Detecting and Identifying Anomalous Effects in Complex Signals

2021 ◽  
Vol 82 (10) ◽  
pp. 1668-1678
Author(s):  
V. V. Geppener ◽  
B. S. Mandrikova
Keyword(s):  
2009 ◽  
Vol 179 (2) ◽  
pp. 218
Author(s):  
V.I. Kaevitser ◽  
V.M. Razmanov
Keyword(s):  

2016 ◽  
Vol 2016 (16) ◽  
pp. 1-7
Author(s):  
Alfredo Restrepo Palacios ◽  
Jorge L Mayorga
Keyword(s):  

2021 ◽  
Author(s):  
Jüri Engelbrecht ◽  
Kert Tamm ◽  
Tanel Peets
Keyword(s):  

2002 ◽  
Vol 48 (1) ◽  
pp. 1-7 ◽  
Author(s):  
V. A. Akulichev ◽  
V. V. Bezotvetnykh ◽  
S. I. Kamenev ◽  
E. V. Kuz’20min ◽  
Yu. N. Morgunov ◽  
...  

2021 ◽  
Vol 31 (12) ◽  
pp. 2130037
Author(s):  
Visarath In ◽  
Antonio Palacios

This article reviews recent progress in signal frequency up-conversion and down-conversion, both theory and experiments with network implementations. The fundamental idea is to exploit the inherent symmetry of networks to produce collective behavior in which certain oscillators tend to oscillate at different frequencies. This concept is significantly different from other techniques, e.g. master-slave systems, in the sense that the collective behavior arises naturally from the mutual interactions of the individual units, and without any external forcing. In this manuscript, we present a comprehensive review of the basic ideas, methods, and experiments of the symmetry-based phenomenon of frequency conversion. In addition, we present a review of a device implementation of a broad spectrum analyzer, which motivated the development of systematic methods to up- and down-convert frequencies of oscillations. This device is made up of large parallel arrays of analog nonlinear oscillators with the ability to receive complex signals containing multiple frequencies and instantaneously lock-on or respond to a received signal in a few oscillation cycles.


Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.


2014 ◽  
Vol 519-520 ◽  
pp. 1051-1056
Author(s):  
Jie Guo ◽  
An Quan Wei ◽  
Lei Tang

This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.


Sign in / Sign up

Export Citation Format

Share Document