Dr. Kupervasser Oleg 8-916-4516193 (10.00-22.00) 8-499-134-3965 (20.00-22.00)

1. Dr. Kupervasser Oleg8-916-4516193 (10.00-22.00)8-499-134-3965 (20.00-22.00)

2. OLEG KUPERVASSERe-mail: olegkup@yahoo.comaddress: Russia, Vavilova 54-1-71 Moscow 119296Date of birth: 01/02/1966Citizenship: Russia, Israel Home page: http://leah.haifa.ac.il/~skogan/Apache/mydata1/Oleg_home/Oleg_home.html

4. Education • 2004 Haifa University: Three dimensional protein structure. Postdoctorate.(http://leah.haifa.ac.il/~skogan/Apache/mydata1/main.html) ; Perl, DHTML, Fortran, C++ languages • 2001-2002 ATLAS college: Bioinformatics for Hi-Tech people. Final project: Three dimensional protein structure, under supervision of Professor Edward N. Trifonov from Weizmann Institute of Science. Including: Basic knowledge in Biology, Bioinformatics, Programming (C++, SQL, JAVA, DHTML, Bio-PERL, UNIX, MATLAB) • Ph.D. 1992-1999 Weizmann Institute of Science, Faculty of Physics. Research project: Nonlinear dynamics (Analytical methods and numerical algorithms for the solution of nonlinear physics equations). Fortran, C++ languages • 1991-1992 Tel-Aviv University, Faculty of Engineering, department of Physical Electronics, Ph.D student, Tel Aviv, Free electron laser. Fortran languages • M.Sc. 1983-1989 Moscow Institute of Radioengineering, Electronics and Automation. (theoretical and experimental electro-optics, laser, fiber optics and radars) Diploma honoris causa . Fortran language.

5. Employment • 2008-2010 Algorithm developer in Moscow State University, Russia, Moscow.Algorithm developer. C++ language Computer drug design, nano-systems computer modeling; solvent influence on molecules interaction; took part in scientific conferences; submitted six papers and one published in high impact factor journal • 2009-2009 Algorithm developer in UltraSpect, Image processing in Nuclear medicine. C++ language • 2008-2008 Algorithm developer in Vayar Vision, Israel. Images search in Internet ("Google" for images). C++ language This company develops a search engine for images. For some picture this search engine looks for similar pictures in the given data basis of images. This similarity is not based on objects recognition. The similarity is based on not semantic characters (for example, a number of the boundaries pixels over image segments and etc.). • 2005-2008 Rafael-Technion: Image processing, multiple view geometry of smooth bodies, Navigation systems; took part in scientific conferences; two published papers; Matlab language It is creation of algorithms in the field of image processing, computer vision for navigation of rockets. For Rafael (the leading Israeli company in the field of rocket weapons) programs was developed for navigation of rockets by means of the video images and the known terrain map. This is inverse problem with respect to the problem solved by means of Google Earth. In Google Earth for a given trajectory and orientation the correspondent images can be found. In the developed method a trajectory and orientation can be found on the basis of a video. From this experience expansion of Google Earth can be developed, allowing video-navigation for a flying plane, a rocket or a car from a video film. • 2003-2004 Algorithm developer in Intel , Israel. Image processing in digital TV, C++ language • 2002-2002 Algorithm developer in UltraSpect, Israel. Image processing in Nuclear medicine, C++ languages • 2000-2001 Algorithm developer in Orbotech LTD, Israel. Recognition of glass defects and plate marks, Matlab, C++ languages. • 1999-2000 Electronics engineer in Tower Semiconductor LTD, Israel. Design flash-memory, Excel

6. Student physics Olympiad in Moscow (1 prize) • Student physics Olympiad in USSA (2 prize) • About 27 publications in physics, image processing

7. Continuum solution model. Moscow State University

8. Process of solutesolvationin solvent Continuum solution model.

9. Gs =Gcav +Gnp+Gpol, Gcav–Hydrophobic component Gnp-Van-der-Waals component Gpol- polarization component Three components of Gibbs energy

10. Methods for finding of polarization component Gpol. • Exact numerical methods: • Solution of Poisson equation in 3D • Solution of equivalent equation for charge on solvent excluded surface (PCM) • Simplified PCM: COSMO – water (ε=78) is changed to metal (ε=∞). • Exact analytical method for spherical cavity полости: • Multipole moments method, • Mirror charges method • Heuristic model: • Generalized Born, • Surface Generalized Born.

11. About solute surface construction in РСМ

12. Protein Loop-Lock StructureHaifa University We must decompose a protein on set of closed loops. The developed Internet site solves this task.

13. Protein Loop-Lock Structure We must decompose a protein on set of closed loops. The developed Internet site solves this task.

16. Images Similarity Engine.Vayar Vision

17. Similarity Engine Similarity Engine : Finding images similar to some given image in some image database. • First step: basic features definition • Indexing of all images in database by help these features • Indexing of some given images by help these features • Finding image (or images) with maximum feature similarity from database

18. large number of investigation exists in the field of images similarity. But the current Engine: 1) Can generate almost infinity number of features used for images similarity. 2) The system can learned from mistakes. 3) The system can be easily adaptive to some new definition of similarity 4) The set of used feature has no semantic sense. We don’t make recognition of objects on image and use such no sense features as number of pixels on object boundaries and etc.

