LEADER 03991nam 22006135 450 001 9910983089003321 005 20250630101745.0 010 $a3-031-71093-2 024 7 $a10.1007/978-3-031-71093-3 035 $a(CKB)36338358700041 035 $a(MiAaPQ)EBC31743917 035 $a(Au-PeEL)EBL31743917 035 $a(DE-He213)978-3-031-71093-3 035 $a(EXLCZ)9936338358700041 100 $a20241002d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNoise signals $eModelling and Analyses /$fby Vitalii Babak, Artur Zaporozhets, Yurii Kuts, Mykhailo Fryz, Leonid Scherbak 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (232 pages) 225 1 $aStudies in Systems, Decision and Control,$x2198-4190 ;$v567 311 08$a3-031-71092-4 327 $aChapter 1. Problems of Noise Signals Research -- Chapter 2. Linear Models of Stochastic Noise Signals -- Chapter 3. Periodic Models of Noise Signals -- Chapter 4. Method of Envelope and Phase in the Tasks of Identification of Narrowband Noise Signals -- Chapter 5. Identification of Vibration Noise Signals of Electric Power Facilities -- Chapter 6. Examples of Stochastic Noise Signals Identification -- Chapter 7. Identification of Air Pollution Sources. 330 $aThe book meticulously details a constructive mathematical model of a stochastic noise process, specifically a linear random process and its characteristics. Theoretical reasoning on the relationship between random processes with independent increments and those with independent values, known as random processes of white noise, is provided. The model of a linear random process serves as a mathematical representation of colored noises in various hues. Characteristics of both non-stationary and stationary linear random processes are elucidated, with emphasis on their ergodic properties, crucial for practical applications. The study also encompasses the vector linear random process, portraying a model of multi-channel noise signals. A novel contribution to the theory of random functions is the development of a constructive model of a conditional linear random process. This involves determining its distribution laws in the form of a characteristic function and relevant statistical characteristics, which can serve as potential indicators for identifying stochastic noise processes. The book revisits research on periodic stochastic models, examining cyclic, rhythmic, natural, and artificial phenomena, processes, and signals. A comprehensive analysis of the linear periodic random process is conducted, and the identification characteristics of periodic models of stochastic noise signals are explored. Significant attention is directed toward employing contour and phase methods as a theoretical foundation for addressing narrow-band noise signal identification challenges. 410 0$aStudies in Systems, Decision and Control,$x2198-4190 ;$v567 606 $aElectrical engineering 606 $aSignal processing 606 $aNoise control 606 $aElectrical and Electronic Engineering 606 $aDigital and Analog Signal Processing 606 $aNoise Control 615 0$aElectrical engineering. 615 0$aSignal processing. 615 0$aNoise control. 615 14$aElectrical and Electronic Engineering. 615 24$aDigital and Analog Signal Processing. 615 24$aNoise Control. 676 $a621.3 700 $aBabak$b Vitalii$01437507 701 $aZaporozhets$b Artur$01437508 701 $aKuts$b Yurii$01785708 701 $aFryz$b Mykhailo$01785709 701 $aScherbak$b Leonid$01785710 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983089003321 996 $aNoise signals$94317199 997 $aUNINA