The Experts below are selected from a list of 1320 Experts worldwide ranked by ideXlab platform
Junfeng Jiang - One of the best experts on this subject based on the ideXlab platform.
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a multiple events recognition scheme based on improved feature vectors for fiber optic Perimeter Security system
2019 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, 2020Co-Authors: Junfeng JiangAbstract:In this paper, we propose an improved feature extraction based multiple events recognition scheme for fiber optic Perimeter Security system. In the scheme, four common types of Security sensing events, namely, background noises, waggling the fence, cutting the fence and climbing the fence are collected based on a dual Mach-Zehnder interferometry vibration sensor. Variational mode decomposition in frequency domain, sample entropy in irregularity and zero crossing rate in time domain are considered as the feature description of the given Security sensing events. A series of experiments have been implemented by a radial basis foundation neural network, which shows that the proposed recognition scheme can accurately discriminate the three kinds of man-made intrusions from the background noises. The average identification rates of 98.42% and 100% are achieved for the three types of intrusions and background noises, respectively, which can fully satisfy the field application requirements, the recognition response time is also good of real time performance, which can be controlled less than 1.6 s. Therefore, the proposed events recognition scheme can provide a quite promising field application prospect in the fiber optic Perimeter Security system.
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Variational Mode Decomposition-Based Event Recognition in Perimeter Security Monitoring With Fiber Optic Vibration Sensor
IEEE Access, 2019Co-Authors: Junfeng Jiang, Zhongyuan Xu, Zichun ZhouAbstract:Recognition of different kinds of human intrusions from the environmental disturbance with high efficiency is still a challenging task in Perimeter Security monitoring with fiber optic vibration sensor, since the vibration signals induced by these events are highly similar to each other, and it can not be directly discriminated by the signals. In this paper, an intelligent event recognition scheme is proposed to improve its performance in complicated environmental applications. In this event recognition scheme, a variational mode decomposition based kurtosis feature combined with a zero crossing rate feature is used as the input feature vectors. A support vector machine is used to classify the input feature vectors into the corresponding categories. A series of field tests show that the proposed scheme can accurately and rapidly classify wind disturbance and three typical patterns of human intrusions such as waggling the fence, climbing the fence and knocking the fence. The average identification rate of 100.0% and 96.9% are achieved for the wind disturbance and human intrusion events, respectively. The recognition processing time can be controlled less than 0.4 s. Thus, the intelligent event recognition scheme can fully satisfy the online monitoring requirements for practical applications.
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Probabilistic Event Discrimination Algorithm for Fiber Optic Perimeter Security Systems
Journal of Lightwave Technology, 2018Co-Authors: Junfeng Jiang, Zhichen Li, Pengcheng LiAbstract:This paper proposes an event discrimination algorithm with probabilistic output for fiber optic Perimeter Security systems. Multiscale permutation entropy and the zero-crossing rate are employed to increase the efficiency of the algorithm and extract intrusion features. A probabilistic support vector machine is used to calculate multiple event probabilities by solving a convex quadratic programming problem. The experimental results demonstrate that the proposed algorithm can distinguish six intrusion events at an average recognition rate of 92.68% and in a processing time of 0.32 s. Compared with traditional discrimination methods, the proposed algorithm obtains more detailed information (probabilities) of intrusion events. The recognition results are obtained after analyzing the probabilities, which not only reduces the decision-making costs but also reduces the losses from erroneous decisions. Therefore, the proposed high-efficiency feature extraction method and reliable discrimination algorithm can be used to improve the monitoring efficiency of fiber optic Perimeter Security systems.
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An Improved Positioning Algorithm in a Long-Range Asymmetric Perimeter Security System
Journal of Lightwave Technology, 2016Co-Authors: Miao Tian, Junfeng Jiang, Zhichen Li, Jianchang An, Tianhua Xu, Tao Wang, Wenjie Zheng, Fan WuAbstract:In this paper, an improved positioning algorithm is proposed for a long-range asymmetric Perimeter Security system. This algorithm employs zero-crossing rate to detect the disturbance starting point, and then utilizes an improved empirical mode decomposition to obtain the effective time-frequency distribution of the extracted signal. In the end, a cross-correlation is used to estimate the time delay of the effective extracted signal. The scheme is also verified and analyzed experimentally. The field test results demonstrate that the proposed scheme can achieve a detection of 96.60% of positioning errors distributed within the range of 0-±20 m at the sensing length of 75 km, which significantly improves the positioning accuracy for the long-range asymmetric fence Perimeter application.
