A system for measuring Raman scattering light was constructed from a refracting telescope using a Fresnel lens and a Nd:YAG laser of wavelength 355 nm. Raman scattering signals from H₂ gas released into open air and atmospheric N₂ were simultaneously measured, and the concentration of H₂ gas was calculated based on the ratio of these signals. The result showed that H₂ gas concentration of 14 vol.% could be detected at a distance of 10 m. The sensitivity of the system was evaluated using a gas cell filled with H₂ gas mixture, in which H₂ gas concentration of 0.6 vol.% could be detected at a distance of 13 m.

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In this research, we examined process of silica ablation using laser plasma soft X-rays. We observed ionic and neutral species emitted from silica surfaces by irradiation with laser plasma soft X-rays. We mainly detected atomic species such as O⁺, Si⁺ and Si neutrals. Kinetic energies of detected ions are much higher than that of thermally evaporated atoms. There is an ablation threshold F_th at 60 mJ/cm², and ablation rate R obeys a law R = 1/αln (F/F_th) beyond the threshold, where α is an effective absorption coefficient of 9.0×10⁵ cm^-1. The energy of laser plasma soft X-rays absorbed in silica surface at the threshold can be estimated to be 55 kJ/cm³, which is comparable with the binding energy of SiO₂ of 76 kJ/cm³. These results suggest that the most of the bonds in silica glass are broken by absorption of soft X-ray, that several percent of the atoms are ionized, and that repulsive ions are emitted together with neutral atoms. The process enables us to fabricate nano structures.

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Laser-based remote sensing using a vibration detection system has been developed using a photorefractive crystal to reduce the effect of concrete surface-roughness. An electric field was applied to the crystal and the reference beam was phase shifted to increase the detection efficiency (DE). The DE increased by factor of 8.5 times compared to that when no voltage and no phase shifting were applied. Vibration from concrete defects can be detected at a distance of 5 m from the system. A vibration-canceling system has also developed that appears to be promising for canceling vibrations between the laser system and the concrete. Finally, we have constructed a prototype system that can be transported in a small truck.

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In recent years, synaptic plasticity, which is dependent on the order and time interval of pre- and post-synaptic spikes (STDP: spike-timing-dependent synaptic plasticity), has been observed by physiological experiments. There is a type of STDP, which is characterized by a symmetric time window (a Mexican-hat-type window). It has been reported that a Mexican-hat window participates in an inhibitory interneuron. In this study, we investigate a memory of temporal sequences patterns using P-HNN (Pulse-type Hardware Neural Network) with STDP (Mexican-hat time window). To be more specific, we propose the construction of a P-HNN with STDP. In addition, we examine the temporal sequences patterns which were memorized by using a P-HNN with STDP. As a result, we show that a P-HNN with STDP memorize temporal sequences output voltage patterns which obeyed the temporal sequences input current patterns.

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The plethysmogram is measured at different points of human body by using the photo interrupter, which sensitively depends on the physical and mental situation of human body. In this paper the statistical method of the data-analysis is investigated to discuss the dependence of plethysmogram on stress and aging. The first one is the representation method based on the return map, which provides usuful information for the waveform, the flucuation in phase and the fluctuation in amplitude. The return map method makes it possible to understand the fluctuation of plethymogram in amplitude and in phase more clearly and globally than in the conventional power spectrum method. The second is the Lisajous plot and the correlation function to analyze the phase difference between the plethysmograms of the right finger tip and of the left finger tip. The third is the R-index, from which we can estimate “the age of the blood flow”. The R-index is defined by the global character of plethysmogram, which is different from the usual APG-index. The stress- and age-dependence of plethysmogram is discussed by using these methods.

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A treadmill is a useful apparatus for the gait training and evaluation. However, many differences are reported between treadmill and overground walking. Experimental comparisons of the muscle activity of the leg and the heart rate have been carried out. However, the dynamic comparison has not been performed. The dynamic evaluation of the overground walking and the treadmill walking using a compass-type walking model (CTWM) which is a simple bipedal walking model, then their comparison is discussed. It is confirmed that the walking simulation using the CTWM can simulate the difference of that walk, it is clarified that there are the differences of the kick impulse on the ground and the turning impulse of the foot to the variation of the belt speed and then differences are the main factor of two walking.

