The support vector machine has received wide acceptance for its high generalization ability in real world classification applications. But a drawback is that it uniquely classifies each pattern to one class or none. This is not appropriate to be applied in classification problem involves overlapping patterns. In this paper, a novel multi-model classifier (DR-SVM) which combines SVM classifier with kNN algorithm under rough set technique is proposed. Instead of classifying the patterns directly, patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. The classification operation is carried out on these new sets. Simulation results on synthetic data set and benchmark data sets indicate that, compared with conventional classifiers, more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM.

This paper deals with stability analysis of hybrid systems. Such systems are characterized by a combination of continuous dynamics and logic based switching between discrete modes. Lyapunov theory is a well known methodology for the stability analysis of linear and nonlinear systems in control system literature. Construction of Lyapunov functions for hybrid systems is generally a difficult task, but once these functions are defined, stabilization of the system is straight-forward. The sum of squares (SOS) decomposition and semidefinite programming has also provided an efficient methodology for analysis of nonlinear systems. The computational method used in this paper relies on the SOS decomposition of multivariate polynomials. By using SOS, we construct a (some) Lyapunov function(s) for the hybrid system. The reduction techniques provide numerical solution of large-scale instances; otherwise they will be practically unsolvable. The introduced method can be used for hybrid systems with linear or nonlinear vector fields. Some examples are given to demonstrate the capabilities of the proposed approach.

Full-diversity transmission for space-time block codes (STBCs) with multiple transmit antennas can be achieved by using coordinate interleaved orthogonal designs (CIODs). To effectively evaluate the performance of CIODs, we derive union upper and lower bounds on the symbol-error rate (SER) and a corresponding asymptotic diversity order of symmetric structured CIOD, in particular, with two transmit antennas over quasi-static spatially uncorrelated/correlated frequency-nonselective Rayleigh fading channels. Some numerical results are provided to verify our analysis.

This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). As an approach to this problem, a cooperative algorithm with autonomous distributed agents has been proposed. The algorithm is able to plan deadlock-free routes even though the buffer capacity is less, but includes reformability at the point that computation time of that case increases drastically. This paper proposes an initial task assignment method to reduce computation time on planning routes. Results of computational experiments show effectiveness of the proposed method.

The computational complexity of the optimum maximum likelihood detector (OMLD) does not allow its utility for multi-user detection (MUD) in code division multiple access (CDMA) systems. As proposed in this letter, particle swarm optimization (PSO) with soft decision offers a much more efficient option with few parameters to be adjusted, flexibility to implement, that gives a much faster convergence compared to OMLD. It outperforms the conventional detector, the genetic algorithm approach and the standard suboptimal detectors considered in the literature.

In a wireless sensor network (WSN), a large number of sensor nodes are deployed over a wide area and multi-hop communications are required between the nodes. Managing numerous sensor nodes is a very complicated task, especially when the energy issue is involved. Even though a number of ad-hoc management and network structuring approaches for WSNs have been proposed, a management framework covering the entire network management infrastructure from the messaging protocol to the network structuring algorithm has not yet been proposed. In this paper we introduce a management framework for WSNs called SNOWMAN (SeNsOr netWork MANagement) framework. It employs the policy-based management approach for letting the sensor nodes autonomously organize and manage themselves. Moreover, a new light-weight policy distribution protocol called TinyCOPS-PR and policy information base (PIB) are also developed. To facilitate scalable and localized management of sensor networks, the proposed SNOWMAN constructs a 3-tier hierarchy of regions, clusters, and sensor nodes. The effectiveness of the proposed framework is validated through actual implementation and simulation using ns-2. The simulation results reveal that the proposed framework allows smaller energy consumption for network management and longer network lifetime than the existing schemes such as LEACH and LEACH-C for practical size networks.

In order to obtain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the generalization error is minimized. However, since the generalization error is inaccessible in practice, the model parameters are usually determined so that an estimator of the generalization error is minimized. The regularized subspace information criterion (RSIC) is such a generalization error estimator for model selection. RSIC includes an additional regularization parameter and it should be determined appropriately for better model selection. A meta-criterion for determining the regularization parameter has also been proposed and shown to be useful in practice. In this paper, we show that there are several drawbacks in the existing meta-criterion and give an alternative meta-criterion that can solve the problems. Through simulations, we show that the use of the new meta-criterion further improves the model selection performance.

