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.

For a property π on graphs, the edge-contraction problem with respect to π is defined as a problem of finding a set of edges of minimum cardinality whose contraction results in a graph satisfying the property π. This paper gives a lower bound for the approximation ratio for the problem for any property π that is hereditary on contractions and determined by biconnected components.

In advanced ASIC/SoC physical designs, interconnect parasitic extraction is one of the important factors to determine the accuracy of timing analysis. We present a formula-based method to efficiently extract interconnect capacitances of interconnects with dummy fills for VLSI designs. The whole flow is as follows: 1) in each process, obtain capacitances per unit length using a 3-D field solver and then create formulas, and 2) in the actual design phase, execute a well-known 2.5-D capacitance extraction. Our results indicated that accuracies of the proposed formulas were almost within 3% error. The proposed formula-based method can extract interconnect capacitances with high accuracy for VLSI circuits. Moreover, we present formulas to evaluate the effect of dummy fills on interconnect capacitances. These can be useful for determining design guidelines, such as metal density before the actual design, and for analyzing the effect of each structural parameter during the design phase.

We propose a definitional question answering system that extracts phrases using syntactic patterns which are easily constructed manually and can reduce the coverage problem. Experimental results show that our phrase extraction system outperforms a sentence extraction system, especially for selecting concise answers, in terms of recall and precision, and indicate that the proper text unit of answer candidates and the final answer has a significant effect on the system performance.

A time domain (TD) speech enhancement technique to improve SNR in noise-contaminated speech is proposed. Additional supplementary scheme is applied to estimate the degree of noise of noisy speech. This is estimated from a function, which is previously prepared as the function of the parameter of the degree of noise. The function is obtained by least square (LS) method using the given degree of noise and the estimated parameter of the degree of noise. This parameter is obtained from the autocorrelation function (ACF) on frame-by-frame basis. This estimator almost accurately estimates the degree of noise and it is useful to reduce noise. The proposed method is based on two-stage processing. In the first stage, subtraction in time domain (STD), which is equivalent to ordinary spectral subtraction (SS), is carried out. In the result, the noise is reduced to a certain level. Further reduction of noise and by-product noise residual is carried out in the second stage, where blind source separation (BSS) technique is applied in time domain. Because the method is a single-channel speech enhancement, the other signal is generated by taking the noise characteristics into consideration in order to apply BSS. The generated signal plays a very important role in BSS. This paper presents an adaptive algorithm for separating sources in convolutive mixtures modeled by finite impulse response (FIR) filters. The coefficients of the FIR filter are estimated from the decorrelation of two mixtures. Here we are recovering only one signal of interest, in particular the voice of primary speaker free from interfering noises. In the experiment, the different levels of noise are added to the clean speech signal and the improvement of SNR at each stage is investigated. The noise types considered initially in this study consist of the synthesized white and color noise with SNR set from 0 to 30 dB. The proposed method is also tested with other real-world noises. The results show that the satisfactory SNR improvement is attained in the two-stage processing.

In general, a corner model with best- and worst-case delay conditions is used in static timing analysis (STA). The best- and worst-case delays of a stage are defined as the fastest and slowest delays from a cell input to the next cell input. In this paper, we present a methodology for determining the parameters that yield the best- and worst-case delays when interconnect structural parameters have the minimum and maximum values with process variations. We also present analysis results of our circuit model using the methodology. The min and max conditions for the time constant are found to be (+Δw, +Δt, +Δh) & (-Δw, -Δt, -Δh), respectively. Here, +Δ or -Δ means the max or min corner value of each parameter variation, where w is the width, t is the interconnect thickness, and h is the height. Best and worst conditions for delay time are as follows: 1) given a circuit with an optimum driver, dense interconnects, and small branch capacitance, the best and worst conditions are respectively (-Δw, +Δt, +Δh) & (+Δw, +Δt, -Δh), 2) given driver and/or via resistances that are higher than the interconnect resistance, dense interconnects, and small branch capacitance, they are (-Δw, -Δt, +Δh) & (+Δw, +Δt, -Δh), and 3) for other conditions, they are (+Δw, +Δt, +Δh) & (-Δw, -Δt, -Δh). Moreover, if there must be only one condition each for the best- and worst-case delays, they are (+Δw, +Δt, +Δh) & (-Δw, -Δt, -Δh).

