Logic-based QCA implementation of a 4

Amiri, Mohammad Amin; Mahdavi, Mojdeh; Mirzakuchaki, Sattar

doi:10.1109/ieeegcc.2009.5734314

Cited by 9 publications

(11 citation statements)

References 9 publications

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“…2.1 Basic Definitions 2.1.1 Definition of Fuzzy Set: Let 𝑋 be a universal set, 𝐴 ̃⊆ 𝑋. 𝐴 ̃is called a fuzzy/non-exact set that contains ordered pairs, 𝐴 ̃= {(𝑥, 𝜇 𝐴 ̃(𝑥)), ∀𝑥 ∈ 𝑋} where 𝜇 𝐴 ̃(𝑥) is membership function of 𝑥 ∈ 𝐴 ̃(i.e., a characteristic/indicator function for 𝐴 ̃that shows to what degree 𝑥 ∈ 𝐴 ̃), if the height of fuzzy set is one, then fuzzy set is normal, where the height of a fuzzy set is the largest membership value attained by any point in the set (Ameen, 2015;Dharani, K;Selvi, D, 2018;Mahdavi-Amiri, N;Nasseri, SH, 2006;Mahdavi-Amiri;NezamNasseri;Seyed Hadi, 2007; Sakawa, Fundamentals of fuzzy set theory, 1993; Sakawa, Interactive multiobjective linear programming with fuzzy parameters, 1993). Formulation (4.2-3) is one of the fuzzy set kinds.…”

Section: Preliminaries Of Fuzzy Concepts and Polyhedral Set Typesmentioning

confidence: 99%

“…The probability distribution space (𝛺, 2 𝛺 , 𝑃) of an STLPP in many cases is unknown, undetermined, and un-specified since it has fuzzy information, unknown distribution, then should be determined/specified as first step in solution procedures. Further, an STLPP's under fuzzy information on probability and described by fuzzy linear inequalities polyhedral set are called Stochastic Transportation Linear Programming Problems with Fuzzy Uncertainty Unknown Information on Probability Distribution Space STLPPFI (A. Edward Samuel;M. Venkatachalapathy, 2011;Masri, Hatem, 2005;Masri, Hatem, 2009;Masri, Hatem, 2010;Ameen, 2015;Appati, Justice Kwame;Gogovi, Gideon Kwadwo;Fosu, Gabriel Obed, 2015;Dharani, K;Selvi, D, 2018;Guo, Haiying;Wang, Xiaosheng;Zhou, Shaoling, 2015;Mahdavi-Amiri, N;Nasseri, SH, 2006;Mahdavi-Amiri;NezamNasseri;Seyed Hadi, 2007) and (Sengamalaselvi, 2017; Interactive multiobjective linear programming with fuzzy parameters, 1993; Sakawa, Fundamentals of fuzzy set theory, 1993).…”

Section: Introduction To Stlppfimentioning

confidence: 99%

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Solving Stochastic Transportation Electricity Problem With Fuzzy Information on Probability Distribution Using Matlab Program

Azeez,

Ameen

2024

SJUOZ

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This study focuses on MATLAB code programs of the entire stages of solving Stochastic Transportation Linear Programming Problems with Fuzzy Uncertainty Information on Probability Distribution Space (STLPPFI) with its algorithm outlines. A MATLAB code program of STLPPFI problem solver with algorithm outlines are proposed to solve STLPPFI model problems, and it utilizes many concepts as Alpha-Cut technique, Truth Degrees technique, Linear Fuzzy Membership Function (LFMF), Trapezoidal Fuzzy Number , Triangular Fuzzy Number , Linear Fuzzy Ranking Function (LFRF), Expectation Weighted Summation technique (EWS) and analyzing cases via second condition test of alpha-cut technique. The STLPPFI problem solver is utlized to convert STLPPFI into its corresponding equivalent Deterministic Transportation Linear Programming Problem (DTLPP) via defuzzifying from fuzziness on probability distribution space and derandomization randomness of problem formulation respectively. In addition, Dual-Simplex algorithm method with Vogel Approximation Algorithm Method (VAM) are used to obtain optimal solution from DTLPP. All MATLAB code programs with their proposed algorithm outlines are new except Dual-Simplex and VAM. The MATLAB code program of STLPPFI problem solver are more efficient along with a numerical example on electricity field illustrating practicability of this proposed MATLAB code program with its algorithm. Finally, the solution procedure illustrates the MATLAB code program of the proposed method is practical and applicable in the fields of energy and industry as it facilitates the method of transforming the energy at the lowest cost, least time running and is commercially applicable. Comparative comments are provided between Dual-Simplex and VAM in solution process.