This paper presents 3D keyframe animation watermarking using orientation interpolators. 3D keyframe animation consists of a number of transform nodes, including a geometrical node from the initial model and several interpolator nodes that represent object movement. Therefore, the proposed algorithm randomly selects transform nodes with orientation interpolator nodes, then resamples the quaternion components to maintain a uniform key time. Thereafter, watermark bits are embedded into quaternion components with large rotation angles. Experimental results verify the robustness of the proposed algorithm to geometrical and timeline attacks, along with the subjective and objective quality of its invisibility.

In this paper, we present a new fast Fourier transform (FFT) algorithm to reduce the table size of twiddle factors required in pipelined FFT processing. The table size is large enough to occupy significant area and power consumption in long-point FFT processing. The proposed algorithm can reduce the table size to half, compared to the radix-22 algorithm, while retaining the simple structure. To verify the proposed algorithm, a 2048-point pipelined FFT processor is designed using a 0.18 µm CMOS process. By combining the proposed algorithm and the radix-22 algorithm, the table size is reduced to 34% and 51% compared to the radix-2 and radix-22 algorithms, respectively. The FFT processor occupies 1.28 mm2 and achieves a signal-to-quantization-noise ratio (SQNR) of more than 50 dB.

Automatic prosody labeling is the task of automatically annotating prosodic labels such as syllable stresses or break indices into speech corpora. Prosody-labeled corpora are important for speech synthesis and automatic speech understanding. However, the subtleness of physical features makes accurate labeling difficult. Since errors in the prosodic labels can lead to incorrect prosody estimation and unnatural synthetic sound, the accuracy of the labels is a key factor for text-to-speech (TTS) systems. In particular, mora accent labels relevant to pitch are very important for Japanese, since Japanese is a pitch-accent language and Japanese people have a particularly keen sense of pitch accents. However, the determination of the mora accents of Japanese is a more difficult task than English stress detection in a way. This is because the context of words changes the mora accents within the word, which is different from English stress where the stress is normally put at the lexical primary stress of a word. In this paper, we propose a method that can accurately determine the prosodic labels of Japanese using both acoustic and linguistic models. A speaker-independent linguistic model provides mora-level knowledge about the possible correct accentuations in Japanese, and contributes to reduction of the required size of the speaker-dependent speech corpus for training the other stochastic models. Our experiments show the effectiveness of the combination of models.

In order to find paths guaranteed by Quality of Service (QoS), the link state database (LSDB), containing QoS constraint information, and residing in routers, needs to be well managed. However, there is a trade-off between the exact reflection of the current link status and the update cost to calculate and maintain this data. In order to perfectly reflect the current link state, each router immediately notifies its neighbors whenever link state information changes. However, this may degrade the performance of the router. On the other hand, if current link state information is not updated routinely, route setup requests may be rejected because of the discrepancy between the current link state information and the previously updated link state information in the LSDB. Therefore, we need link state update (LSU) algorithms making it possible to appropriately update the LSDB. In addition, to facilitate implementation, they also should have low-complexity and must be adaptive under the variation of network conditions. In this paper, we propose an enhanced simple-adaptive (ESA) LSU algorithm, to reduce the generation of LSU messages while maintaining simplicity and adaptivity. The performance of this algorithm is compared with five existing algorithms by rigorous simulations. The comparision shows that the ESU algorithm can adapt to changes in network conditions and its performance is superior to existing LSU algorithms.

This letter proposes a new weighted fair queueing algorithm, which adjusts dynamically each flow's service probability according to its weight and average packet length and then uses the service probability parameters to implement fair queueing. This solves the main drawback of traditional weighted fair queueing algorithms--the packet-based tracing of weight parameters. In addition, this letter proposes a novel service probability calculation method which solves the unfairness problem induced by the variable packet length.

In this paper, we propose two weighting techniques to improve performances of query expansion in biomedical document retrieval, especially when a short biomedical term in a query is expanded with its synonymous multi-word terms. When a query contains synonymous terms of different lengths, a traditional IR model highly ranks a document containing a longer terminology because a longer terminology has more chance to be matched with a query. However, such preference is clearly inappropriate and it often yields an unsatisfactory result. To alleviate the bias weighting problem, we devise a method of normalizing the weights of query terms in a long multi-word biomedical term, and a method of discriminating terms by using inverse terminology frequency which is a novel statistics estimated in a query domain. The experiment results on MEDLINE corpus show that our two simple techniques improve the retrieval performance by adjusting the inadequate preference for long multi-word terminologies in an expanded query.

