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Nonlinear Ocean Dynamics: Synthetic Aperture Radar [Pehme köide]

(Professor, Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala Darussalam, Banda Aceh, Indonesia)
  • Formaat: Paperback / softback, 462 pages, kõrgus x laius: 235x191 mm, kaal: 910 g, 100 illustrations (8 in full color); Illustrations, unspecified
  • Ilmumisaeg: 12-Feb-2021
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 012820785X
  • ISBN-13: 9780128207857
Teised raamatud teemal:
Nonlinear Ocean Dynamics: Synthetic Aperture RadarSuurem pilt
  • Formaat: Paperback / softback, 462 pages, kõrgus x laius: 235x191 mm, kaal: 910 g, 100 illustrations (8 in full color); Illustrations, unspecified
  • Ilmumisaeg: 12-Feb-2021
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 012820785X
  • ISBN-13: 9780128207857
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128207857.html
Märksõnad:

Nonlinear Ocean Dynamics: Synthetic Aperture Radar delivers the critical tools needed to understand the latest technology surrounding the radar imaging of nonlinear waves, particularly microwave radar, as a main source to understand, analyze and apply concepts in the field of ocean dynamic surface. Filling the gap between modern physics quantum theory and applications of radar imaging of ocean dynamic surface, this reference is packed with technical details associated with the potentiality of synthetic aperture radar (SAR). The book also includes key methods needed to extract the value-added information necessary, such as wave spectra energy, current pattern velocity, internal waves, and more. This book also reveals novel speculation of a shallow coastal front: named as Quantized Marghany's Front.

Rounding out with practical simulations of 4-D wave-current interaction patterns using using radar images, the book brings an effective new source of technology and applications for today’s coastal scientists and engineers.

  • Solves specific problems surrounding the nonlinearity of ocean surface dynamics in synthetic aperture radar data
  • Helps develop new algorithms for retrieving ocean wave spectra and ocean current movements from synthetic aperture radar
  • Includes over 100 equations that illustrate how to follow examples in the book
Preface xi
1 Nonlinear ocean motion equations: Introduction and overview
1.1 Introduction
1(1)
1.2 What is meant by ocean dynamics?
1(2)
1.3 What is meant by nonlinear?
3(1)
1.4 Classification of ocean dynamic flows
3(10)
1.5 Ocean dynamic circulation
13(2)
1.6 What is the difference between circulation and vorticity?
15(1)
1.7 Primitive equation of ocean dynamics
16(10)
1.8 Navier-Stokes equations
26(4)
1.9 Turbulence
30(2)
1.10 Equations of motion in a rotating frame
32(5)
1.11 Conservation equation of ocean waves
37(1)
1.12 Water level exchange and water flow
38(2)
1.13 Dispersion relation for water waves
40(1)
1.14 Nonlinear water flow and wave propagation
41(1)
1.15 Energy equation of fluid flow
42(3)
References
43(2)
2 Quantization of ocean dynamics
2.1 Seawater quantum molecules
45(2)
2.2 Ocean dynamics mimic quantum mechanics
47(1)
2.3 Similarities and differences between quantum field theory and ocean dynamics
48(1)
2.4 Quantum spin of seawater
48(1)
2.5 Kitaev spin seawaters
49(3)
2.6 Hamiltonian mechanics for ocean dynamics
52(5)
2.7 Incompressible flow with Schrodinger equations
57(2)
2.8 Quantum mechanics of Coriolis force
59(4)
2.9 Quantization of barotropic flow
63(2)
2.10 Quantization of vorticity flow
65(4)
2.11 Quantum turbulence
69(7)
2.12 Particle theory of ocean waves
76(2)
2.13 Schrodinger equation for description of nonlinear Sea-state
78(2)
2.14 Hamiltonian formulation for water wave equation
80(5)
References
82(3)
3 Quantization of synthetic aperture microwave radar
3.1 Quantize concept of aperture
85(2)
3.2 Aperture antenna
87(3)
3.3 Quantization of electromagnetic wave and Maxwell's equations
90(6)
3.4 Microwave radar photons
96(2)
3.5 Microwave cavity main concept
98(2)
3.6 Microwave photon generation by Josephson junctions
100(4)
3.7 Radar systems
104(1)
