Most of previous works have presented the dynamic spectrum allocation (DSA) gain achieved by utilizing the time or regional variations in traffic demand between multi-network operators (NOs). In this paper, we introduce the functionalities required for the entities related with the spectrum sharing and allocation and propose a spectrum allocation algorithm while considering the long-term priority between NOs, the priority between multiple class services, and the urgent bandwidth request. To take into account the priorities among the NOs and the priorities of multiple class services, a spectrum sharing metric (SSM) is proposed, while a negotiation procedure is proposed to treat the urgent bandwidth request.

The adaptive predistorter and the negative feedback system are known as methods to compensate for the nonlinear distortion of a power amplifier. Although the feedback method is a simple technique, its instability impedes the capability of high-feedback gain to achieve a high-compensation effect. On the other hand, the predistorter requires a long time for convergence of the adaptive predistorters. In this paper, we propose a nonlinear distortion compensation method for a narrow-band signal. In this method, an adaptive predistorter and negative feedback are combined. In addition, to shorten the convergence time to minimize nonlinear distortion, a variable step-size (VS) method is also applied to the algorithm to determine the parameters of the adaptive predistorter. Using computer simulations, we show that the proposed scheme achieves both five times faster convergence speed than that of the predistorter and three times higher permissible delay time in the feedback amplifier than that of a negative feedback only amplifier.

Simulations are often deployed to evaluate proposed mechanisms or algorithms in Mobile Ad Hoc Networks (MANET). In MANET, the impacts of some simulation parameters are noticeable, such as transmission range, data rate etc. However, the effect of mobility model is not clear until recently. Random Waypoint (RWP) is one of the most applied nodal mobility models in many simulations due to its clear procedures and easy employments. However, it exhibits the two major problems: decaying average speed and border effect. Both problems will overestimate the performance of the employed protocols and applications. Although many recently proposed mobility models are able to reduce or eliminate the above-mentioned problems, the concept of Diverse Average Speed (DAS) has not been introduced. DAS aims to provide different average speeds within the same speed range. In most mobility models, the average speed is decided when the minimum and maximum speeds are set. In this paper, we propose a novel mobility model, named General Ripple Mobility Model (GRMM). GRMM targets to provide a uniform nodal spatial distribution and DAS without decaying average speed. The simulations and analytic results have demonstrated the merits of the outstanding properties of the GRMM model.

It is very important to guarantee the quality of the industrial products by means of visual inspection. In order to reduce the soldering defect with terminal deformation and terminal burr in the manufacturing process, this paper proposes a 3D visual inspection system based on a stereo vision with single camera. It is technically noted that the base line of this single camera stereo was precisely calibrated by the image processing procedure. Also to extract the measuring point coordinates for computing disparity; the error is reduced with original algorithm. Comparing its performance with that of human inspection using industrial microscope, the proposed 3D inspection could be an alternative in precision and in processing cost. Since the practical specification in 3D precision is less than 1 pixel and the experimental performance was around the same, it was demonstrated by the proposed system that the soldering defect with terminal deformation and terminal burr in inspection, especially in 3D inspection, was decreased. In order to realize the inline inspection, this paper will suggest how the human inspection of the products could be modeled and be implemented by the computer system especially in manufacturing process.

A new spread spectrum clock generator (SSCG) using two-point delta-sigma modulation is presented in this paper. Not only the divider is varied, but also the voltage controlled oscillator is modulated. This technique can enhance the modulation bandwidth so that the effect of EMI suppression is improved with lower order ΣΔ modulator and can simultaneously optimize the jitter and the modulation profile. In addition, the method of two-path is applied to the loop filter to reduce the capacitance value such that the total integration can be achieved. The proposed SSCG has been fabricated in a 0.35 µm CMOS process. The clock of 400 MHz with center spread ratios of 1.25% and 2.5% are verified. The peak EMI reduction is 19.73 dB for the case of 2.5%. The size of chip area is 0.900.89 mm2.

The surface amino groups of plasma-polymerized films prepared from various nitrogen-containing monomers were quantitatively characterized for bioelectronic and biomedical applications. X-ray photoelectron spectroscopy (XPS) measurements were conducted on two kinds of surfaces: pristine surfaces of plasma-polymerized film prepared using various nitrogen-containing monomers, and theirs surfaces whose amino groups had been derivatized by a primary-amine-selective reagent carrying an XPS label. The XPS data showed that the maximum surface density of amino groups for this film was 8.41013 cm-2. Amino groups constituted 14-64% of all surface nitrogen atoms (NH/N), depending on the monomer used.

