Robust multiple classification of known signals in additive noise-an asymptotic weak signal approach

1993 ◽  
Vol 39 (2) ◽  
pp. 594-608 ◽  
Author(s):  
O. Hossjer ◽  
M. Moncef
Keyword(s):  
Additive Noise ◽  
Weak Signal ◽  
Multiple Classification ◽  
Signal Approach

scholarly journals Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

2020 ◽  
Vol 13 (2) ◽  
pp. 537-551
Author(s):  
Shuai Zhang ◽  
Xingyou Huang ◽  
Jinzhong Min ◽  
Zhigang Chu ◽  
Xiaoran Zhuang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Weak Signal ◽  
Post Processing ◽  
Algorithm Performance ◽  
Radar Echoes ◽  
Fuzzy Logic Method ◽  
Anomalous Propagation ◽  
Logic Method

Abstract. To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions.

scholarly journals Physical properties and culture-specific factors as principles of semantic categorisation of the Gújjolaay Eegimaa noun class system

2012 ◽  
Vol 23 (1) ◽  
pp. 129-163 ◽  
Author(s):  
Serge Sagna,
Keyword(s):  
Physical Properties ◽  
Cognitive Linguistics ◽  
Detailed Examination ◽  
Family Resemblance ◽  
Class System ◽  
Multiple Classification ◽  
Specific Factors ◽  
Noun Class ◽  
Atlantic Language

AbstractThis paper investigates the semantic bases of class membership in the noun class system of Gújjolaay Eegimaa (Eegimaa henceforth), a Niger-Congo and Atlantic language of the BAK group spoken in Southern Senegal. The question of whether semantic principles underlie the overt classification of nouns in Niger-Congo languages is a controversial one. There is a common perception of Niger-Congo noun class systems as being mainly semantically arbitrary. The goal of the present paper is to show that physical properties and culture-specific factors are central principles of semantic categorisation in the Eegimaa noun class system. I argue that the Eegimaa overt grammatical classification of nouns into classes is a semantic categorisation system whereby categories are structured according to prototypicality, family resemblance, metaphorical and metonymic extensions and chaining processes, as argued within the framework of Cognitive Linguistics. I show that the categorisation of entities in the Eegimaa nominal classification system productively makes use of physical properties such as shape as well as using culture-specific, less productive parameters for the semantic categorisation of entities denoted by nouns. The analysis proposed here also shows that the cases of multiple morphosyntactic classifications of nouns reflect multiple conceptual categorisation strategies. A detailed examination of the formal and semantic instances of multiple classification reveals the existence of conceptual correlations between the physical properties and the culture-specific semantic parameters of categorisation used in the Eegimaa noun class system.

scholarly journals Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

2019 ◽  
Author(s):  
Shuai Zhang ◽  
Xingyou Huang ◽  
Jinzhong Min ◽  
Hengheng Zhang
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Weak Signal ◽  
Post Processing ◽  
Algorithm Performance ◽  
Radar Echoes ◽  
Fuzzy Logic Method ◽  
Anomalous Propagation ◽  
Logic Method

Abstract. In order to obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After the comprehensive analysis of the computational efficiency and radar system characteristics, a fuzzy logic method similar to the MetSignal algorithm is adopted, but its performance is improved significantly in weak signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at far range to be misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is introduced into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show that the performance of the algorithm has been significantly improved, especially with respect to the classification of meteorological echoes in weak signal regions.

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