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Section: Preliminaries Of Fuzzy Concepts and Polyhedral Set Typesmentioning

confidence: 99%

Section: Introduction To Stlppfimentioning

confidence: 99%

Solving Stochastic Transportation Electricity Problem With Fuzzy Information on Probability Distribution Using Matlab Program

Azeez,

Ameen

2024

SJUOZ

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“…Each QCA cell is synchronously latched and unlatched with the changing of the clock signal and therefore the information is distributed through the cells. [12][13][14][15]…”

Section: A Review Of Qcamentioning

confidence: 99%

Single Electron Fault Modeling in Basic Quantum Devices

Mahdavi¹,

Mirzakuchaki

Moghaddasi

et al. 2011

Jpn. J. Appl. Phys.

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Quantum cellular automata represents an emerging technology at the nanotechnology level. There are various faults which may occur in quantum cellular automata cells. One of these faults is the single electron fault that can happen during manufacturing or operation of quantum cellular automata circuits. The behavior of single electron fault in quantum cellular automata devices is not similar to either previously investigated faults or conventional complementary metal oxide semiconductor (CMOS) logic. A detailed simulation based logic level modeling of single electron fault for quantum cellular automata binary wire and majority gate is represented in this paper. Results show that if a single electron fault occurs in a binary wire, the logic value of that wire will be inverted and if a single electron fault occurs in a majority gate, the logic value of that gate will be changed.

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“…Both pessimistic scenario and ideal situation of the SCA attack is anticipated that presumes that QCA is a significant substitute to endeavour power analysis attack. QCAbased cryptography designs are constructed in [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Cryptographic models of nanocommunicaton network using quantum dot cellular automata: A survey

Debnath¹,

Das

2021

IET Quantum Communication

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Security of information is an important issue during the transmission of information. The device that performs the encoding and decoding process is called the encoder and decoder, respectively. The major issues to develop such devices are circuit complexity and power dissipation at the nanoscale level. This can be fixed by utilising the features of reversible logic with Quantum dot cellular automata (QCA) device as a promising archetype for an alternative to the traditional transistor-based device. QCA has high protection against side-channel attacks, especially from power analysis attacks. All those features of QCA influence the researchers to explore several QCA-based cryptographic models of nanocommunication for secure data transmission. Here, the existing QCAbased circuits for cryptographic architecture are explored and compared concerning their circuit complexity, device area, and operating speeds. The working principles of each of those existing architectures have been demonstrated in detail. Besides, the embedding of reversible logic in QCA cryptographic architecture is also explored. The architectures are compared in terms of the logic used in the design. Finally, the major key issues are identified and addressed in the context of cryptographic models for future secure data transmission processes.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Logic-based QCA implementation of a 4

Cited by 9 publications

References 9 publications

Solving Stochastic Transportation Electricity Problem With Fuzzy Information on Probability Distribution Using Matlab Program

Solving Stochastic Transportation Electricity Problem With Fuzzy Information on Probability Distribution Using Matlab Program

Single Electron Fault Modeling in Basic Quantum Devices

Cryptographic models of nanocommunicaton network using quantum dot cellular automata: A survey

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