{"title":"二维随机信号能量特性的采样技术","authors":"V. Syuzev, A. V. Proletarsky, D. Mikov, I. Deykin","doi":"10.18287/2412-6179-co-1074","DOIUrl":null,"url":null,"abstract":"The article is devoted to methods of discretization of energy characteristics of two-dimensional random signals when simulating random signals using the original harmonic method, which is a generalization of the well-known algorithm proposed by V. S. Pugachev for the two-dimensional case. Requirements imposed on the sampling method are aimed at reducing the computational complexity of the simulation method and increasing its flexibility thanks to removing restrictions on the form of autocorrelation functions and spectral energy density functions. The use of the simulation error as a criterion for quality assessment is proposed. The discretization method is considered for signals given both on unlimited definition intervals and on limited ones. The article demonstrates results of the software system implementation in which the original simulation method is realized using the described sampling methods in both cases. The proposed technique is shown to be robust and efficient, with the results obtained being of independent scientific and technical value and showing promise for developing new effective spectral techniques of simulating signals for the use in intelligent decision support systems.","PeriodicalId":46692,"journal":{"name":"Computer Optics","volume":"27 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Techniques of sampling the energy characteristics of two-dimensional random signals\",\"authors\":\"V. Syuzev, A. V. Proletarsky, D. Mikov, I. Deykin\",\"doi\":\"10.18287/2412-6179-co-1074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article is devoted to methods of discretization of energy characteristics of two-dimensional random signals when simulating random signals using the original harmonic method, which is a generalization of the well-known algorithm proposed by V. S. Pugachev for the two-dimensional case. Requirements imposed on the sampling method are aimed at reducing the computational complexity of the simulation method and increasing its flexibility thanks to removing restrictions on the form of autocorrelation functions and spectral energy density functions. The use of the simulation error as a criterion for quality assessment is proposed. The discretization method is considered for signals given both on unlimited definition intervals and on limited ones. The article demonstrates results of the software system implementation in which the original simulation method is realized using the described sampling methods in both cases. The proposed technique is shown to be robust and efficient, with the results obtained being of independent scientific and technical value and showing promise for developing new effective spectral techniques of simulating signals for the use in intelligent decision support systems.\",\"PeriodicalId\":46692,\"journal\":{\"name\":\"Computer Optics\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Optics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18287/2412-6179-co-1074\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18287/2412-6179-co-1074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 1
摘要
本文研究了利用普加乔夫(V. S. Pugachev)在二维情况下提出的著名算法的推广——原始谐波法(original harmonic method)对二维随机信号进行模拟时能量特性的离散化方法。对采样方法提出的要求是为了降低模拟方法的计算复杂度,并通过消除对自相关函数和谱能量密度函数形式的限制来增加其灵活性。提出了将仿真误差作为质量评价标准的方法。考虑了无限定义区间和有限定义区间信号的离散化方法。本文演示了软件系统实现的结果,在这两种情况下,使用所描述的采样方法实现了原始的仿真方法。结果表明,该方法鲁棒性好、效率高,具有独立的科学技术价值,为智能决策支持系统开发新的有效的信号模拟频谱技术提供了前景。
Techniques of sampling the energy characteristics of two-dimensional random signals
The article is devoted to methods of discretization of energy characteristics of two-dimensional random signals when simulating random signals using the original harmonic method, which is a generalization of the well-known algorithm proposed by V. S. Pugachev for the two-dimensional case. Requirements imposed on the sampling method are aimed at reducing the computational complexity of the simulation method and increasing its flexibility thanks to removing restrictions on the form of autocorrelation functions and spectral energy density functions. The use of the simulation error as a criterion for quality assessment is proposed. The discretization method is considered for signals given both on unlimited definition intervals and on limited ones. The article demonstrates results of the software system implementation in which the original simulation method is realized using the described sampling methods in both cases. The proposed technique is shown to be robust and efficient, with the results obtained being of independent scientific and technical value and showing promise for developing new effective spectral techniques of simulating signals for the use in intelligent decision support systems.
期刊介绍:
The journal is intended for researchers and specialists active in the following research areas: Diffractive Optics; Information Optical Technology; Nanophotonics and Optics of Nanostructures; Image Analysis & Understanding; Information Coding & Security; Earth Remote Sensing Technologies; Hyperspectral Data Analysis; Numerical Methods for Optics and Image Processing; Intelligent Video Analysis. The journal "Computer Optics" has been published since 1987. Published 6 issues per year.