scholarly journals Detection and estimation of weak pulse signal in chaotic background noise

2017 ◽  
Vol 66 (9) ◽  
pp. 090503
Author(s):  
Su Li-Yun ◽  
Sun Huan-Huan ◽  
Wang Jie ◽  
Yang Li-Ming
Keyword(s):  
Background Noise ◽  
Pulse Signal ◽  
Detection And Estimation ◽  
Weak Pulse

scholarly journals Detection and Extraction of Weak Pulse Signals in Chaotic Noise with PTAR and DLTAR Models

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Su ◽  
Li Deng ◽  
Wanlin Zhu ◽  
Shengli Zhao
Keyword(s):  
Roc Curve ◽  
Linear Form ◽  
Hypothesis Test ◽  
Pulse Signal ◽  
Support Vector ◽  
Detector Performance ◽  
Threshold Autoregressive ◽  
Profile Least Squares ◽  
Area Under Roc Curve ◽  
Weak Pulse

With the development in communications, the weak pulse signal is submerged in chaotic noise, which is very common in seismic monitoring and detection of ocean clutter targets, and is very difficult to detect and extract. Based on the threshold autoregressive model, pulse linear form, Markov chain Monte Carlo (MCMC), and profile least squares (PrLS) algorithm, phase threshold autoregressive (PTAR) model and double layer threshold autoregressive (DLTAR) model are proposed for detection and extraction of weak pulse signals in chaotic noise, respectively. Firstly, based on noisy chaotic observation, phase space is reconstructed according to Takens’s delay embedding theorem, and the phase threshold autoregressive (PTAR) model is presented to detect weak pulse signals, and then the MCMC algorithm is applied to estimate parameters in the PTAR model; lastly, we obtain one-step prediction error, which is used to realize adaptively detection of weak signals with the hypothesis test. Secondly, a linear form for the pulse signal and PTAR model is fused to build a DLTAR model to extract weak pulse signals. The DLTAR model owns two kinds of parameters, which are affected mutually. Here, the PrLS algorithm is applied to estimate parameters of the DLTAR model and ultimately extract weak pulse signals. Finally, accurate rate (Acc), receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) are used as the detector performance index; mean square error (MSE), mean absolute percent error (MAPE), and relative error (Re) are used as the extraction accuracy index. The presented scheme does not need prior knowledge of chaotic noise and weak pulse signals, and simulation results show that the proposed PTAR-DLTAR model is significantly effective for detection and extraction of weak pulse signals under chaotic interference. Specifically, in very low signal-to-interference ratio (SIR), weak pulse signals can be detected and extracted compared with support vector machine (SVM) class and neural network model.

Detection of weak pulse signal via stochastic resonance

2006 ◽  
Author(s):  
Mingyang Wang ◽  
Yiyu Zhou ◽  
Le Han ◽  
Wenli Jiang
Keyword(s):  
Stochastic Resonance ◽  
Pulse Signal ◽  
Weak Pulse

scholarly journals Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liyun Su ◽  
Meini Li ◽  
Shengli Zhao ◽  
Ting Xie
Keyword(s):  
Sensor Fusion ◽  
Risk Model ◽  
Hypothesis Test ◽  
Pulse Signal ◽  
Optimal Decision ◽  
Conditional Probability Density ◽  
Distributed Sensor ◽  
Local Linear ◽  
Weak Pulse ◽  
Fusion Detection

This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short-term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS-LLAR) model is constructed to obtain the one-step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.

scholarly journals Detection and parameter estimation of weak pulse signal based on strongly coupled Duffing oscillators

2019 ◽  
Vol 68 (8) ◽  
pp. 080501
Author(s):  
Bao-Feng Cao ◽  
Peng Li ◽  
Xiao-Qiang Li ◽  
Xue-Qin Zhang ◽  
Wang-Shi Ning ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Pulse Signal ◽  
Strongly Coupled ◽  
Weak Pulse ◽  
Coupled Duffing Oscillators

scholarly journals Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Liyun Su ◽  
Xiu Ling
Keyword(s):  
Neural Network ◽  
Pulse Signal ◽  
Least Square ◽  
Data Matrix ◽  
Support Vector ◽  
Model Parameters ◽  
Elman Neural Network ◽  
Useful Signal ◽  
Weak Pulse ◽  
Space Data

