12730309
Adaptive Neural-Network Control for Redundant Nonholonomic Mobile Modular Manipulators
This paper discusses the trajectory following issue for redundant nonholonomic mobile modular manipulators. Dynamic model is established and an adaptive neural-network controller is developed to control the end-effector to follow a desired spacial trajectory. The proposed algorithm doesn't need any priori dynamics and provides a new solution for stabilization of redundant robotic self-motions. Simulation results for a real robot demonstrate the proposed algorithm is effective.
16004582
Formation Control for a Multiple Robotic System Using Adaptive Neural Network
Due to the limitations of sensors, each member of a decentralized system can only deal with local information respectively. A description of local relationship within formation pattern is proposed in this paper. Furthermore, a NN control approach with robust term is proposed to control individual motion. By using such individual control method, all robots will finally form a unique formation. Based on properties of such control strategy, we propose a modified artificial potential approach to realize obstacle avoidance.
15288744
Cost/performance tradeoff of n-select square root implementations
Hardware square-root units require large numbers of gates even for iterative implementations. In this paper we present four low-cost high-performance fully-pipelined n-select implementations (nS-Root) based on a non-restoring-remainder square root algorithm. The nS-Root uses a parallel array of carry-save adders (CSAs). For a square root bit calculation, a CSA is used once. This means that the calculations can be fully pipelined. It also uses the n-way root-select technique to speedup the square root calculation. The cost/performance evaluation shows that n=2 or n=2.5 is a suitable solution for designing a high-speed fully pipelined square root unit while keeping the low-cost.
6625111
On incremental and robust subspace learning
Principal Component Analysis (PCA) has been of great interest in computer vision and pattern recognition. In particular, incrementally learning a PCA model, which is computationally efficient for large-scale problems as well as adaptable to reflect the variable state of a dynamic system, is an attractive research topic with numerous applications such as adaptive background modelling and active object recognition. In addition, the conventional PCA, in the sense of least mean squared error minimisation, is susceptible to outlying measurements. To address these two important issues, we present a novel algorithm of incremental PCA, and then extend it to robust PCA. Compared with the previous studies on robust PCA, our algorithm is computationally more efficient. We demonstrate the performance of these algorithms with experimental results on dynamic background modelling and multi-view face modelling.
5262698
An integrated algorithm of incremental and robust PCA
Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.
28848292
Rights Protection for Data Cubes
We propose a rights protection scheme for data cubes. The scheme embeds ownership information by modifying a set of selected cell values. The embedded message will not affect the usefulness of data cubes in the sense that the sum queries at any aggregation level are not affected. At the same time, the errors introduced to individual cell values are under control. The embedded message can be detected with a high probability even in the presence of typical data cube attacks. The proposed scheme can thus be used for protecting data cubes from piracy in an open, distributed environment.
15368149
Hierarchical fuzzy filter method for unsupervised feature selection
The problem of feature selection has long been an active research topic within statistics and pattern recognition. So far, most methods of feature selection focus on supervised data where class information is available. For unsupervised data, the related methods of feature selection are few. The presented article demonstrates a way of unsupervised feature selection, which is a two-level filter model removing the redundant and irrelevant features, respectively. The redundant features are eliminated using any clustering algorithm, and a new method is proposed to remove the irrelevant features: first rank the features according to their relevance to cluster and then a subset of relevant features is selected using the Fuzzy Feature Evaluation Index (FFEI) with some changes and extensions. The experimental results have shown the effectiveness of the proposed method for high-dimensional data. Our major contributions are: (1) to present a new hierarchical filter method for unsupervised feature selection; (2) to propose a new algorithm for removing the irrelevant features; (3) to extend the FFEI, and present a method for calculating the approximate weight of feature in FFEI, which improves the efficiency and robustness of the method.