A hidden Markov model (HMM)-based parameter estimation scheme is proposed for wideband speech recovery. In each Markov state, the estimation efficiency is improved using a new mapping function derived from the weighted least squares of vector deviations. The experimental results reveal that the performance of the proposed scheme is superior to that combining the HMM and Gaussian mixture model (GMM).

Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article, we review recent advances in the kernel methods, with emphasis on scalability for massive problems.

Optical buffer memory for 10-Gb/s data signal is demonstrated experimentally using a polarization bistable vertical-cavity surface-emitting laser (VCSEL). The optical buffer memory is based on an optical AND gate function and the polarization bistability of the VCSEL. Fast AND gate operation responsive to 50-ps-width optical pulses is achieved experimentally by increasing the detuning frequency between an injection light into the VCSEL and a lasing light from the VCSEL. A specified bit is extracted from the 10-Gb/s data signal by the fast AND gate operation and is stored as the polarization state of the VCSEL by the polarization bistability. The corresponding numerical simulations are also performed using two-mode rate equations taking into account the detuning frequency. The simulation results confirm the fast AND gate operation by increasing the detuning frequency as well as the experimental results.

A compact (13.38.05.6 mm) 40 Gbit/s 1.55-µm electroabsorption (EA) modulator monolithically integrated distributed feedback (DFB) laser diode (EML) [1] module integrated with a driver IC has been developed. Its compactness was realized by employing a broadband feed-through and a bias tee which were accurately designed by 3-dimensional (3D) electromagnetic simulation. It was confirmed that the simulation results of the frequency response and the actual measurement results are corresponding well. Clear eye opening of the 40 Gbit/s optical output waveform of the fabricated EML module was observed. Degradation was not observed even when the 40 Gbit/s electrical signal was launched into the module via the flexible printed circuit (FPC).

Synchronization poses a major challenge in ultra wideband (UWB) systems due to low signal duty cycles in UWB. This study develops an effective synchronization scheme for frame-differential IR-UWB receivers to improve the synchronization speed. The proposed parallel search mechanism reduces the search region of the symbol boundaries to only a single frame duration. Moreover, only one delay element is needed in each branch, since a shared looped delay-line (SLD) is also proposed to lower the implementation complexity of the parallel search mechanism. Simulations and performance analysis show that the proposed scheme achieves a lower mean square error and a higher probability of detection than other alternatives.

We present a novel high-speed transmitter consisting of a frequency modulated DBR laser and optical filters. The refractive index modulation in the phase control region of the DBR laser allows high-speed frequency modulation. The generated frequency modulated signal is converted to an intensity modulated signal using the edge of the optical filter pass band. We present theoretical simulations of high-speed modulation characteristics and extension of transmission reach. With the proposed transmitter, we review the experimental demonstration of 180-km transmission of a 10-Gb/s signal with a tuning range of 27 nm and 60-km transmission of a 20-Gb/s signal.

In this paper, a novel method of partially placing electromagnetic band-gap (EBG) unit cells on both the power and ground planes in multi-layer PCBs and packages is proposed; it can not only sufficiently eliminate simultaneous switching noise (SSN), but also prevent severe degradation of signal quality in high-speed systems with imperfect reference planes resulting from the perforated structures of uni-planar EBG unit cells. On the assumption that the noise sources and noise-sensitive devices exist only in specific areas, the proposed method partially arranges the EBG unit cells on both the power and ground planes, but only around the critical areas. The SSN suppression performance of the proposed structure is verified by a simulation and measurements.

A method using genetic algorithms for path generation have been proposed; however, this method is limited to particular applications, and there are limitations on the types of paths that can be represented. This paper therefore proposes a path generation method that is applicable to more general-purpose applications compared to previous methods based on a new design of the genotype used in the genetic algorithm.

Some wireless OFDMA communication systems support the frequency reuse factor of 1. In order to reduce co-channel interference (CCI) caused by neighbor cells, the fractional frequency reuse (FFR) can be employed. A promising frequency partitioning policy and subcarrier allocation for FFR are essential. In this letter, we employ an efficient frequency partitioning mechanism with less interference and propose an efficient subcarrier allocation algorithm to maximize the sum of users capacity under FFR. We show that the proposed algorithm has higher spectral efficiency than the conventional method as well as significantly high system fairness.

In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.

We present our recent work on monolithically integrated devices comprising a variety of functional elements such as high speed optical transmitters and receivers, electro-absorption modulators integrated with tunable dispersion compensators and fast-tunable wavelength converters.

