This paper describes a new approach to detect a fingerprint core location using the extended relational graph, which is generated by the segmentation of the ridge directional image. The extended relational graph presents the adjacency between segments of the directional image and the boundary information between segments of the directional image. The boundary curves generated by the boundary information in the extended relational graph is approximated to the straight lines. The fingerprint core location is calculated as center of the gravity in the points of intersection of these approximated lines. Experimental results show that 90.8% of the 130 fingerprint samples are succeeded to detect the core location.

The scalability of routing protocol has been considered as a key issue in large-scaled wavelength routed networks. Hierarchical routing scales well by yielding enormous reductions in routing table length, but it also increases path length. This increased path length in wavelength-routed networks leads to increased blocking probability because longer paths tend to have less free wavelength channels. However, if the routes assigned to longer paths have greater wavelength resources, we can expect that the blocking probability will not increase. In this paper, we propose a distributed node-clustering method that maximizes the number of lightpaths between nodes. The key idea behind our method is to construct node-clusters that have much greater wavelength resources from the ingress border nodes to the egress border nodes, which increases the wavelength resources on the routes of lightpaths between nodes. We evaluate the blocking probability for lightpath requests and the maximum table length in simulation experiments. We find that the method we propose significantly reduces the table length, while the blocking probability is almost the same as that without clustering.

In this paper, we propose a broadband 3-dB rat-race ring coupler that uses tightly coupled lines. An aperture compensation technique that can simplify the fabrication of tightly coupled lines, is also discussed here. The effective bandwidth of the proposed rat-race coupler with a return loss better than -20 dB can be increased by 14.3%, in comparison with that of March's. Its isolation is always below -20 dB and the phase shift errors less than 6.

This paper describes a pattern classifier for detecting frontal-view faces via learning a decision boundary. The proposed classifier consists of two major parts for improving classification accuracy: the implicit modeling of both the face and the near-face classes resulting in an extended discriminative feature set, and the subsequent composite Support Vector Machines (SVMs) for speeding up the classification. For the extended discriminative feature set, Principal Component Analysis (PCA) or Independent Component Analysis (ICA) is performed for the face and near-face classes separately. The projections and distances to the two different subspaces are complementary, which significantly enhances classification accuracy of SVM. Multiple nonlinear SVMs are trained for the local facial feature spaces considering the general multi-modal characteristic of the face space. Each component SVM has a simpler boundary than that of a single SVM for the whole face space. The most appropriate component SVM is selected by a gating mechanism based on clustering. The classification by utilizing one of the multiple SVMs guarantees good generalization performance and speeds up face detection. The proposed classifier is finally implemented to work in real-time by cascading a boosting based face detector.

We analyze the noise sensitivity of the focal length computation, the principal point estimation, and the orthogonality enforcement for single-view 3-D reconstruction based on vanishing points and orthogonality. We point out that due to the nonlinearity of the problem the standard statistical optimization is not very effective. We present a practical compromise for avoiding the computational failure and preserving high accuracy, allowing a consistent 3-D shape in the presence of however large noise.

A new impulse noise detection algorithm is presented, which can successfully remove impulse noise from corrupted images while preserving image details. The impulse detection algorithm is combined with median filtering to achieve noise removal. The main advantage of the proposed algorithm is that it can detect the impulse noise with high accuracy while reducing the probability of detecting image details as impulses. Also, it can be applied iteratively to improve the quality of restored images. It is efficient and low in complexity. Furthermore, it requires no previous training. Extensive experimental results show that the proposed approach significantly outperforms many well-known techniques.

Non-supersingular elliptic curves are important for the security of pairing-based cryptosystems. But there are few suitable non-supersingular elliptic curves for pairing-based cryptosystems. This letter introduces a method which allows the existing method to generate more non-supersingular elliptic curves suitable for pairing-based cryptosystems when the embedding degree is 6.

