In this contribution we propose an a priori signal-to-noise ratio (SNR) estimator based on a probabilistic speech model. Since the a priori SNR is an important means for speech enhancement algorithms, such as weighting rule calculation for noise reduction or speech presence probability computation, its diligent estimation is of wide interest. As a basis for this estimator a Gaussian mixture mod...

Distance-to-target weighting methods are widely used in life cycle impact assessment. The methods rank impacts as being more important the further away society's activities are from achieving the desired targets for the pollutants. However, we feel that the scientific bases of the distance-to-target methods still need more clarification. This article illustrates how multiattribute value theory ...

In response to the uncertainty, nonlinearity and open-loop instability of active magnetic levitation control system, and neural network PID quadratic optimal controller are designed using optimum control theory. Introducing supervised Hebb learning rule, constraint control for positioning errors and control increment weighting are realized by adjusting weighting coefficients, using weighed sum-...

This paper looks at unobserved components models and examines the implied weighting patterns for signal extraction. There are three main themes. The rst is the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is how setting up models with t distributed disturbances leads to weighting patterns which are robust ...

First we modify the basic (binary) context-tree weighting method such that the past symbols x1 D; x2 D; ; x0 are not needed by the encoder and the decoder. Then we describe how to make the context-tree depth D infinite, which results in optimal redundancy behavior for all tree sources, while the number of records in the context tree is not larger than 2T 1: Here T is the length of the source se...

This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...

This paper performs systematic comparative studies on rough set based class imbalance learning. We compare the strategies of weighting, re-sampling and filtering used in the rough set based methods for class imbalance learning. Weighting is better than re-sampling, and re-sampling is better than filtering. The weighted rough set based method achieves the best performance in class imbalance lear...

The article presents the method of forming associative rules from database SIEM system for detecting cyber incidents, which is based on theory fuzzy sets and methods data mining. On basis conducted analysis, a conclusion was made about expediency incidents in special information communication systems (SICS) by applying rule-oriented methods. necessity mining technologies, particular, to supplem...

This paper aims to present an improved rule-based fingerprint classification using coherence method and a fuzzy rule-based (or simply fuzzy-based) fingerprint classification. The improved classification using coherence method has been applied to fingerprint images that do not previously produce any singular points while the fuzzy rule-based classification is designed based on the uncertainty in...

this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...