A speech enhancement method is proposed that can be implemented efficiently due to its use of wavelet packet transform. The proposed method uses a modified spectral subtraction with noise estimation by a least-squares line method and with an overweighting gain per subband with nonlinear structure, where the overweighting gain is used for suppressing the residue of musical noise and the subband is used for applying the weighted values according to the change of signals. The enhanced speech by our method has the following properties: 1) the speech intelligibility can be assured reliably; 2) the musical noise can be reduced efficiently. Various assessments confirmed that the performance of the proposed method was better than that of the compared methods in various noise-level conditions. Especially, the proposed method showed good results even at low SNR.

Non-malleability is an important security property of commitment schemes. The property means security against the man-in-the-middle attack, and it is defined and proved in the simulation paradigm using the corresponding simulator. Many known non-malleable commitment schemes have the common drawback that their corresponding simulators do not work in a straight-line manner, requires rewinding of the adversary. Due to this fact, such schemes are proved non-malleable only in the stand-alone cases. In the multiple-instances setting, i.e., when the scheme is performed concurrently with many instances of itself, such schemes cannot be proved non-malleable. The paper shows an efficient commitment scheme proven to be non-malleable even in the multiple-instances setting, based on the KEA1 and DDH assumptions. Our scheme has a simulator that works in a straight-line manner by using the KEA1-extractor instead of the rewinding strategy.

This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.

In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.

The present paper describes a quality enhancement of speech corrupted by additive background noise in a single channel system. The proposed approach is based on the introduction of perceptual criteria using a frequency-weighting filter in a subtractive-type enhancement process. This newly developed algorithm allows for an automatic adaptation in the time and frequency of the enhancement system and finds a suitable noise estimate according to the frequency of the corrupted speech. Experimental results show that the proposed approach can efficiently remove additive noise related to various types of noise corruption.

This paper proposes effective energy feature normalization methods for robust speech recognition in noisy environments. We first develop an energy subtraction method and a modified method for the Log-energy Dynamic Range Normalization (ERN) using inverse function. We then present the hybrid method combining the energy subtraction and the modified ERN. Using Aurora2.0 database for representative evaluations, a significant performance improvement over the ERN method is demonstrated.

The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80's by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the two kinds of SOM for nonuniform input data is investigated. Furthermore, we show its application to clustering and confirm its efficiency by comparing with the k-means method.

The element-by-element finite element method (EBE-FEM) combined with the preconditioned conjugate gradient (PCG) technique is employed in this paper to calculate the coupling capacitances of multi-level high-density three-dimensional interconnects (3DIs). All capacitive coupling 3DIs can be captured, with the effects of all geometric and physical parameters taken into account. It is numerically demonstrated that with this hybrid method in the extraction of capacitances, an effective and accurate convergent solution to the Laplace equation can be obtained, with less memory and CPU time required, as compared to the results obtained by using the commercial FEM software of either MAXWELL 3D or ANSYS.

In this paper, we propose a new region extraction method using chroma key with a two-tone checker pattern background. The method solves the problem in conventional chroma key techniques that foreground objects become transparent if their colors are the same as the background color. The method utilizes the adjacency condition between two-tone regions of the background and the geometrical information of the background grid line. The procedure of the proposed method consists of four steps: 1) background color extraction, 2) background grid line extraction, 3) foreground extraction, and 4) image composition. As to background color extraction, a color space approach is used. As to background grid line extraction, it is difficult to extract background grid line by a color space approach because the color of this region may be a composite of two background colors and different from them. Therefore, the background grid line is extracted from adjacency conditions between two background colors. As to foreground extraction, the boundary between the foreground and the background is detected to recheck the foreground region whose color is same as the background, and the background region whose color is same as the foreground. To detect regions whose colors are same as the background, the adjacency conditions with the background grid line are utilized. As to image composition, the process that smoothes the color of the foreground's boundary against the new background is carried out to create natural images. Experimental results show that the foreground objects can be segmented exactly from the background regardless of the colors of the foreground objects.

In vector analysis, it is important to classify three flow primitives as translation, rotation and divergence. These three primitives can be detected utilizing line integral and surface integral according to the knowledge of vector analysis. In this paper, we introduce a method for extracting these three primitives utilizing edges in an image based on vector analysis, namely edge field analysis. The edge has the information of inclination. However, the edge has no information of the direction unlike vector. Hence, line integral and surface integral can not be directly applied to detect these three primitives utilizing edges. We firstly formulate the problem and describe the algorithm for detecting the three primitives in vector analysis. We then propose an algorithm for estimating three primitives regarding edge image as pseudo-vector field. For illustration, we apply edge field analysis to quasi-motion extraction and feature extraction. We also show the experimental results in terms of estimating the center of the flowers, the cell body of neuron, the eye of the storm, the center of the explosion and so on.

Concurrency is one of the most important issues in system-level design. Interleaving among parallel processes can cause an extremely large number of different behaviors, making design and verification difficult tasks. In this work, we propose a synchronization verification method for system-level designs described in the SpecC language. Instead of modeling the design with timed FSMs and using a model checker for timed automata (such as UPPAAL or KRONOS), we formulate the timing constraints with equalities/inequalities that can be solved by integer linear programming (ILP) tools. Verification is conducted in two steps. First, similar to other software model checkers, we compute the reachability of an error state in the absence of timing constraints. Then, if a path to an error state exists, its feasibility is checked by using the ILP solver to evaluate the timing constraints along the path. This approach can drastically increase the sizes of the designs that can be verified. Abstraction and abstraction refinement techniques based on the Counterexample-Guided Abstraction Refinement (CEGAR) paradigm are applied.

