Speech-stream detection in short-wave channel based on empirical mode decomposition and higher-order statistics

To capture the presence of speech embedded in nonspeech events and background noise in shortwave non-cooperative communication, an algorithm for speech-stream detection in noisy environments is presented based on Empirical Mode Decomposition （EMD） and statistical properties of higher-order cumulants of speech signals. With the EMD, the noise signals can be decomposed into different numbers of IMFs. Then, the fourth-order cumulant （ FOC ） can be used to extract the desired feature of statistical properties for IMF components. Since the higher-order eumulants are blind for Gaussian signals, the proposed method is especially effective regarding the problem of speech-stream detection, where the speech signal is distorted by Gaussian noise. With the self-adaptive decomposition by EMD, the proposed method can also work well for non-Gaussian noise. The experiments show that the proposed algorithm can suppress different noise types with different SNRs, and the algorithm is robust in real signal tests.

Higher-Order Statistics and Nonlinear Processes in Space Plasmas

Statistics of order 2 (variance, auto and cross-correlation functions, auto and cross-power spectra) and 3 (skewness, auto and cross-bicorrelation functions, auto and cross-bispectra) are used to analyze the wave-particle interaction in space plasmas. The signals considered here are medium scale electron density irregularities and ELF/ULF electrostatic turbulence. Nonlinearities are mainly observed in the ELF range. They are independently pointed out in time series associated with fluctuations in electronic density and in time series associated with the measurement of one electric field component. Peaks in cross-bicorrelation function and in mutual information clearly show that, in well delimited frequency bands, the wave-particle interactions are nonlinear above a certain level of fluctuations. The way the energy is transferred within the frequencies of density fluctuations is indicated by a bi-spectra analysis.

Higher-Order Statistics for Automatic Weld Defect Detection

Image processing and image analysis are the main aspects for obtaining information from digital image owing to the fact that this techniques give the desired details in most of the applications generally and Non-Destructive testing specifically. This paper presents a proposed method for the automatic detection of weld defects in radiographic images. Firstly, the radiographic images were enhanced using adaptive histogram equalization and are filtered using mean and wiener filters. Secondly, the welding area is selected from the radiography image. Thirdly, the Cepstral features are extracted from the Higher-Order Spectra (Bispectrum and Trispectrum). Finally, neural networks are used for feature matching. The proposed method is tested using 100 radiographic images in the presence of noise and image blurring. Results show that in spite of time consumption, the proposed method yields best results for the automatic detection of weld defects in radiography images when the features were extracted from the Trispectrum of the image.

Identification of the Wave-Particles Interaction in Space Plasma Using Higher-Order Statistics

The interaction of wave-particles and wave-wave in the space plasmas are essentially non-linear or non-Gaussian processes. Using the higher-order statistical analyses methods (higher-order moments and bi-tri correlation or bi-tri spectrum), its physical properties can be described. The question addressed in this paper is that of the usefulness of higher-order statistical analysis for identification of the wave-particles interaction in space plasmas. The signals handled are from the ARCAD-3 ISOPROBE experiment on ELF frequency range, then strong electrostatic turbulence and electron density irregularities. Second and third order statistical analyses are applied: first, on time series associated with each type of measurement, then, on the two types. All results are presented for one typical case. Correlation functions estimated over the corresponding time intervals point out the existence of a, non-linear interaction between these fluctuations and electrostatic filed.

Pipeline thickness estimation using the dispersion of higher-order SH guided waves

Thickness measurement plays an important role in the monitoring of pipeline corrosion damage. However, the requirement for prior knowledge of the shear wave velocity in the pipeline material for popular ultrasonic thickness measurement limits its widespread application. This paper proposes a method that utilizes cylindrical shear horizontal(SH) guided waves to estimate pipeline thickness without prior knowledge of shear wave velocity. The inversion formulas are derived from the dispersion of higher-order modes with the high-frequency approximation. The waveform of the example problems is simulated using the real-axis integral method. The data points on the dispersion curves are processed in the frequency domain using the wave-number method. These extracted data are then substituted into the derived formulas. The results verify that employing higher-order SH guided waves for the evaluation of thickness and shear wave velocity yields less than1% error. This method can be applied to both metallic and non-metallic pipelines, thus opening new possibilities for health monitoring of pipeline structures.

Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

The high penetration of distributed generation(DG)has set up a challenge for energy management and consequently for the monitoring and assessment of power quality(PQ).Besides,there are new types of disturbances owing to the uncontrolled connections of non-linear loads.The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems.Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions.In this context,we propose a measurement method that postulates the use of two-dimensional(2D)diagrams based on higher-order statistics(HOSs)and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign.Being suitable for both PQ and reliability applications,the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform,extracting the individual customers’pattern fingerprint,and compressing the data from both time and spatial aspects.The method allows a continuous and robust performance needed in the SG framework.Consequently,the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.

