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Radu Dogaru
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2020 – today
- 2024
- [c35]Radu Dogaru, Ioana Dogaru:
VRES-CNN: A Tiny Convolutional Image Classifier with Versatile Choice of Hyperparameters. ECAI 2024: 1-6 - 2022
- [c34]Radu Dogaru, Adrian-Dumitru Mirica, Ioana Dogaru:
The C-CNN model: Do we really need multiplicative synapses in convolutional neural networks? COMM 2022: 1-4 - 2021
- [c33]Radu Dogaru, Ioana Dogaru:
A Python Framework for Fast Modelling and Simulation of Cellular Nonlinear Networks and other Finite-difference Time-domain Systems. CSCS 2021: 221-226 - [i3]Radu Dogaru, Ioana Dogaru:
NL-CNN: A Resources-Constrained Deep Learning Model based on Nonlinear Convolution. CoRR abs/2102.00227 (2021) - [i2]Radu Dogaru, Ioana Dogaru:
A Python Framework for Fast Modelling and Simulation of Cellular Nonlinear Networks and other Finite-difference Time-domain Systems. CoRR abs/2102.10340 (2021) - [i1]Radu Dogaru, Ioana Dogaru:
LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy. CoRR abs/2106.15350 (2021) - 2020
- [c32]Radu Dogaru, Ioana Dogaru:
Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy. ECAI 2020: 1-4 - [c31]Iulian-Ionut Felea, Radu Dogaru:
Improving Light-weight Convolutional Neural Networks for Face Recognition Targeting Resource Constrained Platforms. ESANN 2020: 199-204
2010 – 2019
- 2017
- [c30]Mihai Bucurica, Ioana Dogaru, Radu Dogaru:
Novel Applications of Complexity Inspired RDT Transform for Low Complexity Embedded Speech Recognition in Automotive Environments. CSCS 2017: 375-378 - 2016
- [j7]Raluca Boia, Corneliu Florea, Laura Florea, Radu Dogaru:
Logo localization and recognition in natural images using homographic class graphs. Mach. Vis. Appl. 27(2): 287-301 (2016) - [c29]Mihai Bucurica, Ioana Dogaru, Radu Dogaru:
Improving computational efficiency for implementing a sound propagation simulation environment using Python and GPU. ECAI 2016: 1-4 - [c28]Radu Dogaru, Ioana Dogaru:
An efficient local receptive field feature extractor for handwriting recognition. COMM 2016: 105-108 - [c27]Radu Dogaru, Ioana Dogaru:
A super fast vector support classifier using novelty detection and no synaptic tuning. COMM 2016: 373-376 - 2015
- [c26]Ioana Dumitrache, Alina-Elena Sultana, Radu Dogaru:
Feature Extraction from the Contour of Skin Lesions Using Box Counting Principle. CSCS 2015: 548-553 - [c25]Radu Dogaru, Ioana Dogaru:
A Low Cost High Performance Computing Platform for Cellular Nonlinear Networks Using Python for CUDA. CSCS 2015: 593-598 - [c24]Ioana Dogaru, Radu Dogaru:
Using Python and Julia for Efficient Implementation of Natural Computing and Complexity Related Algorithms. CSCS 2015: 599-604 - [c23]Mihai Bucurica, Radu Dogaru:
Shape object classification using echoes inside a virtual environment application. ECAI 2015 - [c22]Mihai Bucurica, Radu Dogaru:
A comparison between Extreme Learning Machine and Fast Support Vector Classifier. ECAI 2015 - [c21]Mihai Bucurica, Radu Dogaru, Ioana Dogaru:
A comparison of Extreme Learning Machine and Support Vector Machine classifiers. ICCP 2015: 471-474 - 2014
- [c20]George Valentin Stoica, Radu Dogaru, Cristina Elena Stoica:
Speeding-up image processing in reaction-diffusion cellular neural networks using CUDA-enabled GPU platforms. ECAI 2014: 39-42 - [c19]Camelia Moldovan
, Radu Dogaru:
Fast support vector classifier applied to microarray data. ECAI 2014: 67-72 - [c18]Ioana Dumitrache, Alina-Elena Sultana, Radu Dogaru:
Automatic detection of skin melanoma from images using natural computing approaches. COMM 2014: 1-4 - 2013
- [c17]Radu Dogaru:
Applications of Emergent Computation in Reaction-Diffusion CNNs for Image Processing. CSCS 2013: 370-377 - [p1]Radu Dogaru, Ioana Dogaru:
Binary Synchronization of Complex Dynamics in Cellular Automata and its Applications in Compressed Sensing and Cryptography. Selected Topics in Nonlinear Dynamics and Theoretical Electrical Engineering 2013: 81-95 - 2010
- [j6]Radu Dogaru, Ioana Dogaru, Hyongsuk Kim
:
Chaotic Scan: A Low Complexity Video Transmission System for Efficiently Sending Relevant Image Features. IEEE Trans. Circuits Syst. Video Technol. 20(2): 317-321 (2010)
