- Tamás Roska, full member of HAS, the head of the scientific council
 Address: 1111 Budapest, Lágymányosi u. 11.Room number: L 519Phone: +36 1 279 6151, +36 1 279 6155, +36 1 209 5263Fax: +36 1 209 5264E-mail: roskaEZT_TOROLJE_KI@EZT_TOROLJE_KIsztaki.huDepartment:
Publications[ order by time]
[ order by categories ]
[ order by authors]
Bezák, T- 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)
Borostyánkői, ZS- 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)
Brendel, M- 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)
- Gradient computation of continuous-time cellular neural/nonlinear networks with linear templates via the CNN universal machine.
- Adaptive image sensing and enhancement using the cellular neural network universal machine.
- Adaptive image sensing and enhancement using the adaptive cellular neural network universal machine.
- Functional representations of retina channels via the RefineC retina simulator
- 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)
- Implementing the multilayer retinal model on the complex-cell CNN-UM chip prototype
- A new computational model for CNN-UMS and its computational complexity
- Properties of the adaptive integration formula to compute the CNN dynamic equations
- Multi-channel spatio-temporal topographic processing for visual search and navigation
- Supervised and unsupervised art-like classifications of binary vectors on the CNN universal machine.
- A realistic mammalian retinal model implemented on the complex cell CNN universal machine.
- Basic mammalian retinal effects on the prototype complex cell CNN uiversal machine.
- A CNN framework for modeling parallel processing in a mammalian retina.
- Implementing a retinal visual language in CNN: a neuromorphic study.
- A CNN model framework and simulator for biological sensory systems.
- A qualitative model-framework for spatio-temporal effects in vertebrate retinas.
Authors: Bálya, D; Roska, B; Németh, E; Roska, T; Werblin, FDate: 2000.Published by: Proceedings of the 6th IEEE international workshop on cellular neural networks and their applications. (CNNA 2000). Catania (Page: 165-170)
- Analogikai celluláris számítógépek. Egy új számítógépelv.
- 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)
- Face and eye detection by CNN algorithms.
- CNN algorithms in face detection systems. A review. (Research report of the Analogical and Neural Computing Laboratory, DNS-6-1998.)
Bártfay, G- 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)
Bölöni, L- Hyperacuity in time: a CNN modell of a time-coding pathway of sound localization.
- Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.) (Page: 30)
- A Cellular Neural Network model of the time-coding pathway of sound localization-hyperacuity in time.
- A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.) (Page: 25)
Carmona, R- Implementing the multilayer retinal model on the complex-cell CNN-UM chip prototype
- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
- Learning on CNN universal machine chips.
- A 0.5mm CMOS CNN analog random access memory chip for massive image processing.
Authors: Carmona, R; Espejo, S; Dominguez-Castro, R; Rodriguez-Vázquez, A; Roska, T; Kozek, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998. (Page: 271-276)
Carmona, RA- 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)
Carmona-Galan, R.- 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]
- 3D multi-layer vision architecture for surveillance and reconnaissance applications.
Chua, LO- High-performance Viterbi decoder with circularly connected 2-D CNN unilateral cell array
- The CNN universal machine: 10 years later
- New spatial-temporal patterns and the first programmable on-chip bifurcation test bed
- Automatic detection and tracking of moving image target with CNN-UM via target probability fusion of multiple features
- Initiation and tracking of DIM target via fusion of feature probabilities with CNN-UM.
- New spatial-temporal patterns and the first programmable on-chip bifurcation test-bed.
- On the relationship between CNNs and PDEs.
- Optimal path finding with space- and time-variant metric weights via multi-layer CNN.
- CNN dynamics represents a broader class than PDEs.
- Cellular Neural Networks and visual computing.
- New spatial-temporal patterns and the first programmable on-chip bifurcation test-bed. (Research report of the Analogical and Neural Computing Laboratory DNS-6-2001.)
- Optimal path finding with space variant metric weights via multilayer CNN-UM.
- Dependant distance potential source algorithm for optimal path finding with the analogic CNN.
- CNN universal chips for solving problems in object-oriented dynamic image coding.
- The cellular neural network (CNN) and the CNN universal machine: concept, architecture and operation modes.
- Robust optical flow detection based on the distance transform with the CNN nonlinear circuits.
- Morphology and autowave metric on CNN applied to Bubble-Debris classification.
- Very low bit-rate video coding using cellular neural network universal machine.
- An um CMOS analog random acces memory chip for TeraOPS speed multimedia video processing.
- Implementation of Binary and gray-scale mathematical morphology on the CNN universal machine.
- Estimating optical flow with cellular neural networks.
- Spatio-temporal CNN algorithm for object segmentation and object recognition.
- Computing with front propagation: evolving interfaces and active contour models in continuous-time CNN. (Research report of the Analogical and Neural Computing Laboratory, DNS-9-1998.)
- Implementation of arbitrary Boolean functions on the CNN universal machine.
- Analysis of time-varying cellular neural networks for quadratic global optimization
- Some methods for practical halftoning on the CNN universal machine.
- A 0.5mm CMOS CNN analog random access memory chip for massive image processing.
Authors: Carmona, R; Espejo, S; Dominguez-Castro, R; Rodriguez-Vázquez, A; Roska, T; Kozek, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998. (Page: 271-276)
- Functional measurements of the first analog input/output CNN universal chip. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1997.)
- Bubble-debris classification via binary morphology and autowave metric on CNN.
- Object-oriented image analysis for very-low-bitrate video-coding systems using the CNN universal machine.
- Very low bit-rate video coding using cellular neural network universal machine. ( Memorandum of the Electronics Research Laboratory, UCB/ERL M97/46.)
- Image segmentation and edge detection via constrained diffusion and adaptive morphology: a CNN approach to bubble/debris image enhancement.
- LocRule user's guide. Version 2.2. ( Research report of the Analogical and Neural Computing Laboratory DNS-2-1997.)
- New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips.
- Spatial logic algorithm using basic morphological analogic CNN operators.
- Morphological operators on the CNN universal machine.
- Implementation of binary and gray-scale mathematical morphology on the CNN universal machine. ( Memorandum of the Electronics Research Laboratory, UCB/ERL M96/19)
- CNN model for identifying colors under different illumination condition via Land's experiments.
- Analogue combinatorics and cellular automata - key algorithms and layout design.
- Objectumorientált képanalízis CNN univeerzális gép felhasználásával.
- On object-oriented video coding using the CNN universal machine.
- An object-oriented approach to video coding via the CNN universal machine.
- New results and measurements related to dynamic image coding using CNN universal chips. (Memorandum of the Electronics Research Laboratory, UCB/ERL M96/58)
- Multi-scale image analysis on the CNN universal machine.
- Global optimization through time-varying Cellular Neural Networks.
- CNN universal chips crank up the computing power
- Intelligent image resolution enhancement by the CNN universal machine and its relevance to TV picture enhancement
- The analogic cellular neural network as a bionic eye
- Analogic CNN algorithms for some image compression and restoration tasks
- Translating neuromorphic CNN visual models to the analogic visual microprocessors
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.)
Authors: Roska, T; Chua, LO; Wolf, D; Kozek, T; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.) (Page: 19)
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques
- On a framework of complexity of computations on flows - implemented on the CNN universal machine. (Research report of the Analogical and Neural Computing Laboratory DNS-15-1995.)
- Analogic CNN algorithms and applications - chip-development system and case studies
- Smart image scanning algorithms for the CNN universal machine
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.)
Authors: Kozek, T; Chua, LO; Roska, T; Wolf, D; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.) (Page: 13)
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples
- The world of analogic CNN spatiotemporal dynamics - a review
- Stored program cellular neural networks - an introduction
- Cellular neural networks - a tutorial on programmable nonlinear dynamics in space
- Novel types of analogic CNN algorithms for recognizing bank-notes
- Novel types of analogic CNN algorithms for recognizing bank-notes. (Memorandum UCB/ERL M94/29.)
- The analogic cellular neural network as a bionic eye.(Memorandum UCB/ERL M94/70.)
- Analogic CNN algorithms for some image compression and restoration tasks. (Memorandum UCB/ERL M94/30.)
- Analog combinatorics and cellular automata - key algorithms and layout design
- Analog combinatorics and cellular automata - key algorithms and layout design. (Research report of the Analogical and Neural Computing Laboratory. DNS-7-1994.)
- Random variations in CNN templates: theoretical models and empirical studies
- Solving partial differential equations by CNN. (Research report of the Analogical and Neural Computing Laboratory. DNS-2-1994.)
- Deblurring of images by cellular neural networks with applications to microscopy
- Some examples of preprocessing analog images with discrete-time cellular neural networks
- A current-mode DTCNN universal chip
- A fast, complex and efficient test implementation of the CNN universal machine
- Design of linear cellular neural networks for motion sensitive filtering
- Stability of cellular neural networks with dominant nonlinear and delay-type templates
- Solving partial differential equations by CNN (Research report of the Dual and Neural Computing Systems Laboratory DNS-4-1993)
- Solving partial differential equations by CNN
- Language, compiler, and operating system for the CNN supercomputer. Memorandum UCB/ERL M93/34
- Color image processing by CNN
- The CNN universal machine: an analogic array computer
- Genetic algorithm for CNN template learning
- Image halftoning with cellular neural networks
- The CNN paradigm - a short tutorial
- The CNN paradigm
- The CNN is universal as the turing machine
- The analogic CNN paradigm - programmable nonlinear dynamics in space
- A two-layer Radon transform cellular neural network
- Optically realized feedforward-only cellular neural networks
- Stability of cellular neural networks with dominant nonlinear and delay-type templates. (Memo UCB/ERL No. M92/121.)
