Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks

In this paper we propose a new method for common visual pattern identi cation via Directed Graphs. For this we match common feature points between two images and then apply hierarchical clustering methods to one of them to discriminate between di erent visual patterns. In order to achieve this last task we introduce a technique to obtain an asymmetric dissimilarity function AX(x; x1) between the nodes X of the network N = (X;Ax). For each node, the method weighs the distance between each node and the distance with all the other neighbours. A dendrogram is later obtained as the output of the hierarchical clustering method. Finally we show a criteria to select one of the multiple partitions that conform the dendrogram.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Adaptive MCA-Matched Filter Algorithms for Binary Detection

In this work, we present a method for signal-to-noise ratio maximization using a linear filter based on minor component analysis of the noise covariance matrix. As we will see, the greatest benefits are obtained when both filter and signal design are treated as a single problem. This general problem is then related to the minimization of the probability of error of a digital communication. In particular, the classical binary detection problem is considered when nonstationary and (possibly) nonwhite additive Gaussian noise is present. Two algorithms are given to solve the problem at hand with cuadratic and linear computational complexity with respect to the dimension of the problem.Sociedad Argentina de Informática e Investigación Operativ

Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks

In this paper we propose a new method for common visual pattern identi cation via Directed Graphs. For this we match common feature points between two images and then apply hierarchical clustering methods to one of them to discriminate between di erent visual patterns. In order to achieve this last task we introduce a technique to obtain an asymmetric dissimilarity function AX(x; x1) between the nodes X of the network N = (X;Ax). For each node, the method weighs the distance between each node and the distance with all the other neighbours. A dendrogram is later obtained as the output of the hierarchical clustering method. Finally we show a criteria to select one of the multiple partitions that conform the dendrogram.Sociedad Argentina de Informática e Investigación Operativa (SADIO

On a Theoretical Background for Computing Reliable Approximations of the Barankin Bound

The Barankin bound is locally the greatest possible lower bound for the variance of any unbiased estimator of a deterministic pa- rameter, under certain relatively mild conditions. Much more essential, Barankin's work determines the su cient and necessary conditions un- der which an unbiased estimator with nite variance exists. Nevertheless, the computing of this bound, along with the proof of existence or non- existence of the estimator, has shown to be extremely challenging in most cases. Thereby, many approaches have been made to attain easily com- putable approximations of the bound, given it exists. Focusing on the rather central matter of existence, we provide a simple theoretical frame within which our approximations of the bound give a clear insight on whether an unbiased estimator does exist.Sociedad Argentina de Informática e Investigación Operativa (SADIO

On a Theoretical Background for Computing Reliable Approximations of the Barankin Bound

The Barankin bound is locally the greatest possible lower bound for the variance of any unbiased estimator of a deterministic pa- rameter, under certain relatively mild conditions. Much more essential, Barankin's work determines the su cient and necessary conditions un- der which an unbiased estimator with nite variance exists. Nevertheless, the computing of this bound, along with the proof of existence or non- existence of the estimator, has shown to be extremely challenging in most cases. Thereby, many approaches have been made to attain easily com- putable approximations of the bound, given it exists. Focusing on the rather central matter of existence, we provide a simple theoretical frame within which our approximations of the bound give a clear insight on whether an unbiased estimator does exist.Sociedad Argentina de Informática e Investigación Operativa (SADIO

MiniMax Affine Estimation of Parameters of Multiple Damped Complex Exponentials

Multiple damped complex exponentials are of great practical importance as they are useful for describing many technological situations. Several estimators have been developed for the parameters of these complex exponentials. In this paper, we apply the MiniMax affine estimator to this problem in order to obtain a better performance (in terms of the mean squared error) than other unbiased estimators. Through simulations, this estimator is shown to have a reduced mean squared error, especially for the adverse case of lower signal-to-noise ratio. Additionally, a closed form expression for the MiniMax affine estimator is presented.Sociedad Argentina de Informática e Investigación Operativ

Common Visual Pattern Recognition Using Hierarchical Clustering Methods with Asymmetric Networks