Kalman filters are effective channel estimators but they have the drawback of having heavy calculations when filtering needs to be done in each sample for a large number of subcarriers. In our paper we obtain the steady-state Kalman gain to estimate the channel state by utilizing the characteristics of pilot subcarriers in OFDM, and thus a larger portion of the calculation burden can be eliminated. Steady-state value is calculated by transforming the vector Kalman filtering in to scalar domain by exploiting the filter charactertics when pilot subcarriers are used for channel estimation. Kalman filters operate optimally in the steady-state condition. Therefore by avoiding the convergence period of the Kalman gain, the proposed scheme is able to perform better than the conventional method. Also, driving noise variance of the channel is difficult to obtain practical situations and accurate knowledge is important for the proper operation of the Kalman filter. Therefore, we extend our scheme to operate in the absence of the knowledge of driving noise variance by utilizing received Signal-to-Noise Ratio (SNR). Simulation results show considerable estimator performance gain can be obtained compared to the conventional Kalman filter.

We show that simultaneous perturbation can be used as an algorithm for on-line learning, and we report our theoretical investigation on generalization performance obtained with a statistical mechanical method. Asymptotic behavior of generalization error using this algorithm is on the order of t to the minus one-third power, where t is the learning time or the number of learning examples. This order is the same as that using well-known perceptron learning.

In communication systems, ZCZ sequences and perfect sequences play important roles in removing multiple-access interference (MAI) and synchronization, respectively. Based on an uncorrelated polyphase base sequence set, a novel construction method, which can produce mutually orthogonal (MO) ZCZ polyphase sequence (PS) sets and perfect PSs, is presented. The obtained ZCZ PSs of each set are of ideal periodic cross-correlation functions (PCCFs), in other words, the PCCFs between such two different sequences vanishes, and the sequences between different sets are orthogonal. On the other hand, the proposed perfect PSs include Frank perfect PSs as a special case and the family size of the former is quite larger than that of the latter.

There are two main methods for pandemic simulations: the SEIR model and the MAS model. The SEIR model can deal with simulations quickly for many homogeneous populations with simple ordinary differential equations; however, the model cannot accommodate many detailed conditions. The MAS model, the multi-agent simulation, can deal with detailed simulations under the many kinds of initial and boundary conditions with simple social network models. However, the computing cost will grow exponentially as the population size becomes larger. Thus, simulations in the large-scale model would hardly be realized unless supercomputers are available. By combining these two methods, we may perform the pandemic simulations in the large-scale model with lower costs. That is, the MAS model is used in the early stage of a pandemic simulation to determine the appropriate parameters to be used in the SEIR model. With these obtained parameters, the SEIR model may then be used. To investigate the validity of this combined method, we first compare the simulation results between the SEIR model and the MAS model. Simulation results of the MAS model and the SEIR model that uses the parameters obtained by the MAS model simulation are found to be close to each other.

This study involves implementing an intelligent controller using the fuzzy control algorithm to minimize cold weld and splash in inverter AC spot welding. This study presents an experimental curve of a welding output current and the maximum value of the Instantaneous Heating Rate (IHRmax) using the contact diameter of an electrode as the parameter. It also presents the experimental curve of a welding output current and the slope (S) of the instantaneous dynamic resistance using the instantaneous contact area of an electrode as the parameter. To minimize cold weld and splash, this study proposes an intelligent controller that controls the optimum welding current in real time by estimating the contact diameter of an electrode and the contact area of the initial welding part.

This paper proposes an approach to image segmentation using Iterated Graph Cuts based on local texture features of wavelet coefficients. Using Haar Wavelet based Multiresolution Analysis, the low-frequency range (smoothed image) is used for the n-link and the high-frequency range (local texture features) is used for the t-link along with the color histogram. The proposed method can segment an object region having not only noisy edges and colors similar to the background, but also heavy texture change. Experimental results illustrate the validity of our method.

The spike timings of neurons are irregular and are considered to be a one-dimensional point process. The Bayesian approach is generally used to estimate the time-dependent firing rate function from sequences of spike timings. It can also be used to estimate the firing rate from only a single sequence of spikes. However, the rate function has too many degrees of freedom in general, so approximation techniques are often used to carry out the Bayesian estimation. We applied the transfer matrix method, which efficiently computes the exact marginal distribution, to the estimation of the firing rate and developed an algorithm that enables the exact results to be obtained for the Bayesian framework. Using this estimation method, we investigated how the mismatch of the prior hyperparameter value affects the marginal distribution and the firing rate estimation.

Recently, it has become important to rapidly detect human subjects buried under collapsed houses, rubble and soil due to earthquakes and avalanches to reduce the casualties in a disaster. Such detection systems have already been developed as one kind of microwave displacement sensors that are based on phase difference generated by the motion of the subject's breast. Because almost all the systems consist of single transmitter and receiver pair, it is difficult to rapidly scan a wide area. In this paper, we propose a single-frequency multistatic radar system to detect breathing human subjects which exist in the area surrounded by the transmitting and receiving array. The vibrating targets can be localized by the MUSIC algorithm with the complex amplitude in the Doppler frequency. This algorithm is validated by the simulated signals synthesized with a rigorous solution of a dielectric spherical target model. We show experimental 3D localization results using a developed multistatic Doppler radar system around 250 MHz.

Particle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.