Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （...Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （CIR） aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm （VGA） for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm （BP）, it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP （multi-layer perception） classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.展开更多
String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that...String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.展开更多
Playing an increasingly important role in the security protection of the network information systems,the intrusion detection system(IDS) becomes a hotspot of research interest nowadays.However,this technology in the k...Playing an increasingly important role in the security protection of the network information systems,the intrusion detection system(IDS) becomes a hotspot of research interest nowadays.However,this technology in the kernel to many of these systems,namely string searching algorithm,has not received enough attention.By utilizing the concurrent mechanisms(multi-threading) provided by modern operation systems,such work can be divided symmetrically and thus improve the throughput of the corresponding application effectively.Presented in this work is a paralleled string searching algorithm-PBM,an algorithm based on the famous Boyer-Moore(BM) string searching algorithm.Taken as a dividable process,the string searching work is distributed between many cooperating threads of execution in the PBM algorithm,while each of them searches the target pattern in their respective share of the target strings.As compared with the traditional string searching algorithms,the PBM algorithm can do the pattern matching work faster by increasing the data processing throughput,thus adapting better to the drastic increase in the network band width.A simplification of the PBM algorithm that can be used as a multi-string searching algorithm is also suggested with supporting simulations,which is a promising approach when the number of target patterns is limited.展开更多
Pattern matching is a very important algorithm used in many applications such as search engine and DNA analysis. They are aiming to find a pattern in a text. This paper proposes a Pattern Matching Algorithm Using Chan...Pattern matching is a very important algorithm used in many applications such as search engine and DNA analysis. They are aiming to find a pattern in a text. This paper proposes a Pattern Matching Algorithm Using Changing Consecutive Characters (PMCCC) to make the searching pro- cess of the algorithm faster. PMCCC enhances the shift process that determines how the pattern moves in case of the occurrence of the mismatch between the pattern and the text. It enhances the Berry Ravindran (BR) shift function by using m consecutive characters where m is the pattern length. The formal basis and the algorithms are presented. The experimental results show that PMCCC made enhancements in searching process by reducing the number of comparisons and the number of attempts. Comparing the results of PMCCC with other related algorithms has shown significant enhancements in average number of comparisons and average number of attempts.展开更多
文摘Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （CIR） aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm （VGA） for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm （BP）, it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP （multi-layer perception） classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.
文摘String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.
基金This work is supported by National Science Foundatinon Grant60273035"Software Performance Assure and Recovery"
文摘Playing an increasingly important role in the security protection of the network information systems,the intrusion detection system(IDS) becomes a hotspot of research interest nowadays.However,this technology in the kernel to many of these systems,namely string searching algorithm,has not received enough attention.By utilizing the concurrent mechanisms(multi-threading) provided by modern operation systems,such work can be divided symmetrically and thus improve the throughput of the corresponding application effectively.Presented in this work is a paralleled string searching algorithm-PBM,an algorithm based on the famous Boyer-Moore(BM) string searching algorithm.Taken as a dividable process,the string searching work is distributed between many cooperating threads of execution in the PBM algorithm,while each of them searches the target pattern in their respective share of the target strings.As compared with the traditional string searching algorithms,the PBM algorithm can do the pattern matching work faster by increasing the data processing throughput,thus adapting better to the drastic increase in the network band width.A simplification of the PBM algorithm that can be used as a multi-string searching algorithm is also suggested with supporting simulations,which is a promising approach when the number of target patterns is limited.
文摘Pattern matching is a very important algorithm used in many applications such as search engine and DNA analysis. They are aiming to find a pattern in a text. This paper proposes a Pattern Matching Algorithm Using Changing Consecutive Characters (PMCCC) to make the searching pro- cess of the algorithm faster. PMCCC enhances the shift process that determines how the pattern moves in case of the occurrence of the mismatch between the pattern and the text. It enhances the Berry Ravindran (BR) shift function by using m consecutive characters where m is the pattern length. The formal basis and the algorithms are presented. The experimental results show that PMCCC made enhancements in searching process by reducing the number of comparisons and the number of attempts. Comparing the results of PMCCC with other related algorithms has shown significant enhancements in average number of comparisons and average number of attempts.