The concept of LR(k) validity is represented as an abstract interpretation of a refinement of the derivation semantics of a given grammar. Also the algorithm of LR(k) parsing is represented as an abstract interpretation of the refined semantics. Such representations of LR formalisms provide us with more intuitive and easier means by which to understand LR parsing.

This paper proposes a method to determine a single frequency for interconnect RL extraction. Resistance and inductance of interconnects depend on frequency, and hence the extraction frequency strongly affects the modeling accuracy of interconnects. The proposed method determines an extraction frequency based on the transfer characteristic of interconnects. By choosing the frequency where the transfer characteristic becomes maximum, the extracted RL values achieve the accurate modeling of the waveform. Experimental results show that the proposed method provides accurate transition waveforms over various interconnect topologies.

In this paper the attention is focused on the numerical study of hyperchaotic 2D-scroll attractors via the Adomian decomposition method. The approach, which provides series solutions of the system equations, is first applied to weakly-coupled Chua's circuits with Hermite interpolating polynomials. Then the method is successfully utilized for achieving hyperchaos synchronization of two coupled Chua's circuits. The reported examples show that the approach presents two main features, i.e., the system nonlinearity is preserved and the chaotic solution is provided in a closed form.

The notion of correlation intractable function ensembles (CIFEs) was introduced in an attempt to capture the "unpredictability" property of random oracles [12]: If O is a random oracle then it is infeasible to find an input x such that the input-output pair (x,O(x)) has some desired property. In this paper, we observe relationships between zero-knowledge protocols and CIFEs. Specifically, we show that, in the non-uniform model, the existence of CIFEs implies that 3-round auxiliary-input zero-knowledge (AIZK) AM interactive proofs exist only for BPP languages. In the uniform model, we show that 3-round AIZK AM interactive proofs with perfect completeness exist only for easy-to-approximate languages. These conditional triviality results extend to constant-round AIZK AM interactive proofs assuming the existence of multi-input CIFEs, where "multi-input" means that the correlation intractability is satisfied with respect to multiple input-output pairs. Also, as a corollary, we show that any construction of uniform multi-input CIFEs from uniform one-way functions proves unconditionally that constant-round AIZK AM interactive proofs with perfect completeness only for easy-to-approximate languages.

In this paper, we propose a robust statistical framework for extracting scenes from a baseball broadcast video. We apply multi-stream hidden Markov models (HMMs) to control the weights among different features. To achieve a large robustness against new scenes, we used a common simple structure for all the HMMs. In addition, scene segmentation and unsupervised adaptation were applied to achieve greater robustness against differences in environmental conditions among games. The F-measure of scene-extracting experiments for eight types of scene from 4.5 hours of digest data was 77.4% and was increased to 78.7% by applying scene segmentation. Furthermore, the unsupervised adaptation method improved precision by 2.7 points to 81.4%. These results confirm the effectiveness of our framework.

Automated information extraction systems from biomedical text have been reported. Some systems are based on manually developed rules or pattern matching. Manually developed rules are specific for analysis, however, new rules must be developed for each new domain. Although the corpus must be developed by human effort, a machine-learning approach automatically learns the rules from the corpus. In this article, we present a system for automatically extracting protein-protein interaction information from biomedical text with support vector machines (SVMs). We describe the performance of our system and compare its ability to extract protein-protein interaction information with that of other systems.

In this paper we propose a road object extraction technique based on an active contour model (snake) considering inertia and differential features in a movie. Different energy functions can be applicable to snake in order to use information of various objects and various environments. Using many methods for tracking a moving object, snake can be applied to a scene frame by frame. Initial positions of the control points in a frame can refer to the results in the previous frame. We focus on the inertia that works between object shapes in the previous and present frames. In this research inertia is the tendency of a control point to resist its changes in its state of motion in an image space. We introduce an external energy for snake based on inertia of control points. Internal energy functions based on differential features of road geometry are also introduced to extract straight, circular and S-shaped road segments smoothly. The proposed method is applied to extract road geometry from a movie taken by a camera equipped on the flont of a vehicle. Experimental results indicate the availability of the proposed method which is to extract road geometry smoothly and to improve its robustness.

This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.

In this paper, we propose a combination-based novel technique of dithered subband coding with spectral subtraction for improving the perceptual quality of coded audio at low bit rates. It is well known that signal-correlated distortion is audible when the audio signal is quantized at bit rates lower than the lower bound of perceptual coding. We show that this problem can be overcome by applying the dithering quantization process in each subband. Consequently, the quantization noise is rendered into a signal-independent white noise; this noise is then estimated and removed by spectral subtraction at the decoder. Experimental results show an effective improvement by the proposed method over the conventional one in terms of better SNR and human listening test results. The proposed method can be combined with other existing or future coding methods such as perceptual coding to improve their performance at low bit rates.