In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.

scholarly journals Statistical Detection of Weak Pulse Signal under Chaotic Noise Based on Elman Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Su ◽  
Li Deng ◽  
Wanlin Zhu ◽  
Shengli Zhao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Signal Detection ◽  
Prediction Error ◽  
Pulse Signal ◽  
Learning Ability ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Elman Neural Network ◽  
Weak Pulse

Weak signal detection is a significant problem in modern detection such as mechanical fault diagnosis. The uniqueness of chaos and good learning ability of neural networks provide new ideas and framework for weak signal detection field. In this paper, Elman neural network is applied to detect and recover weak pulse signal in chaotic noise. For detection problem of weak pulse signal under chaotic noise, based on short-term predictability of chaotic observations, phase space reconstruction for observed signals is carried out. And Elman deep learning adaptive detection model (EDAD model) is established for weak pulse signal detection, and a hypothesis test is used to detect weak pulse signal from the prediction error. For the recovery of weak pulse signal under chaotic noise, a double-layer Elman deep neural network recovery model (DEDR model) is proposed, which is based on the Elman deep learning network model and single-point jump model for weak pulse signal, and it is optimized with goal of minimizing mean square prediction error of the Elman model. The profile least squares method is applied to estimate parameters of the DEDR model for difficult recovery of weak pulse signal because the DEDR model is essentially a semiparametric model with parametric and nonparametric parts. In the end, simulation experiments show that the model built in this paper can effectively detect and recover weak pulse signal in the background of chaotic noise.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

Use of a Mild-Gain Hearing Aid by Middle-Age Normal-Hearing Adults Who Do and Do Not Self-Report Trouble Hearing in Background Noise

2020 ◽  
Vol 29 (3) ◽  
pp. 419-428
Author(s):  
Jasleen Singh ◽  
Karen A. Doherty
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Middle Age ◽  
Hearing Aid ◽  
Normal Hearing ◽  
Self Report ◽  
Hearing Aid Use ◽  
Hearing Handicap ◽  
Before And After ◽  
Hearing Problems

Purpose The aim of the study was to assess how the use of a mild-gain hearing aid can affect hearing handicap, motivation, and attitudes toward hearing aids for middle-age, normal-hearing adults who do and do not self-report trouble hearing in background noise. Method A total of 20 participants (45–60 years of age) with clinically normal-hearing thresholds (< 25 dB HL) were enrolled in this study. Ten self-reported difficulty hearing in background noise, and 10 did not self-report difficulty hearing in background noise. All participants were fit with mild-gain hearing aids, bilaterally, and were asked to wear them for 2 weeks. Hearing handicap, attitudes toward hearing aids and hearing loss, and motivation to address hearing problems were evaluated before and after participants wore the hearing aids. Participants were also asked if they would consider purchasing a hearing aid before and after 2 weeks of hearing aid use. Results After wearing the hearing aids for 2 weeks, hearing handicap scores decreased for the participants who self-reported difficulty hearing in background noise. No changes in hearing handicap scores were observed for the participants who did not self-report trouble hearing in background noise. The participants who self-reported difficulty hearing in background noise also reported greater personal distress from their hearing problems, were more motivated to address their hearing problems, and had higher levels of hearing handicap compared to the participants who did not self-report trouble hearing in background noise. Only 20% (2/10) of the participants who self-reported trouble hearing in background noise reported that they would consider purchasing a hearing aid after 2 weeks of hearing aid use. Conclusions The use of mild-gain hearing aids has the potential to reduce hearing handicap for normal-hearing, middle-age adults who self-report difficulty hearing in background noise. However, this may not be the most appropriate treatment option for their current hearing problems given that only 20% of these participants would consider purchasing a hearing aid after wearing hearing aids for 2 weeks.

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

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