In this paper we propose a new method for common visual pattern identi cation via Directed Graphs. For this we match common feature points between two images and then apply hierarchical clustering methods to one of them to discriminate between di erent visual patterns. In order to achieve this last task we introduce a technique to obtain an asymmetric dissimilarity function AX(x; x1) between the nodes X of the network N = (X;Ax). For each node, the method weighs the distance between each node and the distance with all the other neighbours. A dendrogram is later obtained as the output of the hierarchical clustering method. Finally we show a criteria to select one of the multiple partitions that conform the dendrogram.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Métodos secuenciales de Monte Carlo aplicados a modelos ocultos de Markov con proceso de estado y de medición correlacionados

En el marco del filtrado Bayesiano, se presenta un modelo en el cual el proceso de medición y el estado siguiente son condicionalmente dependientes, dado el conjunto de observaciones pasadas y el estado actual. Además se busca la distribución de predicción para este modelo, y la distribución de filtrado se halla con una simple actualización a partir de la de predicción. La distribución requerida se aproxima utilizando el muestreo secuencial de importancia, y la distribución queda representada por un conjunto de muestras aleatorias, que son obtenidas a partir de una función de importancia, y se le asigna a las mismas un peso de acuerdo a su relevancia.Sociedad Argentina de Informática e Investigación Operativ

On a Theoretical Background for Computing Reliable Approximations of the Barankin Bound

The Barankin bound is locally the greatest possible lower bound for the variance of any unbiased estimator of a deterministic pa- rameter, under certain relatively mild conditions. Much more essential, Barankin's work determines the su cient and necessary conditions un- der which an unbiased estimator with nite variance exists. Nevertheless, the computing of this bound, along with the proof of existence or non- existence of the estimator, has shown to be extremely challenging in most cases. Thereby, many approaches have been made to attain easily com- putable approximations of the bound, given it exists. Focusing on the rather central matter of existence, we provide a simple theoretical frame within which our approximations of the bound give a clear insight on whether an unbiased estimator does exist.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Blood pressure long term regulation: A neural network model of the set point development

<p>Abstract</p> <p>Background</p> <p>The notion of the nucleus tractus solitarius (NTS) as a comparator evaluating the error signal between its rostral neural structures (RNS) and the cardiovascular receptor afferents into it has been recently presented. From this perspective, stress can cause hypertension via set point changes, so offering an answer to an old question. Even though the local blood flow to tissues is influenced by circulating vasoactive hormones and also by local factors, there is yet significant sympathetic control. It is well established that the state of maturation of sympathetic innervation of blood vessels at birth varies across animal species and it takes place mostly during the postnatal period. During ontogeny, chemoreceptors are functional; they discharge when the partial pressures of oxygen and carbon dioxide in the arterial blood are not normal.</p> <p>Methods</p> <p>The model is a simple biological plausible adaptative neural network to simulate the development of the sympathetic nervous control. It is hypothesized that during ontogeny, from the RNS afferents to the NTS, the optimal level of each sympathetic efferent discharge is learned through the chemoreceptors' feedback. Its mean discharge leads to normal oxygen and carbon dioxide levels in each tissue. Thus, the sympathetic efferent discharge sets at the optimal level if, despite maximal drift, the local blood flow is compensated for by autoregulation. Such optimal level produces minimum chemoreceptor output, which must be maintained by the nervous system. Since blood flow is controlled by arterial blood pressure, the long-term mean level is stabilized to regulate oxygen and carbon dioxide levels. After development, the cardiopulmonary reflexes play an important role in controlling efferent sympathetic nerve activity to the kidneys and modulating sodium and water excretion.</p> <p>Results</p> <p>Starting from fixed RNS afferents to the NTS and random synaptic weight values, the sympathetic efferents converged to the optimal values. When learning was completed, the output from the chemoreceptors became zero because the sympathetic efferents led to normal partial pressures of oxygen and carbon dioxide.</p> <p>Conclusions</p> <p>We introduce here a simple simulating computational theory to study, from a neurophysiologic point of view, the sympathetic development of cardiovascular regulation due to feedback signals sent off by cardiovascular receptors. The model simulates, too, how the NTS, as emergent property, acts as a comparator and how its rostral afferents behave as set point.</p