3.8 What is meant by echolocation detecting and ranging?
105(1)
3.9 Why quantum synthetic aperture radar is necessary
106(1)
3.10 What is meant by quantum SAR?
107(1)
3.11 What are the classifications of quantum SAR?
107(2)
3.12 Classical and quantum radar equations
109(3)
3.13 Quantum SAR illumination
112(1)
3.14 Quantum theory of SAR system
113(6)
References
116(3)
4 Quantum mechanism of nonlinear ocean surface backscattering
4.1 What is meant by scattering?
119(1)
4.2 Comparison between coherent and incoherent multiple scattering
120(3)
4.3 What is the role of spin in understanding scattering?
123(2)
4.4 Spin of scattering of particles
124(1)
4.5 Scattering of identical particles
125(1)
4.6 Schrodinger equation for scattering particles
125(1)
4.7 How do the Lippmann-Schwinger equation and the scattering amplitude generalize when spin is included?
126(2)
4.8 Seawater atom-photon scattering
128(2)
4.9 Scattering from roughness surface
130(1)
4.10 Mathematical depiction of SAR backscattering cross-section
131(1)
4.11 Wave function of SAR backscattering cross-section
132(4)
4.12 Quantization of Bragg scattering
136(5)
References
139(2)
5 Relativistic quantum mechanics of ocean surface dynamic in synthetic aperture radar
5.1 What is meant by relativity?
141(1)
5.2 Relativistic quantum mechanics versus ordinary quantum mechanics
142(2)
5.3 SAR backscatter in relativistic quantum mechanics
144(2)
5.4 Duality of wave packages in relativistic quantum mechanics
146(2)
5.5 Relativities of SAR time pulse range traveling
148(3)
5.6 SAR space-time invariance interval
151(3)
5.7 How is quantum entanglement consistent with the time relativity?
154(2)
5.8 SAR time dilation
156(1)
5.9 SAR length contraction in polarized data
156(7)
References
161(2)
6 Novel relativistic theories of ocean wave nonlinearity imagine mechanism in synthetic aperture radar
6.1 What is meant by waves and flows?
163(1)
6.2 Description of ocean waves
164(2)
6.3 How sea waves are formed based on Spooky Action at a Distance
166(1)
6.4 What is doing the waving?
167(2)
6.5 Hamiltonian formula for nonlinear wave description
169(3)
6.6 SAR image mechanism for ocean wave
172(9)
6.7 Relativistic theory of SAR velocity bunching
181(1)
6.8 Relativistic theory of the ocean wavelength in SAR images
182(2)
6.3 Relativistic theory of incidence angle in SAR wave images
184(3)
6.10 Relativistic theory of range bunching
187(4)
References
189(2)
7 Quantum nonlinear techniques for retrieving ocean wave spectral parameters from synthetic aperture radar
7.1 Simplification of the magic concept of SAR Doppler shift frequency
191(2)
7.2 SAR sensors for ocean wave simulation
193(2)
7.3 Sea surface backscatter based on the Kirchhoff approximation
195(1)
7.4 Imaging Ocean wave parameters in single polarization SAR data
196(1)
7.5 How to relate wave fields to SAR images
197(1)
7.6 SAR wave retrieval algorithms
198(1)
7.7 Quantum spectra estimation using quantum Fourier transform
199(4)
7.8 Multilooking and cross-spectral analysis
203(3)
7.9 Quantum Monte Carlo wave spectral simulation
206(1)
7.10 SAR wave spectra simulated using diffusion quantum Monte Carlo
207(8)
References
212(3)
