We propose a new receiving method for an information-providing system that uses LED-based traffic lights as the transmitter. We analyzed the improvements obtained when 2-dimentional image sensor replaced the conventional single-element photodiode. First, we discuss the maximum receiver's field of view (FOV) when using the 2-dimentional image sensor at a particular focal length. We analyzed the best vertical inclination for both lanes and quantified the improvements in terms of the enhancement of received signal-noise ratio (SNR) when different numbers of pixels were applied. Our results indicate that using more pixels increases the received SNR and the service area becomes wider compared to the conventional single-element system. Consequently, receivable information within the service area also increased. We also found that the optimum number of pixels to accomplish a reliable communication system is 5050 because performance degradation occured with a larger number of pixels.

In this paper, we present a numerical method for the equiripple approximation of minimum phase FIR digital filters. Many methods have been proposed for the design of such filters. Many of them first design a linear phase filter whose length is twice as long, and then factorize the filter to obtain the minimum phase. Although these methods theoretically guarantee its optimality, it is difficult to control the ratio of ripples between different bands. In the conventional lowpass filter design, for example, when different weights are given for its passband and stopband, one needs to iteratively design the filter by trial and error to achieve the ratio of the weights exactly. To address this problem, we modifies well-known Parks-McClellan algorithm and make it possible to directly control the ripple ratios. The method iteratively solves a set of linear equations with controlling the ratio of ripples. Using this method, the equiripple solutions are obtained quickly.

Lee et al. recently proposed the first identity-based key agreement protocols for a multiple PKG environment where each PKG has different domain parameters in ICCSA 2005. However, this letter demonstrates that Lee et al.'s scheme does not include the property of implicit key authentication which is the fundamental security requirement, making it vulnerable to an impersonation attack.

In this paper we propose a novel spatial fading simulator to evaluate the performance of an array antenna and show its spatial stochastic characteristics by computer simulation based on parameters verified by experimental data. We introduce a cavity-excited circular array (CECA) as a fading simulator that can simulate realistic mobile communication environments. To evaluate the antenna array, two stochastic characteristics are necessary. The first one is the fading phenomenon and the second is the angular spread (AS) of the incident wave. The computer simulation results with respect to fading and AS show that CECA works well as a spatial fading simulator for performance evaluation of an antenna array. We first present the basic structure, features and design methodology of CECA, and then show computer simulation results of the spatial stochastic characteristics. The results convince us that CECA is useful to evaluate performance of antenna arrays.

An optical flow processor architecture is proposed. It offers accuracy and image-size scalability for video segmentation extraction. The Hierarchical Optical flow Estimation (HOE) algorithm [1] is optimized to provide an appropriate bit-length and iteration number to realize VLSI. The proposed processor architecture provides the following features. First, an algorithm-oriented data-path is introduced to execute all necessary processes of optical flow derivation allowing hardware cost minimization. The data-path is designed using 4-SIMD architecture, which enables high-throughput operation. Thereby, it achieves real-time optical flow derivation with 100% pixel density. Second, it has scalable architecture for higher accuracy and higher resolution. A third feature is the CMOS-process compatible on-chip 2-port DRAM for die-area reduction. The proposed processor has performance for CIF 30 fr/s with 189 MHz clock frequency. Its estimated core size is 6.025.33 mm2 with six-metal 90-nm CMOS technology.

A novel class of microstrip bandpass filter is configured using the impedance transformers and an improved stepped impedance resonator (SIR). This SIR is composed of a central narrow strip section with an aperture on ground and two wide strip sections at the two sides. This low-high-low SIR resonator has a promising capability in achieving an extremely large ratio of first two resonant frequencies for design of a bandpass filter with ultra-broad stopband. The two quarter-wavelength transformers with low and high impedances, referred as to impedance- and admittance-inverters, are modeled and utilized as alternative types of inductive and capacitive coupling elements with highly tightened degrees for wideband filter design. After extensive investigation is made on the two transformers and the proposed SIR, the two novel bandpass filters are constructed, designed and implemented. Two sets of predicted and measured frequency responses over a wide frequency range both quantitatively exhibit their several attractive features, such as ultra-broad stopband with deep rejection and broadened dominant passband with low insertion loss.

