An efficient scheme for the evaluation of coding performance of high speed links incorporating joint error behaviour based on Monte Carlo Method

While channel coding is a standard method of improving a system’s energy efficiency in digital communications, its practice does not extend to high-speed links. Increasing demands in network speeds are placing a large burden on the energy efficiency of high-speed links and render the benefit of channel coding for these systems a timely subject. The low error rates of interest and the presence of residual intersymbol interference (ISI) caused by hardware constraints impede the analysis and simulation of coded high-speed links. Focusing on the residual ISI and combined noise as the dominant error mechanisms, this paper analyses error correlation through concepts of error region, channel signature, and correlation distance. This framework provides a deeper insight into joint error behaviours in high-speed links, extends the range of statistical simulation for coded high-speed links, and provides a case against the use of biased Monte Carlo methods in this setting

THz Plasma Waves in Field-Effect-Transistors: A Monte Carlo Study

Sensing with electromagnetic waves having frequencies in the Terahertz-range is a very attractive investigative method with applications in fundamental research and industrial settings. Up to now, a lot of sources and detectors are available. However, most of these systems are bulky and have to be used in controllable environments such as laboratories. In 1993 Dyakonov and Shur suggested that plasma waves developing in field-effect-transistors can be used to emit and detect THz-radiation. Later on, it was shown that these plasma waves lead to rectification and allows for building efficient detectors. In contrast to the prediction that these plasma waves lead to new promising solid-state sources, only a few weak sources are known up to now. This work studies THz plasma waves in semiconductor devices using the Monte Carlo method in order to resolve this issue. A fast Monte Carlo solver was developed implementing a nonparabolic bandstructure representation of the used semiconductors. By investigating simplified field-effect-transistors it was found that the plasma frequency follows under equilibrium conditions the analytical predictions. However, no current oscillations were found at room temperature or with a current flowing in the channel. For more complex structures, consisting of ungated and gated regions, it was found that the plasma frequency does not follow the value predicted by the dispersion relation of the gated nor the ungated device.

Markov chain montecarlo method applied to rounding zeros of compositional data: first approach

As stated in Aitchison (1986), a proper study of relative variation in a compositional data set should be based on logratios, and dealing with logratios excludes dealing with zeros. Nevertheless, it is clear that zero observations might be present in real data sets, either because the corresponding part is completely absent –essential zeros– or because it is below detection limit –rounded zeros. Because the second kind of zeros is usually understood as “a trace too small to measure”, it seems reasonable to replace them by a suitable small value, and this has been the traditional approach. As stated, e.g. by Tauber (1999) and by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000), the principal problem in compositional data analysis is related to rounded zeros. One should be careful to use a replacement strategy that does not seriously distort the general structure of the data. In particular, the covariance structure of the involved parts –and thus the metric properties– should be preserved, as otherwise further analysis on subpopulations could be misleading. Following this point of view, a non-parametric imputation method is introduced in Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000). This method is analyzed in depth by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2003) where it is shown that the theoretical drawbacks of the additive zero replacement method proposed in Aitchison (1986) can be overcome using a new multiplicative approach on the non-zero parts of a composition. The new approach has reasonable properties from a compositional point of view. In particular, it is “natural” in the sense that it recovers the “true” composition if replacement values are identical to the missing values, and it is coherent with the basic operations on the simplex. This coherence implies that the covariance structure of subcompositions with no zeros is preserved. As a generalization of the multiplicative replacement, in the same paper a substitution method for missing values on compositional data sets is introduced

Estimation of the probability of congestion using Monte Carlo method in OPS networks

In networks with small buffers, such as optical packet switching based networks, the convolution approach is presented as one of the most accurate method used for the connection admission control. Admission control and resource management have been addressed in other works oriented to bursty traffic and ATM. This paper focuses on heterogeneous traffic in OPS based networks. Using heterogeneous traffic and bufferless networks the enhanced convolution approach is a good solution. However, both methods (CA and ECA) present a high computational cost for high number of connections. Two new mechanisms (UMCA and ISCA) based on Monte Carlo method are proposed to overcome this drawback. Simulation results show that our proposals achieve lower computational cost compared to enhanced convolution approach with an small stochastic error in the probability estimation

Meta-análisis : aportaciones metodológicas a la síntesis cuantitativa de la evidencia : un estudio de simulación Monte-Carlo.

Analizar los procedimientos sistemáticos para la síntesis de resultados; ofrecer alternativas metodológicas a los problemas detectados en el proceso de realización de un meta-análisis; y establecer un conjunto de pautas istemáticas para la realización de revisiones de resultados de investigación. La primera parte presenta la conceptualización del meta-análisis como una perspectiva para la información de resultados. Después se describen y analizan las alternativas metodológicas de integración meta-analítica. Por último se evalúa el funcionamiento de las propuestas metodológicas determinando la adecuación a las características comunes de desarrollo de un estudio meta-analítico. Se utiliza el método analítico-descriptivo y la simulación Monte Carlo, que permite comparar alternativas según criterios objetivos. Se trata de generar conjuntos de datos que respondan a modelos predeterminados. A los datos así generados se les aplica la técnica objeto de estudio y se comprueba su comportamiento en las distintas condiciones experimentales. Se muestra la superioridad de los modelos jerárquicos lineales en la síntesis cuantitativa de la evidencia en el ámbito de las Ciencias Sociales, puesto que sus estimadores están escasamente sesgados, son altamente eficientes, robustos y sus pruebas de contraste muestran potencia por encima de los niveles nominales. La síntesis de resultados responde a la necesidad de racionalizar ante la acumulación de conocimientos fruto del avance científico. De entre las alternativas, el meta-análisis es la herramienta más adecuada para la síntesis cuantitativa. Es un tipo de investigación centrado en el análisis de la generalización de resultados de estudios primarios permitiendo establecer el estado de la investigación en un ámbito concreto y elaborar modelos relacionales. Sus principales problemas son de tipo metodológico y procedimental. La adaptación de métodos estadísticos tradicionales de análisis de varianza y regresión, es un gran avance, pero no son del todo adecuados al meta-análisis. Por tanto, los procedimientos de integración propuestos desde los modelos jerárquicos lineales son una alternativa válida, sencilla y eficaz a los tradicionales procedimientos meta-analíticos de integración de resultados.