Traffic sign recognition usually consists of two stages: detection and classification. In this paper, we describe the classification stage using the ring-partitioned method. The proposed method uses a specified grayscale image in the pre-processing step and ring-partitioned matching in the matching step. The method does not need carefully prepared many samples of traffic sign images for the training process, alternatively only the standard traffic signs are used as the reference images. The experimental results show the effectiveness of the method in the matching of occluded, rotated, and illumination problems of the traffic sign images with the fast computation time.

Visual secret sharing (VSS) scheme is a perfect secure method that protects a secret image by breaking it into shadows. Unlike other secret sharing schemes, the VSS scheme can be easily decoded by the human visual sight when staking the shadows. We replace a pixel in the secret image by m sub pixels in the shadow image and the value m is called as pixel expansion. In general, most papers are dedicated to find the minimum m for a VSS scheme, i.e. a smaller shadow size. However, it seems that no one studies how to trade the shadow size for the contrast. In this paper, we take the lead in studying size-adjustable VSS schemes such that one can choose appropriate shadow size and the recovered image contrast for practical use.

The transient scattering characteristics of millimeter waves from a cylindrical object near a flat boundary were measured by the 50 GHz scatterometer to evaluate the multiple interactions of scattered waves with the objects and the boundary. Both perfectly conducting and dielectric cylinders are considered as a scattering object. The pulse intensities including waves scattered first from the object and then from the flat boundary or vice versa are shown to be significantly influenced by the distance from the object to the boundary, depending on the refractive index of the object. The observed higher order responses including the multiple scattering between the object and the boundary are also discussed. A preliminary comparison of the measured and calculated pulse responses for the perfectly conducting object is presented at slightly oblique incidence on a flat boundary.

Howling is very annoying problem to the hearing-aid users and it limits the maximum usable gain of hearing aids. We propose a new feedback cancellation system by inserting a time-varying decorrelation filter in the forward path. We use a second-order all-pass filter with control parameters whose time variation is implemented using a low-frequency modulator. A noticeable reduction of weight-vector misalignment is achievable using our proposed method.

An infrared (IR) transcutaneous remote control was designed for use in the totally implantable middle ear system. Considering the IR reflection, absorption and scattering effect of the skin, the required IR radiant intensity is calculated. After we have implemented the designed control, the transcutaneous operation experiment was carried out using a porcine skin.

This paper proposes the application of tree-structured clustering to the processing of noisy speech collected under various SNR conditions in the framework of piecewise-linear transformation (PLT)-based HMM adaptation for noisy speech. Three kinds of clustering methods are described: a one-step clustering method that integrates noise and SNR conditions and two two-step clustering methods that construct trees for each SNR condition. According to the clustering results, a noisy speech HMM is made for each node of the tree structure. Based on the likelihood maximization criterion, the HMM that best matches the input speech is selected by tracing the tree from top to bottom, and the selected HMM is further adapted by linear transformation. The proposed methods are evaluated by applying them to a Japanese dialogue recognition system. The results confirm that the proposed methods are effective in recognizing digitally noise-added speech and actual noisy speech issued by a wide range of speakers under various noise conditions. The results also indicate that the one-step clustering method gives better performance than the two-step clustering methods.

In this paper, we present the method for identifying an Adaptive Neuro-Fuzzy Networks (ANFN) with Takagi-Sugeno-Kang (TSK) fuzzy type based on fuzzy granulation. We also develop a systematic approach to generating fuzzy if-then rules from a given input-output data. The proposed ANFN is designed by the use of fuzzy granulation realized via context-based fuzzy clustering. This clustering technique builds information granules in the form of fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The experimental results reveal that the proposed model yields a better performance in comparison with Linguistic Models (LM) and Radial Basis Function Networks (RBFN) based on context-based fuzzy clustering introduced in the previous literature for Box-Jenkins gas furnace data and automobile MPG prediction.