Since a human object is an important element of the moving pictures being processed by mobile terminals, establishing a human object extraction method encourages dissemination of new applications. In accordance with the requirement of mobile applications, this paper proposes a low-cost human object extraction method, which consists of a face object and a hair object extraction based on their color information and a simple body extraction utilizing the position information of the face object. In the proposed method, skin color and hair color are estimated through color space segmentation, and a human object is effectively extracted by using a radial active contour model. Simulation results of the human object extraction with the use of XScale processor claims that QCIF 15 fps video sequences can be processed in real time.

Rendering realistic trees is quite important for simulating a 3D natural scene. Separating the trunk from its background is the first step toward the 3D model construction of the tree. In this paper, a three-phase algorithm is developed to extract the trunk structure of the tree and hence segment the trunk from the image. Some experiments were conducted and results confirmed the feasibility of proposed algorithm.

This paper describes a new talker direction estimation method for front-end processing to capture distant-talking speech by using a microphone array. The proposed method consists of two algorithms: One is a TDOA (Time Delay Of Arrival) estimation algorithm based on a weighted CSP (Cross-power Spectrum Phase) analysis with an average speech spectrum and CSP coefficient subtraction. The other is a talker direction estimation algorithm based on ML (Maximum Likelihood) estimation in a time sequence of the estimated TDOAs. To evaluate the effectiveness of the proposed method, talker direction estimation experiments were carried out in an actual office room. The results confirmed that the talker direction estimation performance of the proposed method is superior to that of the conventional methods in both diffused- and directional-noise environments.

In this paper, an efficient bottom-up extraction approach is presented to generate accurate behavioral models of PLL designs more quickly by using Verilog-AMS language. The main idea is to use a special "characterization mode" such that we can use only one input pattern to get all required circuit parameters with parasitic effects. After carefully adjustment, all parameters in our behavioral models can be measured at the outputs of the PLL system without simulating each block separately. Therefore, this approach is more suitable to accurately model protected IPs or flattened post-layout netlists. In the experimental results, we will build an accurate PLL behavioral model for demonstration and compare the results with HSPICE simulation and traditional behavioral models.

Contract signing is a practical application of the fair exchange of digital signatures. This application used to be realized by directly adopting the results of the fair exchange of signatures, which do not completely meet the requirements of the signing of a secret contract. The assistance of a trusted third party (TTP) and some cryptographic technology are required to allow two parties to exchange their signatures through the network in a fair manner because these two parties potentially may be dishonest or mistrust each other. This paper presents a subtle method of preventing the off-line TTP from gaining the exchanged signature and the corresponding message when a dispute occurs between the two parties wherein the TTP is required to take part in the exchange procedure. An advanced concept, the non-disclosure property, is proposed in order to prevent a party from misusing evidence left during the exchange process. Two approaches, namely the secret divide method and the convertible signature are demonstrated. To satisfy the properties of the traditional paper-based contract signing, the technique of multi-signature scheme is used in the proposed protocols.

This paper proposes a new non-destructive methodology to estimate physical parameters for LSIs. In order to resolve the estimation accuracy degradation issue for low-k dielectric films, we employ a parallel-plate capacitance measurement and a wire resistance measurement in our non-destructive method. Due to (1) the response surface functions corresponding to the parallel-plate capacitance measurement and the wire resistance measurement and (2) the searching of the physical parameter values using our cost function and simulated annealing, the proposed method attains higher precision than that of the existing method. We demonstrate the effectiveness of our method by application to our 90 nm SoC process using low-k materials.

Simple closed-form expressions for efficiently calculating on-chip interconnect capacitances are presented. The formulas are expressed with second-order polynomial functions which do not include exponential functions. The runtime of the proposed formulas is about 2-10 times faster than those of existing formulas. The root mean square (RMS) errors of the proposed formulas are within 1.5%, 1.3%, 3.1%, and 4.6% of the results obtained by a field solver for structures with one line above a ground plane, one line between ground planes, three lines above a ground plane, and three lines between ground planes, respectively. The proposed formulas are also superior in accuracy to existing formulas.

In a conventional method based on quadrature 2D Gabor wavelets to extract iris features, the iris recognition is performed by a 256-byte iris code, which is computed by applying the Gabor wavelets to a given area of the iris. However, there is a code redundancy because the iris code is generated by basis functions without considering the characteristics of the iris texture. Therefore, the size of the iris code is increased unnecessarily. In this paper we propose a new feature extraction algorithm based on independent component analysis (ICA) for a compact iris code. We implemented the ICA to generate optimal basis functions which could represent iris signals efficiently. In practice the coefficients of the ICA expansions are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing individual's iris patterns. Additionally, we introduce a method to refine the ICA basis functions for improving the recognition performance. Experimental results show that our proposed method has a similar equal error rate as a conventional method based on the Gabor wavelets, and the iris code size of our proposed methods is five times smaller than that of the Gabor wavelets.