- Stability and dynamics of delay-type general and cellular neural networks
- The dual CNN analog software - a programmable analog, nonlinear, dynamic, 3D computing array plus logic to form a "multi-screen theater" on silicon. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-2-1992.)
Authors: Roska, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1992.Published by: The dual CNN analog software - a programmable analog, nonlinear, dynamic, 3D computing array plus logic to form a "multi-screen theater" on silicon. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-2-1992.) (Page: 28)
- Detecting moving and standing objects using cellular neural network
- The CNN universal machine Part 2: Programmability and applications
- The CNN universal machine and supercomputer. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-18-1992.)
- Cellular neural networks with non-linear and delay-type template elements and non-uniform grids
- Cellular neural networks with nonlinear and delay-type template elements
- Cellular neural networks: theory and circuit design
- Genetic algorithm for CNN template learning. (Memo UCB/ERL No. M92/82.)
- Programmable analogue VLSI CNN chip with local digital logic
- 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
- The CNN universal machine Part 1: The architecture
Csapodi, M- A mammogram diagnostic workstation.
- 20 msec focal plane image processing.
- Analogikai celluláris számítógépek. Egy új számítógépelv.
- 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)
- Invertible operations on a cellular neural network universal machine.
- High speed calculation of cryptographic hash functions by CNN chips.
- Mammogram and echocardiogram analysis by using cellular neural network technology.
- 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.)
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- An embedded use of 2D cryptography schemes in video coding using the CNN universal marchine architecture. Part 1: 2D cryptography via CNN - an introduction. (Research report of the Analogical and Neural Computing Laboratory, DNS-12-1996.)
Authors: Csapodi, M; Roska, T; Wandewalle, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: An embedded use of 2D cryptography schemes in video coding using the CNN universal marchine architecture. Part 1: 2D cryptography via CNN - an introduction. (Research report of the Analogical and Neural Computing Laboratory, DNS-12-1996.) (Page: 29)
- Dynamic analogic CNN algorithms for a complex recognition task - a first step towards a bionic eyeglass.
- Adaptive histogram equalization with Cellular Neural Networks.
- Dynamic analogic CNN algorithms for a complex recognition task - a first step a bionic eyeglass. (Research report of the Analogical and Neural Computing Laboratory DNS-4-1995.)
- Some novel analogic CNN algorithms for object rotation, 3D interpolation-approximation, and a "door-in-a-floor" problem
Domínguez-Castro, R- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
- An um CMOS analog random acces memory chip for TeraOPS speed multimedia video processing.
- Learning on CNN universal machine chips.
- 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)
Espejo, S- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
- 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)
- Learning on CNN universal machine chips.
- A 0.5mm CMOS CNN analog random access memory chip for massive image processing.
Authors: Carmona, R; Espejo, S; Dominguez-Castro, R; Rodriguez-Vázquez, A; Roska, T; Kozek, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998. (Page: 271-276)
- 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)
Fehér, B- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- Per-pixel integration time controlled image sensor
- Various implementations of topographic, sensory, cellular wave computers
- Analogic cellular PDE machines.
- A development system for prototyping and interfacing CNN chips and for analogic algorithm design.
- 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)
- CNN chip prototyping and development systems.
- 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 computational infrastructure of analogic CNN computing - Part I: The CNN-UM chip prototyping system.
- Fault-tolerant design of analogic CNN templates and algorithms - Part I: The binary output case.
- Fault tolerant design of analogic CNN templates and algorithms. Part I: The binary output case.(Research report of the Analogical and Neural Computing Laboratory, DNS-3-1998.)
- Fault tolerant CNN template design and optimatization based on chip measurements.
- Functional measurements of the first analog input/output CNN universal chip. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1997.)
- 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)
- Distance preserving 1D turing-pattern models via CNN, implementing of complex-valued CNN, and solving a simple inverse pattern problem (detection).( Research report of the Analogical and Neural Computing Laboratory, DNS-3-1996.)
- Distance preserving 1D turing-wave models via CNN, implementation of complex-valued CNN and solving a simple inverse pattern problem (detection).
- VISCUBE: a multi-layer vision chip.
- 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]
- 3D multi-layer vision architecture for surveillance and reconnaissance applications.
- 3D integrated scalable focal-plane processor array.
- High performance processor array for image processing.
- Digital implementation of the cellular sensor-computers
- 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)
- Collision prediction via the CNN universal machine.
Hirakawa, S- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
Horváth, E. Á.- Periodicity enhancement of two-mode stochastic oscillators in a CNN type architecture.
Authors: Máté, G.; Horváth, E. Á.; Káptalan, E.; Tunyagi, A.; Néda, Z.; 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: 313-317.)Download article: [html]
Hámori, J- 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)
- Hyperacuity in time: a CNN modell of a time-coding pathway of sound localization.
- CNN models of receptive field dynamics of the central visual system neurons.
- Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.) (Page: 30)
- CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.(Research report of the Neuromorphic Information Technology Graduate Center. NIT-2-1966.)
- CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.
- Cellular Neural Network realizations of neuron models with diverse spiking patterns.
- A Cellular Neural Network model of the time-coding pathway of sound localization-hyperacuity in time.
- A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.) (Page: 25)
- An analogic phenomenological CNN algorithm to model the mouth detection task of the inferotemporal cortex discovered by I. Fujita
- Analogic CNN models of some qualitative pattern recognition tasks in the inferotemporal cortex. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-1-1995.)
- Some cortical spiking neuron models using CNN
Authors: Lotz, K; Vidnyánszky, Z; Roska, T; Vandewalle, J; Hámori, J; Jacobs, A; Werblin, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1994.Published by: Proceedings of the third IEEE international workshop on cellular neural networks and their applications. CNNA-94. Rome, 1994 (Page: 41-46)
- A CNN model of the feed-forward part of the LGN (Research report of the Dual and Neural Computing Systems Laboratory DNS-6-1993)
- 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)
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
Hídvégi, T- 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)
Jiménez-Garrido, F- Exploration of spatial-temporal dynamic phenomena in a 32
- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
- 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)
Kasem, I- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
- Analogic CNN algorithms in bronchogenic carcinoma analysis
Katona, A- 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)
Keresztes, P- 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)
- CASTLE: an emulated digital architecture; design issues, new results.
- An emulated digital architecture implementing the CNN universal machine.
Kozek, T- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- CNN universal chips for solving problems in object-oriented dynamic image coding.
- Morphology and autowave metric on CNN applied to Bubble-Debris classification.
- 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)
- An um CMOS analog random acces memory chip for TeraOPS speed multimedia video processing.
- Analogic Macro Code (AMC). Extended assembly language for CNN computers. Version 1.1.(Research report of the Analogical and Neural Computing Laboratory, DNS-10-1998.)
- A 0.5mm CMOS CNN analog random access memory chip for massive image processing.
Authors: Carmona, R; Espejo, S; Dominguez-Castro, R; Rodriguez-Vázquez, A; Roska, T; Kozek, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998. (Page: 271-276)
- New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips.
- New results and measurements related to dynamic image coding using CNN universal chips. (Memorandum of the Electronics Research Laboratory, UCB/ERL M96/58)
- Multi-scale image analysis on the CNN universal machine.
- A double time-scale CNN for solving two-dimensionalnavier-stokes equations.
- CNN universal chips crank up the computing power
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.)
Authors: Roska, T; Chua, LO; Wolf, D; Kozek, T; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.) (Page: 19)
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques
- Smart image scanning algorithms for the CNN universal machine
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.)
Authors: Kozek, T; Chua, LO; Roska, T; Wolf, D; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.) (Page: 13)
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples
- Stored program cellular neural networks - an introduction
- Cellular neural networks - a tutorial on programmable nonlinear dynamics in space
- Solving partial differential equations by CNN. (Research report of the Analogical and Neural Computing Laboratory. DNS-2-1994.)
- Analogic cellular neural network processing
- A double time-scale CNN for solving 2-D Navier-Stokes equations
- Solving partial differential equations by CNN (Research report of the Dual and Neural Computing Systems Laboratory DNS-4-1993)
- Solving partial differential equations by CNN
- Genetic algorithm for CNN template learning
- The CNN paradigm - a short tutorial
- 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)
- Genetic algorithm for CNN template learning. (Memo UCB/ERL No. M92/82.)
Káptalan, E.- Periodicity enhancement of two-mode stochastic oscillators in a CNN type architecture.
Authors: Máté, G.; Horváth, E. Á.; Káptalan, E.; Tunyagi, A.; Néda, Z.; 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: 313-317.)Download article: [html]
- 3D echocardiography powered by CNN technology.
- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- A mammogram diagnostic workstation.
- 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)
- 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)
- Fault-tolerant design of analogic CNN templates and algorithms - Part I: The binary output case.
- Local contrast measures for detection of microalcifications. (Research report of the Analogical and Neural Computing Laborarory, DNS-3-1999)
- Fault tolerant design of analogic CNN templates and algorithms. Part I: The binary output case.(Research report of the Analogical and Neural Computing Laboratory, DNS-3-1998.)
- Fault tolerant CNN template design and optimatization based on chip measurements.
- 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.)
- Analogic CNN program library version 6.2. (Research report of the Analogical and Neural Computing Laboratory DNS-7-1995.)
- Mammogram analysis using CNN algorithms
- Design of analogic CNN algorithms for mammogram analysis
Linán, G- 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)
Lotz, K- 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)
- Hyperacuity in time: a CNN modell of a time-coding pathway of sound localization.
- Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.) (Page: 30)
- Cellular Neural Network realizations of neuron models with diverse spiking patterns.
- A Cellular Neural Network model of the time-coding pathway of sound localization-hyperacuity in time.
- A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.) (Page: 25)
- An analogic phenomenological CNN algorithm to model the mouth detection task of the inferotemporal cortex discovered by I. Fujita
- Analogic CNN models of some qualitative pattern recognition tasks in the inferotemporal cortex. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-1-1995.)
- Cellular neural networks. Part 2. Application examples - a tutorial
- Some cortical spiking neuron models using CNN
Authors: Lotz, K; Vidnyánszky, Z; Roska, T; Vandewalle, J; Hámori, J; Jacobs, A; Werblin, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1994.Published by: Proceedings of the third IEEE international workshop on cellular neural networks and their applications. CNNA-94. Rome, 1994 (Page: 41-46)
- 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)
- A multilayer cellular neural network model of the two pathways of the retina related to dark adaptation. (Katholieke Universiteit Leuven. Department Elektrotechniek. ESAT-SISTA/TR 1993-33I)
Authors: Lotz, K; Vandewalle, J; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1993.Published by: A multilayer cellular neural network model of the two pathways of the retina related to dark adaptation. (Katholieke Universiteit Leuven. Department Elektrotechniek. ESAT-SISTA/TR 1993-33I) (Page: 12)
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
Lábos, E- 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)
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
László, K- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- 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)
- Video compression algorithms on the CNN visual microprocessor - early segmentation and basic motion estimation.
- CNN models of receptive field dynamics of the central visual system neurons.
- Early segmentation in video compression using CNN processors.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
Máté, G.- Periodicity enhancement of two-mode stochastic oscillators in a CNN type architecture.
Authors: Máté, G.; Horváth, E. Á.; Káptalan, E.; Tunyagi, A.; Néda, Z.; 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: 313-317.)Download article: [html]
Nemes, L- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- 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)
- Prefiltering and classification CNN algorithms for a chromosome scoring and aberration detection system.
Authors: Nemes, L; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 international symposium on nonlinear theory and its applications. NOLTA'98. Proceedings. Crans-Montana, 1998. Vol. 2. Crans Montana, Polytechniques et Univ. Romandes, 1998.(Presses Polytechniques et Universitaires Romandes.) (Page: 683-686)
- Implementation of arbitrary Boolean functions on the CNN universal machine.
- LocRule user's guide. Version 2.2. ( Research report of the Analogical and Neural Computing Laboratory DNS-2-1997.)
- Improved scoring and semi-automatic screening of human peripheral blood chromosomes by CNN visual system.
- Analogic CNN algorithms for 3D interpolation-approximation and object rotation using controlled switched templates.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- Cellular neural networks for image deconvolution and enhancement: a microscopy toolkit. (Research report of the Analogical and Neural Computing Laboratory DNS-11-1995.)
- Cellular neural networks for image deconvolution and enhancement: a microscopy toolkit
- A CNN model of oscillation and chaos in ant colonies: a case study
- Analogic CNN algorithm for object rotation using controlled switched templates. (Research report of the Analogical and Neural Computing Laboratory DNS-3-1995.)
- Deblurring of images by cellular neural networks with applications to microscopy
- Some novel analogic CNN algorithms for object rotation, 3D interpolation-approximation, and a "door-in-a-floor" problem
- The network behind spatio-temporal patterns: building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.
Authors: Rekeczky, CS; Roska, T; Nemeth, E; Werblin, FSDate: 2001.Published by: INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS (Volume no.: 29, Issue no.: 2, Page: 197-234)
- The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.)
Authors: Rekeczky, CS; Roska, B; Nemeth, E; Wang, M; Weblin, F; Roska, TDate: 1998.Published by: The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.) (Page: 34)
Nishio, Y- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
- Image segmentation by soft computing on the CNN universal machine - a case study of echocardiography.
- CNN based adaptive smoothing and some novel types of nonlinear operators for grey-scale image processing
- Analogic CNN algorithms in bronchogenic carcinoma analysis
Nishitani, H- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
- Analogic CNN algorithms in bronchogenic carcinoma analysis
Néda, Z.- Periodicity enhancement of two-mode stochastic oscillators in a CNN type architecture.
Authors: Máté, G.; Horváth, E. Á.; Káptalan, E.; Tunyagi, A.; Néda, Z.; 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: 313-317.)Download article: [html]
- Cellular neural networks for NP-hard optimization.
- Stochastic optimatization of spin-glasses on cellular neural/nonlinear network based processors.
Négyessy, L- 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)
- CNN models of receptive field dynamics of the central visual system neurons.
- POAC (programmable optical array computer) applied for target recognition and tracking
- Laptop POAC: a compact optical implementation of CNN-UM
- A CNN image-compression algorithm for improved utilization of on-chip resources
- 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)
- Evolution of the programmable optical array computer (POAC)
- Flexibly programmable opto-electronic analogic CNN computer (POAC) implementation applying an efficient, unconventional optical correlator architecture
- Programmable optical CNN implementation based on the template pixels' angular coding.
- Basic mammalian retinal effects on the prototype complex cell CNN uiversal machine.
- Programmable opto-electronic CNN implementation provides a new and powerful tool for image processing applications. (Research report of the Analogical and Neural Computing Laboratory DNS-9-2001.)
- Programmable analogic cellular optical computer using bacteriorhodopsin as analog rewriteable image memory.
- Design aspects of an optical correlator based CNN implementation.
- Dennis Gabor as the initiator of optical computing. Importance and prospects of optical computing and an optical implementation of the CCN-UM computer.
- An advanced joint Fourier transform correlator (JTC).
- 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.
- CNN models of receptive field dynamics of the central visual system neurons.
- CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.(Research report of the Neuromorphic Information Technology Graduate Center. NIT-2-1966.)
- CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.
- 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)
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
- CNN as curve shortening flow computer
- 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)
- Implementing the multilayer retinal model on the complex-cell CNN-UM chip prototype
- Exploration of spatial-temporal dynamic phenomena in a 32
- On the effect of boundary conditions on CNN dynamics: stability and instability; bifurcation processes and chaotic phenomena
- New spatial-temporal patterns and the first programmable on-chip bifurcation test bed
- Analog addition/subtraction on the CNN-UM chip with short-time superimposition of input signals
- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
- New spatial-temporal patterns and the first programmable on-chip bifurcation test-bed.
- New spatial-temporal patterns and the first programmable on-chip bifurcation test-bed. (Research report of the Analogical and Neural Computing Laboratory DNS-6-2001.)
- Application of direction constrained and bipolar waves for pattern recognition.
- CNN chip prototyping and development systems.
- 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)
Puffer, F- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.)
Authors: Roska, T; Chua, LO; Wolf, D; Kozek, T; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.) (Page: 19)
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.)
Authors: Kozek, T; Chua, LO; Roska, T; Wolf, D; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.) (Page: 13)
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples
- Analogic CNN algorithm for 3D interpolation-approximation. Research report of the Analogical and Neural Computing Laboratory DNS-2-1995.)
- ACL (an analogical CNN language) Version 2.0. User's manual. (Research report of the Analogical and Neural Computing Laboratory. DNS-3-1994.)
- On a CNN chip-prototyping system
- Some novel analogic CNN algorithms for object rotation, 3D interpolation-approximation, and a "door-in-a-floor" problem
- DUALCOMP dual CNN compiler to CNN-HAC1 board. Version 2.0. 1992. User's guide
- 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)
- Detecting moving and standing objects using cellular neural network
- The CNN workstation. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-10-1992.)
- Optical tracking system for automatic guided vehicles using cellular neural networks. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-15-1992)
- Optical tracking system for automatic guided vehicles using cellular neural networks
- Analogic CNN algorithms for 3D interpolation-approximation and object rotation using controlled switched templates.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- 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)
- 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.
- A stored program 2nd order/3-layer complex cell CNN-UM.
Rekeczky, Cs.- 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]
- 3D multi-layer vision architecture for surveillance and reconnaissance 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)
- Exploration of spatial-temporal dynamic phenomena in a 32
- Multi-channel spatio-temporal topographic processing for visual search and navigation
- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
- Analogic cellular PDE machines.
- A realistic mammalian retinal model implemented on the complex cell CNN universal machine.
- Basic mammalian retinal effects on the prototype complex cell CNN uiversal machine.
- Implementing a retinal visual language in CNN: a neuromorphic study.
- Dennis Gabor as the initiator of optical computing. Importance and prospects of optical computing and an optical implementation of the CCN-UM computer.
- The network behind spatio-temporal patterns: building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.
Authors: Rekeczky, CS; Roska, T; Nemeth, E; Werblin, FSDate: 2001.Published by: INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS (Volume no.: 29, Issue no.: 2, Page: 197-234)
- Cellular arrays.
- Calculating local and global PDEs by analogic diffusion and wave algorithms.
- Analogic cellular PDE machines. (Research report of the Analogical and Neural Computing Laboratory DNS-2-2001.)
- 3D echocardiography powered by CNN technology.
- Morphology and autowave metric on CNN applied to Bubble-Debris classification.
- 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)
- A nonlinear wave mertic and its CNN implementation for object classification.
- CNN based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography.
- Spatio-temporal CNN algorithm for object segmentation and object recognition.
- CNN-based difference-controlled adaptive non-linear image filters.
