Modeling the Algorithm of Cumulative Sums in the Applied Problems of Detecting the Signals with Random Time of Occurrence in non-Gaussian Noise

Artyushenko, V. M.; Volovach, V. I.

doi:10.1109/ieeeconf51389.2021.9416051

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…To calculate the number of maximum probable intervals within which an event is likely to occur requires first defining the method of defining a threshold. e proposed model uses a Cumulative Sum Algorithm (CuSum) [48,49] which is because the magnitude σ t � σ(y 1 , y 2 , . .…”

Section: Methodsmentioning

confidence: 99%

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An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

Zhao

Guiqiu

et al. 2021

Security and Communication Networks

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Data mining in real-time data streams is associated with multiple types of uncertainty, which often leads the respective categorizers to make erroneous predictions related to the presence or absence of complex events. But recognizing complex abnormal events, even those that occur in extremely rare cases, offers significant support to decision-making systems. Therefore, there is a need for robust recognition mechanisms that will be able to predict or recognize when an abnormal event occurs or will occur on a data stream. Considering this need, this paper presents an Intuitionistic Tumbling Windows event calculus (ITWec) methodology. It is an innovative data analysis system that combines for the first time in the literature a set of multiple systems for Complex Abnormal Event Recognition (CAER). In the proposed system, the probabilities of the existence of a high-level complex abnormal event for each period are initially calculated nonparametrically, based on the probabilities of the low-level events associated with it. Because cumulative results are sought in consecutive, nonoverlapping sections of the data stream, the method uses the clearly defined rules of initialization and termination of the tumbling windows method, where there is an explicit determination of the time interval within which several blocks of a particular stream are investigated window. Finally, the number of maximum probable intervals in which an event is likely to occur based on a certain probability threshold is calculated, based on a parametric representation of intuitively fuzzy sets.

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Section: Methodsmentioning

confidence: 99%

“…e threshold value k provides a balance between the mean detection delay time and the mean time between false detections. e change detection functions used to detect positive and negative deviations are defined as follows [48,49]:…”

Section: Methodsmentioning

confidence: 99%

An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

Zhao

Guiqiu

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Second-order cyclostationarity, or wide-sense cyclostationarity, appears to be the intrinsic property exhibited by processes generating signals belonging to different classes. Thus, there are plenty of examples where it was reasonably taken into account in many research fields and engineering applications, including communication signals with various modulation schemes [ 6 ], radio signals used in passive radars [ 7 , 8 ], electromagnetic measurements with near-field probes [ 9 , 10 ], spectral sensing in cognitive radio [ 11 ], mechanical vibration of rotary machines [ 12 , 13 ], radio astronomy [ 14 , 15 ], power analysis of electric circuits [ 16 ], generalized detection [ 17 , 18 ] under conditions of the least certainty, electromagnetic compatibility [ 19 , 20 ], detection of vital signs in radar response [ 21 , 22 ], drone navigation signals [ 23 ] and others.…”

Section: Introductionmentioning

confidence: 99%

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

Shevgunov

Efimov

Guschina

2022

Sensors

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This article addresses the problem of estimating the spectral correlation function (SCF), which provides quantitative characterization in the frequency domain of wide-sense cyclostationary properties of random processes which are considered to be the theoretical models of observed time series or discrete-time signals. The theoretical framework behind the SCF estimation is briefly reviewed so that an important difference between the width of the resolution cell in bifrequency plane and the step between the centers of neighboring cells is highlighted. The outline of the proposed double-number fast Fourier transform algorithm (2N-FFT) is described in the paper as a sequence of steps directly leading to a digital signal processing technique. The 2N-FFT algorithm is derived from the time-smoothing approach to cyclic periodogram estimation where the spectral interpolation based on doubling the FFT base is employed. This guarantees that no cyclic frequency is left out of the coverage grid so that at least one resolution element intersects it. A numerical simulation involving two processes, a harmonic amplitude modulated by stationary noise and a binary-pulse amplitude-modulated train, demonstrated that their cyclic frequencies are estimated with a high accuracy, reaching the size of step between resolution cells. In addition, the SCF components estimated by the proposed algorithm are shown to be similar to the curves provided by the theoretical models of the observed processes. The comparison between the proposed algorithm and the well-known FFT accumulation method in terms of computational complexity and required memory size reveals the cases where the 2N-FFT algorithm offers a reasonable trade-off.

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Spectral Correlation Density Estimation Using 2N-FFT Interpolation

Shevgunov¹

2021

2021 International Conference on Engineering Management of Communication and Technology (EMCTECH)

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Modeling the Algorithm of Cumulative Sums in the Applied Problems of Detecting the Signals with Random Time of Occurrence in non-Gaussian Noise

Cited by 4 publications

References 5 publications

An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

Spectral Correlation Density Estimation Using 2N-FFT Interpolation

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