Fast- and slow-scale bifurcations have been observed in electrical power circuits with multiple inputs. This paper studies the bifurcation phenomena in an electric circuit with multiple inputs. First, the circuit model and the waveform behavior are shown. Then, discrete maps are defined. Finally, the bifurcation phenomena in the circuit are analyzed using the discrete maps.

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Nagano proposed a mathematical model of coupled Dictyostelium systems and demonstrated the strong tendency of in-phase synchronization. In this study, we investigate the mechanisms of this phenomenon by obtaining bifurcation sets and analyzing the phase differences of responses. From the analysis results, the model has a wide parameter range giving a stable 1-periodic solution. Thus, the one-to-one synchronization is maintained regardless of changes in the environment parameters. For example, even if the frequency ratio of two oscillators is extremely unbalanced, the model with an appropriate coupling factor shows a 1-periodic solution.

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In this study, we have performed two-dimensional simulations of multi-component systems by employing the freeenergy functional F{Φ_α (r)}. First, we conducted a simulation of a mixture of AB amphiphilic molecules and S solvent to verify the stability of a vesicle structure. In the study, we found the range of the radius R to retain a vesicle structure under certain conditions. Then, we performed a simulation of a mixture of AB amphiphilic molecules, CD amphiphilic molecules, and S solvent to examine phase-separation dynamics on a vesicle membrane. The results showed the difference of phase separation among the radius R and χ parameter (χ_AC and χ_BD), enabling us to obtain the χ_AC, χ_BD- R phase diagram

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In the absence of sensory stimuli, continuous neuronal firings are observed in cortical networks. Such self-sustained ongoing activity is referred to as "spontaneous activity," the dynamics of which is characterized by (1) low firing frequency, (2) irregularity, and (3) asynchronous firings among neurons. Despite numerous theoretical attempts, the mechanism that underlies the spontaneous firing activity has remained unclear. Recently, Teramae et al. proposed a neuronal network model with excitatory postsynaptic potentials (EPSPs) obeying a lognormal distribution, as observed in physiological experiments. The model successfully reproduced the key features of the spontaneous activity. Their model, however, focused mainly on the lognormal distribution of the network connectivity, where the correlation of EPSPs observed between bidirectionally coupled neurons was disregarded. The present paper introduces the correlated EPSPs to the lognormal network model and shows that a physiologically plausible level of such correlation causes (i) synchronous firings among neurons, (ii) extremely high firing frequencies observed in a group of neurons, and (iii) intermittent switching between asynchronous and synchronous firing states.

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A minimum-error method was developed in our previous study to estimate model parameters from spike train data experimentally recorded from inferior olive neurons. Our method characterized the neuronal firing dynamics using 67 spatiotemporal features. The closest match between experimental and simulated data in the feature space determined two parameters that control the neural network dynamics of the simulation model. This approach, however, has not been fully verified, because the true parameter values were unknown for the experimental data. In the present study, we attempt to validate the minimum-error method using simulated spike data, for which the true parameter values are known, as the test data. Our performance evaluation on the test data confirmed that the minimum-error method is effective for resolving the inverse problem even when the simulation model is an imperfect representation of the experimental data.

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In many nonlinear applications, hardware architectures for the particle swarm optimization (PSO) algorithm are implemented to obtain the optimum solution. However, the conventional hardware implementation is inefficient when the arithmetic equations in the application become complex. To overcome this problem, this paper presents a two-level pipeline structure for the PSO algorithm based on field-programmable gate array (FPGA) technology. The proposed hardware employs two novel features. First, a generic particle calculation block (GPCB) is adopted to support different kinds of PSO algorithm. Second, a twolevel pipeline approach is adopted, which helps to increase the calculation speed of the proposed hardware. The experimental results demonstrate that the proposed architecture achieves higher performance than conventional hardware.

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In our previous study, we enhanced the predictive power of the principal component portfolio (PCP) model by applying a nonlinear prediction model. However, here we point out that this modification destroys the no-correlation relationship among the principal components, and accordingly the portfolio effect of risk reduction is weakened. To solve this problem, we mixed the advantages of the PCP model and our nonlinear portfolio model. To confirm the validity of this, we performed some investment simulations with real stock data and confirmed that our new portfolio model improves the predictive power and risk-reduction power simultaneously, that is, it improves the efficiency and safety of portfolio management.

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We propose how to optimize the embedding dimension and the delay time used for local linear prediction, which can be optimized adaptively to the local structure of the embedded state space. In particular, we assumed that the observed timeseries data are sometimes noisy and not long enough. In this case, even if the dynamics generating the time-series data is stable and stationary, it is advantageous to change the embedding parameters dynamically. For this optimization, we applied the bagging algorithm to local linear prediction, and we optimized the parameters so as to minimize the prediction risk estimated by an ensemble of bagging predictors. To confirm its validity, we performed numerical simulations using chaotic time-series data and real financial data.

