Fingerprint Feature Recognition of Frequency Hopping Radio with FCBF-NMI Feature Selection

2020 ◽  
pp. 819-831
Author(s):  
Hongguang Li ◽  
Ying Guo ◽  
Zisen Qi ◽  
Ping Sui ◽  
Linghua Su
Keyword(s):  
Feature Selection ◽  
Feature Recognition ◽  
Frequency Hopping ◽  
Fingerprint Feature

scholarly journals A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Feng Zhao ◽  
Zhiyuan Chen ◽  
Islem Rekik ◽  
Peiqiang Liu ◽  
Ning Mao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Functional Connectivity ◽  
Evaluation Method ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Superior Performance ◽  
Selection Strategy ◽  
Dynamic Functional Connectivity ◽  
Discriminative Feature ◽  
Fingerprint Feature

The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.

scholarly journals Frequency-Hopping Transmitter Fingerprint Feature Classification Based on Kernel Collaborative Representation Classifier

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ping Sui ◽  
Ying Guo ◽  
Kun-feng Zhang ◽  
Honguang Li
Keyword(s):  
Noise Suppression ◽  
Feature Space ◽  
Frequency Hopping ◽  
Collaborative Representation ◽  
Feature Classification ◽  
Line Integral ◽  
Wave Atoms ◽  
Non Gaussian ◽  
Fingerprint Feature

Noncooperation frequency-hopping (FH) transmitter fingerprint feature classification is a significant but challenging issue for FH transmitter recognition, since not only is it sensitive to noise but also it has the nonlinear, non-Gaussian and nonstability characteristics, which make it difficult to guarantee the classification in the original signal space. To address these problems, a method of frequency-hopping transmitter fingerprint feature classification based on kernel collaborative representation classifier is proposed in this paper. First, the noise suppression pretreatment of the FH transmitter signal is carried out by using the wave atoms frame method. Then, the nuances of the FH transmitters in the feature space are characterized by the surrounding-line integral bispectra features. And finally, incorporating the kernel function, a classifier which can generalize a linear algorithm to nonlinear counterpart is constructed for the final transmitter fingerprint feature classification. Extensive experiments on real-world FH transmitter “turn-on” transient signals demonstrate the robust classification of our method.

Feature Selection Strategies and Perceptual Expertise in Configuration Search Tasks

2011 ◽  
Author(s):  
Lindsey M. Kitchell ◽  
Francisco J. Parada ◽  
Brandi L. Emerick ◽  
Tom A. Busey
Keyword(s):  
Feature Selection ◽  
Perceptual Expertise ◽  
Search Tasks ◽  
Selection Strategies
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