- Computing with front propagation: evolving interfaces and active contour models in continuous-time CNN. (Research report of the Analogical and Neural Computing Laboratory, DNS-9-1998.)
- The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.)
Authors: Rekeczky, CS; Roska, B; Nemeth, E; Wang, M; Weblin, F; Roska, TDate: 1998.Published by: The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.) (Page: 34)
- Mammogram and echocardiogram analysis by using cellular neural network technology.
- Bubble-debris classification via binary morphology and autowave metric on CNN.
- The use of CNN technology in echocardiography: spatio-temporal nonlinear filtering and contour detection. (Research report of the Analogical and Neural Computing Laboratory, DNS-7-1997.)
- Image segmentation and edge detection via constrained diffusion and adaptive morphology: a CNN approach to bubble/debris image enhancement.
- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
- Improved scoring and semi-automatic screening of human peripheral blood chromosomes by CNN visual system.
- Image segmentation by soft computing on the CNN universal machine - a case study of echocardiography.
- CNN based self-adjusting nonlinear filters.(Research report of the Analogical and Neural Computing Laboratory, DNS-4-1996.)
- CNN based self-adjusting nonlinear filters.
- Rotation invariant detection of moving and standing objects using analogic cellular neural network algorithms based on ring codes
- Diminishment and enlargement of binary pictures using slightly space variant cellular neural network architecture
- CNN based adaptive smoothing and some novel types of nonlinear operators for grey-scale image processing
- Analogic CNN algorithms in bronchogenic carcinoma analysis
- Mammogram analysis using CNN algorithms
- Design of analogic CNN algorithms for mammogram analysis
- Rotation invariant detection of the objects using analogic cellular neural network algorithms
- Enhancing X-ray mammograms by analogic CNN operations: some experiments. (Neuromorphic Information Technology, Graduate Center report NIT-2-1994.)
Rodriguez-Vázquez, A- 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)
- Review of CMOS implementations of the CNN universal machine-type visual microprocessors.
- A 0.5mm CMOS CNN analog random access memory chip for massive image processing.
Authors: Carmona, R; Espejo, S; Dominguez-Castro, R; Rodriguez-Vázquez, A; Roska, T; Kozek, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998. (Page: 271-276)
Rodríguez-Vázguez, Á- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
Rodríguez-Vázquez, A- Bio-inspired 0.35 (mikro)m CMOS time-to-digital converter with 29.3 ps LSB
- Implementing the multilayer retinal model on the complex-cell CNN-UM chip prototype
- Exploration of spatial-temporal dynamic phenomena in a 32
- Learning on CNN universal machine chips.
- 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)
Rodríguez-Vázquez, A.- 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]
- 3D multi-layer vision architecture for surveillance and reconnaissance applications.
Roska, B- A CNN framework for modeling parallel processing in a mammalian retina.
- Implementing a retinal visual language in CNN: a neuromorphic study.
- A qualitative model-framework for spatio-temporal effects in vertebrate retinas.
Authors: Bálya, D; Roska, B; Németh, E; Roska, T; Werblin, FDate: 2000.Published by: Proceedings of the 6th IEEE international workshop on cellular neural networks and their applications. (CNNA 2000). Catania (Page: 165-170)
- The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.)
Authors: Rekeczky, CS; Roska, B; Nemeth, E; Wang, M; Weblin, F; Roska, TDate: 1998.Published by: The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.) (Page: 34)
- Per-pixel integration time controlled image sensor
- Various implementations of topographic, sensory, cellular wave computers
- Cellular wave computers for brain-like spatial-temporal sensory computing
- Cellular wave computers and CNN technology - an SoC architecture with xK processors and sensor arrays
- High-performance Viterbi decoder with circularly connected 2-D CNN unilateral cell array
- High dynamic range perception with spatially variant exposure
- Adaptive perception with locally-adaptable sensor array
- Adaptive perception with locally adaptable sensor array
- Stability of multi-layer cellular neural/non-linear networks
- Bayesian incorporation of multiple scales in optical flow estimation
- POAC (programmable optical array computer) applied for target recognition and tracking
- Laptop POAC: a compact optical implementation of CNN-UM
- CNN as curve shortening flow computer
- A CNN image-compression algorithm for improved utilization of on-chip resources
- Functional representations of retina channels via the RefineC retina simulator
- Holistic feature extraction from handwritten words on wave computers
- 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)
- Feature extraction CNN algorithms for artificial immune systems
- An artificial immune system for visual applications with CNN-UM
- An artificial immune system based visual analysis model and its real-time terrain surveillance application
- Implementing the multilayer retinal model on the complex-cell CNN-UM chip prototype
- A new computational model for CNN-UMS and its computational complexity
- Evolution of the programmable optical array computer (POAC)
- Flexibly programmable opto-electronic analogic CNN computer (POAC) implementation applying an efficient, unconventional optical correlator architecture
- Exploration of spatial-temporal dynamic phenomena in a 32
- Stability of multi-layer cellular neural/non-linear networks including a 2-layer complex cell CNN-UM
- Properties of the adaptive integration formula to compute the CNN dynamic equations
- Multi-channel spatio-temporal topographic processing for visual search and navigation
- Proactive, adaptive, cellular sensory-computer architecture via extending the CNN univesal machine
- Computational and computer complexity of analogic cellular wave computers. (Research report of the Analogical and Neural Computing Laboratory DNS-5-2003)
- Computational and computer complexity of analogic cellular wave computers
- The CNN universal machine: 10 years later
- Bonyolultság és egyszerűség analogikai hullám-számítógépekben és néhány idegi jelenség modelljében
- On the effect of boundary conditions on CNN dynamics: stability and instability; bifurcation processes and chaotic phenomena
- New spatial-temporal patterns and the first programmable on-chip bifurcation test bed
- High speed road boundary detection on the images for autonomous vehicle with the multi-layer CNN
- Automatic detection and tracking of moving image target with CNN-UM via target probability fusion of multiple features
- Analog addition/subtraction on the CNN-UM chip with short-time superimposition of input signals
- Intimate integration of shape codes and linguistic framework in handwriting recognition via wave computers
- Immune response inspired CNN algorithms for many-target detection
- A Bio-inspired two-layer mixed-signal flexible programmable chip for early vision
Authors: Carmona, R; Jiménez-Garrido, F; Domínguez-Castro, R; Espejo, S; Roska, T; Rekeczky, CS; Petrás, I; Rodríguez-Vázguez, ÁDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2003.Published by: IEEE TRANSACTIONS ON NEURAL NETWORKS (Volume no.: 14, Issue no.: 5, Page: 1313-1336)
- Initiation and tracking of DIM target via fusion of feature probabilities with CNN-UM.
- New spatial-temporal patterns and the first programmable on-chip bifurcation test-bed.
- Supervised and unsupervised art-like classifications of binary vectors on the CNN universal machine.
- On the relationship between CNNs and PDEs.
- Computational and computer complexity of analogic cellular wave computers.
- Programmable optical CNN implementation based on the template pixels' angular coding.
- Basic mammalian retinal effects on the prototype complex cell CNN uiversal machine.
- Analogic cellular PDE machines.
- A realistic mammalian retinal model implemented on the complex cell CNN universal machine.
- Basic mammalian retinal effects on the prototype complex cell CNN uiversal machine.
- Gradient computation of continuous-time cellular neural/nonlinear networks with linear templates via the CNN universal machine.
- A CNN framework for modeling parallel processing in a mammalian retina.
- Adaptive image sensing and enhancement using the cellular neural network universal machine.
- Optimal path finding with space- and time-variant metric weights via multi-layer CNN.
- Toward visual microprocessors. Invited paper.
- CNN dynamics represents a broader class than PDEs.
- Cellular Neural Networks and visual computing.
- Implementing a retinal visual language in CNN: a neuromorphic study.
- Programmable opto-electronic CNN implementation provides a new and powerful tool for image processing applications. (Research report of the Analogical and Neural Computing Laboratory DNS-9-2001.)
- Programmable analogic cellular optical computer using bacteriorhodopsin as analog rewriteable image memory.
- Design aspects of an optical correlator based CNN implementation.
- Dennis Gabor as the initiator of optical computing. Importance and prospects of optical computing and an optical implementation of the CCN-UM computer.
- An advanced joint Fourier transform correlator (JTC).
- Notes on the complexity of computations on analogic wave computers. (Research report of the Analogical and Neural Computing Laboratory DNS-8-2001.)
- AnaLogic wave computers - wave-type algorithms: canonical description, computer classes, and computational complexity.
- The network behind spatio-temporal patterns: building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.
Authors: Rekeczky, CS; Roska, T; Nemeth, E; Werblin, FSDate: 2001.Published by: INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS (Volume no.: 29, Issue no.: 2, Page: 197-234)
- Cellular arrays.
- Calculating local and global PDEs by analogic diffusion and wave algorithms.
- Analogic cellular PDE machines. (Research report of the Analogical and Neural Computing Laboratory DNS-2-2001.)
- 3D echocardiography powered by CNN technology.
- New spatial-temporal patterns and the first programmable on-chip bifurcation test-bed. (Research report of the Analogical and Neural Computing Laboratory DNS-6-2001.)
- Optimal path finding with space variant metric weights via multilayer CNN-UM.
- Dependant distance potential source algorithm for optimal path finding with the analogic CNN.
- A CNN model framework and simulator for biological sensory systems.
- A development system for prototyping and interfacing CNN chips and for analogic algorithm design.
- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- A mammogram diagnostic workstation.
- CNN universal chips for solving problems in object-oriented dynamic image coding.