This letter deals with multiuser detection under imprecise knowledge of the received signature codes of all active users for multicarrier code division multiple access (MC-CDMA) systems. The weight vector of the modified multiple constrained minimum variance (MMCMV) is found by projecting the multiple constrained minimum variance (MCMV) weight vector onto a vector subspace constructed from the eigenstructure of the correlation matrix. However, MMCMV still cannot handle the large code-mismatch. Shaping the noise subspace with all estimated active spreading codes, we present an effective approach to achieve more robust capabilities than the MMCMV. Computer simulations show the effectiveness of the proposed detector.

We demonstrate a novel display structure for color electronic paper for the first time. Fully transparent amorphous oxide TFT array is directly deposited onto color filter array and combined with E Ink Imaging Film. Taking advantage of the transparent property of the oxide TFT, the color filter and TFT array are positioned at the viewing side of the display. This novel "Front Drive" display structure facilitates the alignment of the color filter and TFT dramatically.

We have been developing a secure and reliable distributed storage system, which uses a secret sharing scheme. In order to efficiently store data in the system, this paper introduces an optimal share transfer problem, and proves it to be, generally, NP-hard. It is also shown that the problem can be resolved into a Steiner tree problem. Finally, through computational experiments we perform the comparison of heuristic algorithms for the Steiner tree problem.

The roaming services with the predefined security associations among the entities in various networks are especially complex. We propose a novel architecture to support future context-aware interoperator roaming services throughout 4G networks by using Roaming Coordinators. We design a secure context management model for the practical use of Smart Cards in the secure roaming services. Our architecture solves the interoperator roaming management problems while minimizing the processing overhead on the mobile nodes.

We explain how network failures were caused by a natural disaster, describe the restoration steps that were taken, and present lessons learned from the recovery. At 21:26 on December 26th (UTC+9), 2006, there was a serious undersea earthquake off the coast of Taiwan, which measured 7.1 on the Richter scale. This earthquake caused significant damage to submarine cable systems. The resulting fiber cable failures shut down communications in several countries in the Asia Pacific networks. In the first post-earthquake recovery step, BGP routers detoured traffic along redundant backup paths, which provided poor quality connection. Subsequently, operators engineered traffic to improve the quality of recovered communication. To avoid filling narrow-bandwidth links with detoured traffic, the operators had to change the BGP routing policy. Despite the routing-level first aid, a few institutions could not be directly connected to the R&E network community because they had only a single link to the network. For these single-link networks, the commodity link was temporarily used for connectivity. Then, cable connection configurations at the switches were changed to provide high bandwidth and next-generation Internet service. From the whole restoration procedure, we learned that redundant BGP routing information is useful for recovering connectivity but not for providing available bandwidth for the re-routed traffic load and that collaboration between operators is valuable in solving traffic engineering issues such as poor-quality re-routing and lost connections of single-link networks.

This paper presents a new strategy to detect and diagnose fault of a manipulator based on the expression with a Probabilistic Production Rule (PPR). Production Rule (PR) is widely used in the field of computer science as a tool of formal verification. In this work, first of all, PR is used to represent the mapping between highly quantized input and output signals of the dynamical system. By using PR expression, the fault detection and diagnosis algorithm can be implemented with less computational effort. In addition, we introduce a new system description with Probabilistic PR (PPR) wherein the occurrence probability of PRs is assigned to them to improve the robustness with small computational burden. The probability is derived from the statistic characteristics of the observed input and output signals. Then, the fault detection and diagnosis algorithm is developed based on calculating the log-likelihood of the measured data for the designed PPR. Finally, some experiments on a controlled manipulator are demonstrated to confirm the usefulness of the proposed method.

The thin film transistor liquid crystal display (TFT-LCD) image has nonuniform brightness, which is a major difficulty in finding the Mura defect region. To facilitate Mura segmentation, globally widely varying background signal must be flattened and then Mura signal must be enhanced. In this paper, Mura signal enhancement and background-signal-flattening methods using wavelet coefficient processing are proposed. The wavelet approximation coefficients are used for background-signal flattening, while wavelet detail coefficients are employed to magnify the Mura signal on the basis of an adapted contrast sensitivity function (CSF). Then, for the enhanced image, trimodal thresholding segmentation technique and a false-region elimination method based on the human visual system (HVS) are employed for reliable Mura segmentation. The experimental results show that the proposed algorithms produce promising results and can be applied to automated inspection systems for finding Muras in a TFT-LCD image.