8 Polarimetric synthetic aperture radar for wave spectra refraction using inversion SAR wave spectra model
8.1 What is meant by polarimetric synthetic aperture radar?
215(1)
8.2 Polarimetric matrix formulations and SAR data representation
215(2)
8.3 The coherency matrix THV (for single-look or multilook)
217(1)
8.4 Circular polarization-based covariance matrix CRL
217(1)
8.5 Estimation of azimuth slopes using orientation angle
218(1)
8.6 Alpha parameter sensitivity to the range of traveling waves
219(3)
8.7 Examined POLSAR and AIRSAR data
222(1)
8.8 Wave spectra model
223(1)
8.9 Two-dimensional quantum Fourier transform for retrieving SAR wave spectra
224(1)
8.10 Quasilinear transform
225(6)
8.11 Modeling significant wave height using azimuth cutoff model
231(4)
8.12 AIRSAR/POLSAR cross-spectrum inversion
235(3)
8.13 Differences between deep and shallow water waves
238(1)
8.14 Quantum of wave refraction
238(2)
8.15 Wave refraction graphical method
240(7)
References
245(2)
9 Wavelet transform and particle swarm optimization algorithms for automatic detection of internal wave from synthetic aperture radar
9.1 Introduction
247(1)
9.2 What is meant by internal wave?
247(2)
9.3 Simplification of internal wave generation mechanisms
249(1)
9.4 Mathematical description of internal waves
250(3)
9.5 Kelvin-Helmholtz instability
253(1)
9.6 Internal wave imaging in SAR
254(2)
9.7 Internal wave radar backscatter cross-section
256(2)
9.8 Internal wave detection using two-dimensional wavelet transform
258(2)
9.9 Particle swarm optimization (PSO) algorithm
260(2)
9.10 Tested SAR data
262(1)
9.11 Backscatter distribution along with internal wave in SAR data
263(4)
9.12 Automatic detection of internal wave using two-dimensional wavelet transform
267(2)
9.13 Internal wave packet detection by PSO
269(3)
9.14 Why do internal waves occur in the Andaman Sea?
272(3)
References
273(2)
10 Modeling wave pattern cycles using advanced interferometry altimeter satellite data
10.1 Microwave altimeter
275(1)
10.2 Principles of altimeters
275(1)
10.3 Types of radar altimeter frequencies
276(1)
10.4 How does a radio altimeter work?
277(1)
10.5 How is surface height estimated by radio altimeter?
278(1)
10.6 Pulse-limited altimetry
278(1)
10.7 Altimeter sensors
279(1)
10.8 Principles of synthetic aperture radar altimeterinterferometry
280(1)
10.9 Altimeter interferometry technique
281(2)
10.10 InSAR precision procedures altimeter scheme
283(2)
10.11 Delay-Doppler altimeter
285(1)
10.12 CRYOSAT-2 SIRAL data acquisitions
286(1)
10.13 Cycle of significant wave heights and powers: Case study of west coast of Australia
287(10)
References
295(2)
11 Multiobjective genetic algorithm for modeling Rossby wave and potential velocity patterns from altimeter satellite data
11.1 What is meant by Rossby wave?
297(3)
11.2 Rossby waves algebraic portrayal Coriolis
300(1)
11.3 Rossby waves causing convergence and divergence zones
301(2)
11.4 Collinear analysis for modeling Rossby wave patterns from satellite altimeter
303(1)
11.5 Rossby wave spectra patterns using fast Fourier transform
304(1)
11.6 Multiobjective algorithm for modeling Rossby waves in altimeter data
305(4)
11.7 Rossby wave population of solutions
309(1)
11.8 Fitness procedures for simulation of Rossby wave patterns
310(3)
11.9 Cross-over and mutation for Rossby wave reconstruction from altimeter data
313(1)
11.10 Velocity potential patterns in the southern Indian Ocean from Jason-2
313(5)
11.11 Pareto algorithm simulation of water parcel sinking due to vorticity potential velocity
318(2)
11.12 How can Rossby waves mobilize water mass parcels and heavy debris?
320(5)
References
323(2)
12 Nonlinear sea surface current mathematical and retrieving models in synthetic aperture radar
12.1 Introduction
325(1)
12.2 What is meant by ocean current?
325(1)
12.3 Ocean current theory
326(1)
12.4 Ocean current measurements
327(5)
12.5 Governing equations of inviscid motion
332(4)
12.6 Wind-driven current
336(3)
12.7 Ekman spiral
339(1)
12.8 Quantum theory of the Ekman spiral
340(3)
12.9 SAR Doppler shift frequency
343(2)
12.10 SAR Doppler frequency shift model formulation
345(3)
12.11 Radial current velocity based on Doppler spectral intensity
348(1)
12.12 Robust model for simulating surface current in SAR imaging
349(2)
12.13 Tidal current direction estimation
351(1)
12.14 Ocean current retrieving from SAR data, case study: East coast of Malaysia
351(6)
12.15 Quantization of large scale eddy in SAR image
357(4)
References
358(3)
