The symbolization of land polygon is an important part of cartography. In the mapping of traditional present land-use maps, the symbol of land polygons was usually filled by the method of filling or plotting, but thes...The symbolization of land polygon is an important part of cartography. In the mapping of traditional present land-use maps, the symbol of land polygons was usually filled by the method of filling or plotting, but these methods can't solve the spatial conflicts of the symbol. According to the principle of cartography, the rule of how to symbolize the land polygon was summarized, and a new method that can generate and deploy the land symbols was presented. By making use of C# programming language and Arc Engine developing components, the algorithm can generate land symbols presenting triangle and adjust the coordinate of the symbol. Through mapping the present land-use map of Honghe county, this algorithm can reduce 88.84% of the spatial conflicts error rate compared with the traditional methods. It improves the accuracy and efficiency of the map symbolic.展开更多
Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants' foraging beh...Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants' foraging behavior, is one of the most recent metaheuristic technique. These techniques are used for solving optimization problems. Multiple-Input Multiple-Output (MIMO) detection problem is an NP-hard combinatorial optimization problem. We present heuristic and metaheuristic approaches for symbol detection in multi-input multi-output (MIMO) system. Since symbol detection is an NP-hard problem so ACO is particularly attractive as ACO algorithms are one of the most successful strands of swarm intelligence and are suitable for applications where low complexity and fast convergence is of absolute importance. Maximum Likelihood (ML) detector gives optimal results but it uses exhaustive search technique. We show that 1-Opt and ACO based detector can give near-optimal bit error rate (BER) at much lower complexity levels. Comparison of ACO with another nature inspired technique, Particle Swarm Optimization (PSO) is also discussed. The simulation results suggest that the proposed detectors give an acceptable performance complexity trade-off in comparison with ML and VBLAST detectors.展开更多
基金Project(41061043)support by the National Natural Science Foundation of China
文摘The symbolization of land polygon is an important part of cartography. In the mapping of traditional present land-use maps, the symbol of land polygons was usually filled by the method of filling or plotting, but these methods can't solve the spatial conflicts of the symbol. According to the principle of cartography, the rule of how to symbolize the land polygon was summarized, and a new method that can generate and deploy the land symbols was presented. By making use of C# programming language and Arc Engine developing components, the algorithm can generate land symbols presenting triangle and adjust the coordinate of the symbol. Through mapping the present land-use map of Honghe county, this algorithm can reduce 88.84% of the spatial conflicts error rate compared with the traditional methods. It improves the accuracy and efficiency of the map symbolic.
文摘Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants' foraging behavior, is one of the most recent metaheuristic technique. These techniques are used for solving optimization problems. Multiple-Input Multiple-Output (MIMO) detection problem is an NP-hard combinatorial optimization problem. We present heuristic and metaheuristic approaches for symbol detection in multi-input multi-output (MIMO) system. Since symbol detection is an NP-hard problem so ACO is particularly attractive as ACO algorithms are one of the most successful strands of swarm intelligence and are suitable for applications where low complexity and fast convergence is of absolute importance. Maximum Likelihood (ML) detector gives optimal results but it uses exhaustive search technique. We show that 1-Opt and ACO based detector can give near-optimal bit error rate (BER) at much lower complexity levels. Comparison of ACO with another nature inspired technique, Particle Swarm Optimization (PSO) is also discussed. The simulation results suggest that the proposed detectors give an acceptable performance complexity trade-off in comparison with ML and VBLAST detectors.
