- Ákos Zarándy, doctor of HAS, research advisor

Curriculum vitae
Dr. Ákos Zarándy
Personal Information Name: Ákos Zarándy Date of birth: Oct 6th, 1967 Workplace: Computer and Automation Research Institute of the Hungarian Academy of Sciencies (MTA-SZTAKI) Address: POB: 63, 1518 Budapest, Hungary Phone: +36 1 279-6131 Fax: +36 1 209-5264 Email: zarandy@sztaki.hu Citizenship: Hungarian Professional Experience 2007-present Senior Research Fellow (MTA-SZTAKI, Hungary) 2005-2006 CTO (Eutecus Inc. Berkeley, California, USA) 2000-2005 CEO (AnaLogic Computers Ltd, Budapest, Hungary, Spin-off company of SZTAKI) 1997-2005 Research Fellow (MTA-SZTAKI, Hungary) 1992-1997 PhD student (MTA-SZTAKI, Hungary) 1993, 95, 96, 98 Visiting scholar or post doc at University of California at Berkeley Education 2010 DSC of the Hungarian Academy of Sciencies 1997 Ph.D. in Computer science, Hungarian Academey of Sciences: "CNN Universal Machines: spatial logic, colors, and illusions" 1992 MsC, Electrical Engineering, Technical University of Budapest, Hungary Fields of interest Focal-plane sensor-processors: architecture, design, and development Cellular Neural Network research: VLSI implementations, algorithm development IR imaging: sensor interface development Human and artificial vision, color vision: modelling Image processing: algorithms, embedded implementation System integration: ultra-high speed, low power, embedded image processing devices Major technical achievements 2006-2007: 8x8 nanoantenna sensor interface including per pixel analogue amplifiers and programmable digital filters (100dB signal to noise ratio total) (Project Leader) 2005-2006: Focal-plane sensor-processor array with 64 fully programmable on-chip processors: XENON (Project Leader) 2004-2005: Locally adaptive image sensor development 2002-2005: Biologically motivated collision warning focal-plane sensor-processor array: LOCUST (Group Leader) 2002-2003 Bi-i: Industrial grade camera capable over 25,000 FPS visual decision making (capturing and real-time evaluation) Product of the year, Vision 2003, Stuttgart (Project Leader) 1996-2000: CNN Chip Prototyping System (chief developer) Publication Summary 26 peer-reviewed published (or accepted) papers 5 Book chapters 45 conference contributions 300 total number of citations (excluding self citation) 2 patents Most Important International Collaborations 1994- present: Focal-plane sensor-processor chip development and testing, Microelectronic Institute Seville, Spain, Prof. Angel Rodriguez-Vazquez and his group 2002-present Active contour algorithm implementation on sensor-processor array School of Electrical and Electronic Engineering The University of Manchester, Dr. Piotr Dudek 2000-present: Neuromorph modell implementations on sensor processor arrays Friedrich Miescher Institute, Dr. Botond Roska 1998-present Robot guidance algorithms for sensor-processor array Department of Electronic and Electrical Systems, University of Catania, Dr. Paolo Arena Teaching 2005-present Peter Pazmany Catholic University, „Electronic components of the computing, communicating and sensing devices” Grants and Scholarships 2005-2007: Nanoantenna sensor-processor array development: Eutecus Inc, Notre Dame University (two US grants: N00014-05C-0370 and HQ0006-05-C-7268, over $1,5M) role: PI 2002-2003 RobotEyepair (Hungarian grant IKTA4-044) Budget: 420k Euro. Role: PI 2004-2007 SPARK: Spatial-temporal Patterns for Action-oriented perception in Roving robots, (FP6-2003-IST-2) EU FP6, role: participant 2002-2005: LOCUST: Life-Like Object Detection For Collision Avoidance Using Spatio-Temporal Image Processing. (IST-2001-38097) EU FP5-FP6. Role: participant 2000-2003 DICTAM: Dynamic Image Computing Using Tera-Speed Analogic Visual Microprocessors. (IST-1999-19007) EU FP5. role: participant Honors and Awards 2007 Charles Simonyi Award 2007 Microprocessor Report Innovation Award, San Jose, California: Superfast Sensor-Processors Break New Ground in Digital Imaging 2005 Best paper award, “Per-pixel integration time controlled image sensor” ECCTD Ireland 2003 Product of the year at Stuttgart Vision fair: Bi-i camera 2002 John von Kemeny Award 2000 Denes Gabor Patent Award 1999 General Electrics Scholarship Spcecial issues, sessions sessions 2004: Special issue: IEEE Transactions on Circuits and Systems—I: Fundamental Theory and Applications (TCAS-I), „CNN Technology and Active Wave Computing: Analog-and-logic cellular machines integrating sensors and/or actuators” guest co-editor 2003: Special session: IEEE International symphosium on Circuits and Systems (ISCAS) „Spatiotemporal cellular vision systems” co-organizer 2002: Journal of Circuits, Systems, and Computers, „CNN Technology and Visual Microprocessors”, guest co-editor
Publications[ order by time]
[ order by categories ]
[ order by authors]
2010.- Retinal approaching object detector model implementation and validation.
