For fast egomotion of a camera, computing feature correspondence and motion parameters by global search becomes highly time-consuming. Therefore, the complexity of the estimation needs to be reduced for real-time applications. In this paper, we propose a compound omnidirectional vision sensor and an algorithm for estimating its fast egomotion. The proposed sensor has both multi-baselines and a large field of view (FOV). Our method uses the multi-baseline stereo vision capability to classify feature points as near or far features. After the classification, we can estimate the camera rotation and translation separately by using random sample consensus (RANSAC) to reduce the computational complexity. The large FOV also improves the robustness since the translation and rotation are clearly distinguished. To date, there has been no work on combining multi-baseline stereo with large FOV characteristics for estimation, even though these characteristics are individually are important in improving egomotion estimation. Experiments showed that the proposed method is robust and produces reasonable accuracy in real time for fast motion of the sensor.

Alumina supported cobalt-ferrum catalysts were prepared using wet impregnation method by applying 3 different conditions, namely hotplate (A), sonication (B) and soaking (C). The alumina supported cobalt-ferrum catalysts were applied in the synthesis of multi-walled carbon nanotubes (MWNTs) using catalytic chemical vapour deposition (CCVD) technique. The morphology and particle size of the cobalt-ferrum catalysts and the MWNTs yield were examined by field emission-scanning electron microscopy (FE-SEM) while the surface elemental composition of the samples was obtained by energy dispersive X-ray analysis (EDX). The morphology of catalysts A, B and C were found to be different, the particle sizes were ranged from 20-40 nm. The diameters of the MWNTs yield from samples A, B and C were found to be related to the catalyst particle size, thus the smaller the catalyst particle, the thinner the MWNTs obtained. The MWNTs with smaller diameter were obtained with higher purity and quality becuase the nanotube surface are free from amorphous carbon. Therefore, different catalyst preparation methods resulted in different sizes of the catalyst particle in order to synthesize MWNTs with desired diameter.

The joint source-channel coding problem is considered. The joint source-channel coding theorem reveals the existence of a code for the pair of the source and the channel under the condition that the error probability is smaller than or equal to ε asymptotically. The separation theorem guarantees that we can achieve the optimal coding performance by using the two-stage coding. In the case that ε = 0, Han showed the joint source-channel coding theorem and the separation theorem for general sources and channels. Furthermore the ε-coding theorem (0 ≤ ε <1) in the similar setting was studied. However, the separation theorem was not revealed since it is difficult in general. So we consider the separation theorem in this setting.

We propose a novel adaptive segment repair mechanism to improve traditional MPLS (Multi-Protocol Label Switching) failure recovery. The proposed mechanism protects one or more contiguous high failure probability links by dynamic setup of segment protection. Simulations demonstrate that the proposed mechanism reduces failure recovery time while also increasing network resource utilization.

In this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.

This paper considers a two-echelon repair model where several series systems comprising multiple items are operated in each base. We propose a basic model and two modified models. For two models, approximation methods are developed to derive the system availability. The difference between the basic model and the first modified model is whether the normal items in failed series systems are available as spare or not. The second modified model relaxes the assumptions of the first modified model to reflect more realistic situation. We perform numerical analysis for the models to compare their system availabilities and verify the accuracy of the approximation methods.

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.

A sound field reproduction method is proposed that uses blind source separation and a head-related transfer function. In the proposed system, multichannel acoustic signals captured at distant microphones are decomposed to a set of location/signal pairs of virtual sound sources based on frequency-domain independent component analysis. After estimating the locations and the signals of the virtual sources by convolving the controlled acoustic transfer functions with each signal, the spatial sound is constructed at the selected point. In experiments, a sound field made by six sound sources is captured using 48 distant microphones and decomposed into sets of virtual sound sources. Since subjective evaluation shows no significant difference between natural and reconstructed sound when six virtual sources and are used, the effectiveness of the decomposing algorithm as well as the virtual source representation are confirmed.

An algorithm for blind signal separation (BSS) of convolutive mixtures is presented. In this algorithm, the BSS problem is treated as multidimensional independent component analysis (ICA) by introducing an extended signal vector which is composed of current and previous samples of signals. It is empirically known that a number of conventional ICA algorithms solve the multidimensional ICA problem up to permutation and scaling of signals. In this paper, we give theoretical justification for using any conventional ICA algorithm. Then, we discuss the remaining problems, i.e., permutation and scaling of signals. To solve the permutation problem, we propose a simple algorithm which classifies the signals obtained by a conventional ICA algorithm into mutually independent subsets by utilizing temporal structure of the signals. For the scaling problem, we prove that the method proposed by Koldovský and Tichavský is theoretically proper in respect of estimating filtered versions of source signals which are observed at sensors.

In this paper, a design method of two-dimensional (2-D) orthogonal symmetric wavelets is proposed by using a lattice structure for multi-dimensional (M-D) linear-phase paraunitary filter banks (LPPUFB), which the authors have proposed as a previous work and then modified by Lu Gan et al. The derivation process for the constraints on the second-order vanishing moments is shown and some design examples obtained through optimization with the constraints are exemplified. In order to verify the significance of the constraints, some experimental results are shown for Lena and Barbara image.

