Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5580639
Hua Lian, Bo-Ning Hu, Rui-Mei Zhao, Yanli Hou
A blind watermarking algorithm based on wavelet transform domain is proposed. This algorithm use two-level wavelet transform on the original image. Reference the coefficients of level detail sub-band in one-level wavelet transform and adjust the two-level wavelet coefficients in the same direction adaptive to achieve embedded watermark information. The result shows that the embedded watermark has good transparency and robustness, and the watermark can be extracted from the watermarked image without the original image.
{"title":"Design of digital watermarking algorithm based on wavelet transform","authors":"Hua Lian, Bo-Ning Hu, Rui-Mei Zhao, Yanli Hou","doi":"10.1109/ICMLC.2010.5580639","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580639","url":null,"abstract":"A blind watermarking algorithm based on wavelet transform domain is proposed. This algorithm use two-level wavelet transform on the original image. Reference the coefficients of level detail sub-band in one-level wavelet transform and adjust the two-level wavelet coefficients in the same direction adaptive to achieve embedded watermark information. The result shows that the embedded watermark has good transparency and robustness, and the watermark can be extracted from the watermarked image without the original image.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122413277","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580663
Wei Li, Zhan-You Sha, Bin Wang
The Digitally Controlled Potentiometers (DCP) is a new type of electronic device with great developing foreground, which can replace the traditional mechanical potentiometer in many fields. The programmable gain amplifier, programmable filter and others programmable analogy devices can be built using SCM and DCP through programming. Thereby it is realizable to “Set the analogy device onto the bus” (controlling analogy modules through bus by MCU). The methods presented all have practical significance.
{"title":"The topology and test technology of Digitally Controlled Potentiometers","authors":"Wei Li, Zhan-You Sha, Bin Wang","doi":"10.1109/ICMLC.2010.5580663","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580663","url":null,"abstract":"The Digitally Controlled Potentiometers (DCP) is a new type of electronic device with great developing foreground, which can replace the traditional mechanical potentiometer in many fields. The programmable gain amplifier, programmable filter and others programmable analogy devices can be built using SCM and DCP through programming. Thereby it is realizable to “Set the analogy device onto the bus” (controlling analogy modules through bus by MCU). The methods presented all have practical significance.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122722709","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580974
Qing He, Ning Li, Zhongzhi Shi
Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high.
{"title":"A HSC-based sample selection method for support vector machine","authors":"Qing He, Ning Li, Zhongzhi Shi","doi":"10.1109/ICMLC.2010.5580974","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580974","url":null,"abstract":"Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121906230","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580731
Yubo Yuan, F. Cao
Support vector machine (SVM) is one of the most popular machine learning method and educed from a binary data classification problem. In this paper, a new duality theory named canonical duality theory is presented to solve the normal model of SVM. Several examples are illustrated to show that the exact solution can be obtained after the canonical duality problem being solved. Moreover, the support vectors can be located by non-zero elements of the canonical dual solution.
{"title":"Canonical duality solution to support vector machine","authors":"Yubo Yuan, F. Cao","doi":"10.1109/ICMLC.2010.5580731","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580731","url":null,"abstract":"Support vector machine (SVM) is one of the most popular machine learning method and educed from a binary data classification problem. In this paper, a new duality theory named canonical duality theory is presented to solve the normal model of SVM. Several examples are illustrated to show that the exact solution can be obtained after the canonical duality problem being solved. Moreover, the support vectors can be located by non-zero elements of the canonical dual solution.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122022454","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580720
Jinglin Yang, Han-Xiong Li
The expression of genes could be used for tumor subtype classification, clinical diagnosis and prognosis outcome prediction, but the underlying mechanism remains unknown. It is possible for data-based machine learning method to be employed for phenotype classification problem. But high dimensionality and small sample size make many machine learning methods fail. In this research, a PCA based sequential feature space learning method is proposed for gene selection. A two level feature selection process is conducted. In the first level PCA decomposition is conducted to obtain the orthogonal axis, and then features are projected and evaluated on the orthogonal axis. In second level, the features that have large projections are selected to form the feature space. Then the projections of all features onto the feature space are evaluated. Only features that have large projections both on orthogonal axis and feature subspace are selected as the feature subset. Then a neural network (NN) is employed to learn the classification model. The PCA based feature space learning is processed in a sequential manner until the classification performance is under pre-specified threshold and stable. The proposed methods have been applied to two gene microarray databases and showing good results.
