Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward...Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward the border. Based on the property, an audio steganalysis of DSSS based on statistical moments of histogram is proposed. The statistical moments of the histogram in DCT domain and its frequency domain and the statistical moments of the histogram of the wavelet coefficients of every level in frequency domain are calculated as the features of classification. Support Vector Machine (SVM) is exploited as the classifier. Experimental results show that the proposed technique is effective on the DSSS embedding in DCT domain using different embedding length, and the average detection rate is 91.75%.展开更多
This study aims to establish a rationale for the Rice University rule in determining the number of bins in a histogram. It is grounded in the Scott and Freedman-Diaconis rules. Additionally, the accuracy of the empiri...This study aims to establish a rationale for the Rice University rule in determining the number of bins in a histogram. It is grounded in the Scott and Freedman-Diaconis rules. Additionally, the accuracy of the empirical histogram in reproducing the shape of the distribution is assessed with respect to three factors: the rule for determining the number of bins (square root, Sturges, Doane, Scott, Freedman-Diaconis, and Rice University), sample size, and distribution type. Three measures are utilized: the average distance between empirical and theoretical histograms, the level of recognition by an expert judge, and the accuracy index, which is composed of the two aforementioned measures. Mean comparisons are conducted with aligned rank transformation analysis of variance for three fixed-effects factors: sample size (20, 35, 50, 100, 200, 500, and 1000), distribution type (10 types), and empirical rule to determine the number of bins (6 rules). From the accuracy index, Rice’s rule improves with increasing sample size and is independent of distribution type. It outperforms the Friedman-Diaconis rule but falls short of Scott’s rule, except with the arcsine distribution. Its profile of means resembles the square root rule concerning distributions and Doane’s rule concerning sample sizes. These profiles differ from those of the Scott and Friedman-Diaconis rules, which resemble each other. Among the seven rules, Scott’s rule stands out in terms of accuracy, except for the arcsine distribution, and the square root rule is the least accurate.展开更多
Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algori...Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algorithm,the Kalman filter(KF),which is only suitable for linear problems,is replaced by the extended Kalman filter(EKF),which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient(HOG)of the target.The multi-target tracking framework was constructed with YOLO V5 target detection algorithm.An efficient and longrunning Traffic Flow Statistical framework(TFSF)is established based on the tracking framework.Virtual lines are set up to record the movement direction of vehicles to more accurate and detailed statistics of traffic flow.In order to verify the robustness and accuracy of the traffic flow statistical framework,the traffic flow in different scenes of actual road conditions was collected for verification.The experimental validation shows that the accuracy of the traffic statistics framework reaches more than 93%,and the running speed under the detection data set in this paper is 32.7FPS,which can meet the real-time requirements and has a particular significance for the development of intelligent transportation.展开更多
基金Supported by the National Natural Science Foundation of China (No.60772032)
文摘Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward the border. Based on the property, an audio steganalysis of DSSS based on statistical moments of histogram is proposed. The statistical moments of the histogram in DCT domain and its frequency domain and the statistical moments of the histogram of the wavelet coefficients of every level in frequency domain are calculated as the features of classification. Support Vector Machine (SVM) is exploited as the classifier. Experimental results show that the proposed technique is effective on the DSSS embedding in DCT domain using different embedding length, and the average detection rate is 91.75%.
文摘This study aims to establish a rationale for the Rice University rule in determining the number of bins in a histogram. It is grounded in the Scott and Freedman-Diaconis rules. Additionally, the accuracy of the empirical histogram in reproducing the shape of the distribution is assessed with respect to three factors: the rule for determining the number of bins (square root, Sturges, Doane, Scott, Freedman-Diaconis, and Rice University), sample size, and distribution type. Three measures are utilized: the average distance between empirical and theoretical histograms, the level of recognition by an expert judge, and the accuracy index, which is composed of the two aforementioned measures. Mean comparisons are conducted with aligned rank transformation analysis of variance for three fixed-effects factors: sample size (20, 35, 50, 100, 200, 500, and 1000), distribution type (10 types), and empirical rule to determine the number of bins (6 rules). From the accuracy index, Rice’s rule improves with increasing sample size and is independent of distribution type. It outperforms the Friedman-Diaconis rule but falls short of Scott’s rule, except with the arcsine distribution. Its profile of means resembles the square root rule concerning distributions and Doane’s rule concerning sample sizes. These profiles differ from those of the Scott and Friedman-Diaconis rules, which resemble each other. Among the seven rules, Scott’s rule stands out in terms of accuracy, except for the arcsine distribution, and the square root rule is the least accurate.
基金This work is supported by the Qingdao People’s Livelihood Science and Technology Plan(Grant 19-6-1-88-nsh).
文摘Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algorithm,the Kalman filter(KF),which is only suitable for linear problems,is replaced by the extended Kalman filter(EKF),which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient(HOG)of the target.The multi-target tracking framework was constructed with YOLO V5 target detection algorithm.An efficient and longrunning Traffic Flow Statistical framework(TFSF)is established based on the tracking framework.Virtual lines are set up to record the movement direction of vehicles to more accurate and detailed statistics of traffic flow.In order to verify the robustness and accuracy of the traffic flow statistical framework,the traffic flow in different scenes of actual road conditions was collected for verification.The experimental validation shows that the accuracy of the traffic statistics framework reaches more than 93%,and the running speed under the detection data set in this paper is 32.7FPS,which can meet the real-time requirements and has a particular significance for the development of intelligent transportation.