2000 – 2009
- 2009
- [j5]Radu Dogaru, Ioana Dogaru, Hyongsuk Kim
:
Binary Chaos Synchronization in Elementary Cellular Automata. Int. J. Bifurc. Chaos 19(9): 2871-2884 (2009) - 2008
- [b1]Radu Dogaru:
Systematic Design for Emergence in Cellular Nonlinear Networks: With Applications in Natural Computing and Signal Processing. Studies in Computational Intelligence 95, Springer 2008, ISBN 978-3-540-76800-5 - [j4]Radu Dogaru, Manfred Glesner:
A fast and compact classifier based on sorting in an iteratively expanded input space. Int. J. Intell. Syst. 23(5): 607-618 (2008) - [c16]Peter Zipf
, Heiko Hinkelmann, Hui Shao, Radu Dogaru, Manfred Glesner:
An area-efficient FPGA realisation of a codebook-based image compression method. FPT 2008: 349-352 - [c15]Radu Dogaru, Shi Wang, Hyongsuk Kim:
Fast Selection of Cellular Automata Genes Based on Uncertainty Profiles of Cells. GEM 2008: 216-221 - 2005
- [c14]Peter Zipf
, Oliver Soffke, Andre Schumacher, Radu Dogaru, Manfred Glesner:
Programmable and Reconfigurable Hardware Architectures for the Rapid Prototyping of Cellular Automata. FPL 2005: 329-334 - [c13]Peter Zipf
, Oliver Soffke, Andre Schumacher, Clemens Schlachta, Radu Dogaru, Manfred Glesner:
A Hardware-in-the-Loop System to Evaluate the Performance of Small-World Cellular Automata. FPL 2005: 335-340 - 2004
- [c12]Radu Dogaru, Ioana Dogaru, Manfred Glesner:
CPCA: a multiplierless neural PCA. IJCNN 2004: 2689-2692 - 2003
- [c11]Pedro Julián, Radu Dogaru, Leon O. Chua:
Exploiting piecewise linear features: multinested and simplicial cellular neural/nonlinear networks. ISCAS (3) 2003: 650-653 - [c10]Radu Dogaru, Ioana Dogaru, Manfred Glesner:
Compact image compression using simplicial and ART neural systems with mixed signal implementations. ISCAS (5) 2003: 689-692 - [c9]Radu Dogaru, Cristian Chitu, Manfred Glesner:
A Versatile Cellular Neural Circuit Based on a Multi-nested Approach: Functional Capabilities and Applications. VLSI-SOC 2003: 356-361 - 2002
- [j3]Leon O. Chua, Sook Yoon, Radu Dogaru:
A Nonlinear Dynamics Perspective of Wolfram's New Kind of Science Part I: Threshold of Complexity. Int. J. Bifurc. Chaos 12(12): 2655-2766 (2002) - [j2]Radu Dogaru, Pedro Julián
, Leon O. Chua, Manfred Glesner:
The simplicial neural cell and its mixed-signal circuit implementation: an efficient neural-network architecture for intelligent signal processing in portable multimedia applications. IEEE Trans. Neural Networks 13(4): 995-1008 (2002) - [c8]Pedro Julián, Radu Dogaru, Martin Haenggi, Leon O. Chua:
A search algorithm for the design of multinested cellular neural networks. ISCAS (1) 2002: 617-620 - 2001
- [c7]Pedro Julián
, Radu Dogaru, Leon O. Chua:
A piecewise-linear simplicial coupling cell for CNN gray-level image processing. ISCAS (3) 2001: 109-112 - [c6]Radu Dogaru, Pedro Julián
, Leon O. Chua:
A robust and efficient universal CNN cell circuit using simplicial neuro-fuzzy inferences for fast image processing. ISCAS (3) 2001: 493-496 - 2000
- [j1]Radu Dogaru, Leon O. Chua:
Mutations of the "Game of Life": a generalized Cellular Automata Perspective of Complex Adaptive Systems. Int. J. Bifurc. Chaos 10(8): 1821-1866 (2000)
1990 – 1999
- 1999
- [c5]Radu Dogaru, Marinel Alangiu, Matthias Rychetsky, Manfred Glesner:
Perceptrons revisited: the addition of a non-monotone recursion greatly enhances their representation and classification properties. IJCNN 1999: 862-867 - 1997
- [c4]Radu Dogaru, A. T. Murgan, Leon O. Chua:
Do we really need multiplier-based synapses for neuro-fuzzy classifiers? ICNN 1997: 995-999 - 1996
- [c3]Radu Dogaru, A. T. Murgan, Cristina Comaniciu:
Fast signal recognition and detection using ART1 neural networks and nonlinear preprocessing units based on time delay embeddings. ESANN 1996 - [c2]Radu Dogaru, A. T. Murgan, Stefan Ortmann, Manfred Glesner:
Searching for robust chaos in discrete time neural networks using weight space exploration. ICNN 1996: 688-693 - 1995
- [c1]Radu Dogaru, A. T. Murgan:
Chaotic Resonance Theory, a New Approach for Pattern Storage and Retrieval in Neural Networks. ICNN 1995: 3048-3052
Coauthor Index

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