Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5357720
Yuanbin Wang, Bin Zhang, Tianshun Yao
The projective recovery of 3D point structure from multiple images has been one of the classical problems in computer vision. Existing methods for projective reconstruction usually require a priori estimation of a consistent set of projective depths which in turn require the estimation of the projection matrices or the fundamental matrices in advance. Those methods are usually nonlinear, time-consuming, and sometimes inaccurate. This paper presents a direct and linear method for projective reconstruction. First, a 3D point structure is characterized by representing other points as linear combinations of some reference points. Next, cross ratios of projective depths are derived linearly. Then coefficients of the representations scaled by ratios of the projective depths are derived linearly. Projective invariants of these points are ratios of these values.
{"title":"A linear and direct method for projective reconstruction","authors":"Yuanbin Wang, Bin Zhang, Tianshun Yao","doi":"10.1109/ICICISYS.2009.5357720","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5357720","url":null,"abstract":"The projective recovery of 3D point structure from multiple images has been one of the classical problems in computer vision. Existing methods for projective reconstruction usually require a priori estimation of a consistent set of projective depths which in turn require the estimation of the projection matrices or the fundamental matrices in advance. Those methods are usually nonlinear, time-consuming, and sometimes inaccurate. This paper presents a direct and linear method for projective reconstruction. First, a 3D point structure is characterized by representing other points as linear combinations of some reference points. Next, cross ratios of projective depths are derived linearly. Then coefficients of the representations scaled by ratios of the projective depths are derived linearly. Projective invariants of these points are ratios of these values.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5358043
K. Khandani, A. Jalali, M. Alipoor
In this paper a new method to design PID controllers for time delay systems is presented. Particle Swarm Optimization (PSO) technique is used to obtain optimal parameters of a two-degree-of-freedom (2-DOF) PID controller. Set point tracking is an objective that is to be achieved in presence of disturbance. At first a PD controller as a disturbance rejection controller is designed, and then in the outer loop the main PID controller for tracking the input signal is placed. Since disturbance rejection and set point tracking should be satisfied alongside each other, some considerations are taken into account to choose the most appropriate controller. Using this method, a better response can be achieved in comparison with genetic algorithm.
{"title":"Particle Swarm Optimization based design of disturbance rejection PID controllers for time delay systems","authors":"K. Khandani, A. Jalali, M. Alipoor","doi":"10.1109/ICICISYS.2009.5358043","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5358043","url":null,"abstract":"In this paper a new method to design PID controllers for time delay systems is presented. Particle Swarm Optimization (PSO) technique is used to obtain optimal parameters of a two-degree-of-freedom (2-DOF) PID controller. Set point tracking is an objective that is to be achieved in presence of disturbance. At first a PD controller as a disturbance rejection controller is designed, and then in the outer loop the main PID controller for tracking the input signal is placed. Since disturbance rejection and set point tracking should be satisfied alongside each other, some considerations are taken into account to choose the most appropriate controller. Using this method, a better response can be achieved in comparison with genetic algorithm.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115434783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5357867
Wei Jiang, G. Yi, Qingshuang Zeng
An improved particle filter for nonlinear, non-Gaussian estimation is proposed in this paper. The algorithm consists of a particle filter that uses a proximal support vector regression (PSVR) based re-weighting scheme to re-approximate the posterior density and avoid sample impoverishment. A regression function is obtained by PSVR over the weighted sample set and each sample is re-weighted via this function. Then, posterior density of the state is re-approximated to maintain the effectiveness and diversity of samples. Two experimental results demonstrate that the efficiency of the proposed algorithm compared with the generic particle filter and Markov Chain Monte Carlo (MCMC) particle filter.
{"title":"Application of proximal support vector regression to particle filter","authors":"Wei Jiang, G. Yi, Qingshuang Zeng","doi":"10.1109/ICICISYS.2009.5357867","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5357867","url":null,"abstract":"An improved particle filter for nonlinear, non-Gaussian estimation is proposed in this paper. The algorithm consists of a particle filter that uses a proximal support vector regression (PSVR) based re-weighting scheme to re-approximate the posterior density and avoid sample impoverishment. A regression function is obtained by PSVR over the weighted sample set and each sample is re-weighted via this function. Then, posterior density of the state is re-approximated to maintain the effectiveness and diversity of samples. Two experimental results demonstrate that the efficiency of the proposed algorithm compared with the generic particle filter and Markov Chain Monte Carlo (MCMC) particle filter.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115500385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5358040
Huajie Xu, Xiaoming Hu, Bing Yang, Juan Xu
In the environment with objects moving randomly, the positions of moving objects can be modeled as a range of possible values, associated with a probability density function. Data mining of such positions of uncertain moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a density-based probabilistic clustering algorithm for uncertain moving objects is proposed, based on DBSCAN algorithm and probabilistic index on uncertain moving objects. Simulation results show that the proposed algorithm outperforms other density-based clustering algorithm for uncertain moving objects in accuracy and update rate needed for clustering.