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Medical lasers used as ophthalmic photocoagulator are briefly reviewed. The author describes some new approaches to develop a novel laser source for these applications, and then discusses future trends and some options for medical applications. At present, the domestic market of laser equipment for medical applications is smaller than that for industrial applications, which has restricted constant growth. In this paper, laser sources for photocoagulation are mainly discussed. Photocoagulators are influenced by the development of laser sources. New applications at extremely high bright orange fiber laser photocoagulators are expected from the perspective of life cycle of new products.

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A semi-separated co-culture system for spatio-temporal recording of the electrical activities from superior cervical ganglion (SCG) neurons and ventricular myocytes (VMs) was developed by using a handmade “H-shaped” chamber placed on microelectrode arrays (MEA). The chamber was made of polydimethylsyloxane (PDMS) and consisted of two chambers which were connected with a pathway. 16-20 hours after dissemination of SCG neurons into one chamber, the dissociated VMs were disseminated into the other chamber. 4days after dissemination of VMs, SCG neurons and VMs conjugated only at the pathway. Spontaneous electrical activities of SCG neurons and VMs were observed several days after the dissemination of VMs. Constant-voltage stimualtion (1 V, 1 ms, biphasic square pulses) was applied to SCG neurons at the frequency of 10 Hz using 32 electrodes. After applying electrical stimulation to SCG neurons, the contraction rate of VMs in three samples increased by 55±5.6%, 64±8.8%, 280±160%, respectively. This result suggests that neuromuscular junctions were formed between SCG neurons and VMs.

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To understand the relationship between neuronal-network functions and single-neuron activity, construction of artificial neuronal network is one of the promising approaches. Cell patterning is a useful technique to get single-neuron-based networks in vitro. Here in this work, we propose a simple method to get simple neuronal networks, based on neurite-outgrowth guidance. Our method, referred to as “micropipette drawing” is a quite simple photomask-free technique. Growth-guiding patterns are drawn with a micropipette containing cell-adhesive solution on non-adhesive substrates. Guiding structures of approximately 10 μm width were successfully drawn and rat hippocampal neurons were cultured on the patterns. The patterned neuronal networks could be maintained for more than a week.

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This paper shows that it is possible to decrease the computational cost of Dynamic Programming methods for problems of planning a certain kind of Stochastic Discrete Event System (SDES), if those methods are constructed based on the modeling method called Symbolic Binary Coding Scheme (SBCS). The considered property of such a system is that the number of following states from an origin state is rather smaller than the number of possible situational-inputs (SIs) which the system suffers. Here, the SI is one of the components involved in a model by the SBCS, and a kind of augmented disturbance. The property raises the conjecture that some SIs may have the same influence to the system, and if so, they can be united to an SI which typifies them. This conjecture can be achieved by algebraically simplifying the state transition function, which is modeled as a set of elementary binary functions by the SBCS, with the aid of Computer Algebra Systems. That unification obviously decreases the computational cost of DP methods. In computer illustrations, the effectiveness of employing that unification is shown through results by applying the Value Iteration method to elevator operation problems of small scale.

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In this paper, we propose a new Particle Swarm Optimization (PSO), which is based on successive optimization in its parameter space, in order to overcome the difficulty for applying PSO to complex and high dimensional nonlinear optimization problems. The proposed PSO consists of two types of optimization procedures; optimization in its decision variable space and optimization in its parameter space. Some numerical simulations using six types of typical benchmark problems verify the performance of the proposed PSO.

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A control technology using Reinforcement Learning (RL) and Radial Basis Function (RBF) Network has been developed to reduce environmental load substances exhausted from power and industrial plants. This technology consists of the statistic model using RBF Network, which estimates characteristics of plants with respect to environmental load substances, and RL agent, which learns the control logic for the plants using the statistic model. In this technology, it is necessary to design an appropriate reward function given to the agent immediately according to operation conditions and control goals to control plants flexibly. Therefore, we propose an automatic reward adjusting method of RL for plant control. This method adjusts the reward function automatically using information of the statistic model obtained in its learning process. In the simulations, it is confirmed that the proposed method can adjust the reward function adaptively for several test functions, and executes robust control toward the thermal power plant considering the change of operation conditions and control goals.