13 Relativistic quantum of nonlinear three-dimensional front signature in synthetic aperture radar imagery
13.1 What is meant by quantum coastal front?
361(3)
13.2 Signature of a front in a single SAR image
364(4)
13.3 Relativity of front signatures in polarimetric SAR
368(3)
13.4 How does the tidal cycle effect front signature in SAR images?
371(1)
13.5 Speckles impact on front signature in SAR images
371(3)
13.6 Anisotropic diffusion algorithm for speckle reductions
374(2)
13.7 3-D front model
376(6)
13.8 3-D front topology reconstruction in SAR data
382(2)
13.9 Quantized Marghany's front
384(5)
References
386(3)
14 Automatic detection of nonlinear turbulent flow in synthetic aperture radar using quantum multiobjective algorithm
14.1 Introduction
389(1)
14.2 What is meant by quantum turbulence?
390(1)
14.3 Turbulence imagined in SAR data
390(1)
14.4 Can a quantum algorithm automatically detect turbulent flow in SAR images?
391(1)
14.5 Quantum computing
392(2)
14.6 Quantum machine learning
394(1)
14.7 Quantum multiobjective evolutionary algorithm (QMEA)
395(1)
14.8 Generation of qubit populations
396(1)
14.9 Generation of turbulent flow population pattern
397(1)
14.10 Quantum nondominated sort and elitism (QNSGA-II)
398(3)
14.11 Quantum Pareto optimal solution
401(1)
14.12 Automatic detection of turbulent flow in SAR images
401(6)
14.13 Role of Pareto optimization in QNSGA-II
407(1)
14.14 Quantum coherence of turbulent flow magnitudes in SAR imaging and QNSGA-II
408(3)
References
409(2)
15 Four-dimensional along-track interferometry for retrieving sea surface wave-current interaction
15.1 What is meant by four-dimensional and why?
411(1)
15.2 Does n-dimensional exist?
412(1)
15.3 Physics of Interferometry
413(2)
15.4 What is synthetic aperture interferometry?
415(2)
15.5 Interferograms
417(1)
15.6 Phase unwrapping
418(1)
15.7 Understanding SAR interferograms
419(2)
15.8 Along-track interferometry
421(1)
15.9 Quantum of along-track interferometry
422(2)
15.10 Quantum Hopfield algorithm for ATI phase unwrapping
424(3)
15.11 Quantum ATI Hopfield algorithm application to TanDEM-X satellite data
427(1)
15.12 In situ measurement
428(2)
15.13 Retrieving current from ATI TanDEM-X satellite data using qHop algorithm
430(4)
15.14 Four-dimensional ATI quantum algorithm for wave-current interaction
434(3)
15.15 4-D visualization of wave-current sea level interactions
437(1)
15.16 Relativistic quantum 4-D of sea surface reconstruction in TanDEM data
438(5)
References
440(3)
Index 443
Distinguished Professor Dr. Maged Marghany, the visionary behind the innovative theory titled "Quantized Marghanys Front," currently holds the esteemed position of Director at Global Geoinformation in Malaysia. Acknowledged globally for his exceptional contributions, Dr. Marghany achieved recognition by Stanford University, USA, by being listed among the top 2% of scientists for four consecutive years - 2020, 2021, 2022, and 2023. Furthermore, his profound impact is reflected in the recognition of two of his books, which were acknowledged among the best genetic algorithm books of all time. Dr. Marghany's ongoing commitment to excellence continues to shape the landscape of scientific thought and geoinformation expertise.

Additionally, Dr. Maged Marghany achieved the remarkable distinction of being ranked first among oil spill scientists in a global list spanning the last 50 years, compiled by the prestigious Universidade Estadual de Feira de Santana in Brazil. His expertise also extended to the role of a prominent visiting professor at Syiah Kuala University in Indonesia.

In previous roles, Dr. Marghany directed the Institute of Geospatial Applications at the University of Geomatica College. His educational journey includes a post-doctoral degree in radar remote sensing, a PhD in environmental remote sensing, and a Master of Science in physical oceanography. With over 250 papers and influential books," Dr. Marghany s significant contributions shape global perspectives in remote sensing, geospatial applications, and environmental science.