- Bio-inspired looming object detector algorithm on the Eye-RIS focal plane-processor system.
- Digital processor array implementation aspects of a 3D multi-layer vision architecture.
Authors: Földesy, Péter; Carmona-Galan, R.; Zarándy, Ákos; Rekeczky, Cs.; Rodríguez-Vázquez, A.; Roska, TamásEditor: Roska, Tamás; Gilli, Marco; Zarándy, ÁkosDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2010. 02. 03.Published by: 12th international workshop on cellular nanoscale networks and their applications. CNNA 2010. Berkeley, 2010. (Page: 329-332.)Download article: [html]
- Displacement calculation algorithm on a heterogeneous multi-layer cellular sensor processor array.
- Cellular multi-core processor carrier chip for nanoantenna integration and experiments.
- Low-power processor array design strategy for solving computationally intensive 2D topographic problems.
- A 3-D chip architecture for optical sensing and concurrent processing.
Authors: Rodríguez-Vázquez, A.; Carmona, R.; Domínguez Matas, C.; Suárez-Cambre, M.; Brea, V.; Pozas, F.; Linán, G.; Földesy, Péter; Zarándy, Ákos; Rekeczky, CsabaEditor: Berghmans, F.; Mignani, A. G.; van Hof, C. A.Department: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2010.Published by: Optical sensing and detection. Brussels, 2010. (Proceedings of SPIE 7726.) (Page: 772613-1-772613-12.)Download article: [html]
- Many-core processor architectures for topographic computations. Beyond parallelism.
2004.- Topographic and non-topographic neural network based computational platform for UAV applications
- Cellular multiadaptive analogic achitecture: a computational framework for UAV applications
- Bi-i: a standalone cellular vision system. Part II: Topographic and non-topographic algorithms and related applications
- Receptive field atlas and related CNN models
Authors: Gál, V; Hámori, J; Roska, T; Bálya, D; Borostyánkői, ZS; Brendel, M; Lotz, K; Négyessy, L; Orzó, L; Petrás, I; Rekeczky, CS; Takács, J; Venetiáner, P; Vidnyánszky, Z; Zarándy, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2004.Published by: INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS (Volume no.: 14, Issue no.: 2, Page: 551-584)
- Analog VLSI based Bayesian multi-scale optical flow estimation. (Research report of the Analogical and Neural Computing Laboratory DNS-7-2004)
- Bayesian incorporation of multiple scales in optical flow estimation
- High dynamic range perception with spatially variant exposure
- Adaptive perception with locally-adaptable sensor array
- Adaptive perception with locally adaptable sensor array
- Bi-i: a standalone cellular vision system. Part I. Architecture and ultra high frame rate processing examples
2000.- Dennis Gabor as the initiator of optical computing: Importance and prospects of optical computing and an optical implementation of the CNN-UM computer.
- An optical CNN implementation with stored programmability.
- An analogic CNN engine board with the 64x64 analog I/O CNN-UM chip.
- Analogikai celluláris számítógépek. Egy új számítógépelv.
- Futási eredmények az analogikai CNN-UM vizuális mikroprocesszorokon.
- 20 msec focal plane image processing.
- A mammogram diagnostic workstation.
- CNN universal chips for solving problems in object-oriented dynamic image coding.
- CNN technology in action.
Authors: Zarándy, Á; Espejo, S; Földesy, P; Kék, L; Linán, G; Rekeczky, C; Rodriguez-Vázquez, A; Roska, T; Szatmári, I; Szirányi, T; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Proceedings of the 6th IEEE international workshop on cellular neural networks and their applications. (CNNA 2000). Catania, 2000. (Page: 79-81)
- A development system for prototyping and interfacing CNN chips and for analogic algorithm design.
1999.- A new type of analogic CNN agorithmfor printed circuit board layout error detection.
- Fault-tolerant design of analogic CNN templates and algorithms - Part I: The binary output case.
- Noise estimation and measures for detection of clustered microalcifications.