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A High-Efficiency Multiple Events Discrimination Method in Optical Fiber Perimeter Security System
Journal of Lightwave Technology, 2015Co-Authors: Miao Tian, Junfeng Jiang, Zhenyang Ding, Qinnan Chen, Chang He, Haofeng Hu, Xuezhi ZhangAbstract:This paper proposes an integrated scheme to distinguish invasive events in optical fiber dual Mach-Zehnder Interferometry based Perimeter Security system. This algorithm combined empirical mode decomposition, kurtosis characteristics with radial basis function neural network, which can improve the recognition rate of event discrimination and increase the variety of intrusion events. Several experiments demonstrate that the proposed scheme can discriminate four common invasive events (climbing the fence, knocking the cable, cutting the fence, and waggling the fence) with an average recognition rate above 85.75%, which can satisfy actual application requirements.
Suzanne J. Matthews - One of the best experts on this subject based on the ideXlab platform.
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UEMCON - A Raspberry Pi Mesh Sensor Network for Portable Perimeter Security
2019 IEEE 10th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2019Co-Authors: Cullen D. Johnson, Nikhil Shyamkumar, Preston C. Haney, Harry L. Moore, Thomas A. Babbitt, Brian H. Curtin, Rachelle H. David, Emmet D. Dunham, Suzanne J. MatthewsAbstract:Wireless sensor networks play an important role for Perimeter monitoring in remote environments. While commercial wireless sensor networks for providing audio-visual monitoring exist, they are often expensive to deploy. In this paper, we describe and implement a wireless mesh network consisting of inexpensive battery-operated Raspberry Pi nodes. The choice of the Raspberry Pi enables the construction of cost-effective sensor nodes that are extendable and expendable. We conduct a series of test to illustrate the efficacy of our network in a building monitoring use case. Our nodes can be built for as little as 49.00 per node and is capable of node-to-node transmission of up to 50 feet. Custom sleep states enable battery life to last 14 hours on 4 AA batteries. Our results support our thesis that an all-Pi mesh sensor is capable of providing portable Perimeter Security.
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A Raspberry Pi Mesh Sensor Network for Portable Perimeter Security
2019 IEEE 10th Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON), 2019Co-Authors: Cullen Johnson, Brian Curtin, Nikhil Shyamkumar, Rachelle David, Emmet Dunham, Preston C. Haney, Harry L. Moore, Thomas A. Babbitt, Suzanne J. MatthewsAbstract:Wireless sensor networks play an important role for Perimeter monitoring in remote environments. While commercial wireless sensor networks for providing audio-visual monitoring exist, they are often expensive to deploy. In this paper, we describe and implement a wireless mesh network consisting of inexpensive battery-operated Raspberry Pi nodes. The choice of the Raspberry Pi enables the construction of cost-effective sensor nodes that are extendable and expendable. We conduct a series of test to illustrate the efficacy of our network in a building monitoring use case. Our nodes can be built for as little as 49.00 per node and is capable of node-to-node transmission of up to 50 feet. Custom sleep states enable battery life to last 14 hours on 4 AA batteries. Our results support our thesis that an all-Pi mesh sensor is capable of providing portable Perimeter Security.
Xiangdong Huang - One of the best experts on this subject based on the ideXlab platform.
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fully modelling based intrusion discrimination in optical fiber Perimeter Security system
Optical Fiber Technology, 2018Co-Authors: Xiangdong Huang, Haojie ZhangAbstract:Abstract In order to develop an efficient, accurate and richly functional intrusion discrimination scheme in the optical fiber Perimeter Security, this paper proposed a fully modelling based scheme for the DMZI vibration system. In this scheme, data modelling is applied in both the feature extraction stage and the pattern classification stage. By means of incorporating the coefficients of AR modelling into the feature vector, the intrusion characteristics are described in a brief, overall and essential way, which helps to recognize more intrusion types than the existing schemes. Moreover, owing to that the sigmoid modelling is applied in the classifier design, the proposed scheme is endowed with a particular function of estimating occurrence probabilities for all intrusion types. Besides, the adoption of AdaBoostSVM technique further enhances the classification rate. Experimental results showed that, with the above techniques incorporated, our proposed discrimination scheme can identify 6 intrusion types with the average classification rate 87.14 % using only 4-length feature patterns, which presents vast potentials for DMZI applications.