We present a practical method of dealing with the influences of floating dummy metal fills, which are inserted to assist planarization by chemical-mechanical polishing (CMP) process, in extracting interconnect capacitances for system-on-chip (SoC) designs. The method is based on reducing the thicknesses of dummy metal layers according to electrical field theory. We also clarify the influences of dummy metal fills on the parasitic capacitance, signal delay, and crosstalk noise. Moreover, we address that interlayer dummy metal fills have more significant influences than intralayer ones in terms of the impact on coupling capacitances. When dummy metal fills are ignored, the error of capacitance extraction can be more than 30%, whereas the error of the proposed method is less than about 10% for many practical geometries. We also demonstrate, by comparison with capacitance results measured for a 90-nm test chip, that the error of the proposed method is less than 8%.

This paper deals with the use of neural network rule extraction techniques based on the Genetic Programming (GP) to build intelligent and explanatory evaluation systems. Recent development in algorithms that extract rules from trained neural networks enable us to generate classification rules in spite of their intrinsically black-box nature. However, in the original decompositional method looking at the internal structure of the networks, the comprehensive methods combining the output to the inputs using parameters are complicated. Then, in our paper, we utilized the GP to automatize the rule extraction process in the trained neural networks where the statements changed into a binary classification. Even though the production (classification) rule generation based on the GP alone are applicable straightforward to the underlying problems for decision making, but in the original GP method production rules include many statements described by arithmetic expressions as well as basic logical expressions, and it makes the rule generation process very complicated. Therefore, we utilize the neural network and binary classification to obtain simple and relevant classification rules in real applications by avoiding straightforward applications of the GP procedure to the arithmetic expressions. At first, the pruning process of weight among neurons is applied to obtain simple but substantial binary expressions which are used as statements is classification rules. Then, the GP is applied to generate ultimate rules. As applications, we generate rules to prediction of bankruptcy and creditworthiness for binary classifications, and the apply the method to multi-level classification of corporate bonds (rating) by using the financial indicators.

Self-connection can enlarge the memory capacity of an associative memory based on the neural network. However, the basin size of the embedded memory state shrinks. The problem of basin size is related to undesirable stable states which are spurious. If we can destabilize these spurious states, we expect to improve the basin size. The inverse function delayed (ID) model, which includes the Bonhoeffer-van der Pol (BVP) model, has negative resistance in its dynamics. The negative resistance of the ID model can destabilize the equilibrium states on certain regions of the conventional neural network. Therefore, the associative memory based on the ID model, which has self-connection in order to enlarge the memory capacity, has the possibility to improve the basin size of the network. In this paper, we examine the fundamental characteristics of an associative memory based on the ID model by numerical simulation and show the improvement of performance compared with the conventional neural network.

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.

Footsteps, with different shoes of heels, sneakers, leathers or even bare footed, will appear in different grounds of concrete, wood, etc. If a footstep is discriminable, the application to various fields can be considered. In this paper, the feature extraction of a footstep is investigated. We focus on the recognizing a certain kind of footstep waveforms under the restricted condition. We propose a new methodology using the feature parameter such as the peak frequency set by the mel-cepstrum analysis, the walking intervals and the similarity of spectrum envelope. It is shown for personal identification that the performance of the proposed method is effective.

Recently, automatic speech recognition in a car has practical uses for applications like car-navigation and hands-free telephone dialers. For noise robustness, the current successes are based on the assumption that there is only a stationary cruising noise. Therefore, the recognition rate is greatly reduced when there is music or news coming from a radio or a CD player in the car. Since reference signals are available from such in-vehicle units, there is great hope that echo cancellers can eliminate the echo component in the observed noisy signals. However, previous research reported that the performance of an echo canceller is degraded in very noisy conditions. This implies it is desirable to combine the processes of echo cancellation and noise reduction. In this paper, we propose a system that uses echo cancellation and spectral subtraction simultaneously. A stationary noise component for spectral subtraction is estimated through the adaptation of an echo canceller. In our experiments, this system significantly reduced the errors in automatic speech recognition compared with the conventional combination of echo cancellation and spectral subtraction.