The emergence of intelligent and sophisticated attack techniques makes web services more vulnerable than ever which are becoming an important business tool in e-commerce. Many techniques have been proposed to remove the security vulnerabilities, yet have limitations. This paper proposes an adaptive mechanism for a web-server intrusion-tolerant system (WITS) to prevent unknown patterns of attacks by adapting known attack patterns. SYN flooding attacks and their adaptive defense mechanisms are simulated as a case study to evaluate the performance of the proposed adaptation mechanism.

Question answering (QA) is the task of retrieving an answer in response to a question by analyzing documents. Although most of the efforts in developing QA systems are devoted to dealing with electronic text, we consider it is also necessary to develop systems for document images. In this paper, we propose a method of document image retrieval for such QA systems. Since the task is not to retrieve all relevant documents but to find the answer somewhere in documents, retrieval should be precision oriented. The main contribution of this paper is to propose a method of improving precision of document image retrieval by taking into account the co-occurrence of successive terms in a question. The indexing scheme is based on two-dimensional distributions of terms and the weight of co-occurrence is measured by calculating the density distributions of terms. The proposed method was tested by using 1253 pages of documents about the major league baseball with 20 questions and found that it is superior to the baseline method proposed by the authors.

The effect of intermediate frequency magnetic fields or, very-low-frequency magnetic fields (VLFMF) on living biological cells was investigated using a highly sensitive mutagenesis assay method. A bacterial gene expression system for mutation repair (umu system) was used for the sensitive evaluation of damage in DNA molecules. Salmonella typhimurium TA1535 (pSK1002) were exposed to VLFMF (20 kHz and 600 µT) in a specially designed magnetic field loading chamber. The experiment results showed the possibility of applying the umu assay for sensitive and effective evaluation of damage in DNA molecules. No effects from exposure to 20 kHz and 600 µT magnetic fields in terms of damage in DNA molecules were observed.

Data clustering is a technique for grouping similar data items together for convenient understanding. Conventional data clustering methods, including agglomerative hierarchical clustering and partitional clustering algorithms, frequently perform unsatisfactorily for large text collections, since the computation complexities of the conventional data clustering methods increase very quickly with the number of data items. Poor clustering results degrade intelligent applications such as event tracking and information extraction. This paper presents an unsupervised document clustering method which identifies topic keyword clusters of the text corpus. The proposed method adopts a multi-stage process. First, an aggressive data cleaning approach is employed to reduce the noise in the free text and further identify the topic keywords in the documents. All extracted keywords are then grouped into topic keyword clusters using the k-nearest neighbor approach and the keyword clustering technique. Finally, all documents in the corpus are clustered based on the topic keyword clusters. The proposed method is assessed against conventional data clustering methods on a web news corpus. The experimental results show that the proposed method is an efficient and effective clustering approach.

The performance of integrated segmentation and recognition of handwritten numeral strings relies on the classification accuracy and the non-character resistance of the underlying character classifier, which is variable depending on the techniques of pattern normalization, feature extraction, and classifier structure. In this paper, we evaluate the effects of 12 normalization functions and four selected feature types on numeral string recognition. Slant correction (deslant) is combined with the normalization functions and features so as to create 96 feature vectors, which are classified using two classifier structures. In experiments on numeral string images of the NIST Special Database 19, the classifiers have yielded very high string recognition accuracies. We show the superiority of moment normalization with adaptive aspect ratio mapping and the gradient direction feature, and observed that slant correction is beneficial to string recognition when combined with good normalization methods.

This paper presents a new algorithm for efficiently detecting silhouette voxels in volume objects. The high performance of the algorithm is partly due to its ability to exclude all the gradient vectors not associated with silhouettes from further consideration. A judicious re-arrangement of the voxels enhances its efficiency. We have studied its performance through computer simulations. The results indicate a manifold improvement over conventional algorithms. A parallel version of the algorithm has also been described in the paper. Its performance is quite understandably impressive.