{"title":"PCA based sequential feature space learning for gene selection","authors":"Jinglin Yang, Han-Xiong Li","doi":"10.1109/ICMLC.2010.5580720","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580720","url":null,"abstract":"The expression of genes could be used for tumor subtype classification, clinical diagnosis and prognosis outcome prediction, but the underlying mechanism remains unknown. It is possible for data-based machine learning method to be employed for phenotype classification problem. But high dimensionality and small sample size make many machine learning methods fail. In this research, a PCA based sequential feature space learning method is proposed for gene selection. A two level feature selection process is conducted. In the first level PCA decomposition is conducted to obtain the orthogonal axis, and then features are projected and evaluated on the orthogonal axis. In second level, the features that have large projections are selected to form the feature space. Then the projections of all features onto the feature space are evaluated. Only features that have large projections both on orthogonal axis and feature subspace are selected as the feature subset. Then a neural network (NN) is employed to learn the classification model. The PCA based feature space learning is processed in a sequential manner until the classification performance is under pre-specified threshold and stable. The proposed methods have been applied to two gene microarray databases and showing good results.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122073124","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580625
Yipiao Chen, Yu-Zhong Chen
How to design an energy efficient routing algorithm is a hot topic in the research of wireless sensor networks. In this paper, based on the analysis of some typical cluster-based routing algorithms, a novel cluster-based energy efficient routing algorithm is proposed to solve the hot spot problem involved in inter-cluster routing and optimize network lifetime. Clusters are formed by local competition and the role of cluster head is rotated among sensor nodes periodically to balance energy consumption in WSN. Furthermore, Particle Swarm Optimization algorithm is utilized to search optimal inter-cluster routing path for the optimization of network lifetime. Simulation results prove the effectiveness of the routing algorithm proposed in this paper.
{"title":"A novel energy efficient routing algorithm for wireless sensor networks","authors":"Yipiao Chen, Yu-Zhong Chen","doi":"10.1109/ICMLC.2010.5580625","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580625","url":null,"abstract":"How to design an energy efficient routing algorithm is a hot topic in the research of wireless sensor networks. In this paper, based on the analysis of some typical cluster-based routing algorithms, a novel cluster-based energy efficient routing algorithm is proposed to solve the hot spot problem involved in inter-cluster routing and optimize network lifetime. Clusters are formed by local competition and the role of cluster head is rotated among sensor nodes periodically to balance energy consumption in WSN. Furthermore, Particle Swarm Optimization algorithm is utilized to search optimal inter-cluster routing path for the optimization of network lifetime. Simulation results prove the effectiveness of the routing algorithm proposed in this paper.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116827495","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580486
Xiaoping Zong, X. Ding
For the purpose of robot visual servo researches, a semi-physical simulation platform of robot visual servo based on position was presented. The image acquisition and processing realistic part based on USB is established and the virtual simulation environment and kinematics model are built with OpenGL. By means of ROBOOP toolbox the dynamics model of robot is set up. With forward and inverse kinematics algorithm trajectory planning is realized. The experiment result indicates that the robot can catch the target following planned trajectory and the simulation platform has important value for robot visual servo researches.
{"title":"Semi-physical simulation of robot visual servo based on position","authors":"Xiaoping Zong, X. Ding","doi":"10.1109/ICMLC.2010.5580486","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580486","url":null,"abstract":"For the purpose of robot visual servo researches, a semi-physical simulation platform of robot visual servo based on position was presented. The image acquisition and processing realistic part based on USB is established and the virtual simulation environment and kinematics model are built with OpenGL. By means of ROBOOP toolbox the dynamics model of robot is set up. With forward and inverse kinematics algorithm trajectory planning is realized. The experiment result indicates that the robot can catch the target following planned trajectory and the simulation platform has important value for robot visual servo researches.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129518416","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580798
K. Jea, Chao-Wei Li, Chih-Wei Hsu, Ru-Ping Lin, S. Yen
In many applications, data-stream sources are prone to dramatic spikes in volume, which necessitates load shedding for data-stream processing systems. In this research, we study the load-shedding problem for frequent-pattern discovery in transactional data streams. A load-controllable mining system with an ε-deficient mining algorithm and three dedicated load-shedding schemes is proposed. When the system is overloaded, a load-shedding scheme is executed to prune a fraction of unprocessed data. From the experimental result, we find that the strategies of load shedding can indeed lighten the system workload while preserving the mining accuracy at an acceptable level.
