A two-step MF signal acquisition method for wireless underground sensor networks

Liang, Wei; Xie, Yong; Chen, Xuhui; Hosam, Osama; Chen, Xiaoyan

doi:10.2298/csis160224019l

Cited by 14 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Endsley [20] is the most concerned model that defines situation awareness as perception, by including the current state and future trend of the element in a space during a time period. Endsley model divides situation awareness into three parts: (1) Perception: it acquires and collects important information in network, which is the fundamental step of situation awareness model, (2) Comprehension: the collected data is integrated and analyzed and then used to evaluate the current situation, and (3) Prediction: the future trend is predicted on the basis of the results in (1) and (2). The data is collected from the management device, monitoring device and security device in the network.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Security Situation Prediction Algorithm Based on HMM in Mobile Network

Liang

Long

Zuo

et al. 2018

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

The increasingly severe network security situation brings unanticipated challenges to mobile networking. Traditional HMM (Hidden Markov Model) based algorithms for predicting the network security are not accurate, and to address this issue, a weighted HMM based algorithm is proposed to predict the security situation of the mobile network. The multiscale entropy is used to address the low speed of data training in mobile network, whereas the parameters of HMM situation transition matrix are also optimized. Moreover, the autocorrelation coefficient can reasonably use the association between the characteristics of the historical data to predict future security situation. Experimental analysis on DARPA2000 shows that the proposed algorithm is highly competitive, with good performance in prediction speed and accuracy when compared to existing design.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, one of largest issues is security threat in mobile network [2][3][4]. People has developed many protection techniques to cope with these threats, such as mobile firewall, intrusion detection, and virus killer.…”

Section: Introductionmentioning

confidence: 99%

A Security Situation Prediction Algorithm Based on HMM in Mobile Network

Liang

Long

Zuo

et al. 2018

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the study of signal vibration frequency, the Fourier transformation method is commonly used to determine the signal; for non-stationary time-varying signals, wavelet transform is generally used to obtain energy spectra on different scales, but wavelet transform is also based on Fourier transform theory. However, there are still limitations of the window function because the frequency of time conversion in Liang et al 5 cannot accurately be described, and the frequency-domain resolution is low. After a large number of measured data analysis, the effective information of the optical fiber vibration signal collected by the system is mostly included in the mid-band frequency, so the method of modern power spectrum estimation with higher frequency-domain resolution is selected to extract the frequency-domain features.…”

Section: Second Stage: Vibration Pattern Recognitionmentioning

confidence: 99%

Events detection and recognition by the fiber vibration system based on power spectrum estimation

Marie

Han

et al. 2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

One of the important successes of optical fiber sensor established for the security system is the detection and the recognition of any type of events. The performance parameters (event recognition, event detection position, and time of detection) are unavoidable and describe the validity of any perimeter detection system. An event recognition is any signal detected within the protected area, and it is related to a non-intrusion event and an intrusion event. To achieve the detection and the recognition events at the real time, an effective two-level vibration recognition method and a technique are proposed and presented in this article. The signal characteristics (short-term energy and short-time overthreshold) have been used and compared to the dynamic threshold to judge the type of event. Then the extraction of the power distribution features on the frequency domain through power spectral estimation on the suspected intrusion signal samples is carried out and finally combined with the time-domain characteristics as feature vector through Support Vector Machine to determine the efficiency and effectiveness of the proposed vibration recognition method. The experimental simulation results show that the proposed method is effective and reliable. With collected data, it can detect and recognize the type of event in real time.

show abstract

“…A two-level identification scheme is proposed, which in first passes through the time-domain features, after which the suspected perturbation position is extracted. en the short-term Fourier transform is used by Liang et al [13] to qualitatively analyze the spectral features. But DiCarlo and Weber [14] have extracted in the complex wavelet domain the energy distribution and vibration duration of different frequency bands as signal characteristics; and finally the support vector machine (SVM) is used to classify them.…”

Section: Introductionmentioning

confidence: 99%

Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm

Marie

Han

et al. 2020

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Distributed optical fiber vibration signal plays a significant role in the communication and safety of any perimeter security monitoring system. It uses light as an information carrier and optical fiber as a means of signal transmission and communication. Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is used to detect the signals generated during events (intrusions or nonintrusion). This paper proposes the time-frequency characteristic (TFC) method for the recognition of the fiber vibration signal and designs and implements the corresponding software function module. The combination of time-domain features and time-frequency-domain features is called TFC; and it is based on the Hilbert transform and on the empirical mode decomposition (EMD) of time-frequency entropy and center-of-gravity frequency that is described. A feature vector is formed, and multiple types of probabilistic neural networks (PNNs) are performed on it to determine whether intrusion events occur. The experimental simulation results show that the monitoring system software can intelligently display the data collected in real time, which demonstrates that the proposed method is effective and reliable in identifying and classifying accurately the types of events. The data processing time is less than 2 s, and the accuracy of the system identification can reach 99%, which ensures the system’s validity.

show abstract

A two-step MF signal acquisition method for wireless underground sensor networks

Cited by 14 publications

References 0 publications

A Security Situation Prediction Algorithm Based on HMM in Mobile Network

A Security Situation Prediction Algorithm Based on HMM in Mobile Network

Events detection and recognition by the fiber vibration system based on power spectrum estimation

Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm

Contact Info

Product

Resources

About