- The cellular neural network (CNN) and the CNN universal machine: concept, architecture and operation modes.
- 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)
- 20 msec focal plane image processing.
- 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.
- Review of CMOS implementations of the CNN universal machine-type visual microprocessors.
- Computing with waves: the analogic cellular computing paradigm for elecrons, photons, and molecules.
- Analogic computing: system aspects of analogic CNN sensor computers.
- A stored program 2nd order/3-layer complex cell CNN-UM.
- Application of direction constrained and bipolar waves for pattern recognition.
- Robust optical flow detection based on the distance transform with the CNN nonlinear circuits.
- Collision prediction via the CNN universal machine.
- Noise estimation and measures for detection of clustered microcalcifications.
Authors: Csapody, M; Petrányi, Á; Liszka, G; Zárándy, Á; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Képfeldolgozók és alakfelismerok 2. konferenciája. (Second conference of the Hungarian Image Processing and Pattern Recognition Society.) Noszvaj, 2000. (Page: 69-74)
- Adaptive image sensing and enhancement using the adaptive cellular neural network universal machine.
- A qualitative model-framework for spatio-temporal effects in vertebrate retinas.
Authors: Bálya, D; Roska, B; Németh, E; Roska, T; Werblin, FDate: 2000.Published by: Proceedings of the 6th IEEE international workshop on cellular neural networks and their applications. (CNNA 2000). Catania (Page: 165-170)
- Analogikai celluláris számítógépek. Egy új számítógépelv.
- Érzékelő számítógépek - távjelenlét.
- Morphology and autowave metric on CNN applied to Bubble-Debris classification.
- CNN chip prototyping and development systems.
- 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)
- A nonlinear wave mertic and its CNN implementation for object classification.
- Very low bit-rate video coding using cellular neural network universal machine.
- Computer-sensors: spatial-temporal computers for analog array signals, dynamically integrated with sensors.
- The computational infrastructure of analogic CNN computing - Part I: The CNN-UM chip prototyping system.
- Towards computer-sensors by using the CNN-UM - via non-equilibrium algorithms.
- CNN based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography.
- Hyperacuity in time: a CNN modell of a time-coding pathway of sound localization.
- Video compression algorithms on the CNN visual microprocessor - early segmentation and basic motion estimation.
- 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)
- An analogic CNN algorithm for following continously moving objects. (Research report of the Analogical and Neural Computing Laboratory, DNS-8-1999.)
- 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 um CMOS analog random acces memory chip for TeraOPS speed multimedia video processing.
- Learning on CNN universal machine chips.
- Face and eye detection by CNN algorithms.
- Networks of language processors: a language theoretic framework for mainly locally connected processor arrays.
Authors: Csuhaj-Varjú, E; Roska, TDate: 1998.Published by: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998. (Page: 137-142)
- Implementation of Binary and gray-scale mathematical morphology on the CNN universal machine.
- CASTLE: an emulated digital architecture; design issues, new results.
- An emulated digital architecture implementing the CNN universal machine.
- Image compression by cellular neural networks.
- The CNN implementation of wave type metric for image analysis and classification.
- Estimating optical flow with cellular neural networks.
- Spatio-temporal CNN algorithm for object segmentation and object recognition.
- CNN computing infrastructure hosting nonlinear spatiotemporal algorithms - a review.
- Analogic CNN computing : architectural, implementation, and algorithmic advances - a review.
- CNN-based difference-controlled adaptive non-linear image filters.
- Computing with front propagation: evolving interfaces and active contour models in continuous-time CNN. (Research report of the Analogical and Neural Computing Laboratory, DNS-9-1998.)
- The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.)
Authors: Rekeczky, CS; Roska, B; Nemeth, E; Wang, M; Weblin, F; Roska, TDate: 1998.Published by: The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.) (Page: 34)
- CNN models of receptive field dynamics of the central visual system neurons.
- Prefiltering and classification CNN algorithms for a chromosome scoring and aberration detection system.
Authors: Nemes, L; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 international symposium on nonlinear theory and its applications. NOLTA'98. Proceedings. Crans-Montana, 1998. Vol. 2. Crans Montana, Polytechniques et Univ. Romandes, 1998.(Presses Polytechniques et Universitaires Romandes.) (Page: 683-686)
- Implementation of arbitrary Boolean functions on the CNN universal machine.
- Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: Hyperacuity in time: a CNN model of a time-coding pathway of sound localization. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1998.) (Page: 30)
- Early segmentation in video compression using CNN processors.
- Analogic Macro Code (AMC). Extended assembly language for CNN computers. Version 1.1.(Research report of the Analogical and Neural Computing Laboratory, DNS-10-1998.)
- Analysis of time-varying cellular neural networks for quadratic global optimization
- Fault tolerant design of analogic CNN templates and algorithms. Part I: The binary output case.(Research report of the Analogical and Neural Computing Laboratory, DNS-3-1998.)
- Fault tolerant CNN template design and optimatization based on chip measurements.
- Invertible operations on a cellular neural network universal machine.
- High speed calculation of cryptographic hash functions by CNN chips.
- CNN feasibility study: document image processing.(Research report of the Analogical and Neural Computing Laboratory, DNS-8-1998.)
- Some methods for practical halftoning on the CNN universal machine.
- A 0.5mm CMOS CNN analog random access memory chip for massive image processing.
Authors: Carmona, R; Espejo, S; Dominguez-Castro, R; Rodriguez-Vázquez, A; Roska, T; Kozek, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1998.Published by: 1998 fifth IEEE international workshop on cellular neural networks and their applications. CNNA 98. Proceedings. London, 1998. (Page: 271-276)
- CNN algorithms in face detection systems. A review. (Research report of the Analogical and Neural Computing Laboratory, DNS-6-1998.)
- CNN template design strategies and fault tolerant CNN template design - a survey.
- 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.)
- Bubble-debris classification via binary morphology and autowave metric on CNN.
- Object-oriented image analysis for very-low-bitrate video-coding systems using the CNN universal machine.
- Very low bit-rate video coding using cellular neural network universal machine. ( Memorandum of the Electronics Research Laboratory, UCB/ERL M97/46.)
- Ultra fast image processing via CNN technology.
- Space variant adaptive CNN - fault tolerance and plasticity.
- Implementation of CNN computing technology.
- The use of CNN technology in echocardiography: spatio-temporal nonlinear filtering and contour detection. (Research report of the Analogical and Neural Computing Laboratory, DNS-7-1997.)
- Image segmentation and edge detection via constrained diffusion and adaptive morphology: a CNN approach to bubble/debris image enhancement.
- LocRule user's guide. Version 2.2. ( Research report of the Analogical and Neural Computing Laboratory DNS-2-1997.)
- New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips.
- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
- 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)
- On additive cellular automata in one and two dimensions.
- Applications of CNN-UM chips in multimedia authentication. In: Research report of the Department of Electrical Engineering, ESAT-COSIC / TR 97-1.
- Spatial logic algorithm using basic morphological analogic CNN operators.
- Morphological operators on the CNN universal machine.
- Implementation of binary and gray-scale mathematical morphology on the CNN universal machine. ( Memorandum of the Electronics Research Laboratory, UCB/ERL M96/19)
- CNN model for identifying colors under different illumination condition via Land's experiments.
- Image compression by CNN.
- Analogue combinatorics and cellular automata - key algorithms and layout design.
- Distance preserving 1D turing-pattern models via CNN, implementing of complex-valued CNN, and solving a simple inverse pattern problem (detection).( Research report of the Analogical and Neural Computing Laboratory, DNS-3-1996.)
- Distance preserving 1D turing-wave models via CNN, implementation of complex-valued CNN and solving a simple inverse pattern problem (detection).
- Improved scoring and semi-automatic screening of human peripheral blood chromosomes by CNN visual system.
- Objectumorientált képanalízis CNN univeerzális gép felhasználásával.
- On object-oriented video coding using the CNN universal machine.
- An object-oriented approach to video coding via the CNN universal machine.
- CNN chip set architectures and the visual mouse.
- Image segmentation by soft computing on the CNN universal machine - a case study of echocardiography.
- CNN based self-adjusting nonlinear filters.(Research report of the Analogical and Neural Computing Laboratory, DNS-4-1996.)
- CNN based self-adjusting nonlinear filters.
- CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.(Research report of the Neuromorphic Information Technology Graduate Center. NIT-2-1966.)
- CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.
- Analogic CNN algorithms for 3D interpolation-approximation and object rotation using controlled switched templates.
- Cellular Neural Network realizations of neuron models with diverse spiking patterns.
- A Cellular Neural Network model of the time-coding pathway of sound localization-hyperacuity in time.
- New results and measurements related to dynamic image coding using CNN universal chips. (Memorandum of the Electronics Research Laboratory, UCB/ERL M96/58)
- Multi-scale image analysis on the CNN universal machine.
- A double time-scale CNN for solving two-dimensionalnavier-stokes equations.
- Methods for constructing physiologically motivated neuromorphic models in CNNs.
- Global optimization through time-varying Cellular Neural Networks.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- An embedded use of 2D cryptography schemes in video coding using the CNN universal marchine architecture. Part 1: 2D cryptography via CNN - an introduction. (Research report of the Analogical and Neural Computing Laboratory, DNS-12-1996.)
Authors: Csapodi, M; Roska, T; Wandewalle, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: An embedded use of 2D cryptography schemes in video coding using the CNN universal marchine architecture. Part 1: 2D cryptography via CNN - an introduction. (Research report of the Analogical and Neural Computing Laboratory, DNS-12-1996.) (Page: 29)
- Dynamic analogic CNN algorithms for a complex recognition task - a first step towards a bionic eyeglass.