A method was developed for automatic recognition of syllable tone types in continuous speech of Mandarin by integrating two techniques, tone nucleus modeling and neural network classifier. The tone nucleus modeling considers a syllable F0 contour as consisting of three parts: onset course, tone nucleus, and offset course. Two courses are transitions from/to neighboring syllable F0 contours, while the tone nucleus is intrinsic part of the F0 contour. By viewing only the tone nucleus, acoustic features less affected by neighboring syllables are obtained. When using the tone nucleus modeling, automatic detection of tone nucleus comes crucial. An improvement was added to the original detection method. Distinctive acoustic features for tone types are not limited to F0 contours. Other prosodic features, such as waveform power and syllable duration, are also useful for tone recognition. Their heterogeneous features are rather difficult to be handled simultaneously in hidden Markov models (HMM), but are easy in neural networks. We adopted multi-layer perceptron (MLP) as a neural network. Tone recognition experiments were conducted for speaker dependent and independent cases. In order to show the effect of integration, experiments were conducted also for two baselines: HMM classifier with tone nucleus modeling, and MLP classifier viewing entire syllable instead of tone nucleus. The integrated method showed 87.1% of tone recognition rate in speaker dependent case, and 80.9% in speaker independent case, which was about 10% relative error reduction as compared to the baselines.

The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Weber's Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.

To improve the computation efficiency of the application specific instruction-set processor (ASIP), a strategy of hardware/software collaborative design is usually utilized. In this process, the auto-customization of specific instruction set has always been a key part to support the automated design of ASIP. The key issue of this problem is how to effectively reduce the huge exponential exploration space in the instruction identification process. To address this issue, we first formulate it as a feasible sub-graph enumeration problem under multiple constraints, and then propose a fast instruction identification algorithm based on a new model called basic convex pattern (BCP). The kernel technique in this algorithm is the transformation from the graph exploration to the formula-based computations. The experimental results have indicated that the proposed algorithm has a distinct reduction in the execution time.

Spread Spectrum (SS) has been widely used for various wireless systems such as cellular systems, wireless local area network (LAN) and so on. Using multiple antennas at the receiver, two-dimensional (2D) RAKE is realized over the time- and the space-domain. However, it should be noted that the 2D-RAKE receiver must detect the bit timing prior to the RAKE combining. In case of deep fading, it is often difficult to detect it due to low signal-to-noise power ratio (SNR). To solve this problem, we propose a new blind 2D-RAKE receiver based on the constant modulus algorithm (CMA). Since it does not need a priori bit timing detection, it is possible to compensate frequency selective fading even in very low SNR environments. The proposed method is particularly suitable for the software defined radio (SDR) architecture. The performance of the proposed method is investigated through computer simulations.

We describe a statistical parametric speech synthesis system developed by a joint group from the Nagoya Institute of Technology (Nitech) and the Nara Institute of Science and Technology (NAIST) for the annual open evaluation of text-to-speech synthesis systems named Blizzard Challenge 2006. To improve our 2005 system (Nitech-HTS 2005), we investigated new features such as mel-generalized cepstrum-based line spectral pairs (MGC-LSPs), maximum likelihood linear transform (MLLT), and a full covariance global variance (GV) probability density function (pdf). A combination of mel-cepstral coefficients, MLLT, and full covariance GV pdf scored highest in subjective listening tests, and the 2006 system performed significantly better than the 2005 system. The Blizzard Challenge 2006 evaluations show that Nitech-NAIST-HTS 2006 is competitive even when working with relatively large speech databases.

Spoken language understanding (SLU) aims to map a user's speech into a semantic frame. Since most of the previous works use the semantic structures for SLU, we verify that the structure is useful even for noisy input. We apply a structured prediction method to SLU problem and compare it to an unstructured one. In addition, we present a combined method to embed long-distance dependency between entities in a cascaded manner. On air travel data, we show that our approach improves performance over baseline models.

We propose a new type of direction-of-arrival estimation method for robot audition that is free from strict head related transfer function estimation. The proposed method is based on statistical pattern recognition that employs a ratio of power spectrum amplitudes occurring for a microphone pair as a feature vector. It does not require any phase information explicitly, which is frequently used in conventional techniques, because the phase information is unreliable for the case in which strong reflections and diffractions occur around the microphones. The feature vectors we adopted can treat these influences naturally. The effectiveness of the proposed method was shown from direction-of-arrival estimation tests for 19 kinds of directions: 92.4% of errors were reduced compared with the conventional phase-based method.

Cognitive Radio is an advanced enabling technology for efficient utilization of vacant spectrum due to its ability to sense the spectrum environment. Various detection methods have been proposed for spectrum sensing, which is the key function in implementing cognitive radio. However most of the existing methods put their interests in detecting TV signal and wireless microphone signals. In this paper, we explore the periodicity of the equally spaced pilot subcarriers in OFDM signal. Simulations in various fading environments show that the proposed cyclostationarity based detection method works well for OFDM signal.