- Local contrast measures for detection of microalcifications. (Research report of the Analogical and Neural Computing Laborarory, DNS-3-1999)
- An emulated digital CNN Implementation.
Authors: Keresztes, P; Zarándy, Á; Roska, T; Szolgay, P; Bezák, T; Hídvégi, T; Jónás, P; Katona, ADepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1999.Published by: JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (Volume no.: 23, Issue no.: 2-3, Page: 291-303)
- The computational infrastructure of analogic CNN computing - Part I: The CNN-UM chip prototyping system.
- CNN chip prototyping and development systems.
- CNN-based models for color vision and visual illusions.
- The computational infrastructure for cellular visual microprocessors.
Authors: Szolgay, P; Zarándy, Á; Zöld, S; Roska, T; Földesy, P; Kék, L; Kozek, T; László, K; Petrás, I; Rekeczky, CS; Szatmári, I; Bálya, DDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1999.Published by: MicroNeuro '99. Proceedings of the seventh international conference on microelectronics for neural, fuzzy, and bio-inspired systems. Granada, 1999. (Page: 54-60)
- The CADETWin application software design system - a tutorial.
Authors: Szolgay, P; László, K; Kék, L; Kozek, T; Nemes, L; Petrás, I; Rekeczky, CS; Szatmári, I; Zarándy, Á; Zöld, S; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1999.Published by: Design automation day on cellular visual microprocessor. Stresa, 1999. (Page: 58-68)
- The art of CNN template design.
1997.- New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips.
- Implementation of large-neighborhood nonlinear templates on the CNN universal machine.
- 0.8-?m CMOS two-dimensional programmable mixed-signal focal-plane array processor with on-chip binary imaging and instructions storage.
Authors: Domínguez-Castro, R; Espejo, S; Rodríguez-Vázquez, A; Carmona, RA; Földesy, P; Zarándy, Á; Szolgay, P; Szirányi, T; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEEE JOURNAL OF SOLID-STATE CIRCUITS (Volume no.: 32, Issue no.: 7, Page: 1013-1026)
- CNN univerzális számítógépek alkalmazása: térbeli logika, színek és illúziók.
- Ultra fast image processing via CNN technology.
- The art of CNN template design.(Research report of the Analogical and Neural Computing Laboratory, DNS-8-1997.)
- Mammogram and echocardiogram analysis by using cellular neural network technology.
- Celluláris és neurális áramkörök alkalmazása mammogramok kiértékelésében.
- Analogic mammogram diagnostic workstation boosted up with cellular neural networks. Version 1.1. (Research report of the Analogical and Neural Computing Laboratory, DNS-3-1997.)
- Functional measurements of the first analog input/output CNN universal chip. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1997.)
- CNN template design strategies and fault tolerant CNN template design - a survey.
1993.- The CNN paradigm - a short tutorial
- Color image processing by CNN
- Language, compiler, and operating system for the CNN supercomputer. Memorandum UCB/ERL M93/34
- The use of CNN models in the subcortical visual pathway
Authors: Roska, T; Hámori, J; Lábos, E; Lotz, K; Orzó, L; Takács, J; Venetianer, PL; Vidnyánszky, Z; Zarándy, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1993.Published by: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS (Volume no.: 40, Issue no.: 3, Page: 182-195)
1992.- CNNHAC cellular neural network simulator using hardware accelerator board. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-11-1992.)
- Some novel capabilities of CNN. Game of life and examples of multipath algorithms. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-3-1992.)
- Some novel capabilities of CNN: Game of life and examples of multipath algorithms
- CNNM multi-layer cellular neural network simulator.Version 2.4. 1992. User's guide. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-12-1992.)
- A digital multiprocessor hardware accelerator board for cellular neural networks: CNN-HAC
Authors: Roska, T; Bártfay, G; Szolgay, P; Szirányi, T; Radványi, A; Kozek, T; Ugray, ZS; Zarándy, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1992.Published by: INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS (Volume no.: 20, Issue no.: 5, Page: 589-599)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
- DUALCOMP dual CNN compiler to CNN-HAC1 board. Version 2.0. 1992. User's guide
- The use of CNN models in the visual parthway. Part II: The amacrine cell in the modified retina model, simple LGN effects and motion related illusions. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-9-1992.)
Authors: Roska, T; Hámori, J; Lábos, E; Lotz, K; Orzó, L; Takács, J; Venetianer, P; Vidnyánszky, Z; Zarándy, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1992.Published by: The use of CNN models in the visual parthway. Part II: The amacrine cell in the modified retina model, simple LGN effects and motion related illusions. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-9-1992.) (Page: 51)
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