Bayesian event detection for sport games with hidden Markov model

Event detection can be defined as the problem of detecting when a target event has occurred, from a given data sequence. Such an event detection problem can be found in many fields in science and engineering, such as signal processing, pattern recognition, and image processing. In recent years, many data sequences used in these fields, especially in video data analysis, tend to be high dimensional. In this paper, we propose a novel event detection method for high-dimensional data sequences in soccer video analysis. The proposed method assumes a Bayesian hidden Markov model with hyperparameter learning in addition to the parameter leaning. This is in an attempt to reduce undesired influences from ineffective components within the high-dimensional data. Implemention is performed by Markov Chain Monte Carlo. The proposed method was tested against an event detection problem with sequences of 40-dimensional feature values extracted from real professional soccer games. The algorithm appears functional.     

Signal estimation with multiple delayed sensors using covariance information

Recursive filtering and smoothing algorithms to estimate a signal from noisy measurements coming from multiple randomly delayed sensors, with different delay characteristics, are proposed. To design these algorithms an innovation approach is used, assuming that the state-space model of the signal is unknown and using only covariance information. To measure the precision of the proposed estimators formulas to calculate the filtering and smoothing error covariance matrices are also derived. The effectiveness of the estimators is illustrated by a numerical simulation example where a signal is estimated using observations from two randomly delayed sensors having different delay properties.     

New recursive estimators from correlated interrupted observations using covariance information

Least-squares linear and quadratic filtering and fixed-point smoothing algorithms are derived to estimate a signal from uncertain observations perturbed by an additive white noise. The random variables describing the uncertainty are correlated only at consecutive time instants, and this correlation, as well as the probability that the signal exists in each observation, is known. Recursive algorithms are obtained without requiring the state-space model generating the signal, but just some moments of both the signal and the additive noise in the observation equation. For the linear estimation algorithms, only the second-order moments are required, and the autocovariance function of the signal must be expressed in a semi-degenerate kernel form. The quadratic estimation algorithms use, in addition, the moments up to the fourth one, and they require the autocovariance and cross-covariance functions of the signal and their second-order powers in a semi-degenerate kernel form. This form for expressing autocovariance functions is not very restrictive, since it covers many general stochastic processes, including stationary and non-stationary processes.     

Photoluminescence and scintillation properties of Eu2O3–BaO–Nb2O5–TeO2 glass and glass ceramics

Journal of Materials Science: Materials in Electronics - 1Eu2O3–3BaO–20Nb2O5–76TeO2 glass and the corresponding glass-ceramics were synthesized with the aim to investigate the...     

A Probability-Based Approach to Comparison of Fuzzy Numbers and Applications to Target-Oriented Decision Making

In this paper, we introduce a new comparison relation on fuzzy numbers based on their alpha-cut representation and comparison probabilities of interval values. Basically, this comparison process combines a widely accepted interpretation of fuzzy sets together with the uncertain characteristics inherent in the representation of fuzzy numbers. The proposed comparison relation is then applied to the issue of ranking fuzzy numbers using fuzzy targets in terms of target-based evaluations. Some numerical examples are used to illuminate the proposed ranking technique as well as to compare with previous methods. More interestingly, according to the interpretation of the new comparison relation on fuzzy numbers, we provide a fuzzy target-based decision model as a solution to the problem of decision making under uncertainty, with which an interesting link between the decision maker's different attitudes about target and different risk attitudes in terms of utility functions can be established. Moreover, an application of the proposed comparison relation to the fuzzy target-based decision model for the problem of fuzzy decision making with uncertainty is provided. Numerical examples are also given for illustration.     

Polynomial fixed-point smoothing of uncertainly observed signals based on covariances

In this article, the least-squares νth-order polynomial fixed-point smoothing problem of uncertainly observed signals is considered, when only some information about the moments of the processes involved is available. For this purpose, a suitable augmented observation equation is defined such that the optimal polynomial estimator of the original signal is obtained from the optimal linear estimator of the augmented signal based on the augmented observations and, hence, a recursive algorithm for this linear estimator is deduced. The proposed estimator does not require the knowledge of the state-space model of the signal, but only the moments (up to the 2νth one) of the signal and observation noise, as well as the probability that the signal exists in the observations.     

Random mutagenesis into the conserved Gly154 of subtilisin E: isolation and characterization of the revertant enzymes

We analyzed the role played by the conserved Gly154, a constituent of the P1 substrate-binding pocket of Bacillus subtilis subtilisin E, in the catalytic properties of the protease. Using an Escherichia coli expression system, the termination codon at position 154 in subtilisin E was first introduced to abolish the catalytic activity through truncation of the C-terminus from amino acid residues 154-275. We then attempted to obtain revertants with substitutions of various amino acids at position 154 by the polymerase chain reaction using a mixture of oligonucleotides. In addition to the Gly residue (wild-type), six amino acid substitutions (Ala, Arg, Leu, Phe, Pro and Thr) gave caseinolytic activity. When assayed with synthetic peptide substrates, most of the revertants showed a considerable decrease in specific activity and a P1 specificity similar to that of the wild-type enzyme. An Ala154 mutant purified from the periplasmic space in E. coli, however, resulted in an up to 2.3-fold preference for Val rather than Pro as a P2 substrate relative to the wild-type. Further, a significant 2-10-fold increase in the catalytic efficiency occurred in the Gly127Ala plus Gly154Ala combination variant, relative to the single Gly127Ala variant, without any change in the restricted specificity. The kinetic data and molecular modeling analysis demonstrate the important role of position 154 in the catalytic efficiency as well as in the substrate specificity of subtilisin E.      

Random mutagenesis into the conserved Gly154 of subtilisin E: isolation and characterization of the revertant enzymes

We analyzed the role played by the conserved Gly154, a constituent of the P1 substrate-binding pocket of Bacillus subtilis subtilisin E, in the catalytic properties of the protease. Using an Escherichia coli expression system, the termination codon at position 154 in subtilisin E was first introduced to abolish the catalytic activity through truncation of the C-terminus from amino acid residues 154-275. We then attempted to obtain revertants with substitutions of various amino acids at position 154 by the polymerase chain reaction using a mixture of oligonucleotides. In addition to the Gly residue (wild-type), six amino acid substitutions (Ala, Arg, Leu, Phe, Pro and Thr) gave caseinolytic activity. When assayed with synthetic peptide substrates, most of the revertants showed a considerable decrease in specific activity and a P1 specificity similar to that of the wild-type enzyme. An Ala154 mutant purified from the periplasmic space in E. coli, however, resulted in an up to 2.3-fold preference for Val rather than Pro as a P2 substrate relative to the wild-type. Further, a significant 2-10-fold increase in the catalytic efficiency occurred in the Gly127Ala plus Gly154Ala combination variant, relative to the single Gly127Ala variant, without any change in the restricted specificity. The kinetic data and molecular modeling analysis demonstrate the important role of position 154 in the catalytic efficiency as well as in the substrate specificity of subtilisin E.     

New design of linear discrete-time predictor using covariance information

A discrete one-stage predictor algorithm using covariance information in linear systems is derived. The algorithm is obtained for white Gaussian observation noise. The signal is a nonstationary or stationary stochastic process. The auto-covariance function of the signal is expressed using a semi-degenerate kernel of discrete-time systems. The semi-degenerate kernel can represent general covariance functions of random processes by a finite sum of nonrandom functions.