In this paper, we introduce a new method for depth perception from a 2D natural scene using scale variation of patterns. As the surface from a 2D scene gets farther away from us, the texture appears finer and smoother. Texture gradient is one of the monocular depth cues which can be represented by gradual scale variations of textured patterns. To extract feature vectors from textured patterns, higher order local autocorrelation functions are utilized at each scale step. The hierarchical linear discriminant analysis is employed to classify the scale rate of the feature vector which can be divided into subspaces by recursively grouping the overlapped classes. In the experiment, relative depth perception of 2D natural scenes is performed on the proposed method and it is expected to play an important role in natural scene analysis.

Shadow detection and removal is important to deal with traffic image sequences. Cast shadow of vehicle may lead to an inaccurate object feature extraction and erroneous scene analysis. Furthermore, separate vehicles can be connected through shadow. Both can confuse object recognition systems. In this paper, a robust method is proposed for detecting and removing active cast shadow in monocular color image sequences. Background subtraction method is used to extract moving blobs in color and gradient dimensions, and the YCrCb color space is adopted for detecting and removing the cast shadow. Even when shadows link different vehicles, it can detect the each vehicle figure using modified mask by shadow bar. Experimental results from town scenes show that proposed method is effective and the classification accuracy is sufficient for general vehicle type classification.

One important question for quantum computing is whether a computational gap exists between models that are allowed to use quantum effects and models that are not. Several types of quantum computation models have been proposed, including quantum finite automata and quantum pushdown automata (with a quantum pushdown stack). It has been shown that some quantum computation models are more powerful than their classical counterparts and others are not since quantum computation models are required to obey such restrictions as reversible state transitions. In this paper, we investigate the power of quantum pushdown automata whose stacks are assumed to be implemented as classical devices, and show that they are strictly more powerful than their classical counterparts under the perfect-soundness condition, where perfect-soundness means that an automaton never accepts a word that is not in the language. That is, we show that our model can simulate any probabilistic pushdown automata and also show that there is a non-context-free language which quantum pushdown automata with classical stack operations can recognize with perfect soundness.

In this paper, we propose an ICI mitigation method for MIMO OFDM using turbo detection technique. In order to reduce the computational complexity, we present a method for dividing the received frequency-domain signals into subbands and the manner of division varies with each iteration, joint soft ICI cancellation and decoding is then performed on each subband. To perform iterative ICI mitigation, the estimation of the time-variant channel using a great quantity of pilot tones is needed, which results in poor spectral efficiency. We then propose a method to reduce the required scatter pilot tones, which is differentially-modulated-pilot scheme. Moreover, the estimation can be constructed based on EM-type algorithms to further reduce the computational complexity. Finally, the results of computer simulations demonstrate that the proposal can provide significant performance improvement.

In this letter, we propose a partial minimum mean-squared error (MMSE) with successive interference cancellation (PMMSESIC) method in frequency domain to mitigate ICI caused by channel variation. Each detection, the proposed method detects the symbol with the largest received signal-to-interference-plus-noise ratio (SINR) among all the undetected symbols, using an MMSE detector that considers only the interference of several neithborhood subcarriers. Analysis and simulations show that it outperforms the MMSE method at relatively high Eb/N0 and its performance is close to the MMSE with successive detection (MMSESD) method in relatively low Doppler frequency region.

In this letter, a new formula is proposed for calculating the polarization entropy, based on the least square method. There is no need to calculate the eigenvalues of a covariance matrix as well as to use logarithms of values. So the time for computing the polarization entropy is reduced. Using polarimetric SAR data, the authors validate the effectiveness of the new formula.

Recently, a novel detector was proposed by the authors for code acquisition in non-Gaussian impulsive channels [3], which dramatically outperforms the conventional squared-sum detector; however, it requires exact knowledge of the non-Gaussian noise dispersion. In this paper, a robust detector is proposed, which employs the signs and ranks of the received signal samples, instead of their actual values, and so does not require knowledge of the non-Gaussian noise dispersion. The acquisition performance of the proposed detector is compared with that of the detector of [3] in terms of the mean acquisition time. The simulation results show that the proposed scheme is not only robust to deviations from the true value of the non-Gaussian noise dispersion, but also has comparable performance to that of the scheme of [3] using exact knowledge of the non-Gaussian noise dispersion.

