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Alexander Soiguine [soi’gin] (Sasha – standard Russian nickname for Alexander)

Alexander Soiguine [soi’gin] (Sasha – standard Russian nickname for Alexander). Physicist, Mathematician, Computer Modeling Scientist PhD in Applied Mathematics, MS in Mathematical Physics 20+ years of hands-on software development in computer simulation

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Alexander Soiguine [soi’gin] (Sasha – standard Russian nickname for Alexander)

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  1. Alexander Soiguine [soi’gin](Sasha – standard Russian nickname for Alexander) • Physicist, Mathematician, Computer Modeling Scientist • PhD in Applied Mathematics, MS in Mathematical Physics • 20+ years of hands-on software development in computer simulation • Former professor of Mathematics and Mechanics • Enthusiast of naturally parallelizable simulation algorithms

  2. Physical Department of St. Petersburg University, Russia. • MS in Mathematical Physics. Major Courses: • Higher Mathematics - General Physics- Atomic Physics - Theoretical Mechanics • - Thermodynamics and Statistical Physics - Optics- Electrodynamics - Quantum Mechanics - Mathematical Hydrodynamics - Methods of Mathematical Physics - Advanced Mathematical Analysis - Theory of Scattering - Operator Theory - Nonlinear Problems of Mathematical Physics- Spectral Theory of Operators - Generalized Functions - Wave Propagation in Random Media • PhD in Applied Mathematics - Numerical Methods for Stochastically Disturbed PDEs (Definitions of stochastic solutions, existence/uniqueness theorems, propagated variance estimations)

  3. Professor of Mathematics and Mechanics at Naval Academy of Russia (St. Petersburg) – top level institution preparing senior officers for Russian Navy. Taught: • Linear Algebra - Probability Theory - Differential Equations - Vector Analysis - Numerical Methods- Coding Theory - Stochastic Processes - Partial Differential Equations - Control Theory - Theory of Optimized Filtration and Estimation- Equations of Mathematical Physics- Electrodynamics - Heat Transfer Processes • Participated in multiple research projects, particularly: - submarine nuclear power plant numerical simulation algorithms - relativistic particle beam dynamics simulation (star war challenge) - acoustic emission simulation in metal construction • Beginning of object-oriented programming

  4. January 1997 – first USA job as software developer at International Metrology Systems - a company producing coordinate measuring machines. Wrote C/C++ code to geometric surface/shape/solid modeling Programming was mainly around: • implementation of Levenberg-Marquardt best fit approximation method for predefined geometrical shapes and spline-type curves/surfaces • creating effective iteration algorithms to accurate and in real-time construction of tangents/normals to curves/surfaces • integration of Matra Datavision Cascade geometrical object library into existing software tool • OpenInventor/VRML 3D visualization routines • implementation of interprocess dynamical data exchange multithreading mechanism

  5. August 1999 – took offer from Cadence to work on math simulation engine for integrated electronic circuits. Architectural, data structure modifications, matrix solver and integration algorithms on-fly switching optimization. Programming issues: • Restructuring circuit matrix nodes data fromlinked list of doubles type into single double values, rewriting device load member functions • Rewriting class member function interfaces for compatibility with NIST SparseLib package • Restructuring circuit matrix load bypass algorithms for new architecture • Creating algorithms to optimize switching between different matrix decomposition and integration schemes The result: Electronic circuit simulation speed increased up to 15 times; simulation began to converge with circuits where it never did before.

  6. What was done: • 2003 – 2004 – Principal Analyst/Engineer at JRM Technologies. Software simulation of physical signatures appeared due to optical, electromagnetic, thermal, chemical phenomena. Implemented both on Windows and Linux platforms. • Algorithms to transform stellar brightness data into irradiation approaching earth surface • Algorithms to calculate spectra of a city site illumination based on given distribution of light sources • Simulation algorithm of radiation propagation from missile plumes • Simulation algorithms of thermal irradiation from vehicles, tanks, detection by phase antennas • Terrain scene material identification algorithms based on satellite hypespectral images

  7. Since August 2004 – computational scientist at Tinsley. Two “from a scratch” huge projects - software tool to visualize/analyze/process optical surface data collected from coordinate measuring machines and interferometers, and software package to interactively control optics processing machines. Some of the first tool numerical blocks: • Algorithms to convert scattered point Cartesian data into regular grid surface data • Algorithms to convert interferometer phase data into regular grid surface data • Algorithms for surface profile analysis • Algorithms to filter regular grid data through cutting off 2D rectangular/circle frequency domains

  8. Algorithms to approximate data through Zernike polynomials • Algorithms for polynomial, moving average, weighted Gaussian, other types of smoothing • Image cropping, clipping, stitching algorithms • Algorithms to create regular surface grid from given aspheric equation • Algorithms to best fit metrology data to analytically predefined aspheric surface • Algorithms to define pixels height/slope statistics in areas close to optics edges • Algorithms of manual and scenario automated pixel editing • Image algebra

  9. The following slides are a few screen shots of the software tool to visualize/analyze/process optical surface data collected from coordinate measuring machines and interferometers.Live demonstration will be available.

  10. How it works. Main data visualization/analysis box

  11. Profile analysis

  12. Zernike approximation interface

  13. Comparing profiles

  14. Interface to create aspherics

  15. We can show how the tool can be used to estimate power losses in one reflection due to dust particles. Typical dust pike effect in phase data:

  16. Profile of disturbance in interferometer phase intensity measurement

  17. To get power loss estimation let’s remove Zernikes up to 8th order. The rms decreases to 2.668nm.

  18. If we consider this Zernike residual as a sample of two-dimensional centered stationary random process with and assume that HeNe wavelength 632.8nm is much bigger than the Zernike residual PV = 39.37nm then the Fourier transform expansion of the reflected wave from normally impinging beam : can be used just up to the first power of . Then we get scattering losses from the power spectrum density: that in this case is 0.05. It would be interesting to get detailed information about field spectral variations depending on dust pike position and its shape parameters, though it is doubtful if the Fresnel diffraction or PDE will work at all. More direct involvement of the Kirchhoff equation looks necessary.

  19. I would be happy to answer your questions if you have some.Thank you.

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