The mobile object (MO) location determination technologies which can be used in intelligent transportation system (ITS) are studied in this paper. The principles and characteristics of wireless location determination ...
详细信息
The mobile object (MO) location determination technologies which can be used in intelligent transportation system (ITS) are studied in this paper. The principles and characteristics of wireless location determination technologies are introduced and the characteristics of GSM useful for location determination are also summarized. An experimental positioning system based on GSM is proposed, and the architecture is described. TOA method based on GSM signals and TDOA method are used in the experimental system. Moreover, the methods are simulated. The performance of the positioning methods is assessed in the simulation environment, and the accuracy for 67% mobile stations (MS) is 70m in urban areas.
It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use t...
详细信息
It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use the principal component analysis (PCA) to project an image into a lower dimensional space, then perform the LDA transform to extract discriminant feature. But some useful discriminant information to the following LDA transform will be lost in the PCA step. To overcome these defects, a face recognition method based on the discrete cosine transform (DCT) and the LDA is proposed. First the DCT is used to achieve dimension reduction, then LDA transform is performed on the lower space to extract features. Two face databases are used to test our method and the correct recognition rates of 97.5 % and 96.0 % are obtained respectively. The performance of the proposed method is compared with that of the PCA + LDA method and the results show that the method proposed outperforms the PCA + LDA method.
Blind separation of independent sources from their nonlinear convoluted mixtures is a more realistic problem than from linear ones. A solution to this problem based on the Entropy Maximization principle is presented. ...
详细信息
Blind separation of independent sources from their nonlinear convoluted mixtures is a more realistic problem than from linear ones. A solution to this problem based on the Entropy Maximization principle is presented. First we propose a novel two-layer network as the de-mixing system to separate sources in nonlinear convolved mixture. In output layer of our network we use feedback network architecture to cope with convoluted mixtures. Then we derive learning algorithms for the two-layer network by maximizing the information entropy. Based on the comparison of the computer simulation results, it can be concluded that the proposed algorithm has a better nonlinear convolved blind signal separation effect than the H.H. Y' s algorithm.
Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e., there are only two views or two dist...
详细信息
A new algorithm for removing random-valued impulse noise is *** use a standardized version of the Rank Ordered Absolute Differences statistic of Garnett et al. [1] to attribute weights to noisy pixels. These weights a...
详细信息
Support vector machine (SVM), as a novel approach in patternrecognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with ...
详细信息
Support vector machine (SVM), as a novel approach in patternrecognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an algorithm by combining SVM classifier with NNC to improve the correct recognition rate. We conduct the experiment on the Cambridge ORL face database. The result shows that our approach outperforms the standard eigenface approach and some other approaches.
In this paper, three dimensions kinematics andkinetics simulation arc discussed for hardware realization ofa physical biped walking-chair robot. The direct and inverseclose-form kinematics solution of the biped walkin...
详细信息
In this paper, three dimensions kinematics andkinetics simulation arc discussed for hardware realization ofa physical biped walking-chair robot. The direct and inverseclose-form kinematics solution of the biped walking-chairis deduced. Several gaits are realized with thekinematics solution, including walking straight on levelfloor, going up stair, squatting down and standing up. ZeroMoment Point(ZMP) equation is analyzed considering themovement of the crew. The simulated biped walking-chairrobot is used for mechanical design, gaits development andvalidation before they are tested on real robot.
Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning ...
详细信息
Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG si...
详细信息
Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative mod...
详细信息
Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative model to eliminate cast shadow on Discriminative Random Fields (DRFs). The method combines different features for Boosting to discriminate cast shadow from moving objects, then temporal and spatial coherence of shadow and foreground are incorporated on Discriminative Random Fields and the problem can be solved by graph cut. Firstly, moving objects are obtained by background subtraction;secondly, shadow candidates can be derived through pre-processing moving objects, in terms of the shadow physical property;thirdly, color information and texture information is derived by comparing shadow and foreground points in current image with corresponding points in background image, which are selected as features for Boosting;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields and discriminate shadow and foreground by graph cut accurately.
暂无评论