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Hybrid Feature Extraction-Based Intrusion Discrimination in Optical Fiber Perimeter Security System
IEEE Photonics Journal, 2017Co-Authors: Xiangdong Huang, Haojie Zhang, Yuedong WangAbstract:This paper proposes a hybrid feature extraction-based intrusion discrimination scheme for an optical fiber Perimeter Security system, which concurrently possesses high classification rate and high efficiency. The high classification rate lies in two aspects: On one hand, plentiful contents (including bandwidth segmentation in frequency domain, kurtosis in statistics, and the zero-crossing rate in time domain) are incorporated into the proposed hybrid feature vector; on the other hand, a configurable filter bank is adopted to reduce the intercoupling between features in the hybrid vector. The high efficiency also arises for two reasons: For one thing, the configurable filter bank works in a pipeline stream; for another, an efficient support vector machine is employed to classify hybrid vectors. Experiments demonstrated that the proposed scheme can accurately identify four common intrusions (fence climbing, knocking the cable, waggling, and fence cutting) with an average recognition rate higher than 94%. Moreover, the recognition efficiency is also high.
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An Event Recognition Scheme Aiming to Improve both Accuracy and Efficiency in Optical Fiber Perimeter Security System
Journal of Lightwave Technology, 1Co-Authors: Xiangdong Huang, Biyao WangAbstract:Developing an intrusion event identification scheme, which concurrently possesses high recognition accuracy and high efficiency, has been an intractable task in optical fiber Perimeter Security systems. To achieve high recognition accuracy, we apply the all-phase filter (APF) bank in frequency division and choose the envelope fiuctuation parameter to describe the waveform feature of APFs' outputs. To achieve high recognition efficiency, we introduce the random forest classifier to recognize intrusion types, which not only alleviates the negative effect arising from occasionality or randomness of intrusions, but also bypasses tedious computation of existing classifiers applied to optical fiber dual Mach-Zehnder Interferometry based Perimeter Security system. Experimental results demonstrate that the proposed system can distinguish 6 typical patterns (kicking the fence, cuttingthefence,wagglingthefence,knockingthefence,climbing the fence, and no intrusion) with the average recognition rate of 96.92%. Moreover, the consumed training time is reduced to about 40% of the Support Vector Machine. Therefore, the proposed scheme has vast potentials in actual applications.
Zichun Zhou - One of the best experts on this subject based on the ideXlab platform.
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Variational Mode Decomposition-Based Event Recognition in Perimeter Security Monitoring With Fiber Optic Vibration Sensor
IEEE Access, 2019Co-Authors: Junfeng Jiang, Zhongyuan Xu, Zichun ZhouAbstract:Recognition of different kinds of human intrusions from the environmental disturbance with high efficiency is still a challenging task in Perimeter Security monitoring with fiber optic vibration sensor, since the vibration signals induced by these events are highly similar to each other, and it can not be directly discriminated by the signals. In this paper, an intelligent event recognition scheme is proposed to improve its performance in complicated environmental applications. In this event recognition scheme, a variational mode decomposition based kurtosis feature combined with a zero crossing rate feature is used as the input feature vectors. A support vector machine is used to classify the input feature vectors into the corresponding categories. A series of field tests show that the proposed scheme can accurately and rapidly classify wind disturbance and three typical patterns of human intrusions such as waggling the fence, climbing the fence and knocking the fence. The average identification rate of 100.0% and 96.9% are achieved for the wind disturbance and human intrusion events, respectively. The recognition processing time can be controlled less than 0.4 s. Thus, the intelligent event recognition scheme can fully satisfy the online monitoring requirements for practical applications.
Biyao Wang - One of the best experts on this subject based on the ideXlab platform.
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An Event Recognition Scheme Aiming to Improve both Accuracy and Efficiency in Optical Fiber Perimeter Security System
Journal of Lightwave Technology, 1Co-Authors: Xiangdong Huang, Biyao WangAbstract:Developing an intrusion event identification scheme, which concurrently possesses high recognition accuracy and high efficiency, has been an intractable task in optical fiber Perimeter Security systems. To achieve high recognition accuracy, we apply the all-phase filter (APF) bank in frequency division and choose the envelope fiuctuation parameter to describe the waveform feature of APFs' outputs. To achieve high recognition efficiency, we introduce the random forest classifier to recognize intrusion types, which not only alleviates the negative effect arising from occasionality or randomness of intrusions, but also bypasses tedious computation of existing classifiers applied to optical fiber dual Mach-Zehnder Interferometry based Perimeter Security system. Experimental results demonstrate that the proposed system can distinguish 6 typical patterns (kicking the fence, cuttingthefence,wagglingthefence,knockingthefence,climbing the fence, and no intrusion) with the average recognition rate of 96.92%. Moreover, the consumed training time is reduced to about 40% of the Support Vector Machine. Therefore, the proposed scheme has vast potentials in actual applications.