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This paper proposes a congestion degree estimation method for train cars using wireless LAN (WLAN) in a simple manner because the public WLAN service area has recently been expanded. WLAN stations (STAs) frequently find a WLAN access point (AP) using WLAN control frames named probe request frames. In addition, many APs for public WLAN services are located in places such as railways and airports. In this paper, we investigate the congestion degree using WLAN control frames. Compared with the case of using the number of WLAN STAs obtained by probe request frames, we clarify that the correlation between the results of evaluation by visual observations and the received signal strength indicator (RSSI) values obtained by the beacon frames of a WLAN AP is higher.

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In cellular networks, when multiple base stations (BSs) of adjoining cells use the same frequency band for transmitting their signals, inter-cell interference (ICI) occurs at user equipment (UE). To suppress ICI, UE needs to know the channel state information (CSI) between each of its surrounding BSs and itself. Therefore, multi-cell channel estimation is required. However, ICI also degrades the channel estimation performance. In this paper, we propose an iterative multicell minimum mean square error (MMSE) channel estimation scheme. To employ the MMSE principle, UE must know the covariance matrix of its estimating channels before conducting estimation. To cope with this contradiction, our proposed scheme estimates and updates the covariance matrix as well as the channels themselves through iterative processing. From computer simulation, we can show that the proposed scheme estimates the covariance matrix effectively, and achieves a higher channel estimation performance than the multi-cell least square (LS) channel estimation scheme.

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This paper describes a navigation system for a blind person that uses an audio guidance system including input from a voice recognition system. The proposed voice recognition system is used in order to easily set the desired destination for the blind person. By using the proposed voice recognition system, the destination can be set by using a microphone. An EasyVR module is used to develop the voice input navigation system. In order to measure the performance of the developed system, the user’s voice is evaluated by using the template matching method. Some experiments on the proposed voice input navigation system are also conducted at different locations with different sound intensity levels. As a result, the satisfactory performance of the developed voice navigation system is confirmed.

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In this paper, we describe the design and evaluation of a behavior control algorithm for a multiple-mobile-robot system in a panel cruising problem. In order to achieve given tasks, fundamental functions such as path planning, path tracking, obstacle avoidance, and energy conservation are required for each mobile robot. Therefore, we propose an intelligent behavior control algorithm that was derived by analyzing the operator's manipulation data. In particular, we focus on the implementation of decision-making skills making it possible to maintain a balance between selfish and altruistic behaviors in a multiple-mobile-robot system, and confirm the usefulness of the proposed behavior control algorithm from several computer simulations.

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The main problem for autonomous underwater vehicle (AUV) is not only how to make a stable underwater vehicle but also how to keep the AUV form its desire trajectory. Some perturbations such as wind, waves, and ocean currents are the crucial factors to disturb the AUV. Those will move AUV's position from for the desired track. To cope with this problem, sliding mode control (SMC) is used to make a robust AUV under a range of ocean disturbances. Moreover, the chattering effect which is produced by SMC will be overcome by fuzzy logic control (FLC). A simulation is presented to analyse the effectiveness of proposed control under some deterministic condition. However, the results show that the movement of AUV under high value of disturbances cannot be solved by the proposed control.

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When team sports coaches instruct their team members, a sample video is needed for an offensive instruction. However, it is very difficult to search a video for the parts of interest, and it takes a long time to compile a database, for example, ball trajectories and player locations, because sports videos have many periods with no play such as timeouts. In particular, information on the ball trajectory is very important for tactical analysis. However, it is currently inputted manually, which is time-consuming. Therefore, we focus on American football, where the ball can rarely be seen. The contribution of this paper is the submission of a method of automatic ball trajectory extraction. First, we remove the no-play periods in team sports videos to leave only play time and reduce the processing time. Second, we extract the ball trajectory to enable tactical analysis. We propose a new approach to ball tracking by focusing on the ball holder prediction. Our method can be applied to situations with heavy occlusion. Finally, we construct a play search and 3D virtual display system using information on the ball trajectory and the Unity, which is the development environment of the 3D virtual visualization. We used it to construct a Virtual 3D map for watching the play from an arbitrary viewpoint.

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We propose an automatic alignment system in simple planes for projection mapping (PM). PM has been used widely and applied in many fields such as the medical, entertainment, and industrial fields. However, there are many problems such as the equipment of PM being too expensive and large. Additionally, we must align the projected images and the target manually in the final step of the mapping procedure. In this study, to solve these problems, we build a compact mobile PM system with automatic alignment. As reported in this paper, we construct the automatic alignment system for simple planes using an inexpensive camera, laptop computer, and mobile projector as the first step towards the mobile PM system. In experiments, we use a plane with a color frame and a nonframe plane (picture-story show) as the simple planes. Using color feature and local feature amounts such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB), we estimate the corners of the plane and project an image to planes automatically. Finally, we evaluate the error calculated by the proposed system.