Liquid-phase detection of biological targets utilizing magnetic marker and superconducting quantum interference device (SQUID) magnetometer is shown. In this method, magnetic markers are coupled to the biological targets, and the binding reaction between them is detected by measuring the magnetic signal from the bound markers. Detection can be done in the liquid phase, i.e., we can detect only the bound markers even in the presence of unbound (free) markers. Since the detection principle is based on the different magnetic properties between the free and bound markers, we clarified the Brownian relaxation of the free markers and the Neel relaxation of the bound markers. Usefulness of the present method is demonstrated from the detection of the biological targets, such as biotin-coated polymer beads, IgE and Candida albicans.

In this paper, we propose designing method for separable-denominator two-dimensional Infinite Impulse Response (IIR) filters (separable 2D IIR filters) by Successive Projection (SP) methods using the stability criteria based on the system matrix. It is generally known that separable 2D IIR filters are stable if and only if each of the denominators is stable. Therefore, the stability criteria of 1D IIR filters can be used for separable 2D IIR filters. The stability criteria based on the system matrix are a necessary and sufficient condition to guarantee stability in 1D IIR filters. Therefore, separable 2D IIR filters obtained by the proposed design method have a smaller error ripple than those obtained by the conventional design method using the stability criterion of Rouche's theorem.

Cluster systems are prevalent infrastructures for offering e-services because of their cost-effectiveness. The objective of our research is to enhance their cost-effectiveness by reducing the minimum number of nodes to meet a given target performance. To achieve the objective, we propose a load balancing algorithm, the Nearest Underloaded algorithm (N algorithm). The N algorithm aims at quick solution of load imbalance caused by request departures while also preventing herd effect. The performance index in our evaluation is the xth percentile capacity which we define based on throughputs and the xth percentile response times. We measured the capacity of 8- to 16-node cluster systems under the N algorithm and existing Least-Loaded (LL) algorithms, which dispatch or transfer requests to the least-loaded node. We found that the N algorithm could achieve larger capacity or could achieve the target capacity with fewer nodes than LL algorithms could.

We propose a new algorithm for the blind source separation (BSS) approach in which independent component analysis (ICA) and frequency subband beamforming interpolation are combined. The slow convergence of the optimization of the separation filters is a problem in ICA. Our approach to resolving this problem is based on the relationship between ICA and null beamforming (NBF). The proposed method consists of the following three parts: (I) a frequency subband selector part for learning ICA, (II) a frequency domain ICA part with direction-of-arrivals (DOA) estimation of sound sources, and (III) an interpolation part in which null beamforming constructed with the estimated DOA is used. The results of the signal separation experiments under a reverberant condition reveal that the convergence speed is superior to that of the conventional ICA-based BSS methods.

Fetal electrocardiogram (FECG) extraction is of vital importance in biomedical signal processing. A promising approach is blind source extraction (BSE) emerging from the neural network fields, which is generally implemented in a semi-blind way. In this paper, we propose a robust extraction algorithm that can extract the clear FECG as the first extracted signal. The algorithm exploits the fact that the FECG signal's kurtosis value lies in a specific range, while the kurtosis values of other unwanted signals do not belong to this range. Moreover, the algorithm is very robust to outliers and its robustness is theoretically analyzed and is confirmed by simulation. In addition, the algorithm can work well in some adverse situations when the kurtosis values of some source signals are very close to each other. The above reasons mean that the algorithm is an appealing method which obtains an accurate and reliable FECG.

Burn-in is a widely used method to improve the quality of products or systems after they have been produced. In this paper, optimal burn-in procedures for a system with two types of failures (i.e., minor and catastrophic failures) are investigated. A new system surviving burn-in time b is put into field operation and the system is used under a warranty policy under which the manufacturer agrees to provide a replacement system for any system that fails to achieve a lifetime of at least w. Upper bounds for optimal burn-in time minimizing the total expected warranty cost are obtained under a more general assumption on the shape of the failure rate function which includes the bathtub shaped failure rate function as a special case.

Assuming there are short time periods in which only one source is active, a new approach for source separation is proposed. An affine projection adaptation algorithm with a non-orthogonal constraint shows excellent noise immunity, a high convergence rate, and good tracking capability to efficiently obtain a solution to the separation filters.

FeedForward (FF-) Blind Source Separation (BSS) systems have some degree of freedom in the solution space. Therefore, signal distortion is likely to occur. First, a criterion for the signal distortion is discussed. Properties of conventional methods proposed to suppress the signal distortion are analyzed. Next, a general condition for complete separation and distortion-free is derived for multi-channel FF-BSS systems. This condition is incorporated in learning algorithms as a distortion-free constraint. Computer simulations using speech signals and stationary colored signals are performed for the conventional methods and for the new learning algorithms employing the proposed distortion-free constraint. The proposed method can well suppress signal distortion, while maintaining a high source separation performance.

From lessons learned in medical data mining projects we show that integration of advanced computation techniques and human inspection is indispensable in medical data mining. We proposed an integrated approach that merges data mining and text mining methods plus visualization support for expert evaluation. We also appropriately developed temporal abstraction and text mining methods to exploit the collected data. Furthermore, our visual discovery system D2MS allowed to actively and effectively working with physicians. Significant findings in hepatitis study were obtained by the integrated approach.

Various trajectories of design, arising from the new methodology of analog network design, are analyzed. Several major criteria suggested for optimal selection of initial approximation to the design process permit the minimization of computer time. The initial approximation point is selected with regard to the previously revealed effect of acceleration of the design process. The concept of separatrix is defined making it possible to determine the optimal position of the initial approximation. The numerical results obtained for passive and active networks prove the possibility of optimal choice of the initial point in design process.