In unidentifiable models, the Bayes estimation has the advantage of generalization performance over the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an alternative approximation method, which we call a subspace Bayes approach. A subspace Bayes approach is an empirical Bayes approach where a part of the parameters are regarded as hyperparameters. Consequently, in some three-layer models, this approach requires much less computational costs than Markov chain Monte Carlo methods. We show that, in three-layer linear neural networks, a subspace Bayes approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation, and theoretically clarify its generalization error and training error. We also discuss the domination over the maximum likelihood estimation and the relation to the variational Bayes approach.

A new system identification based method has been proposed for accurate estimation of vocal tract parameters. An often encountered problem in using the conventional linear prediction analysis is due to the harmonic structure of the excitation source of voiced speech. This harmonic characteristic is coupled with the estimation of autoregressive (AR) coefficients that results in difficulties in estimating the vocal tract filter. This paper models the effective voice source from the residual obtained through the covariance analysis in the first-pass which is then used as input to the second-pass least-square analysis. A better source-filter separation is thus achieved. The formant frequencies and corresponding bandwidths obtained using the proposed method for synthetic vowels are found to be accurate up to a factor of more than three (in percent) compared to the conventional method. Since the source characteristic is taken into account, local variations due to the positioning of analysis window are reduced significantly. The validity of the proposed method is also examined by inspecting the spectra obtained from natural vowel sounds uttered by high-pitched female speaker.

We propose a novel simplified Viterbi equalizer for high symbol rate FWA (Fixed Wireless Access) systems carrying 64QAM signals. Reduced complexity and improved performance are achieved adopting two approaches. The first one is reducing the number of survival paths, taking advantage of the large D/U common in LOS (line of sight) communications. The second one is using a multi-stage process to generate desired signal replicas based on their likelihoods. Computer simulations confirm that the proposed replica generation method offers a performance improvement of about 1 dB and the proposed Viterbi equalizer offers reduced complexity with no performance penalty compared to full Viterbi equalizer.

In this paper we introduce a statistical quality model for delay testing that reflects fabrication process quality, design delay margin, and test timing accuracy. The model provides a measure that predicts the chip defect level that cause delay failure, including marginal small delay. We can therefore use the model to make test vectors that are effective in terms of both testing cost and chip quality. The results of experiments using ISCAS89 benchmark data and some large industrial design data reflect various characteristics of our statistical delay quality model.

We propose two sequence design schemes for an overloaded space-time spreading system with multiple antennas. One scheme is for a system in which the amplitude of user signals needs not be adjusted and provides tradeoffs between the user capacity and diversity order. This scheme has a certain similarity to time-sharing, but its performance is further improved by time-diversity. Another is to achieve full diversity order by varying user signal amplitudes. The diversity orders of the respective schemes are theoretically proved and their performances are demonstrated by simulation.

Modern digital systems design requires us to explore a large and complex design space to find a best configuration which satisfies design requirements. Such exploration requires a sound representation of design space from which design candidates are efficiently generated, each of which then is evaluated. This paper proposes a plan-generation-evaluation framework which supports a complete process of such design space exploration. The plan phase constitutes a design space of all possible design alternatives by means of a formally defined representation scheme of attributed AND-OR graph. The generation phase generates a set of candidates by algorithmic pruning of the design space in an attributed AND-OR graph with respect to design requirements as well as architectural constraints. Finally, the evaluation phase measures performance of design candidates in a pruned graph to select a best one. A complete process of cache design is exemplified to show the effectiveness of the proposed framework.

This paper presents a shortest path search algorithm using a model of excitable reaction-diffusion dynamics. In our previous work, we have proposed a framework of Digital Reaction-Diffusion System (DRDS)--a model of a discrete-time discrete-space reaction-diffusion system useful for nonlinear signal processing tasks. In this paper, we design a special DRDS, called an "excitable DRDS," which emulates excitable reaction-diffusion dynamics and produces traveling waves. We also demonstrate an application of the excitable DRDS to the shortest path search problem defined on two-dimensional (2-D) space with arbitrary boundary conditions.