Detection Method for Satellite Communication Interfering Signals Based on Compressive Sensing

2017 ◽  
Author(s):  
Fuhua Fan ◽  
Shengliang Fang ◽  
Yong Wang
Keyword(s):  
Compressive Sensing ◽  
Satellite Communication ◽  
Detection Method

scholarly journals Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi-Modal Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4152-4158
Keyword(s):  
False Alarm ◽  
Compressive Sensing ◽  
Detection Method ◽  
Probability Of Detection ◽  
Stochastic Gradient Descent ◽  
Activity Detection ◽  
Copula Theory ◽  
Probability Of False Alarm ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Activity detection based on likelihood ratio in the presence of high dimensional multimodal data acts as a challenging problem as the estimation of joint probability density functions (pdfs) with intermodal dependence is tedious. The existing method with above expectations fails due to poor performance in the presence of strongly dependent data. This paper proposes a Compressive Sensing Based Detection method in the Multi-sensor signal using the deep learning method. The proposed Tree copula- Grasshopper optimization based Deep Convolutional Neural Network (TC-GO based DCNN) detection method comprises of three main steps, such as compressive sensing, fusion and detection. The signals are initially collected from the sensors in order to subject them under tensor based compressive sensing. The compressed signals are then fused together using tree copula theory, and the parameters are estimated with the Grasshopper optimization algorithm (GOA). The activity detection is finally performed using DCNN, which is trained with the Stochastic Gradient Descent (SGD) Optimizer. The performance of the proposed method is evaluated based on the evaluation metrics, such as probability of detection and probability of false alarm. The highest probability of detection and least probability of false alarm are obtained as 0.9083, and 0.0959, respectively using the proposed method that shows the effectiveness of the proposed method in activity detection.

Anomaly Event Detection Method Based on Compressive Sensing and Iteration in Wireless Sensor Networks

2014 ◽  
Vol 9 (3) ◽  
Author(s):  
Shihua Cao ◽  
Qihui Wang ◽  
Yaping Yuan ◽  
Junyang Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressive Sensing ◽  
Event Detection ◽  
Detection Method ◽  
Wireless Sensor

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

Detection method and stage in BRCA1/2 mutation carriers

2002 ◽  
Vol 38 (11) ◽  
pp. S163
Author(s):  
R Kaas
Keyword(s):  
Detection Method ◽  
Mutation Carriers

Compressive Sensing for Wireless Networks

2009 ◽  
Author(s):  
Zhu Han ◽  
Husheng Li ◽  
Wotao Yin
Keyword(s):  
Wireless Networks ◽  
Compressive Sensing

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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