A Monte Carlo method for accelerating the computation of animated radiosity sequences

Realistic rendering animation is known to be an expensive processing task when physically-based global illumination methods are used in order to improve illumination details. This paper presents an acceleration technique to compute animations in radiosity environments. The technique is based on an interpolated approach that exploits temporal coherence in radiosity. A fast global Monte Carlo pre-processing step is introduced to the whole computation of the animated sequence to select important frames. These are fully computed and used as a base for the interpolation of all the sequence. The approach is completely view-independent. Once the illumination is computed, it can be visualized by any animated camera. Results present significant high speed-ups showing that the technique could be an interesting alternative to deterministic methods for computing non-interactive radiosity animations for moderately complex scenarios

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Error and complexity of random walk Monte Carlo radiosity

The author studies the error and complexity of the discrete random walk Monte Carlo technique for radiosity, using both the shooting and gathering methods. The author shows that the shooting method exhibits a lower complexity than the gathering one, and under some constraints, it has a linear complexity. This is an improvement over a previous result that pointed to an O(n log n) complexity. The author gives and compares three unbiased estimators for each method, and obtains closed forms and bounds for their variances. The author also bounds the expected value of the mean square error (MSE). Some of the results obtained are also shown

Monte Carlo phase diagram for diblock copolymer melts

The phase diagram for diblock copolymer melts is evaluated from lattice-based Monte Carlo simulations using parallel tempering, improving upon earlier simulations that used sequential temperature scans. This new approach locates the order-disorder transition (ODT) far more accurately by the occurrence of a sharp spike in the heat capacity. The present study also performs a more thorough investigation of finite-size effects, which reveals that the gyroid (G) morphology spontaneously forms in place of the perforated-lamellar (PL) phase identified in the earlier study. Nevertheless, there still remains a small region where the PL phase appears to be stable. Interestingly, the lamellar (L) phase next to this region exhibits a small population of transient perforations, which may explain previous scattering experiments suggesting a modulated-lamellar (ML) phase.

Modeling the structure of amorphous MoS3: a neutron diffraction and reverse Monte Carlo study

A model for the structure of amorphous molybdenum trisulfide, a-MoS3, has been created using reverse Monte Carlo methods. This model, which consists of chains Of MoS6 units sharing three sulfurs with each of its two neighbors and forming alternate long, nonbonded, and short, bonded, Mo-Mo separations, is a good fit to the neutron diffraction data and is chemically and physically realistic. The paper identifies the limitations of previous models based on Mo-3 triangular clusters in accounting for the available experimental data.

Finding the smallest eigenvalue by the inverse Monte Carlo method with refinement

Finding the smallest eigenvalue of a given square matrix A of order n is computationally very intensive problem. The most popular method for this problem is the Inverse Power Method which uses LU-decomposition and forward and backward solving of the factored system at every iteration step. An alternative to this method is the Resolvent Monte Carlo method which uses representation of the resolvent matrix [I -qA](-m) as a series and then performs Monte Carlo iterations (random walks) on the elements of the matrix. This leads to great savings in computations, but the method has many restrictions and a very slow convergence. In this paper we propose a method that includes fast Monte Carlo procedure for finding the inverse matrix, refinement procedure to improve approximation of the inverse if necessary, and Monte Carlo power iterations to compute the smallest eigenvalue. We provide not only theoretical estimations about accuracy and convergence but also results from numerical tests performed on a number of test matrices.

Parallel Monte Carlo approach for integration of the rendering equation

This paper is addressed to the numerical solving of the rendering equation in realistic image creation. The rendering equation is integral equation describing the light propagation in a scene accordingly to a given illumination model. The used illumination model determines the kernel of the equation under consideration. Nowadays, widely used are the Monte Carlo methods for solving the rendering equation in order to create photorealistic images. In this work we consider the Monte Carlo solving of the rendering equation in the context of the parallel sampling scheme for hemisphere. Our aim is to apply this sampling scheme to stratified Monte Carlo integration method for parallel solving of the rendering equation. The domain for integration of the rendering equation is a hemisphere. We divide the hemispherical domain into a number of equal sub-domains of orthogonal spherical triangles. This domain partitioning allows to solve the rendering equation in parallel. It is known that the Neumann series represent the solution of the integral equation as a infinity sum of integrals. We approximate this sum with a desired truncation error (systematic error) receiving the fixed number of iteration. Then the rendering equation is solved iteratively using Monte Carlo approach. At each iteration we solve multi-dimensional integrals using uniform hemisphere partitioning scheme. An estimate of the rate of convergence is obtained using the stratified Monte Carlo method. This domain partitioning allows easy parallel realization and leads to convergence improvement of the Monte Carlo method. The high performance and Grid computing of the corresponding Monte Carlo scheme are discussed.