To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the cl...To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.展开更多
Two-way concrete slabs are widely used around the world for the construction of many types of infrastructures and common buildings. The optimal sensor placement(OSP) in slabs with various opening positions is the most...Two-way concrete slabs are widely used around the world for the construction of many types of infrastructures and common buildings. The optimal sensor placement(OSP) in slabs with various opening positions is the most important issue in structural health monitoring(SHM) to increase reliability. In this study, a novel approach of OSP was evaluated to obtain the number and placement of sensors using examination of the closed loop performance. The nonlinear finite element(NFE) was used to discretize the mechanism behavior of slab. Multi-Objective Optimization based on the coordinate modal assurance criterion(COMAC) and cost considerations was considered in the optimization processes. All of the analysis, discretization and optimization process was designed and developed as a novel approach in Matlab by the author under the name ‘FEMS-COMAC’(FEM analysis of slab with COMAC). The points in the finite element method(FEM) mesh were classified as line by line information along the slab. The OSP in each line was optimized according to the objective function. The slabs with various width, thickness, aspect ratio and opening position were selected as case studies. The results of the OSP using the COMAC algorithm around the slab openings were compared with the novel ‘FEMS-COMAC’ method. The statistical analysis according Mann-Whitney criteria shows that there were significant differences between them in some of the case studies(mean P-value=0.54).展开更多
文摘To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.
文摘Two-way concrete slabs are widely used around the world for the construction of many types of infrastructures and common buildings. The optimal sensor placement(OSP) in slabs with various opening positions is the most important issue in structural health monitoring(SHM) to increase reliability. In this study, a novel approach of OSP was evaluated to obtain the number and placement of sensors using examination of the closed loop performance. The nonlinear finite element(NFE) was used to discretize the mechanism behavior of slab. Multi-Objective Optimization based on the coordinate modal assurance criterion(COMAC) and cost considerations was considered in the optimization processes. All of the analysis, discretization and optimization process was designed and developed as a novel approach in Matlab by the author under the name ‘FEMS-COMAC’(FEM analysis of slab with COMAC). The points in the finite element method(FEM) mesh were classified as line by line information along the slab. The OSP in each line was optimized according to the objective function. The slabs with various width, thickness, aspect ratio and opening position were selected as case studies. The results of the OSP using the COMAC algorithm around the slab openings were compared with the novel ‘FEMS-COMAC’ method. The statistical analysis according Mann-Whitney criteria shows that there were significant differences between them in some of the case studies(mean P-value=0.54).
