A frequent problem of traffic flow characteristics acquisition is data loss, which leads to uneven time series analysis. An effective approach to uneven data analysis is the spectral analysis, which requires obtaining process with a constant sampling interval, for example, by restoring missing data, which leads to the appearance of dating error. Thus, the main purpose of this study is to develop a method and software for wavelet analysis of traffic flow characteristics without restoring the missing data.
To analyze and interpret non-stationary uneven time series obtained from traffic monitoring systems, we propose the wavelet transformation method with adjustment of the sampling intervals, which results in a time-frequency domain with a constant sampling interval. Wavelet analysis is applied to the macroscopic traffic flow characteristics.
We developed the software for traffic flow wavelet analysis on the "ITSGIS" intelligent transport geo-information framework using the attribute-oriented approach.
Wavelet analysis of traffic flows characteristics using Morlet wavelets was accomplished for data analysis of the city of Aarhus, Denmark. Wavelet spectra and scalograms were constructed and analyzed, general dependencies in the frequency distribution of extremes, and differences in spectral power were revealed.
The developed software is being experimentally tested in solving practical problems of municipalities and road agencies in Russia.
The current state of research in the field of computerized electrophysiological gastrointestinal tract (GIT) diagnostics methods in terms of cooperation of medical specialists and IT-engineers is analyzed in the paper. Hardware, software and methodology of lectrogastroenterography (EGEG) are reviewed. The features of the wavelet transform (WT) in the processing of non-stationary signals of EGEG are viewed. The research infrastructure built in recent years in the North-West region of Russia is presented. Prospects are offered: using telemedicine technologies in EGEG and development of open Internet-platform for accumulation and sharing experience between the researchers in this field.
One of the approaches to the detection of network anomalies is the analyses of parameters of functioning of a network. Characteristics, calculated on a wavelet coefficients, indeed, are more sensitive to changes in the number, than the characteristics calculated directly in a row, but this requires more calculations, the spectral-time algorithms, of course, subject to optimize for application in real-time systems. In addition, there are different approaches to the implementation of wavelet expansions, each of which has its place on the informative value (the number of qualifying ratios), the authentic values, the computational complexity of the transformations. The article offers a reasonable approach to the implementation of these algorithms for use in real-time anomaly detection systems.
Machine learning and digital signal processing methods are used in various industries, including in the analysis and classification of seismic signals from surface sources. The developed wave type analysis algorithm makes it possible to automatically identify and, accordingly, separate incoming seismic waves based on their characteristics. To distinguish the types of waves, a seismic measuring complex is used that determines the characteristics of the boundary waves of surface sources using special molecular electronic sensors of angular and linear oscillations. The results of the algorithm for processing data obtained by the method of seismic observations using spectral analysis based on the Morlet wavelet are presented. The paper also describes an algorithm for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves, considers the use of statistical characteristics and MFCC (Mel-frequency cepstral coefficients) parameters, as well as their joint application. At the same time, the following were used as statistical characteristics of the signal: variance, kurtosis coefficient, entropy and average value, and gradient boosting was chosen as a machine learning method; a machine learning method based on gradient boosting using statistical and MFCC parameters was used as a method for determining the distance to the signal source. The training was conducted on test data based on the selected special parameters of signals from sources of seismic excitation of surface waves. From a practical point of view, new methods of seismic observations and analysis of boundary waves make it possible to solve the problem of ensuring a dense arrangement of sensors in hard-to-reach places, eliminate the lack of knowledge in algorithms for processing data from seismic sensors of angular movements, classify and systematize sources, improve prediction accuracy, implement algorithms for locating and tracking sources. The aim of the work was to create algorithms for processing seismic data for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves.
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