In this paper an alternative method to symbolic segmentation is studied. Semantic segmentation be... more In this paper an alternative method to symbolic segmentation is studied. Semantic segmentation being one of the most difficult tasks currently in the computer vision area, and large number of algorithms is being developed. Thus the proposed approach in this paper exploits this large amount of available computational tools by using the algorithm selection approach. That is, let there be a set A of available algorithms for symbolic segmentation, a set of input features F, a set of image attribute A and a selection mechanism S(F, A, A) that selects on a case by case basis the best algorithm. The semantic segmentation is then an optimization process that combines best component segments from multiple results into a single optimal result. The experiments compare three different algorithm selection mechanisms using three selected semantic segmentation algorithms. The results show that using the current state of art algorithms and relatively low accuracy of algorithm selection the accuracy...
In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its p... more In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can theoretically be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated using (general purpose graphic processing unit) GPGPU. The main target of this paper is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum ...
2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013), 2013
ABSTRACT In order to obtain the best result in image understanding it is desirable to select the ... more ABSTRACT In order to obtain the best result in image understanding it is desirable to select the best algorithm on a case by case basis. An algorithm can be selected using only image features, however such selected algorithms will often generate errors due to occlusion, shadows and other environmental conditions. To avoid such errors, it is necessary to understand processing errors on a symbolic level. Using symbolic information to determine the best algorithm is however difficult task because the possible combinations of elements and environmental conditions are almost infinite. Consequently it is impossible to predict best algorithm for all possible combinations of objects, environment conditions and context variations. In this paper we investigate selection of algorithms using symbolic image description and the determination of algorithms' error from high level image description. The proposed method transforms and minimize the high level information contained in the symbolic image description in such manner that will preserve the algorithm selection quality. The transformation takes a high level information label and transforms it into a set of generic features while the minimization uses hierarchy to reduce the specific nature of the information. Both methods of information reduction are used in a Bayesian Network because a BN is well known for using the generalization and hierarchy. As is shown in this paper, such representation efficiently reduces the fine grain high-level symbolic description to a coarser-grain hierarchy that preserves the selection quality but reduces the number of nodes.
2012 12th IEEE International Conference on Nanotechnology (IEEE-NANO), 2012
ABSTRACT We provide several extensions of the new approach to the minimization of reversible circ... more ABSTRACT We provide several extensions of the new approach to the minimization of reversible circuits based on PSE gates and ESOPOS circuits. These circuits realize the Exclusive-Or-Sum-of-Product-Sums (ESOPOS) structure where every output is an exclusive-or of Product-Sum-Exor (PSE) gates which generalize the multi-input Toffoli gates. We also propose a new efficient realization of the PSE gate that uses external-binary, internal-ternary logic.
2011 41st IEEE International Symposium on Multiple-Valued Logic, 2011
... Also, in the presented form it is necessary to keep the two transmission gates T4 and T5 so t... more ... Also, in the presented form it is necessary to keep the two transmission gates T4 and T5 so that the two opposite outputs do not generate spurious feedback signals. ... Driving fully-adiabatic logic circuits using custom high-q mems resonators. ...
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
Abstract In this paper we present an evolutionary approach to the quantum symbolic logic synthes... more Abstract In this paper we present an evolutionary approach to the quantum symbolic logic synthesis that was introduced in [1]. We use a Genetic Algorithm to synthesize quantum circuits from examples, allowing to synthesize functions that are both completely and ...
In this paper an alternative method to symbolic segmentation is studied. Semantic segmentation be... more In this paper an alternative method to symbolic segmentation is studied. Semantic segmentation being one of the most difficult tasks currently in the computer vision area, and large number of algorithms is being developed. Thus the proposed approach in this paper exploits this large amount of available computational tools by using the algorithm selection approach. That is, let there be a set A of available algorithms for symbolic segmentation, a set of input features F, a set of image attribute A and a selection mechanism S(F, A, A) that selects on a case by case basis the best algorithm. The semantic segmentation is then an optimization process that combines best component segments from multiple results into a single optimal result. The experiments compare three different algorithm selection mechanisms using three selected semantic segmentation algorithms. The results show that using the current state of art algorithms and relatively low accuracy of algorithm selection the accuracy...
In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its p... more In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can theoretically be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated using (general purpose graphic processing unit) GPGPU. The main target of this paper is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum ...
2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013), 2013
ABSTRACT In order to obtain the best result in image understanding it is desirable to select the ... more ABSTRACT In order to obtain the best result in image understanding it is desirable to select the best algorithm on a case by case basis. An algorithm can be selected using only image features, however such selected algorithms will often generate errors due to occlusion, shadows and other environmental conditions. To avoid such errors, it is necessary to understand processing errors on a symbolic level. Using symbolic information to determine the best algorithm is however difficult task because the possible combinations of elements and environmental conditions are almost infinite. Consequently it is impossible to predict best algorithm for all possible combinations of objects, environment conditions and context variations. In this paper we investigate selection of algorithms using symbolic image description and the determination of algorithms' error from high level image description. The proposed method transforms and minimize the high level information contained in the symbolic image description in such manner that will preserve the algorithm selection quality. The transformation takes a high level information label and transforms it into a set of generic features while the minimization uses hierarchy to reduce the specific nature of the information. Both methods of information reduction are used in a Bayesian Network because a BN is well known for using the generalization and hierarchy. As is shown in this paper, such representation efficiently reduces the fine grain high-level symbolic description to a coarser-grain hierarchy that preserves the selection quality but reduces the number of nodes.
2012 12th IEEE International Conference on Nanotechnology (IEEE-NANO), 2012
ABSTRACT We provide several extensions of the new approach to the minimization of reversible circ... more ABSTRACT We provide several extensions of the new approach to the minimization of reversible circuits based on PSE gates and ESOPOS circuits. These circuits realize the Exclusive-Or-Sum-of-Product-Sums (ESOPOS) structure where every output is an exclusive-or of Product-Sum-Exor (PSE) gates which generalize the multi-input Toffoli gates. We also propose a new efficient realization of the PSE gate that uses external-binary, internal-ternary logic.
2011 41st IEEE International Symposium on Multiple-Valued Logic, 2011
... Also, in the presented form it is necessary to keep the two transmission gates T4 and T5 so t... more ... Also, in the presented form it is necessary to keep the two transmission gates T4 and T5 so that the two opposite outputs do not generate spurious feedback signals. ... Driving fully-adiabatic logic circuits using custom high-q mems resonators. ...
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
Abstract In this paper we present an evolutionary approach to the quantum symbolic logic synthes... more Abstract In this paper we present an evolutionary approach to the quantum symbolic logic synthesis that was introduced in [1]. We use a Genetic Algorithm to synthesize quantum circuits from examples, allowing to synthesize functions that are both completely and ...
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Papers by Martin Lukac