scholarly journals A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 448
Author(s):  
Han Li ◽  
Yanzhu Hu ◽  
Song Wang
Keyword(s):  
Power Spectrum ◽  
Detection Method ◽  
Multinomial Distribution ◽  
Sample Entropy ◽  
Energy Ratio ◽  
Statistical Characteristics ◽  
Interval Probability ◽  
Blind Signal ◽  
Signal Detector ◽  
Subband Energy

In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.

scholarly journals Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 64
Author(s):  
Han Li ◽  
Yanzhu Hu ◽  
Song Wang
Keyword(s):  
Power Spectrum ◽  
Signal Detection ◽  
False Alarm ◽  
Probability Density ◽  
Beta Distribution ◽  
Detection Method ◽  
Energy Detection ◽  
Energy Ratio ◽  
Noise Variance ◽  
Band Energy

The power-spectrum sub-band energy ratio (PSER) has been applied in a variety of fields, but reports on its statistical properties and application in signal detection have been limited. Therefore, the statistical characteristics of the PSER were investigated and a signal detection method based on the PSER was created in this paper. By analyzing the probability and independence of power spectrum bins, as well as the relationship between F and beta distributions, we developed a probability distribution for the PSER. Our results showed that in a case of pure noise, the PSER follows beta distribution. In addition, the probability density function exhibited no relationship with the noise variance—only with the number of bins in the power spectrum. When Gaussian white noise was mixed with the signal, the resulting PSER followed a doubly non-central beta distribution. In this case, the probability density and cumulative distribution functions were represented by infinite double series. Under the constant false alarm strategy, we established a signal detector based on the PSER and derived the false alarm probability and detection probability of the PSER. The main advantage of this detector is that it did not need to estimate noise variance. Compared with time-domain energy detection and local spectral energy detection, we found that the PSER had better robustness under noise uncertainty. Finally, the results in the simulation and real signal showed that this detection method was valid.

scholarly journals A BP Neural Network-Based Communication Blind Signal Detection Method With Cyber-Physical-Social Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 43920-43935 ◽  
Author(s):  
Xin Liu ◽  
Yanju Zhou ◽  
Zongrun Wang ◽  
Xiaohong Chen
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Bp Neural Network ◽  
Detection Method ◽  
Social Systems ◽  
Blind Signal

scholarly journals Statistical characteristics of surrogate data based on geophysical measurements

2006 ◽  
Vol 13 (4) ◽  
pp. 449-466 ◽  
Author(s):  
V. Venema ◽  
S. Bachner ◽  
H. W. Rust ◽  
C. Simmer
Keyword(s):  
Time Series ◽  
Power Spectrum ◽  
Surrogate Data ◽  
Structure Functions ◽  
Statistical Characteristics ◽  
Historical Time ◽  
Nonlinear Dynamical ◽  
Small Scales ◽  
Measured Distribution ◽  
Geophysical Measurements

Abstract. In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales.

scholarly journals EEG Microstate in Obstructive Sleep Apnea Patients

2020 ◽  
Author(s):  
Xin Xiong ◽  
Yuyan Ren ◽  
Shenghan Gao ◽  
Jianhua Luo ◽  
Jiangli Liao ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Power Spectrum ◽  
Transient State ◽  
Sample Entropy ◽  
Fluctuation Analysis ◽  
Obstructive Sleep ◽  
Healthy Control ◽  
Detrended Fluctuation ◽  
Electroencephalogram Eeg

Abstract Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstate reflecting the transient state of the brain is abnormal is unclear during sleep apnea or hypopnea. We investigated the microstates of sleep EEG in 30 OSA patients and in 10 healthy control volunteers. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). We observed that there was an additional fifth microstate E during apnea or hypopnea in N1 and N3 stages in OSA patients. And the microstate E was correlated with the power spectrum of delta, theta and alpha bands, not correlated with sample entropy, but correlated with DFA in N1-OA/OH stage. Moreover, Global Explained Variance, Mean Duration, Time Coverage and Segment Density of microstate E were positively correlated with DFA. We can interpret that the abnormal transition of brain active areas of OSA patients in N1-OA/OH stages leads to an extra microstate E, which might be related to the change of alpha activity in the cortex. And the generation of microstate E is not correlated with the decrease of EEG complexity, but correlated with the stronger self-similar regularity of EEG signals in OSA patients. These findings indicate that the microstate has the potential as a biomarker of EEG and has potential application value in OSA diagnosis.

scholarly journals Motion Direction Inconsistency-Based Fight Detection for Multiview Surveillance Videos

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chuang Yao ◽  
Xiaoyan Su ◽  
Xuehua Wang ◽  
Xinyi Kang ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Optical Flow ◽  
Detection Method ◽  
Research Field ◽  
Statistical Characteristics ◽  
Public Security ◽  
Accurate Information ◽  
Cyberphysical Systems ◽  
Motion Direction ◽  
Challenging Research

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

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