We present a method for the objective quality evaluation of noise-reduced speech in wideband speech communication services, which utilize speech with a wider bandwidth (e.g., 7 kHz) than the usual telephone bandwidth. Experiments indicate that the amount of residual noise and the distortion of speech and noise, which are quality factors, influence the perceived quality degradation of noise-reduced speech. From the results, we observe the principal relationships between these quality factors and perceived speech quality. On the basis of these relationships, we propose a method that quantifies each quality factor in noise-reduced speech by analyzing signals that can be measured and assesses the overall perceived quality of noise-reduced speech using values of these quality factors. To verify the validity of the method, we perform a subjective listening test and compare subjective quality of noise-reduced speech with its estimation. In the test, we use various types of background noise and noise-reduction algorithms. The verification results indicate that the correlation between subjective quality and its objective estimation is sufficiently high regardless of the type of background noise and noise-reduction algorithm.

Very high-speed data services are demanded in the next generation wireless systems. However, the available bandwidth is limited. The use of multi-input multi-output (MIMO) multiplexing can increase the transmission rate without bandwidth expansion. For high-speed data transmission, however, the channel becomes severely frequency-selective and the achievable bit error rate (BER) performance degrades. In our previous work, we proposed the joint use of iterative frequency-domain parallel interference cancellation (PIC) and two-dimensional (2D) MMSE-FDE for the non-spread single-carrier (SC) transmission in a frequency-selective fading channel. The joint use of PIC and 2D MMSE-FDE can effectively suppress the inter-path interference (IPI) and the inter-code interference (ICI), resulting from the channel frequency-selectivity, and the interference from other antennas simultaneously. An iterative PIC with 2D MMSE-FDE has a high computational complexity. In this paper, to well suppress the interference from other antennas while reducing the computational complexity, we propose to replace 2D MMSE-FDE by 1D MMSE-FDE except for the initial iteration stage and to use multicode spread-spectrum (SS) transmission instead of the non-spread SC transmission. The BER performance of the proposed scheme in a frequency-selective Rayleigh fading channel is evaluated by computer simulation to show that the proposed scheme can basically match the BER performance of 2D MMSE-FDE with lower complexity.

This paper proposes a spectroscopic method and system for preventing spoofing of biometric authentication. One of its focus is to enhance biometrics authentication with a spectroscopic method in a multi-factor manner such that a person's unique 'spectral signatures' or 'spectral factors' are recorded and compared in addition to a non-spectroscopic biometric signature to reduce the likelihood of imposter getting authenticated. By using the 'spectral factors' extracted from reflectance spectra of real fingers and employing cluster analysis, it shows how the authentic fingerprint image presented by a real finger can be distinguished from an authentic fingerprint image embossed on an artificial finger, or molded on a fingertip cover worn by an imposter. This paper also shows how to augment two widely used biometrics systems (fingerprint and iris recognition devices) with spectral biometrics capabilities in a practical manner and without creating much overhead or inconveniencing their users.

This paper presents the selective epitaxial growth (SEG) properties of reduced pressure chemical vapor deposition (RPCVD) at low temperatures (LT) of 675-725 with high aspect ratio mask of dielectric films. The SEG process could be explained in conjunction with the loading effect, the mask pattern shape/size, and the process parameters of RPCVD. The growth rates showed a large non-uniformity up to 40% depending upon the pattern size of the dielectric mask films, but as the SEG film becomes thicker, the growth rate difference converged on 15% between the narrow 2-µm and the wide 100-µm patterns. The evolution of SEG was controlled dominantly by the surface migration control at the initial stage, and converted to the surface topology control. The design of pattern size and distribution with dummy patterns must be useful to accomplish the reliable and uniform LT-SEG.

There are two ways to describe a state machine as an algebraic specification: a behavioral specification and a rewrite specification. In this study, we propose a translation system from behavioral specifications to rewrite specifications to obtain a verification system which has the strong points of verification techniques for both specifications. Since our translation system is complete with respect to invariant properties, it helps us to obtain a counter-example for an invariant property through automatic exhaustive searching for a rewrite specification.

It is said that technology comes out from humanity. What is humanity? The very definition of humanity is emotion. Emotion is the basis for all human expression and the underlying theme behind everything that is done, said, thought or imagined. Making computers being able to perceive and respond to human emotion, the human-computer interaction will be more natural. Several classifiers are adopted for automatically assigning an emotion category, such as anger, happiness or sadness, to a speech utterance. These classifiers were designed independently and tested on various emotional speech corpora, making it difficult to compare and evaluate their performance. In this paper, we first compared several popular classification methods and evaluated their performance by applying them to a Mandarin speech corpus consisting of five basic emotions, including anger, happiness, boredom, sadness and neutral. The extracted feature streams contain MFCC, LPCC, and LPC. The experimental results show that the proposed WD-MKNN classifier achieves an accuracy of 81.4% for the 5-class emotion recognition and outperforms other classification techniques, including KNN, MKNN, DW-KNN, LDA, QDA, GMM, HMM, SVM, and BPNN. Then, to verify the advantage of the proposed method, we compared these classifiers by applying them to another Mandarin expressive speech corpus consisting of two emotions. The experimental results still show that the proposed WD-MKNN outperforms others.