19. Found similar images by help our Engine from terragaleria site

20. Found similar images by help our Engine from terragaleria site

21. Found similar images by help our Engine from terragaleria site

22. Field of use • Images similarity engine in Internet (like Google for words) • Finding relevant images in film for product advertising setting • Finding relevant information about some building or place from photo or film. • Navigation by place recognition from photo or film (GPS equivalent) by help Google Earth • Find similar images for intellectual property rights control

23. Vision Based Navigation from Image Sequence and Digital Terrain Model. Technion

24. Vision Based Navigation from Image Sequence and Digital Terrain Model. It is creation of algorithms in the field of image processing, computer vision for navigation of rockets. For Rafael (the leading Israeli company in the field of rocket weapons) programs was developed for navigation of rockets by means of the video images and the known terrain map. This is inverse problem with respect to the problem solved by means of Google Earth. In Google Earth for a given trajectory and orientation the correspondent images can be found. In the developed method a trajectory and orientation can be found on the basis of a video. From this experience expansion of Google Earth can be developed, allowing video-navigation for a flying plane, a rocket or a car from a video film.

25. Vision Based Navigation from Image Sequence and Digital Terrain Model Two consecutive images Compute n feature correspondences 2n constraints (u, v) Compute pose and ego-motion that best explain the features movement DTM Pose and ego-motion 12 variables

26. Recovering Epipolar Geometry from Images of Smooth SurfacesTechnion

27. We present four methods for recovering the epipolar geometry from images of smooth surfaces. Existing methods for recovering epipolar geometry use corresponding feature points that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM - illumination characteristic points method). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM - outline tangent points method). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two else methods, termed CCPM (curve characteristic points method) and CTPM (curve tangent points method), for search epipolar geometry for images of smooth bodies based on a set of level curves with a constant illumination intensity. The CCPM method is based on search correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolarline to map into the tangential to correspondent isophoto curves epipolar line. Unfortunately these two methods give us only finite subset of solution, which usually include "good" solution, but don't allow us to find this "good" solution among this subset. Exception is the case of epipoles in infinity. The main reason for such result is inexactness of constant brightness assumption for smooth bodies. But outline and illumination characteristic points are not influenced this inexactness. So the first pair of methods gives exact result.

28. Correspondent points

29. Epipolar geometry

30. Image processing in the Nuclear medicine. UltraSpect

31. Diagram of parallel-hole collimator attached to a crystal of a gamma camera. Obliquely incident gamma-rays are absorbed by the septa.

32. Gamma Camera

33. Image Processing Direct problem: To get Image on gamma camera from image of radiated particles distribution Inverse problem: To get image of radiated particles distribution from Image in on gamma camera Slice of Brain

34. Deinterlacing for video. Intel

35. Transform of interlaced video frames to progressive video frames.

36. Main problems:Motion regions, no-smooth boundary

37. Steps of Algorithm. • Motion detection • Interpolation or motion compensation 3) Detection of direction 4) Detection of angles 5) Detection of “zebra” 6) Detection of “high entropy regions” 7) taking into account history and environment 8) Fuzzy boundary between motion and no motion regions 9) Median filtering, Mean filtering over space and time.

38. Detection and recognition of defects on the glass Orbotech LTD

39. Types of defects: bubbles, scratchs, dirtyspots

40. Steps of Algorithm • Segmentation • Descriptors (features) definition 3) Finding P(Xj|Hk) , Xj-Descriptor (N), Hk - type of defect (M) 4) Naive bayes model: X={X1,…,XN} P(X|Hk) =Пj P(Xj|Hk) P (Hk|X) = P(Hk)·P(X|Hk) / P(X) P(X)=ΣkP(Hk)· P(X|Hk)

41. Flash memory designTower Semiconductor LTD

42. SONOS Transistor

43. Flash memory designTower Semiconductor LTD • Silicon–oxide-nitride–oxide–silicon memory transistor, where information is stored as twocharges in nitride at the edges of the channel. • Silicon-Oxide-Nitride-Oxide-Silicon memmory (SONOS) • A SONOS memory cell is formed from a standard polysilicon NMOS transistor with the addition of a small sliver of silicon nitride inserted inside the transistor's gate oxide. The sliver of nitride is non-conductive but contains a large number of charge trapping sites able to hold an electrostatic charge. The nitride layer is electrically isolated from the surrounding transistor, although charges stored on the nitride directly affect the conductivity of the underlying transistor channel. The oxide/nitride sandwich typically consists of a 2 nm thick oxide lower layer, a 5 nm thick silicon nitride middle layer, and a 5—10 nm oxide upper layer.SONOS promises lower programming voltages and higher program/erase cycle endurance than polysilicon-based flash. SONOS distinguished from mainstream flash by the use of silicon nitride (Si3N4) instead of polysilicon for the charge storage material.