Photoinduced Floquet higher-order Weyl semimetal in C_(6) symmetric Dirac semimetals

Topological Dirac semimetals are a parent state from which other exotic topological phases of matter, such as Weyl semimetals and topological insulators, can emerge. In this study, we investigate a Dirac semimetal possessing sixfold rotational symmetry and hosting higher-order topological hinge Fermi arc states, which is irradiated by circularly polarized light. Our findings reveal that circularly polarized light splits each Dirac node into a pair of Weyl nodes due to the breaking of time-reversal symmetry, resulting in the realization of the Weyl semimetal phase. This Weyl semimetal phase exhibits rich boundary states, including two-dimensional surface Fermi arc states and hinge Fermi arc states confined to six hinges.Furthermore, by adjusting the incident direction of the circularly polarized light, we can control the degree of tilt of the resulting Weyl cones, enabling the realization of different types of Weyl semimetals.

Lottery Numbers and Ordered Statistics

The lottery has long captivated the imagination of players worldwide, offering the tantalizing possibility of life-changing wins. While winning the lottery is largely a matter of chance, as lottery drawings are typically random and unpredictable. Some people use the lottery terminal randomly generates numbers for them, some players choose numbers that hold personal significance to them, such as birthdays, anniversaries, or other important dates, some enthusiasts have turned to statistical analysis as a means to analyze past winning numbers identify patterns or frequencies. In this paper, we use order statistics to estimate the probability of specific order of numbers or number combinations being drawn in future drawings.

Higher-order expansions of powered extremes of logarithmic general error distribution

In this paper,Let M_(n)denote the maximum of logarithmic general error distribution with parameter v≥1.Higher-order expansions for distributions of powered extremes M_(n)^(p)are derived under an optimal choice of normalizing constants.It is shown that M_(n)^(p),when v=1,converges to the Frechet extreme value distribution at the rate of 1/n,and if v>1 then M_(n)^(p)converges to the Gumbel extreme value distribution at the rate of(loglogn)^(2)=(log n)^(1-1/v).

Opinion consensus incorporating higher-order interactions in individual-collective networks

In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.

Time-Domain Higher-Order Boundary Element Method for Simulating High Forward-Speed Ship Motions in Waves

The hydrodynamic performance of a high forward-speed ship in obliquely propagating waves is numerically examined to assess both free motions and wave field in comparison with a low forward-speed ship.This numerical model is based on the time-domain potential flow theory and higher-order boundary element method,where an analytical expression is completely expanded to determine the base-unsteady coupling flow imposed on the moving condition of the ship.The ship in the numerical model may possess different advancing speeds,i.e.stationary,low speed,and high speed.The role of the water depth,wave height,wave period,and incident wave angle is analyzed by means of the accurate numerical model.It is found that the resonant motions of the high forward-speed ship are triggered by comparison with the stationary one.More specifically,a higher forward speed generates a V-shaped wave region with a larger elevation,which induces stronger resonant motions corresponding to larger wave periods.The shoaling effect is adverse to the motion of the low-speed ship,but is beneficial to the resonant motion of the high-speed ship.When waves obliquely propagate toward the ship,the V-shaped wave region would be broken due to the coupling effect between roll and pitch motions.It is also demonstrated that the maximum heave motion occurs in beam seas for stationary cases but occurs in head waves for high speeds.However,the variation of the pitch motion with period is hardly affected by wave incident angles.

Effect Evaluation of CBL Combined with Rain Classroom Teaching Method in Medical Statistics

Objective: To explore the application effect of CBL combined with rain classroom teaching method in medical statistics courses. Methods: The undergraduate students of medical imaging technology in 2019 and 2020 in a university were selected as the research objects. A cluster sampling method was used to select 79 undergraduate students from 2019 in the control group and 75 undergraduate students from 2020 in the experimental group. Traditional teaching method and CBL combined with rain classroom teaching method was used in the control group and experimental group respectively. The final examination scores of the two groups were compared. In experimental group, the correlation between the average score in the rain classroom and the final examination score was tested, and the teaching effect was evaluated. Results: The average score of final examination in experimental group and control group was 79.13 ± 10.32 points and 71.54 ± 14.752 points, respectively, which had a statistically significant difference (Z = 2.586, P = 0.012);the final examination scores of the students in the experimental group were positively correlated with the average scores of the rain classroom (r = 0.372, P = 0.001), and the proportion of satisfaction in the experimental group was 94.7%. Conclusion: The CBL combined with rain classroom teaching method can improve the teaching effectiveness of medical statistics courses.

