User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection
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In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.Keywords:
False alarm
Feature (linguistics)
Mode (computer interface)
Urban rail
Urban rail transit
SIGNAL (programming language)
Instantaneous phase
Passenger train
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Wireless sensor networks are expected to enhance the efficiency and to reduce the cost of target detection in area surveillance systems. In order to provide accurate reports for target detection and tracking in realistic environments, not only false alarms but also the impact of weather, terrain, and ground conditions on sensor readings should be taken into account. This paper addresses how to determine a false alarm threshold dynamically and efficiently in order to minimize the false alarm probability and to maximize the probability that no target passes through without being detected. In the proposed dynamic threshold scheme, the threshold changes in accordance with the false alarm rate. This results in a better detection probability and reduces the number of false alarms. The paper proposes to reduce the impact of noise by taking a weighted average of different sensing units' readings for the same target. The fact that sensing units of different types are affected at varying degrees by the environmental factors is exploited here. In addition to analytically characterizing false alarm rate and the role of reputation values, we provide simulation results to show the improvement on the target detection accuracy by the proposed scheme. A real world target detection case is considered and the false alarm probability is reduced by 25% when compared to a single sensor reading and by 17% when compared to an non-weighted averaged reading.
False alarm
False positive rate
Statistical power
Detection theory
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Hilbert spectral analysis
Instantaneous phase
S transform
SIGNAL (programming language)
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False alarm
False positive rate
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Separation or classification of signal-present samples from noise-only samples is studied. The false-alarm probability implies how many noise-only samples are wrongly classified as outliers, and typically it should be smaller than some upper limit. The noise distribution parameters are not known a priori and have to be estimated. Multiple outliers have a strong influence to that estimation and may lead to uncontrollable false-alarm probability. The false-alarm probability control can be improved by robust estimators and/or by forward-detection methods. In this article, the false-alarm probability of the forward methods is analyzed. The forward consecutive mean excision (FCME) algorithm is enhanced to allow better false-alarm control. It is proposed that the forward method using the cell-averaging (CA) constant false-alarm rate (CFAR) technique can be applied for locating the outliers. The results show that its false-alarm probability stays close to the required value even in the presence of multiple outliers.
False alarm
Statistical power
Truncated mean
Robust Statistics
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CFAR (Constant False Alarm Rate) is a key technology in Infrared dim-small target detection system. Because the traditional constant false alarm rate detection algorithm gets the probability density distribution which is based on the pixel information of each area in the whole image and calculates the target segmentation threshold of each area by formula of Constant false alarm rate, the problems including the difficulty of probability distribution statistics and large amount of algorithm calculation and long delay time are existing. In order to solve the above problems effectively, a formula of Constant false alarm rate based on target coordinates distribution is presented. Firstly, this paper proposes a new formula of Constant false alarm rate by improving the traditional formula of Constant false alarm rate based on the single grayscale distribution which objective statistical distribution features are introduced. So the control of false alarm according to the target distribution information is implemented more accurately and the problem of high false alarm that is caused of the complex background in local area as the cloud reflection and the ground clutter interference is solved. At the same time, in order to reduce the amount of algorithm calculation and improve the real-time characteristics of algorithm, this paper divides the constant false-alarm statistical area through two-dimensional probability density distribution of target number adaptively which is different from the general identifying methods of constant false-alarm statistical area. Finally, the target segmentation threshold of next frame is calculated by iteration based on the function of target distribution probability density in image sequence which can achieve the purpose of controlling the false alarm until the false alarm is down to the upper limit. The experiment results show that the proposed method can significantly improve the operation time and meet the real-time requirements on condition of keeping the target detection performance.
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Constant (computer programming)
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Mine and unexploded ordnance (UXO) detection systems must function in highly cluttered environments. Clutter leads to false alarms thereby hindering the detection and identification of targets of interest. Since the end user of the mine or UXO detection technology requires both a high detection rate and a low number of false alarms, technology demonstrations and system evaluations are designed to test these measures of performance. Detection rate and false- alarm rate are highly interdependent and must always be evaluated together. The relationship between the two rates directly affects the overall performance of the sensor in the field. Poor performance of a system in either detection rate or false-alarm rate causes a substantial increase in risk of undetected and thus unmarked or unremediated mines or UXO. A system with a low detection rate will leave many mines or UXO undetected. Performance can be traded between probability of detection and false-alarm rate by changing the system threshold. Raising or lowering the threshold will cause both the detections and false alarms to decrease or increase together. A system with a high false-alarm rate result in an increase in the time required to investigate potential targets. Therefore the rate of advance and rate of clearance decrease. With limited clearance resources, site coverage may become too time consuming or costly for operationally effective clearance, resulting in risk from undetected mines and UXO in areas that have not been searched. An assessment of the connection between detection rate nd false-alarm is presented. This relationship is discussed in the context of several government-sponsored in- field technology demonstrations of prototype and commercially available mine and UXO detection technologies, as well as real clearance operations. Implications of the results of these tests and the measures of performance are discussed in the context of real-world operations, including scenarios for clearance of miens in Bosnia and of UXO at DoD sites.
Unexploded ordnance
False alarm
False positive rate
Detection threshold
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Hilbert spectral analysis
Mode (computer interface)
SIGNAL (programming language)
Instantaneous phase
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Instantaneous phase
Mode (computer interface)
SIGNAL (programming language)
Hilbert spectral analysis
Seismic Noise
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Summary Frame detection is an important step in satellite‐based automatic identification system for its contributions in verifying the presence of automatic identification system signal before frame synchronization. In this paper, a constant false alarm rate frame detector is proposed, which exploits the feature implied in the training sequence, to realize frame detection in the presence of additive white Gaussian noise and frequency offset. False alarm probability is related with a threshold, which is independent of the signal and noise. For fixed false alarm probability, the relationship between detection performance and E b / N 0 is analyzed. Simulations prove that the proposed detector outperforms the detector based on cyclic autocorrelation when message collisions exist. Copyright © 2016 John Wiley & Sons, Ltd.
False alarm
Detection theory
Frame synchronization
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