Interleaved Frequency Division Multiplexing (IFDM) can achieve high diversity gain as well as establishing orthogonal frequency multiplexing by using a comb-shaped frequency spectrum. In IFDM, as the number of repeat transmissions of a modulated symbol is increased, the comb-shaped frequency spectrum should be narrowed, so that the frequency diversity gain is decreased. In addition, IFDM suffers from inter-path interference imposed on the transmitted signal by multipath fading channel. In this paper, a novel frequency spectrum construction is proposed. In the proposed frequency spectrum construction, the comb-shaped frequency spectrum is frequency-shifted for every modulated symbol. As a result, the frequency spectrum of the frame composed of many modulated symbols is widely spread. In addition, the inter-path interference can be suppressed because the modulated symbol is orthogonal, in the frequency domain, to the following symbol. From the computer simulation, the frequency spectrum rotation can achieve better error rate performance thanks to the increase in frequency diversity gain and suppressing inter-path interference.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

In the research field of microwave radar, a range finding method based on standing wave is known to be effective for measuring short distances. In this paper, we focus our attention on audible sound and fundamentally examine the distance estimation method in which acoustical standing wave is used.

The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.

This paper proposes a new algorithm named Adaptive Running Spectrum Filtering (ARSF) to restore the amplitude spectra of speech corrupted by additive noises. Based on the pre-hand noise estimation, adaptive filtering is used in speech modulation spectra according to the noise conditions. The periodic structures in the amplitude spectra are kept against noise distortion. Since the amplitude spectral structures contain the information of fundamental frequency, which is the inverse of pitch period, ARSF algorithm is added into robust pitch detection to increase the accuracy. Compared with the conventional methods, experimental results show that the proposed method significantly improves the robustness of pitch detection against noise conditions with several types and SNRs.

A sweep spectrum analyzer has been improved over the years, but the fundamental method has not been changed before the 'Super Sweep' method appeared. The 'Super Sweep' method has been expected to break the limitation of the conventional sweep spectrum analyzer, a limit of the maximum sweep rate which is in inverse proportion to the square of the frequency resolution. The superior performance of the 'Super Sweep' method, however, has not been experimentally proved yet. This paper gives the experimental evaluation on the 'Super Sweep' spectrum analyzer, of which theoretical concepts have already been presented by the authors of this paper. Before giving the experimental results, we give complete analysis for a sweep spectrum analyzer and express the principle of the super-sweep operation with a complete set of equations. We developed an experimental system whose components operated in an optimum condition as the spectrum analyzer. Then we investigated its properties, a peak level reduction and broadening of the frequency resolution of the measured spectrum, by changing the sweep rate. We also confirmed that the experimental system satisfactorily detected the spectrum at least 30 times faster than the conventional method and the sweep rate was in proportion to the bandwidth of the base band signal to be analyzed. We proved that the 'Super Sweep' method broke the restriction of the sweep rate put on a conventional sweep spectrum analyzer.

The extraction of acoustic features for robust speech recognition is very important for improving its performance in realistic environments. The bi-spectrum based on the Fourier transformation of the third-order cumulants expresses the non-Gaussianity and the phase information of the speech signal, showing the dependency between frequency components. In this letter, we propose a method of extracting short-time bi-spectral acoustic features with averaging features in a single frame. Merged with the conventional Mel frequency cepstral coefficients (MFCC) based on the power spectrum by the principal component analysis (PCA), the proposed features gave a 6.9% relative lower a word error rate in Japanese broadcast news transcription experiments.

In a distant-talking environment, the length of channel impulse response is longer than the short-term spectral analysis window. Conventional short-term spectrum based Cepstral Mean Normalization (CMN) is therefore, not effective under these conditions. In this paper, we propose a robust speech recognition method by combining a short-term spectrum based CMN with a long-term one. We assume that a static speech segment (such as a vowel, for example) affected by reverberation, can be modeled by a long-term cepstral analysis. Thus, the effect of long reverberation on a static speech segment may be compensated by the long-term spectrum based CMN. The cepstral distance of neighboring frames is used to discriminate the static speech segment (long-term spectrum) and the non-static speech segment (short-term spectrum). The cepstra of the static and non-static speech segments are normalized by the corresponding cepstral means. In a previous study, we proposed an environmentally robust speech recognition method based on Position-Dependent CMN (PDCMN) to compensate for channel distortion depending on speaker position, and which is more efficient than conventional CMN. In this paper, the concept of combining short-term and long-term spectrum based CMN is extended to PDCMN. We call this Variable Term spectrum based PDCMN (VT-PDCMN). Since PDCMN/VT-PDCMN cannot normalize speaker variations because a position-dependent cepstral mean contains the average speaker characteristics over all speakers, we also combine PDCMN/VT-PDCMN with conventional CMN in this study. We conducted the experiments based on our proposed method using limited vocabulary (100 words) distant-talking isolated word recognition in a real environment. The proposed method achieved a relative error reduction rate of 60.9% over the conventional short-term spectrum based CMN and 30.6% over the short-term spectrum based PDCMN.