- Adaptive histogram equalization with Cellular Neural Networks.
- CNN universal chips crank up the computing power
- Intelligent image resolution enhancement by the CNN universal machine and its relevance to TV picture enhancement
- The analogic cellular neural network as a bionic eye
- Image compression by CNN. (Research report of the Analogical and Neural Computing Laboratory DNS-13-1995.)
- Analogic CNN algorithms for some image compression and restoration tasks
- Analogic CNN algorithm for 3D interpolation-approximation. Research report of the Analogical and Neural Computing Laboratory DNS-2-1995.)
- Cellular neural networks for image deconvolution and enhancement: a microscopy toolkit. (Research report of the Analogical and Neural Computing Laboratory DNS-11-1995.)
- Cellular neural networks for image deconvolution and enhancement: a microscopy toolkit
- Translating neuromorphic CNN visual models to the analogic visual microprocessors
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.)
Authors: Roska, T; Chua, LO; Wolf, D; Kozek, T; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.) (Page: 19)
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques
- On a framework of complexity of computations on flows - implemented on the CNN universal machine. (Research report of the Analogical and Neural Computing Laboratory DNS-15-1995.)
- The CNN universal machine - a summary of an analogic supercomputer chip architecture for very high speed visual processing
- The CNN universal machine - a host for the application of analogic algorithms
- Classes of analogic CNN algorithms and their practical use in complex image processing tasks
- Analogic CNN program library version 6.2. (Research report of the Analogical and Neural Computing Laboratory DNS-7-1995.)
- Analogic CNN algorithms and applications - chip-development system and case studies
- Rotation invariant detection of moving and standing objects using analogic cellular neural network algorithms based on ring codes
- Diminishment and enlargement of binary pictures using slightly space variant cellular neural network architecture
- CNN based adaptive smoothing and some novel types of nonlinear operators for grey-scale image processing
- Analogic CNN algorithms in bronchogenic carcinoma analysis
- A CNN model of oscillation and chaos in ant colonies: a case study
- Analogic CNN algorithm for object rotation using controlled switched templates. (Research report of the Analogical and Neural Computing Laboratory DNS-3-1995.)
- A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.)
Authors: Lotz, K; Bölöni, L; Roska, T; Hámori, JDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: A CNN model of the time-coding pathway of sound localization - hyperacuity in time. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-4-1995.) (Page: 25)
- An analogic phenomenological CNN algorithm to model the mouth detection task of the inferotemporal cortex discovered by I. Fujita
- Analogic CNN models of some qualitative pattern recognition tasks in the inferotemporal cortex. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-1-1995.)
- Mammogram analysis using CNN algorithms
- Smart image scanning algorithms for the CNN universal machine
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.)
Authors: Kozek, T; Chua, LO; Roska, T; Wolf, D; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.) (Page: 13)
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples
- Cellular neural networks. Part 2. Application examples - a tutorial
- Cellular neural networks. Part 1. From a single cell to a supercomputer - a tutorial
- Dynamic analogic CNN algorithms for a complex recognition task - a first step a bionic eyeglass. (Research report of the Analogical and Neural Computing Laboratory DNS-4-1995.)
- The world of analogic CNN spatiotemporal dynamics - a review
- Stored program cellular neural networks - an introduction
- Cellular neural networks - a tutorial on programmable nonlinear dynamics in space
- Novel types of analogic CNN algorithms for recognizing bank-notes
- Novel types of analogic CNN algorithms for recognizing bank-notes. (Memorandum UCB/ERL M94/29.)
- Design of analogic CNN algorithms for mammogram analysis
- The analogic cellular neural network as a bionic eye.(Memorandum UCB/ERL M94/70.)
- Analogic CNN algorithms for some image compression and restoration tasks. (Memorandum UCB/ERL M94/30.)
- Analog combinatorics and cellular automata - key algorithms and layout design
- Analog combinatorics and cellular automata - key algorithms and layout design. (Research report of the Analogical and Neural Computing Laboratory. DNS-7-1994.)
- ACL (an analogical CNN language) Version 2.0. User's manual. (Research report of the Analogical and Neural Computing Laboratory. DNS-3-1994.)
- Parallel analog image coding and decoding by using cellular neural networks
- Random variations in CNN templates: theoretical models and empirical studies
- Solving partial differential equations by CNN. (Research report of the Analogical and Neural Computing Laboratory. DNS-2-1994.)
- On a CNN chip-prototyping system
- The CNN universal chip: performance and domains of applications
- Analogic cellular neural network processing
- Analogic algorithms running on the CNN universal machine
- Rotation invariant detection of the objects using analogic cellular neural network algorithms
- Deblurring of images by cellular neural networks with applications to microscopy
- Some cortical spiking neuron models using CNN
Authors: Lotz, K; Vidnyánszky, Z; Roska, T; Vandewalle, J; Hámori, J; Jacobs, A; Werblin, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1994.Published by: Proceedings of the third IEEE international workshop on cellular neural networks and their applications. CNNA-94. Rome, 1994 (Page: 41-46)
- Enhancing X-ray mammograms by analogic CNN operations: some experiments. (Neuromorphic Information Technology, Graduate Center report NIT-2-1994.)
- A double time-scale CNN for solving 2-D Navier-Stokes equations
- Techniques for constructing physiologically motivated neuromorphic models in CNN
- Some examples of preprocessing analog images with discrete-time cellular neural networks
- A current-mode DTCNN universal chip
- Some novel analogic CNN algorithms for object rotation, 3D interpolation-approximation, and a "door-in-a-floor" problem
- A fast, complex and efficient test implementation of the CNN universal machine
- A CNN model of the feed-forward part of the LGN (Research report of the Dual and Neural Computing Systems Laboratory DNS-6-1993)
- Design of linear cellular neural networks for motion sensitive filtering
- 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)
- Stability of cellular neural networks with dominant nonlinear and delay-type templates
- Solving partial differential equations by CNN (Research report of the Dual and Neural Computing Systems Laboratory DNS-4-1993)
- Solving partial differential equations by CNN
- Language, compiler, and operating system for the CNN supercomputer. Memorandum UCB/ERL M93/34
- Color image processing by CNN
- The CNN universal machine: an analogic array computer
- CNN models in the retinotopic visual pathway - a review
- A framework for the classification of auditory signals with cellular neural networks (Research report of the Dual and Neural Computing Systems Laboratory DNS-3-1993.)
- A framework for the classification of auditory signals with cellular neural networks
- A multilayer cellular neural network model of the two pathways of the retina related to dark adaptation. (Katholieke Universiteit Leuven. Department Elektrotechniek. ESAT-SISTA/TR 1993-33I)
Authors: Lotz, K; Vandewalle, J; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1993.Published by: A multilayer cellular neural network model of the two pathways of the retina related to dark adaptation. (Katholieke Universiteit Leuven. Department Elektrotechniek. ESAT-SISTA/TR 1993-33I) (Page: 12)
- Genetic algorithm for CNN template learning
- On biological sensory information processing principles relevant to cellular neural networks
- Image halftoning with cellular neural networks
- The CNN paradigm - a short tutorial
- The CNN paradigm
- The CNN is universal as the turing machine
- The analogic CNN paradigm - programmable nonlinear dynamics in space
- A two-layer Radon transform cellular neural network
- Template synthesis of cellular neural networks for information coding and decoding
- Optically realized feedforward-only cellular neural networks
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
- Stability of cellular neural networks with dominant nonlinear and delay-type templates. (Memo UCB/ERL No. M92/121.)
- Stability and dynamics of delay-type general and cellular neural networks
- Programmable cellular neural networks - a state-of-the-art. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-1-1992.)
- Programmable cellular neural networks a state-of-the-art
- Forming an information technology department in turbulent times
- Egyetem - Akadémia együtt!
- DUALCOMP dual CNN compiler to CNN-HAC1 board. Version 2.0. 1992. User's guide
- The dual CNN analog software - a programmable analog, nonlinear, dynamic, 3D computing array plus logic to form a "multi-screen theater" on silicon. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-2-1992.)
Authors: Roska, T; Chua, LODepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1992.Published by: The dual CNN analog software - a programmable analog, nonlinear, dynamic, 3D computing array plus logic to form a "multi-screen theater" on silicon. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-2-1992.) (Page: 28)
- 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)
- Detecting moving and standing objects using cellular neural network
- The CNN universal machine Part 2: Programmability and applications
- The CNN universal machine and supercomputer. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-18-1992.)
- CNN: a paradigm for cellular analog programmable 3D processor arrays with distributed logic and memory
- Cellular neural networks with non-linear and delay-type template elements and non-uniform grids
- Cellular neural networks with nonlinear and delay-type template elements
- The CNN workstation. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-10-1992.)
- Cellular neural networks: theory and circuit design
- Genetic algorithm for CNN template learning. (Memo UCB/ERL No. M92/82.)
- On biological sensory information processing principles relevant to dually computing CNNs. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-4-1992.)
- Programmable analogue VLSI CNN chip with local digital logic
- Optical tracking system for automatic guided vehicles using cellular neural networks. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-15-1992)
- Optical tracking system for automatic guided vehicles using cellular neural networks
- 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
- The CNN universal machine Part 1: The architecture
- Applications of the virtual cellular machine to many-core processors.
- On full-connectivity properties of locally connected oscillatory networks.
- Bionic eyeglass: personal navigation system for visually impaired people.
- VISCUBE: a multi-layer vision chip.
