons. Here we propose a procedure to derive rate models that take into account the fluctuations of the input current and firing-rate ada...
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Rate models are often used to study the behavior of large networks of spiking neurons. Here we propose a procedure to derive rate models that take into account the fluctuations of the input current and firing-rate adaptation, two ubiquitous features in the central nervous system that have been previously overlooked in constructing rate models. The procedure is general and applies to any model of firing unit. As examples, we apply it to the leaky integrate-and-fire (IF) neuron, the leaky IF neuron with reversal potentials, and to the quadratic IF neuron. Two mechanisms of adaptation are considered, one due to an afterhyperpolarization current and the other to an adapting threshold for spike emission. The parameters of these simple models can be tuned to match experimental data obtained from neocortical pyramidal neurons. Finally, we show how the stationary model can be used to predict the time-varying activity of a large population of adapting neurons.
Many different types of integrate-and-fire models have been designed in order to explain how it is possible for a cortical neuron to integrate over many independent inputs while still producing highly variable spike t...
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Many different types of integrate-and-fire models have been designed in order to explain how it is possible for a cortical neuron to integrate over many independent inputs while still producing highly variable spike trains. Within this context, the variability of spike trains has been almost exclusively measured using the coefficient of variation of interspike intervals. However, another important statistical property that has been found in cortical spike trains and is closely associated with their high firing variability is long-range dependence. We investigate the conditions, if any, under which such models produce output spike trains with both interspike-interval variability and long-range dependence similar to those that have previously been measured from actual cortical neurons. We first show analytically that a large class of high-variability integrate-and-fire models is incapable of producing such outputs based on the fact that their output spike trains are always mathematically equivalent to renewal processes. This class of models subsumes a majority of previously published models, including those that use excitation-inhibition balance, correlated inputs, partial reset, or nonlinear leakage to produce outputs with high variability. Next, we study integrate-and-fire models that have (non-Poissonian) renewal point process inputs instead of the Poisson point process inputs used in the preceding class of models. The confluence of our analytical and simulation results implies that the renewal-input model is capable of producing high variability and long-range dependence comparable to that seen in spike trains recorded from cortical neurons, but only if the interspike intervals of the inputs have infinite variance, a physiologically unrealistic condition. Finally, we suggest a new integrate-and-fire model that does not suffer any of the previously mentioned shortcomings. By analyzing simulation results for this model, we show that it is capable of producing output
In this article we generalize concepts of the position automaton and ZPC-structure to the regular K-expressions. We show that the extended ZPC-structure can be built in linear time w.r.t. the size of the K-expression ...
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In this article we generalize concepts of the position automaton and ZPC-structure to the regular K-expressions. We show that the extended ZPC-structure can be built in linear time w.r.t. the size of the K-expression and that the associated position automaton can be deduced from it in quadratic time.
A low-order control algorithm is developed to regulate an infinitely dimensional flexible beam subject to nondecaying, harmonic disturbances of known frequency. The control input is applied at the boundary while the o...
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A low-order control algorithm is developed to regulate an infinitely dimensional flexible beam subject to nondecaying, harmonic disturbances of known frequency. The control input is applied at the boundary while the output of concern is at the opposite end of the flexible structure. Only local displacement and velocity at the boundary are required in the control algorithm, which emulates the behavior of a set of mechanical spring, mass, and damper A virtual spring is integrated with the structure flexibility to establish an internal model for the external disturbance. Such a virtual passive algorithm ensures stability in the presence of structural and parameter uncertainties. It is shown that, by properly choosing the control gains, the output tends to a standstill while the other parts of the system oscillate in such a way as to counteract the harmonic disturbance.
A new method for noise removal of arbitrary surfaces meshes is presented which focuses on the preservation and sharpening of non-linear geometric features such as curved surface regions and feature lines. Our method u...
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A new method for noise removal of arbitrary surfaces meshes is presented which focuses on the preservation and sharpening of non-linear geometric features such as curved surface regions and feature lines. Our method uses a prescribed mean curvature flow (PMC) for simplicial surfaces which is based on three new contributions: 1. the definition and efficient calculation of a discrete shape operator and principal curvature properties on simplicial surfaces that is fully consistent with the well-known discrete mean curvature formula, 2. an anisotropic discrete mean curvature vector that combines the advantages of the mean curvature normal with the special anisotropic behaviour along feature lines of a surface, and 3. an anisotropic prescribed mean curvature flow which converges to surfaces with an estimated mean curvature distribution and with preserved non-linear features. Additionally, the PMC flow prevents boundary shrinkage at constrained and free boundary segments.
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