SSVEP signal classification and recognition based on individual signal mixing template multivariate synchronization index algorithm

2021 ◽  
Vol 64 ◽  
pp. 102304
Author(s):  
Ke Qin ◽  
Raofen Wang
Keyword(s):  
Signal Classification ◽  
Synchronization Index

Non-Stationary Signal Classification Using Joint Frequency Analysis

2003 ◽  
Author(s):  
Somsak Sukittanon ◽  
Les E. Atlas ◽  
James W. Pitton ◽  
Jack McLaughlin
Keyword(s):  
Frequency Analysis ◽  
Signal Classification ◽  
Joint Frequency ◽  
Non Stationary Signal

scholarly journals Electrocardiogram signal classification for automated delineation using bidirectional long short-term memory

2021 ◽  
Vol 22 ◽  
pp. 100507
Author(s):  
Siti Nurmaini ◽  
Alexander Edo Tondas ◽  
Annisa Darmawahyuni ◽  
Muhammad Naufal Rachmatullah ◽  
Jannes Effendi ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Signal Classification ◽  
Short Term ◽  
Term Memory ◽  
Electrocardiogram Signal ◽  
Long Short Term Memory

scholarly journals Signal Classification Algorithms over Time Selective Channels

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1714
Author(s):  
Mohamed Marey ◽  
Hala Mostafa
Keyword(s):  
Block Code ◽  
Classification Problem ◽  
Classification Performance ◽  
Signal Classification ◽  
Final Decision ◽  
Time Block ◽  
Channel Response ◽  
Over Time ◽  
Parallel Fashion

In this work, we propose a general framework to design a signal classification algorithm over time selective channels for wireless communications applications. We derive an upper bound on the maximum number of observation samples over which the channel response is an essential invariant. The proposed framework relies on dividing the received signal into blocks, and each of them has a length less than the mentioned bound. Then, these blocks are fed into a number of classifiers in a parallel fashion. A final decision is made through a well-designed combiner and detector. As a case study, we employ the proposed framework on a space-time block-code classification problem by developing two combiners and detectors. Monte Carlo simulations show that the proposed framework is capable of achieving excellent classification performance over time selective channels compared to the conventional algorithms.

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