- Visual detection and implementation aspects of a UAV see and avoid system.
- Collision avoidance for UAV using visual detection.
- Locally connected oscillatory networks acting as fully connected oscillatory networks.
- On the equivalence between locally and fully connected oscillatory networks.
- Towards a mobile navigation device.
- Periodicity enhancement of two-mode stochastic oscillators in a CNN type architecture.
Authors: Máté, G.; Horváth, E. Á.; Káptalan, E.; Tunyagi, A.; Néda, Z.; 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: 313-317.)Download article: [html]
- 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]
- Isle of Eden in 1D binary cellular automaton as a manifestation of Gödel incompleteness and a proposal for a bridge between analytical results and spatial-temporal logic patterns.
- Cellular wave computing in nanoscale via million processor chips.
- 3D multi-layer vision architecture for surveillance and reconnaissance applications.
- Spatial-temporal patterns in hardware oriented oscillatory CNN architectures.
- Modeling stimulus-driven attentional selection in dynamic natural scenes.
- Cellular neural networks for NP-hard optimization.
- Stochastic optimatization of spin-glasses on cellular neural/nonlinear network based processors.
- Cellular wave computer algorithms with spatial semantic embedding for handwritten text recognition.
- Special issue on cellular wave computing architectures, Part II.
- Anyone can build Chua's circuit: hands-on-experience with chaos theory for high school students.
- Long-range dependence of long-term continuous intracranial electroencephalograms for detection and prediction of epileptic seizures.
- An overview on emerging spatial wave logic for spatial-temporal events via cellular wave computers on flows and patterns.
- Statistical physics on cellular neural network computers.
- 3D integrated scalable focal-plane processor array.
- Artificial immune systems based novelty detection with CNN-UM.
- High performance processor array for image processing.
- A neuromorphic chip that imitates ON brisk transient ganglion cell set in the retinas of rabbits.
- Tactile sensing-processing: interface-cover geometry and the inverse-elastic problem.
- Function-in-layout: Aademonstration with bio-inspired hyperacuity chip.
- Circuits, computers, and beyond Boolean logic.
- Cellular wave computers for nano-tera-scale technology - beyond Boolean, spatial-temporal logicin million processor devices.
- Bio-inspired 0.35 (mikro)m CMOS time-to-digital converter with 29.3 ps LSB
- Human tested saliency map generation in the bionic eyeglass project
- Route number recognition of public transport vehicles via the bionic eyeglass
- Random number generator and Monte Carlo type simulations on the CNN-UM
- Bionic eyeglass: an audio guide for visually impaired
- System aspects of a bionic eyeglass
- Digital implementation of the cellular sensor-computers
- Immune response inspired spatial-temporal target detection algorithms with CNN-UM
- Multi-channel spatio-temporal topographic processing for visual search and navigation
- Analogic cellular PDE machines.
- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- 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)
- Morphology and autowave metric on CNN applied to Bubble-Debris classification.
- 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)
- A nonlinear wave mertic and its CNN implementation for object classification.
- The CNN implementation of wave type metric for image analysis and classification.
- Spatio-temporal CNN algorithm for object segmentation and object recognition.
- Bubble-debris classification via binary morphology and autowave metric on CNN.
- Image segmentation and edge detection via constrained diffusion and adaptive morphology: a CNN approach to bubble/debris image enhancement.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- 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)
- 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)
- Improved scoring and semi-automatic screening of human peripheral blood chromosomes by CNN visual system.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- Cellular neural networks for image deconvolution and enhancement: a microscopy toolkit. (Research report of the Analogical and Neural Computing Laboratory DNS-11-1995.)
- Cellular neural networks for image deconvolution and enhancement: a microscopy toolkit
- Classes of analogic CNN algorithms and their practical use in complex image processing tasks
- On a CNN chip-prototyping system
- Deblurring of images by cellular neural networks with applications to microscopy
- 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)
- Various implementations of topographic, sensory, cellular wave computers
- A development system for prototyping and interfacing CNN chips and for analogic algorithm design.
- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- 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)
- CNN chip prototyping and development systems.
- 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 computational infrastructure of analogic CNN computing - Part I: The CNN-UM chip prototyping system.
- 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)
- An analogic CNN algorithm for following continously moving objects. (Research report of the Analogical and Neural Computing Laboratory, DNS-8-1999.)
- CASTLE: an emulated digital architecture; design issues, new results.
- An emulated digital architecture implementing the CNN universal machine.
- Analogic Macro Code (AMC). Extended assembly language for CNN computers. Version 1.1.(Research report of the Analogical and Neural Computing Laboratory, DNS-10-1998.)
- Functional measurements of the first analog input/output CNN universal chip. (Research report of the Analogical and Neural Computing Laboratory, DNS-4-1997.)
- Ultra fast image processing via CNN technology.
- 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)
- Analogue combinatorics and cellular automata - key algorithms and layout design.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- Analog combinatorics and cellular automata - key algorithms and layout design
- Analog combinatorics and cellular automata - key algorithms and layout design. (Research report of the Analogical and Neural Computing Laboratory. DNS-7-1994.)
- On a CNN chip-prototyping system
- Analogic cellular neural network processing
- 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)
Takács, J- 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)
- A CNN model of the feed-forward part of the LGN (Research report of the Dual and Neural Computing Systems Laboratory DNS-6-1993)
- 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)
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
Tetzlaff, R- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.)
Authors: Roska, T; Chua, LO; Wolf, D; Kozek, T; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.) (Page: 19)
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.)
Authors: Kozek, T; Chua, LO; Roska, T; Wolf, D; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.) (Page: 13)
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples
- Solving partial differential equations by CNN. (Research report of the Analogical and Neural Computing Laboratory. DNS-2-1994.)
Tunyagi, A.- Periodicity enhancement of two-mode stochastic oscillators in a CNN type architecture.
Authors: Máté, G.; Horváth, E. Á.; Káptalan, E.; Tunyagi, A.; Néda, Z.; 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: 313-317.)Download article: [html]
- Distance preserving 1D turing-pattern models via CNN, implementing of complex-valued CNN, and solving a simple inverse pattern problem (detection).( Research report of the Analogical and Neural Computing Laboratory, DNS-3-1996.)
- Distance preserving 1D turing-wave models via CNN, implementation of complex-valued CNN and solving a simple inverse pattern problem (detection).
- Analogic CNN algorithms for 3D interpolation-approximation and object rotation using controlled switched templates.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- Analogic CNN algorithm for 3D interpolation-approximation. Research report of the Analogical and Neural Computing Laboratory DNS-2-1995.)
- Some novel analogic CNN algorithms for object rotation, 3D interpolation-approximation, and a "door-in-a-floor" problem
Ueno, J- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
- Analogic CNN algorithms in bronchogenic carcinoma analysis
Ugray, ZS- 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)
Ushida, A- CNN-based difference-controlled adaptive non-linear image filters.
- Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template.
Authors: Hirakawa, S; Rekeczky, CS; Nishio, Y; Ushida, A; Roska, T; Ueno, J; Kasem, I; Nishitani, HDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1997.Published by: IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (Volume no.: E80-A, Issue no.: 7, Page: 1340-1344)
- Image segmentation by soft computing on the CNN universal machine - a case study of echocardiography.
- CNN based self-adjusting nonlinear filters.(Research report of the Analogical and Neural Computing Laboratory, DNS-4-1996.)
- CNN based self-adjusting nonlinear filters.
- Rotation invariant detection of moving and standing objects using analogic cellular neural network algorithms based on ring codes
- Diminishment and enlargement of binary pictures using slightly space variant cellular neural network architecture
- CNN based adaptive smoothing and some novel types of nonlinear operators for grey-scale image processing
- Analogic CNN algorithms in bronchogenic carcinoma analysis
- Rotation invariant detection of the objects using analogic cellular neural network algorithms
Vandewalle, J- Invertible operations on a cellular neural network universal machine.
- High speed calculation of cryptographic hash functions by CNN chips.
- Some cortical spiking neuron models using CNN
Authors: Lotz, K; Vidnyánszky, Z; Roska, T; Vandewalle, J; Hámori, J; Jacobs, A; Werblin, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1994.Published by: Proceedings of the third IEEE international workshop on cellular neural networks and their applications. CNNA-94. Rome, 1994 (Page: 41-46)
- A multilayer cellular neural network model of the two pathways of the retina related to dark adaptation. (Katholieke Universiteit Leuven. Department Elektrotechniek. ESAT-SISTA/TR 1993-33I)
Authors: Lotz, K; Vandewalle, J; Roska, TDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1993.Published by: A multilayer cellular neural network model of the two pathways of the retina related to dark adaptation. (Katholieke Universiteit Leuven. Department Elektrotechniek. ESAT-SISTA/TR 1993-33I) (Page: 12)
Venetianer, P- The CNN is universal as the turing machine
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
- 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
Venetianer, PL- Image compression by cellular neural networks.
- Image compression by CNN.
- Analogue combinatorics and cellular automata - key algorithms and layout design.
- ACE: A digital floating point CNN emulator engine.
Authors: Fehér, B; Szolgay, P; Roska, T; Radványi, AG; Szirányi, T; Csapodi, M; László, K; Nemes, L; Szatmári, I; Tóth, G; Venetianer, PLDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1996.Published by: 1996 fourth IEEE international workshop on Cellular Neural Networks and their applications proceedings. CNNA-96. Seville, 1996. (Page: 273-278)
- Image compression by CNN. (Research report of the Analogical and Neural Computing Laboratory DNS-13-1995.)