A Study of a Moral Dilemma Stories Teaching Model Focusing on the Development of Higher-Order Thinking --"Synthetic polymers" as an example

Cultivating students'higher-order thinking is one of the important goals of modern education,and innovative teaching model is an effective way to achieve this goal.Aiming at the inadequacy of the existing moral dilemma stories approach in the transformation of knowledge and behavior,this research constructs a new Project Based Learning-Ethical Dilemma Stories(PBL-EDS)Teaching Model applicable to China's secondary education stage based on the innovative features of the moral dilemma stories approach on the core competencies,taking the chemistry subject as an example to carry out practice,and puts forward suggestions for the implementation of the teaching model.Chemistry as an example to carry out the practice,and suggestions are made for the implementation of the teaching model.

Innovations and Challenges in Economic Census Methodology:A Study by the Bureau of Statistics

The Bureau of Statistics has demonstrated a forward-looking strategic approach in its economic census.By leveraging dual innovations in technology and management,and incorporating modern technologies such as big data,cloud computing,and the Internet of Things,it has deepened the reform of the census methodology.Additionally,the Bureau has built a multi-dimensional collaborative network that enhances international cooperation,departmental coordination,and public participation.This approach not only addresses the limitations of traditional statistical methods in a complex economic environment but also improves data quality and census efficiency,providing an accurate and reliable foundation for national economic decision-making.

Research on the Application of PBL+SPOC Blended Teaching Model in Probability and Statistics Course

To cultivate talents with an exploratory spirit and practical skills in the era of information technology,it is imperative to reform teaching methods and approaches.In the teaching process of the Probability and Statistics course,an application-oriented blended teaching model combining problem-based learning and small private online course was explored.By organizing and implementing online and offline teaching activities based on problem-based learning,a multidimensional process-oriented learning assessment system was established.Practice has shown that this model can effectively enhance classroom teaching effectiveness,benefiting the improvement of students’overall skills and mathematical literacy.

Integration of Ideological and Political Education into the Probability Theory and Mathematical Statistics Course:A Teaching Design Based on Estimation and Hypothesis Testing

With the rapid development of higher education in China,colleges and universities are facing new challenges and impacts in talent training.Probability Theory and Mathematical Statistics is one of the important courses in higher education for science and engineering majors and economics and management majors.Its critical role in cultivating students’thinking skills and improving their problem-solving skills is self-evident.Course ideological and political education construction is an important link in college talent training work.Combining ideological and political education with course teaching can help students establish correct value concepts and play a certain role in improving their comprehensive ability and quality.At present,the construction of ideological and political education in the Probability Theory and Mathematical Statistics course still faces some problems,mainly manifested in the lack of attention paid by teachers to course ideological and political education,insufficient exploitation of ideological and political elements,and the simplification of ideological and political education implementation methods.In order to comprehensively deepen the construction of course ideological and political education in line with the actual needs of Probability Theory and Mathematical Statistics course teaching,we should strengthen the construction of teacher teams,improve teachers’ability to carry out course ideological and political education,integrate educational resources,develop educational resources for ideological and political education,and innovate teaching methods to improve the overall effect of ideological and political education integration.

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.

留学生Medical Statistics线上课程建设与远程教学的实践与思考

受政治、经济和全球健康等因素影响,远程教育成为高等教育的一个发展趋势。新冠疫情防控期间,受出入境限制,未能返华的留学生一直以远程教学推进学业,成为其间持续进行远程教学最久的群体。本研究总结临床专业本科留学生主干课程Medical Statistics的校级一流线上课程建设与教学实践,并比较了疫情前后,线下教学与远程教学的留学生期末考试成绩,发现远程教学成绩经历波动后逐渐稳定并接近传统线下教学。本文同时对效果影响因素进行了调研和分析,发现“充分学习和使用远程课程中丰富的资源”和“上网是否容易”是留学生学习效果的主要影响因素。提出建设丰富教学资源对促进医学统计学远程学习效果的重要影响,同时提出对策建议,以期进一步提高远程教学水平,服务高校现代化课程体系建设。

Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method.The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline.Due to the differences in satellite sensors when producing images,subtle but inherent stripes can appear at the stitching positions between the sensors.These stitchingstripes cannot be eliminated by conventional relative radiometric calibration.The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation,classification and interpretation of remote sensing images.Therefore,a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper.First,the inconsistency in grayscales around stripes is eliminated with the statistical method.Second,the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality.Finally,the details of the images are highlighted by a new image enhancement method,which makes the whole image clearer.Comprehensive experiments are performed,and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

The asymptotic relation between the first crossing point and the last exit time of Gaussian order statistics sequences

In this paper,we study the asymptotic relation between the first crossing point and the last exit time for Gaussian order statistics which are generated by stationary weakly and strongly dependent Gaussian sequences.It is shown that the first crossing point and the last exit time are asymptotically independent and dependent for weakly and strongly dependent cases,respectively.The asymptotic relations between the first crossing point and the last exit time for stationary weakly and strongly dependent Gaussian sequences are also obtained.