{"title":"A load-controllable mining system for frequent-pattern discovery in dynamic data streams","authors":"K. Jea, Chao-Wei Li, Chih-Wei Hsu, Ru-Ping Lin, S. Yen","doi":"10.1109/ICMLC.2010.5580798","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580798","url":null,"abstract":"In many applications, data-stream sources are prone to dramatic spikes in volume, which necessitates load shedding for data-stream processing systems. In this research, we study the load-shedding problem for frequent-pattern discovery in transactional data streams. A load-controllable mining system with an ε-deficient mining algorithm and three dedicated load-shedding schemes is proposed. When the system is overloaded, a load-shedding scheme is executed to prune a fraction of unprocessed data. From the experimental result, we find that the strategies of load shedding can indeed lighten the system workload while preserving the mining accuracy at an acceptable level.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129809663","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580709
Seong-Hyeon Choe, Yoon Gi Chung, Sung-Phil Kim
Discrimination of epileptic activity in the electroencephalogram (EEG) signals continuously recorded from the brain may facilitate the effective and accurate diagnosis of epilepsy. This paper proposes a new statistical method combined with a simple classification algorithm that can discriminate epileptic EEG signals from normal signals. The statistical method extracts most significant spectral features by maximizing statistical distance between the epileptic and the normal power spectrums. The power spectrum density of EEG signals is estimated by the multi-taper method. A linear algorithm based on the Fisher discriminant analysis classifies the selected spectral features as either the epileptic or the normal class from the EEG recordings. The results demonstrate that our method could reach >99.6% classification accuracy while its computational complexity appears to be much lower than the previously proposed methods that exhibited similar classification performances. It is suggested that our method may be readily implemented in real time with high accuracy so that it can provide an on-line monitoring tool for clinical epilepsy diagnosis.
{"title":"Statistical spectral feature extraction for classification of epileptic EEG signals","authors":"Seong-Hyeon Choe, Yoon Gi Chung, Sung-Phil Kim","doi":"10.1109/ICMLC.2010.5580709","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580709","url":null,"abstract":"Discrimination of epileptic activity in the electroencephalogram (EEG) signals continuously recorded from the brain may facilitate the effective and accurate diagnosis of epilepsy. This paper proposes a new statistical method combined with a simple classification algorithm that can discriminate epileptic EEG signals from normal signals. The statistical method extracts most significant spectral features by maximizing statistical distance between the epileptic and the normal power spectrums. The power spectrum density of EEG signals is estimated by the multi-taper method. A linear algorithm based on the Fisher discriminant analysis classifies the selected spectral features as either the epileptic or the normal class from the EEG recordings. The results demonstrate that our method could reach >99.6% classification accuracy while its computational complexity appears to be much lower than the previously proposed methods that exhibited similar classification performances. It is suggested that our method may be readily implemented in real time with high accuracy so that it can provide an on-line monitoring tool for clinical epilepsy diagnosis.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128958341","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580839
Ke-Yong Shao, Fei Li, Bei-Yan Jiang, Na Wang, Hongyan Zhang, Wen-Cheng Li
In this paper, a kind of improved method of diploid genetic algorithm (DGA) without considering the dominant and recessive of the allele is given directed at the disadvantages of DGA which are easy to fall into premature convergence and have low efficiency in late period local searching. Improved the genetic operation process by imitating the reproductive processes of diplont and adopting the process of gametes recombination and homologous chromosomes chiasma. United the advantages of genetic algorithm and neural network, a new neural network structure contacted with the diploid genetic algorithm closely is designed. This scheme combines the strong global search capability of genetic algorithm and self-learning ability of neural network. Then applied the method to the complex multi-peak function optimization. Simulation results show that the improved algorithm can keep the population diversity and repressed the premature convergence effectively. The neural network optimization based on diploid genetic algorithm increased the convergence speed and accuracy, and ensured the global optimal.
{"title":"Neural network optimization based on improved diploidic genetic algorithm","authors":"Ke-Yong Shao, Fei Li, Bei-Yan Jiang, Na Wang, Hongyan Zhang, Wen-Cheng Li","doi":"10.1109/ICMLC.2010.5580839","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580839","url":null,"abstract":"In this paper, a kind of improved method of diploid genetic algorithm (DGA) without considering the dominant and recessive of the allele is given directed at the disadvantages of DGA which are easy to fall into premature convergence and have low efficiency in late period local searching. Improved the genetic operation process by imitating the reproductive processes of diplont and adopting the process of gametes recombination and homologous chromosomes chiasma. United the advantages of genetic algorithm and neural network, a new neural network structure contacted with the diploid genetic algorithm closely is designed. This scheme combines the strong global search capability of genetic algorithm and self-learning ability of neural network. Then applied the method to the complex multi-peak function optimization. Simulation results show that the improved algorithm can keep the population diversity and repressed the premature convergence effectively. The neural network optimization based on diploid genetic algorithm increased the convergence speed and accuracy, and ensured the global optimal.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129055931","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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