- Analogic CNN algorithms for some image compression and restoration tasks
- Analogic CNN algorithms for some image compression and restoration tasks. (Memorandum UCB/ERL M94/30.)
- Analog combinatorics and cellular automata - key algorithms and layout design
- Analog combinatorics and cellular automata - key algorithms and layout design. (Research report of the Analogical and Neural Computing Laboratory. DNS-7-1994.)
- ACL (an analogical CNN language) Version 2.0. User's manual. (Research report of the Analogical and Neural Computing Laboratory. DNS-3-1994.)
- On a CNN chip-prototyping system
- Some examples of preprocessing analog images with discrete-time cellular neural networks
- A CNN model of the feed-forward part of the LGN (Research report of the Dual and Neural Computing Systems Laboratory DNS-6-1993)
- 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)
Venetiáner, P- 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)
Vidnyánszky, Z- 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)
- CNN model of the feature-linked synchronized activities in the visual thalamo-cortical system.
- Some cortical spiking neuron models using CNN
Authors: Lotz, K; Vidnyánszky, Z; Roska, T; Vandewalle, J; Hámori, J; Jacobs, A; Werblin, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1994.Published by: Proceedings of the third IEEE international workshop on cellular neural networks and their applications. CNNA-94. Rome, 1994 (Page: 41-46)
- A CNN model of the feed-forward part of the LGN (Research report of the Dual and Neural Computing Systems Laboratory DNS-6-1993)
- 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)
- 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)
- The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
Wang, M- The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.)
Authors: Rekeczky, CS; Roska, B; Nemeth, E; Wang, M; Weblin, F; Roska, TDate: 1998.Published by: The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.) (Page: 34)
Weblin, F- The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.)
Authors: Rekeczky, CS; Roska, B; Nemeth, E; Wang, M; Weblin, F; Roska, TDate: 1998.Published by: The network behind dynamic spatio-temporal patterns - building low-complexity retinal models in CNN based on morphology, pharmacology and physiology.(Research report of the Analogical and Neural Computing Laboratory, DNS-7-1998.) (Page: 34)
Werblin, F- High speed road boundary detection on the images for autonomous vehicle with the multi-layer CNN
- Automatic detection and tracking of moving image target with CNN-UM via target probability fusion of multiple features
- Initiation and tracking of DIM target via fusion of feature probabilities with CNN-UM.
- Implementing a retinal visual language in CNN: a neuromorphic study.
- A qualitative model-framework for spatio-temporal effects in vertebrate retinas.
Authors: Bálya, D; Roska, B; Németh, E; Roska, T; Werblin, FDate: 2000.Published by: Proceedings of the 6th IEEE international workshop on cellular neural networks and their applications. (CNNA 2000). Catania (Page: 165-170)
- Spatial logic algorithm using basic morphological analogic CNN operators.
- CNN model for identifying colors under different illumination condition via Land's experiments.
- Cellular Neural Network realizations of neuron models with diverse spiking patterns.
- Methods for constructing physiologically motivated neuromorphic models in CNNs.
- Intelligent image resolution enhancement by the CNN universal machine and its relevance to TV picture enhancement
- The analogic cellular neural network as a bionic eye
- Analogic CNN algorithms for some image compression and restoration tasks
- Novel types of analogic CNN algorithms for recognizing bank-notes
- Novel types of analogic CNN algorithms for recognizing bank-notes. (Memorandum UCB/ERL M94/29.)
- The analogic cellular neural network as a bionic eye.(Memorandum UCB/ERL M94/70.)
- Analogic CNN algorithms for some image compression and restoration tasks. (Memorandum UCB/ERL M94/30.)
- Some cortical spiking neuron models using CNN
Authors: Lotz, K; Vidnyánszky, Z; Roska, T; Vandewalle, J; Hámori, J; Jacobs, A; Werblin, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1994.Published by: Proceedings of the third IEEE international workshop on cellular neural networks and their applications. CNNA-94. Rome, 1994 (Page: 41-46)
- Techniques for constructing physiologically motivated neuromorphic models in CNN
Wolf, D- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.)
Authors: Roska, T; Chua, LO; Wolf, D; Kozek, T; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques. (Research report of the Analogical and Neural Computing Laboratory DNS-9-1995.) (Page: 19)
- Simulating nonlinear waves and partial differential equations via CNN. Part 1. Basic techniques
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.)
Authors: Kozek, T; Chua, LO; Roska, T; Wolf, D; Tetzlaff, R; Puffer, FDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 1995.Published by: Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples. (Research report of the Analogical and Neural Computing Laboratory DNS-10-1995.) (Page: 13)
- Simulating nonlinear waves and partial differential equations via CNN. Part 2. Typical examples
- Solving partial differential equations by CNN. (Research report of the Analogical and Neural Computing Laboratory. DNS-2-1994.)
- Solving partial differential equations by CNN (Research report of the Dual and Neural Computing Systems Laboratory DNS-4-1993)
- Solving partial differential equations by CNN
Zarándy, A- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- Per-pixel integration time controlled image sensor
- Various implementations of topographic, sensory, cellular wave computers
- High dynamic range perception with spatially variant exposure
- Adaptive perception with locally-adaptable sensor array
- Adaptive perception with locally adaptable sensor array
- Bayesian incorporation of multiple scales in optical flow estimation
- 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)
- Proactive, adaptive, cellular sensory-computer architecture via extending the CNN univesal machine
- Dennis Gabor as the initiator of optical computing. Importance and prospects of optical computing and an optical implementation of the CCN-UM computer.
- A development system for prototyping and interfacing CNN chips and for analogic algorithm design.
- 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)
- 20 msec focal plane image processing.
- 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.
- Analogikai celluláris számítógépek. Egy új számítógépelv.
- CNN chip prototyping and development systems.
- 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 computational infrastructure of analogic CNN computing - Part I: The CNN-UM chip prototyping system.
- 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)
- 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)
- Implementation of Binary and gray-scale mathematical morphology on the CNN universal machine.
- CASTLE: an emulated digital architecture; design issues, new results.
- An emulated digital architecture implementing the CNN universal machine.
- Analogic Macro Code (AMC). Extended assembly language for CNN computers. Version 1.1.(Research report of the Analogical and Neural Computing Laboratory, DNS-10-1998.)
- Fault tolerant design of analogic CNN templates and algorithms. Part I: The binary output case.(Research report of the Analogical and Neural Computing Laboratory, DNS-3-1998.)
- Fault tolerant CNN template design and optimatization based on chip measurements.
- CNN template design strategies and fault tolerant CNN template design - a survey.
- 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.)
- Ultra fast image processing via CNN technology.
- New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips.
- 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)
- Spatial logic algorithm using basic morphological analogic CNN operators.
- Morphological operators on the CNN universal machine.
- Implementation of binary and gray-scale mathematical morphology on the CNN universal machine. ( Memorandum of the Electronics Research Laboratory, UCB/ERL M96/19)
- CNN model for identifying colors under different illumination condition via Land's experiments.
- New results and measurements related to dynamic image coding using CNN universal chips. (Memorandum of the Electronics Research Laboratory, UCB/ERL M96/58)
- CNN universal chips crank up the computing power
- Intelligent image resolution enhancement by the CNN universal machine and its relevance to TV picture enhancement
- Translating neuromorphic CNN visual models to the analogic visual microprocessors
- Analogic CNN algorithms and applications - chip-development system and case studies
- An analogic phenomenological CNN algorithm to model the mouth detection task of the inferotemporal cortex discovered by I. Fujita
- Analogic CNN models of some qualitative pattern recognition tasks in the inferotemporal cortex. (Research report of the Neuromorphic Information Technology, Graduate Center. NIT-1-1995.)
- Mammogram analysis using CNN algorithms
- Cellular neural networks. Part 2. Application examples - a tutorial
- Novel types of analogic CNN algorithms for recognizing bank-notes
- Novel types of analogic CNN algorithms for recognizing bank-notes. (Memorandum UCB/ERL M94/29.)
- Design of analogic CNN algorithms for mammogram analysis
- On a CNN chip-prototyping system
- 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)
- Language, compiler, and operating system for the CNN supercomputer. Memorandum UCB/ERL M93/34
- Color image processing by CNN
- The CNN paradigm - a short tutorial
- 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)
- 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
- 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)
- 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
- Applications of the virtual cellular machine to many-core processors.
- VISCUBE: a multi-layer vision chip.
- Visual detection and implementation aspects of a UAV see and avoid system.
- Collision avoidance for UAV using visual detection.
- 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]
- 3D multi-layer vision architecture for surveillance and reconnaissance applications.
- 3D integrated scalable focal-plane processor array.
- High performance processor array for image processing.
- Digital implementation of the cellular sensor-computers
Zöld, S- A development system for prototyping and interfacing CNN chips and for analogic algorithm design.
- A CNN application development environment and toolkit, CADETWin.
Authors: Roska, T; László, K; Kék, L; Kozek, T; Nemes, L; Rekeczky, C; Szatmári, I; Zarándy, A; Zöld, S; Szolgay, PDepartment: Cellular Sensory and Optical Wave Computing LaboratoryDate: 2000.Published by: Towards the visual microprocessor. VLSI design and the use of cellular neural network universal machines. (Page: 39-58)
- CNN chip prototyping and development systems.
- 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 computational infrastructure of analogic CNN computing - Part I: The CNN-UM chip prototyping system.
- Analogic Macro Code (AMC). Extended assembly language for CNN computers. Version 1.1.(Research report of the Analogical and Neural Computing Laboratory, DNS-10-1998.)
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