{"title":"Density-based probabilistic clustering of uncertain moving objects","authors":"Huajie Xu, Xiaoming Hu, Bing Yang, Juan Xu","doi":"10.1109/ICICISYS.2009.5358040","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5358040","url":null,"abstract":"In the environment with objects moving randomly, the positions of moving objects can be modeled as a range of possible values, associated with a probability density function. Data mining of such positions of uncertain moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a density-based probabilistic clustering algorithm for uncertain moving objects is proposed, based on DBSCAN algorithm and probabilistic index on uncertain moving objects. Simulation results show that the proposed algorithm outperforms other density-based clustering algorithm for uncertain moving objects in accuracy and update rate needed for clustering.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115663251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5358209
Yipeng Zhang, Haifeng You, Xufa Wang
In this paper, we present a hormone based tracking strategy called Hormone based Activation (HA) for mobile target tracking in wireless sensor networks inspired by the bio-endocrine system. Hormone messages are used in sensor networks to coordinate sensor nodes for mobile target tracking in a self-adaptive way. Three kinds of hormones are introduced into sensor network for mobile target tracking: Base Hormone(BH),Wakeup Hormone(WH) and Sleep Hormone(SH).Based on different hormone messages, sensor nodes can choose to be asleep or awake in order to get good tracking results with less energy consumed. Comparative experiment shows that our strategy needs fewer nodes to wake up and has a much higher usage ratio of sensor nodes than other tracking strategies.
{"title":"A hormone based tracking strategy for wireless sensor network","authors":"Yipeng Zhang, Haifeng You, Xufa Wang","doi":"10.1109/ICICISYS.2009.5358209","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5358209","url":null,"abstract":"In this paper, we present a hormone based tracking strategy called Hormone based Activation (HA) for mobile target tracking in wireless sensor networks inspired by the bio-endocrine system. Hormone messages are used in sensor networks to coordinate sensor nodes for mobile target tracking in a self-adaptive way. Three kinds of hormones are introduced into sensor network for mobile target tracking: Base Hormone(BH),Wakeup Hormone(WH) and Sleep Hormone(SH).Based on different hormone messages, sensor nodes can choose to be asleep or awake in order to get good tracking results with less energy consumed. Comparative experiment shows that our strategy needs fewer nodes to wake up and has a much higher usage ratio of sensor nodes than other tracking strategies.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115699769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5357735
Zhang Xiao-yong, Luo Lai-yuan
A novel algorithm for Signal-power-to-Noise-power ratio (SNR) estimation in AWGN channel is proposed in this paper. The relationship between SNR and the distribution of instantaneous phase (DIP) of BPSK signal is investigated, and the SNR estimator is constructed according to this relationship. Correlation coefficients are used to judge the similarity of the DIP between the theoretical one and the one of the received signal. Accurate symbol synchronization, which is the essential prerequisite for most SNR estimators, is not needed for the estimator proposed in this paper. Bias and root-mean-squared error (RMSE) are used to evaluate the performance of the estimator. The results of the Monte Carlo computer simulations show the credible performances of the estimator.
{"title":"Instantaneous phases based SNR estimator for BPSK signals","authors":"Zhang Xiao-yong, Luo Lai-yuan","doi":"10.1109/ICICISYS.2009.5357735","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5357735","url":null,"abstract":"A novel algorithm for Signal-power-to-Noise-power ratio (SNR) estimation in AWGN channel is proposed in this paper. The relationship between SNR and the distribution of instantaneous phase (DIP) of BPSK signal is investigated, and the SNR estimator is constructed according to this relationship. Correlation coefficients are used to judge the similarity of the DIP between the theoretical one and the one of the received signal. Accurate symbol synchronization, which is the essential prerequisite for most SNR estimators, is not needed for the estimator proposed in this paper. Bias and root-mean-squared error (RMSE) are used to evaluate the performance of the estimator. The results of the Monte Carlo computer simulations show the credible performances of the estimator.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"27 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123074323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5357709
Li Feng, Xue Jing-ming
The paper introduce the technique of edge blending process that is the key technique of the seamless tiled system and put forward the improved algorithm of edge blending in order to eliminate the edge of the light generated regional integration and achieve seamless connection. the algorithm is the modification of existing blending algorithm that using two addition parameter p and a, and add Gamma operator to further correction of the pixel brightness. finally, the color/brightness to match the output to be a certain degree of processing. Experimental results show that, the proposed algorithm effectively eliminates the light generated by the edge of the integration region and the image have good transitions, the proposed algorithm reduces the output of the projector color differences by the value of SNR.