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A waiting time prediction system for consultations in hospitals has been developed. The system consists of a main server, control terminals, and monitors for patients, which are controlled using Web applications and Web browsers. The system obtains the following data; time arriving at receptions, examination-start and finish time, and consultation-start and finish time for every patient in real time from other existing medical systems through LAN. Based on these data, the order that doctors consult patients is predicted, and then the waiting times for every patient are estimated in real time. The system was evaluated in a hospital. The average differences between the predicted and actual waiting time were less than 20 minutes for the reserved patients, and less than 60 minutes for non-reserved patients. The predicted waiting times by the system were announced to patients on a display. By interviewing patients, it has been confirmed that the announcement of the waiting time is useful for improving hospital services.

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The arteriosclerosis is on the increase with an aging or change of our living environment. For that reason, diagnosis of the common carotid artery using echocardiogram is doing to take precautions carebropathy. The arteriosclerosis of the common carotid artery is diagnosed using Intima-Media Thickness (IMT) which is obtained from echocardiogram. In order to measure IMT from echocardiogram, it is required to detect a border which is a boundary between vessel tissue layers. The method of border detection requires reproducibility and high accuracy.
The conventional methods to detect the border curve depend on differential value of brightness on the common carotid artery. Therefore, we can't extract a good candidate point by influence of a noise.
In this paper, we propose the high-accuracy detection method by Hough Transform. About high-accuracy, it realized by attaching importance to high reliable candidate point of border.

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Factory built-in type simplified optical coherence tomography (OCT) system was developed for industrial use. The system design was supposed for check of the laser-welded resin. As a first approach, the current simplified OCT system for plant measurement was applied for the validation of the industrial sample; plastic resin. The industrial-use OCT was designed in response to the results. The performances of the measurement speed and range of the developed OCT system were 50scan/s and 5mm, respectively. The low coherence of 18.9μm could clearly distinguish the gap of 2 laser-welded resins. The system became compact and low price, and has the flexibility of epi-optics.

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This paper discusses a control method for the acrobot. The acrobot is a model of a gymnast on a horizontal bar with three links and two active joints and a passive joint. This robot is a non-holonomic and underactuated system. We propose a control method for the acrobot where swing-up stage is performed by genetic programming (GP) and balancing stage is handled by a linear quadratic regulator (LQR). Here, GP searches for the optimum input torque for swing-up so that the acrobot is able to reach the nearly-desired configuration. The LQR is then switched on to stabilize the system. Simulation results show that the proposed method could control the acrobot effectively.

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The purpose of this study is to develop an interactive interface using eye movement to operate a welfare apparatus, such as a feeding device for an orthopedically-impaired person. A part of the purpose is to eliminate special calibration and re-calibration during the operation. Originalities of this proposed system are as follows: eye sensing with evolutionary processing and interactive operation screen based on some physiological knowledge. The proposed system uses a non-contact type interface by the eye movement. An iris is tracked and eye movement is measured by the evolutionary eye sensing (EES) method. An operation screen is divided into 9 areas, which has visual stimulation using the physiological knowledge. A user can select these areas by eye movement and decide by eye fixation. The effectiveness of the proposed system is evaluated by comparison experiments with 20 subjects. The results indicate that the proposed system is easy to use for first-timer, who can become proficient in operation just after a few exercises.

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Over the last quarter century, research in biometric systems has developed at a breathtaking pace and what started with the focus on the fingerprint has now expanded to include face, voice, iris, and behavioral characteristics such as gait. Palmprint is one of the most recent additions, and is currently the subject of great research interest due to its inherent uniqueness, stability, user-friendliness and ease of acquisition. This paper describes an effective and procedurally simple method of palmprint feature extraction specifically for palmprint recognition, although verification experiments are also conducted. This method takes advantage of the correspondences that exist between prominent palmprint features or objects in the spatial domain with those in the frequency or Fourier domain. Multi-dimensional feature vectors are formed by extracting a GA-optimized set of points from the 2-D Fourier spectrum of the palmprint images. The feature vectors are then used for palmprint recognition, before and after dimensionality reduction via the Karhunen-Loeve Transform (KLT). Experiments performed using palmprint images from the ‘PolyU Palmprint Database’ indicate that using a compact set of DFT coefficients, combined with KLT and data preprocessing, produces a recognition accuracy of more than 98% and can provide a fast and effective technique for personal identification.

