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Fractal Functions, Dimensions and Signal Analysis 2021 ed. [Kietas viršelis]

  • Formatas: Hardback, 132 pages, aukštis x plotis: 235x155 mm, weight: 454 g, 59 Illustrations, color; 2 Illustrations, black and white; X, 132 p. 61 illus., 59 illus. in color., 1 Hardback
  • Serija: Understanding Complex Systems
  • Išleidimo metai: 15-Dec-2020
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030626717
  • ISBN-13: 9783030626716
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 132 pages, aukštis x plotis: 235x155 mm, weight: 454 g, 59 Illustrations, color; 2 Illustrations, black and white; X, 132 p. 61 illus., 59 illus. in color., 1 Hardback
  • Serija: Understanding Complex Systems
  • Išleidimo metai: 15-Dec-2020
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030626717
  • ISBN-13: 9783030626716
Kitos knygos pagal šią temą:
This book introduces the fractal interpolation functions (FIFs) in approximation theory to the readers and the concerned researchers in advanced level.  FIFs can be used to precisely reconstruct the naturally occurring functions when compared with the classical interpolants.  

The book focuses on the construction of fractals in metric space through various iterated function systems.  It begins by providing the Mathematical background behind the fractal interpolation functions with its graphical representations and then introduces the fractional integral and fractional derivative on fractal functions in various scenarios.  Further, the existence of the fractal interpolation function with the countable iterated function system is demonstrated by taking suitable monotone and bounded sequences.  It also covers the dimension of fractal functions and investigates the relationship between the fractal dimension and the fractional order of fractal interpolation functions. Moreover, this book explores the idea of fractal interpolation in the reconstruction scheme of illustrative waveforms and discusses the problems of identification of the characterizing parameters. 

In the application section, this research compendium addresses the signal processing and its Mathematical methodologies. A wavelet-based denoising method for the recovery of electroencephalogram (EEG) signals contaminated by nonstationary noises is presented, and the author investigates the recognition of healthy, epileptic EEG and cardiac ECG signals using multifractal measures. 

This book is intended for professionals in the field of Mathematics, Physics and Computer Science, helping them broaden their understanding of fractal functions and dimensions, while also providing the illustrative experimental applications for researchers in biomedicine and neuroscience.

1 Mathematical Background of Deterministic Fractals
1(20)
1.1 Introduction
1(4)
1.2 Iterated Function System
5(4)
1.3 Countable Iterated Function System
9(2)
1.4 Local Countable Iterated Function System
11(4)
1.4.1 Local Iterated Function System
11(1)
1.4.2 Existence and Analytical Properties of LCIFS
12(3)
1.5 Fractal Dimension
15(2)
1.6 Generalized Fractal Dimensions
17(2)
1.6.1 Some Special Cases
18(1)
1.6.2 Limiting Cases of Generalized Fractal Dimensions
19(1)
1.6.3 Range of Generalized Fractal Dimensions
19(1)
1.7 Concluding Remarks
19(2)
2 Fractal Functions
21(16)
2.1 Introduction
21(1)
2.2 Interpolation Functions
22(1)
2.3 Fractal Interpolation Function
23(6)
2.4 Hidden Variable Fractal Interpolation Function
29(3)
2.5 Classical Calculus on Fractal Interpolation Functions
32(3)
2.6 Concluding Remarks
35(2)
3 Fractional Calculus on Fractal Functions
37(24)
3.1 Introduction
37(2)
3.2 Linear Fractal Interpolation Function
39(10)
3.3 Riemann-Liouville Fractional Calculus Quadratic FEF
49(4)
3.4 Fractal Dimension
53(1)
3.5 Fractional Calculus of Quadratic FIF with Variable Scaling Factors
54(5)
3.6 Concluding Remarks
59(2)
4 Fractal Interpolation Function for Countable Data
61(18)
4.1 Introduction
61(1)
4.2 Existence of FIF for Countable Data Set
62(6)
4.3 Fractional Calculus on Interpolation Function of Sequence of Data
68(9)
4.4 Concluding Remarks
77(2)
5 Multifractal Analysis and Wavelet Decomposition in EEG Signal Classification
79(40)
5.1 Introduction
79(1)
5.2 Experimental Signals
79(4)
5.2.1 Synthetic Weierstrass Signals
79(2)
5.2.2 Clinical EEG Signals
81(2)
5.3 Statistical Methods
83(1)
5.3.1 ANOVA Test
83(1)
5.3.2 Box Plot
83(1)
5.3.3 Normal Probability Plot
83(1)
5.4 Development of Multifractal Analysis in EEG Signal Classification
83(12)
5.4.1 Modified Generalized Fractal Dimensions
84(1)
5.4.2 Improved Generalized Fractal Dimensions
85(2)
5.4.3 Advanced Generalized Fractal Dimensions
87(1)
5.4.4 Methods to Analyze the Fractal Time Signals
88(1)
5.4.5 Results and Discussions
89(6)
5.5 Multifractal-Wavelet Based Denoising in EEG Signal Classification
95(22)
5.5.1 Discrete Wavelet Transform
96(3)
5.5.2 Wavelet Denoising of Signals
99(1)
5.5.3 Results and Discussions
100(17)
5.6 Concluding Remarks
117(2)
6 Fuzzy Multifractal Analysis in ECG Signal Classification
119(10)
6.1 Introduction
119(1)
6.2 Fuzzy Multifractal Analysis for Fractal Signals
119(2)
6.2.1 Fuzzy Renyi Entropy
119(1)
6.2.2 Fuzzy Generalized Fractal Dimensions for Signals
120(1)
6.3 Fuzzy Generalized Fractal Dimensions for Deterministic Fractal Waveforms
121(3)
6.4 Experimental ECG Data
124(1)
6.5 Fuzzy Generalized Fractal Dimensions for Clinical ECG Signals
125(2)
6.6 Concluding Remarks
127(2)
References 129
Santo Banerjee was working as Associate Professor, in the Institute for Mathematical Research (INSPEM), University Putra Malaysia, Malaysia till 2020, and also a founder member of the Malaysia-Italy Centre of Excellence in Mathematical Science, UPM, Malaysia. He is now associated with the Department of Mathematics, Politecnico di Torino, Italy. His research is mainly concerned with Nonlinear Dynamics, Chaos, Complexity and Secure Communication. He is a Managing Editor of EPJ Plus (Springer) 

D. Easwaramoorthy received the M.Sc. Degree in Mathematics from the Bharathidasan University, Tiruchirappalli, India in 2008 and the Ph.D. Degree in Mathematics from The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, India in 2013. Currently, he is an Assistant Professor (Senior) in the Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, India.  His research interests include Fractal Analysis, Fuzzy Metric Spaces, Fuzzy Mathematics, Signal & Image Analysis; and Biomedical Signal Analysis. A. Gowrisankar received the M.Sc. and Ph.D.  Degrees in Mathematics from the The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, India in 2012 and 2017 respectively. He got institute postdoctoral fellowship from Indian Institute of Technology Guwahati (IITG), Guwahati, Assam, India in 2017. At present, he is an Assistant Professor in the Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, India.  His broad area of research includes Fractal Analysis, Image Processing, Fractional Calculus and Fractal Functions.