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joe-1661
Double-Staged Syndrome Coding Scheme for Improving Information Transmission Security over the Wiretap Channel
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This paper presents a study of a syndrome coding scheme for different binary linear error correcting codes that refer to the code families such as BCH, BKLC, Golay, and Hamming. The study is implemented on Wyner’s wiretap channel model when the main channel is error-free and the eavesdropper channel is a binary symmetric channel with crossover error probability (0 < Pe ≤ 0.5) to show the security performance of error correcting codes while used in the single-staged syndrome coding scheme in terms of equivocation rate. Generally, these codes are not designed for secure information transmission, and they have low equivocation rates when they are used in the syndrome coding scheme. Therefore, to improve the transmission security when using these codes, a modified encoder which consists of a double-staged syndrome coding scheme, is proposed. Two models are implemented in this paper: the first model utilizes one encoding stage of the conventional syndrome coding scheme. In contrast, the second model utilizes two encoding stages of the syndrome coding scheme to improve the results obtained from the first model. The C++ programming language, in conjunction with the NTL library, is used for obtaining simulation results for the implemented models. The equivocation rate results from the second model were compared to both the results of the first model and of the unsecured transmission (transmission of data without encryption). The comparison revealed that the security performance of the second model is better than the first model and the insecure system, as the equivocation for all the simulated codes over the proposed model reaches at least %97 at the Pe = 0.1.

 

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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Economics And Administrative Sciences
Fuzzy Bridge Regression Model Estimating via Simulation
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      The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin

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