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We propose an algorithm to improve the colorization algorithm using the Lp-norm minimization approach. The proposed algorithm minimizes the Lp-norm of differences between neighboring pixels of a given luminance image to appropriately colorize a chrominance image. Since the Lp-norm minimization problem is difficult to solve, we apply the iterative reweighted least squares (IRLS) algorithm. Numerical examples show the effectiveness of the proposed method.

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The use of digital images has become quite widespread in legal, medical, and private contexts. However, anyone can easily edit or manipulate any digital image on a computer. Thus, an effective method for detecting digital image manipulation is required for retaining authenticity. Image-manipulation detection is applied in investigations of crimes and photographic evidence. In this paper, we describe a method for detecting morphological pattern spectrum-based human-like image manipulation. All conventional techniques, such as metadata and electronic watermarking, cannot consider the same results between an original and rotated original image the same as a human. However, our method can judge whether an original image and rotated original image are the same. In other words, our method judge an image in the same way as the human eye. We determined that this method could detect a manipulated image. And this method can be used for digital image forensics.

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In this paper, we investigate a blind watermarking technique via complex signal processing for images. For improving watermark robustness to JPEG compression, our watermarking employs two complex transforms, referred to as complex wavelet transform and complex smear transform. High frequency components are extracted by the complex wavelet transform in order to prevent a quality degradation of host data due to watermarking. The subband signal is spread by an inverse complex smear transform for enhancing robustness to a variety of attacks. These steps correspond to a detecting scheme in the proposed method. From the spreading signal, we derive an optimum impulse signal as a signature. The signature impulse signal is processed by an inverse system to the detection algorithm for generating a watermark, and the watermark is added to the host image. This watermark is detected by a maximum value detection due to the inserted impulse signal. In addition to a payload of the watermark, various rates of detecting the correct key from a decompressed JPEG image are shown for evaluating the performance of the proposed method.

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As one of the various brain functions, an extension of the body image called the body schema (BS) is known. The BS is required to recognize the positions of our body parts and limbs. To utilize the BS, it is necessary to effectively integrate visual information and haptic information. An extension of the BS often occurs in our daily lives where we use a tool or device by hand; hence, the BS also related to the operational skill of vehicles because the feeling of the car width is an extension of the BS itself. With this background, in our previous study, a lanekeeping assistive method using a weak electrical stimulus based on the consideration of BS extension conditions was presented. It was confirmed in that study that the driving skill was improved using this method through experimental verification using a driving simulator. The induction of the BS extension by the external stimulus was, however, not shown directly in the simulator experiment. Therefore, in this paper, characteristic evidence of the BS extension was investigated from biosignal in the brain measured by near-infrared spectroscopy (NIRS). We monitored the cerebral blood flow (CBF) around the inferior parietal lobule, which is related to the BS. As a result, a significant (p<0.1; Wilcoxon rank-sum) characteristic pattern in the CBF variation was found for all four participants.

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A coronary plaque tissue classification is essential for diagnosis of acute coronary syndromes. We have applied the Averaged Learning Subspace Method (ALSM) with consideration for the neighborhood information, to classify coronary plaque tissues. We have succeeded in classifying the tissues whilst keeping the merit of the subspace method. Simple parameter settings and low computing cost have been realized, and compared to our previous method more accurate classification results have been obtained.

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Doppler ultrasound is a widely used and reliable noninvasive device for blood flow waveform measurement. However, the commercialized blood flow devices are usually in hospital with limited access for public, required monitoring by competent staff and not easily transported. The aim of current study is to examine the potential of a portable blood flow velocity (BFV) Doppler ultrasound system for healthcare application. The effects of visceral fat (VF) level, blood pressure (BP) level (hypertension), aging, regular exercise and exercise-trained programme toward blood flow waveforms and its indices are determined. The peak systolic (S1) velocity and resistive index (RI) are significantly higher (p<0.05) in older population who exercise regularly than who does not. There are significant differences (p<0.05) in the RI and velocity reflection index between normotensive and hypertensive groups. The S1 and peak diastolic (D) are decreased significantly (p<0.05) from normotensive to hypertension groups. Low VF group has significantly increase vascular elasticity than high VF group. In contrast, low VF group has decrese reflected wave velocity. Our findings have broad implication in prevention and monitoring of cardio-related healthproblem that possibly early detection by frequent measurement of BFV, BP and electrocardiogram using this portable device.

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A novel sonication decellularization system is developed to prepare complete decellularized tissue. The objective of the study is to determine the influence of acoustic intensity on sonication parameter and decellularization using sonication treatment. The distribution of acoustic intensity for sonication treatment is first demonstrated by simulation followed by experimental measurement. The relation of acoustic intensity with sonication parameter on decellularization is further investigated. The effect of acoustic intensity on decellularization is evaluated by measuring the residual cell in tissue sample using Hematoxylin-Eosin staining. The simulation shows low residual cells upon high acoustic intensity. From the result, acoustic intensity shows positive correlation with dissolved oxygen concentration and pH and negative correlation with conductivity. The present study concludes that decellularization performed by sonication treatment is dependent on acoustic intensity.

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