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.

In this paper, we propose an analog CMOS circuit which achieves spiking neural networks with spike-timing dependent synaptic plasticity (STDP). In particular, we propose a STDP circuit with symmetric function for the first time, and also we demonstrate associative memory operation in a Hopfield-type feedback network with STDP learning. In our spiking neuron model, analog information expressing processing results is given by the relative timing of spike firing events. It is well known that a biological neuron changes its synaptic weights by STDP, which provides learning rules depending on relative timing between asynchronous spikes. Therefore, STDP can be used for spiking neural systems with learning function. The measurement results of fabricated chips using TSMC 0.25 µm CMOS process technology demonstrate that our spiking neuron circuit can construct feedback networks and update synaptic weights based on relative timing between asynchronous spikes by a symmetric or an asymmetric STDP circuits.

A two-quadrant CMOS current divider using a two-variable second-order Taylor series approximation is proposed. The approximation divider is realized with a compact circuit. The simulation results indicate that the compact divider has with sufficient accuracy, small distortion, and high bandwidth for only 1.8 V supply voltage.

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.

With a Gaussian kernel function, we find that the distance between two classes (DBTC) can be used as a class separability criterion in feature space since the between-class separation and the within-class data distribution are taken into account impliedly. To test the validity of DBTC, we develop a method of tuning the kernel parameters in support vector machine (SVM) algorithm by maximizing the DBTC in feature space. Experimental results on the real-world data show that the proposed method consistently outperforms corresponding hyperparameters tuning methods.

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.

The situations where several population parameters need to be estimated simultaneously arise frequently in wide areas of applications, including reliability modeling, survival analysis and biological study. In this paper, we propose Bayesian methods of estimation of the ordered parameters of the two exponential populations, which incorporate the prior information about the simple order restriction, but sometimes breaks the order restriction. A simulation study shows that the proposed estimators are more efficient (in terms of mean square errors) than the isotonic regression of the maximum likelihood estimators with equal weights. An illustrative example is finally presented.

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.

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.

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).

We propose a user-defined on-demand matching strategy, called O-matching, in which users can control the order of matching arguments of each operation symbol. In ordinary matching schemes it is not important to set the order of matching, however, in on-demand matching schemes, it is very important since an input term may be changed while doing the on-demand matching process. O-matching is suitable to combine with the E-strategy, which is a user-defined reduction strategy in which users can control the order of reducing arguments. We show a sufficient condition under which the E-strategy with O-matching is correct for head normal forms, that is, any reduced term is a head normal form.

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.

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.

An equalizer initialization technique for least mean squares (LMS) algorithm, which can equalize frequency selective multiple input multiple output (MIMO) channels, is presented and analyzed. The proposed method conducts an initial convergence step for superior training prior to running the LMS algorithm. This approach raises the training performance while the complexity of the LMS algorithm, which is known as the simplest training algorithm, is almost the same. The proposed technique is analyzed for the initial convergence and simulated for a possible single carrier MIMO application in single carrier (SC) IEEE802.16-2004 standards. The obtained performance after coding approximates the performance of the recursive least squares (RLS) algorithm as it is presented for 33 and 55 MIMO for comparisons.

The fuzzy controller based on cycle-to-cycle control with output value adjustment factors (OAF) was developed for restoring gait of paralyzed subjects by using functional electrical stimulation (FES). Results of maximum knee flexion and extension controls with neurologically intact subjects suggested that the OAFs would be effective in reaching the target within small number of cycles and in reducing the error after reaching the target. Oscillating responses between cycles were also suppressed. The fuzzy controller was expected to be examined to optimize the OAFs with more subjects including paralyzed patients for clinical application.

Searching mechanisms employed in unstructured overlay networks typically hit multiple peers for the desired content. We propose the use of a simple method that raises the hit rates of unpopular contents and balances the loads by choosing the peer holding the least contents as the provider when multiple candidates exist.

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.

In 2006, Chatterjee and Sarkar proposed a hierarchical identity-based encryption (HIBE) scheme which can support an unbounded number of identity levels. This property is particularly useful in providing forward secrecy by embedding time components within hierarchical identities. In this paper we show that their scheme does not provide the claimed property. Our analysis shows that if the number of identity levels becomes larger than the value of a fixed public parameter, an unintended receiver can reconstruct a new valid ciphertext and decrypt the ciphertext using his or her own private key. The analysis is similarly applied to a multi-receiver identity-based encryption scheme presented as an application of Chatterjee and Sarkar's HIBE scheme.

A reduced complexity quantization error correction method for lattice reduction aided (LRA) vector precoding is proposed. For LRA vector precoding,Babai's approximation procedure can generate quantization errors leading to performance loss. Instead of making a list to correct all possible errors as is done in the existing scheme, we propose a novel method in which only a subset of all possible errors are corrected. The size of the subset is determined by the probability distribution of the number of actual errors. Thus, the computation complexity of our correction procedure is reduced with little performance loss compared with the existing correction scheme.

In this letter, an acoustic environment classification algorithm based on the 3GPP2 selectable mode vocoder (SMV) is proposed for context-aware mobile phones. Classification of the acoustic environment is performed based on a Gaussian mixture model (GMM) using coding parameters of the SMV extracted directly from the encoding process of the acoustic input data in the mobile phone. Experimental results show that the proposed environment classification algorithm provides superior performance over a conventional method in various acoustic environments.