{"title":"Algorithm of edge blending based on the nonlinear function","authors":"Li Feng, Xue Jing-ming","doi":"10.1109/ICICISYS.2009.5357709","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5357709","url":null,"abstract":"The paper introduce the technique of edge blending process that is the key technique of the seamless tiled system and put forward the improved algorithm of edge blending in order to eliminate the edge of the light generated regional integration and achieve seamless connection. the algorithm is the modification of existing blending algorithm that using two addition parameter p and a, and add Gamma operator to further correction of the pixel brightness. finally, the color/brightness to match the output to be a certain degree of processing. Experimental results show that, the proposed algorithm effectively eliminates the light generated by the edge of the integration region and the image have good transitions, the proposed algorithm reduces the output of the projector color differences by the value of SNR.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117198729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5357925
Li Chen, J. Chen, Xintao Gao
Potential Support Vector Machine (P-SVM) is a novel Support Vector Machine (SVM) method. It defines a new optimization model which is different from standard SVM. However, P-SVM method has restrictions in dealing with unbalanced data sets. To solve this problem, an improved P-SVM method used to deal with imbalanced data sets is proposed in this paper. By using different penalty parameters to different slack variables in P-SVM, the new algorithm adjusts penalty parameters more flexible, and effectively improves the low classification accuracy caused by imbalanced samples. From theoretical analyses and experimental results, they have shown that this new method can obtain better classification accuracy than standard SVM and P-SVM in dealing with imbalanced data sets.
{"title":"An improved P-SVM method used to deal with imbalanced data sets","authors":"Li Chen, J. Chen, Xintao Gao","doi":"10.1109/ICICISYS.2009.5357925","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5357925","url":null,"abstract":"Potential Support Vector Machine (P-SVM) is a novel Support Vector Machine (SVM) method. It defines a new optimization model which is different from standard SVM. However, P-SVM method has restrictions in dealing with unbalanced data sets. To solve this problem, an improved P-SVM method used to deal with imbalanced data sets is proposed in this paper. By using different penalty parameters to different slack variables in P-SVM, the new algorithm adjusts penalty parameters more flexible, and effectively improves the low classification accuracy caused by imbalanced samples. From theoretical analyses and experimental results, they have shown that this new method can obtain better classification accuracy than standard SVM and P-SVM in dealing with imbalanced data sets.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121123497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5358155
Tao Wang, Yuan Yao, Lin Han, Dan Zhang, Yuanyuan Zhang
CUDA is a new computing architecture introduced by NVIDIA Corporation, aiming at general purpose computation on GPU. The architecture has strong compute power in the compute-intensive applications and data-intensive applications, so in recent years, how the framework is applied to the scientific computing has become a hot research. The iterative method for solving systems of linear equations in engineering and scientific computing has a very far-ranging application. The algorithm provided with high computing intensity and parallelism is very suitable for CUDA architecture. In this paper, Jacobi iterative method is implemented on CUDA-enable GPU. The experimental results show that this iterative method can effectively make use of the CUDA-enable GPU computing power and achieve good performance.
{"title":"Implementation of Jacobi iterative method on graphics processor unit","authors":"Tao Wang, Yuan Yao, Lin Han, Dan Zhang, Yuanyuan Zhang","doi":"10.1109/ICICISYS.2009.5358155","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5358155","url":null,"abstract":"CUDA is a new computing architecture introduced by NVIDIA Corporation, aiming at general purpose computation on GPU. The architecture has strong compute power in the compute-intensive applications and data-intensive applications, so in recent years, how the framework is applied to the scientific computing has become a hot research. The iterative method for solving systems of linear equations in engineering and scientific computing has a very far-ranging application. The algorithm provided with high computing intensity and parallelism is very suitable for CUDA architecture. In this paper, Jacobi iterative method is implemented on CUDA-enable GPU. The experimental results show that this iterative method can effectively make use of the CUDA-enable GPU computing power and achieve good performance.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127473806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-28DOI: 10.1109/ICICISYS.2009.5358148
Chenwen Wang, D. Li, Ling Shen
Based on the characteristics of the mobile ad hoc network, we discuss the data cache and the replacement algorithms. According to the dynamic topology and limitation capacity of nodes in ad hoc networks, we propose a new cache consistent algorithm integrated with the pull strategy. The algorithm description and flow chart of data replication algorithm are presented in the end.
{"title":"The local consistency of data cache in mobile ad hoc networks","authors":"Chenwen Wang, D. Li, Ling Shen","doi":"10.1109/ICICISYS.2009.5358148","DOIUrl":"https://doi.org/10.1109/ICICISYS.2009.5358148","url":null,"abstract":"Based on the characteristics of the mobile ad hoc network, we discuss the data cache and the replacement algorithms. According to the dynamic topology and limitation capacity of nodes in ad hoc networks, we propose a new cache consistent algorithm integrated with the pull strategy. The algorithm description and flow chart of data replication algorithm are presented in the end.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125014101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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