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Abnormality detection in multi-channel time-series data is important for detecting and understanding changes of states in the data source. We propose a method for automatically detecting abnormality (unusualness) by using correlation-based features of multi-channel time-series data. In general, the feature vectors of normal patterns, which occur frequently in time-series, form the low-dimensional subspace in the feature space. The proposed method measures the abnormality as deviation from the subspace. In the experiment of abnormality detection in electrocardiogram, the proposed method can appropriately detect changes that a doctor also judged to be abnormal.

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It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities.
In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.

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Due to recent advances in biotechnology, we can get activation level of each gene within an organism at a particular point of time. The data is called “gene expression data”. Analysis of gene expression data can provide understanding and insight into gene function and regulatory mechanism. However, these tasks are made more difficult from the empirical nature of array data and the overwhelming number of gene feature. One of our previous works in our field is GASVM. GASVM is a hybrid method of Genetic Algorithm and Support Vector Machine by Saberi et al.⁽¹⁾. GASVM has a large computational cost and a possibility of overfitting. Therefore, we have introduced a new criterion “Confidence Margin” and proposed a new method using it. The experimental result using two famous datasets confirmed the effectiveness of our proposed method.

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Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.

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This paper presents a new Particle Swarm Optimization (PSO) technique using velocity control. In PSO, when a particle finds a local optimal solution, all of the particles gather around it, and cannot escape from it. In the proposed method, we lead the particles from intensification to diversification by adding a random number to the velocity of the particles depending on the distance from gbest, and thereby the particles can search widely in the search space. Moreover, the velocity may not change so much occasionally because the average of random numbers added to velocity is 0. So, we restrain update of pbest of particles depending on the distance from gbest, too. Then, the proposed method is validated through numerical simulations with several functions which are well known as optimization benchmark problems comparing to some PSO methods.

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Profit Sharing is one of exploitation oriented reinforcement learning methods and aims to adapt a system to a given environment. In Profit Sharing, an agent learns a policy based on the reward that is received from the environment when it reaches a goal state. It is important to design a reinforcement function that distributes the received reward to each action rule in the policy. If the reinforcement function satisfies the ineffective rule suppression theorem, the reinforcement function is able to distribute more reward to effective rules than ineffective ones, even in the worst case where an ineffective rule is infinitely selected. The value of the reinforcement function, however, decreases exponentially with distance from the goal state. As a result, the agent fails to learn an appropriate policy when the episode length from an initial state to the goal state is relatively long. In this paper, we report a new dynamic reinforcement function considering the expected value of reward which is distributed to each rule. Using our reinforcement function, the expected value of reward distributed to the effective rules becomes larger than that to the ineffective ones. Even when the episode length becomes long, a decrease in the value of the reinforcement function is able to be suppressed, and thus the agent is able to learn an appropriate policy. We apply our reinforcement function to Sutton's maze problem, and show its effectiveness.

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The portfolio optimizations are generally to optimize the proportion of funds in the static-asset-portfolio. It means that the assets included in the portfolio have already been given before the optimizing process. Therefore we have a critical problem that the assets not included in the portfolio in the first place are never optimized. In order to avoid this problem, we propose a method that optimizes the portfolio consisting of not the given static assets but the dynamically selected assets in this paper. Our method consists of the following two steps, Steps A and B. Step A is to move the valuable assets expected to greatly good influence on the objective function from all assets into the portfolio through a genetic algorithm. Step B is to remove the valueless assets expected to no-good/bad influence on the objective function from the portfolio through a genetic algorithm. In the numerical experiments, we apply the proposed method to the Tokyo Stock Exchange in order to make index funds. We show that our method works well for the dynamic-asset-portfolio optimizations.

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In general, wavelet neural networks have a problem on the curse of dimensionality, i.e. hidden units to be required are exponentially increased with a high input dimension. To solve the above problem, the wavelet neural network incorporating local linear model is proposed. On the network design, however, the number of hidden units are determined by trial and error. In the present paper, a design method based on Bayesian method is proposed for the local linear wavelet neural network. Through computer simulation, the performance of the proposed method is evaluated.

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This paper describes an approach to development of a solution algorithm of a general-purpose for large scale problems using “Local Clustering Organization (LCO)” as a new solution for Job-shop scheduling problem (JSP). Using a performance effective large scale scheduling in the study of usual LCO, a solving JSP keep stability induced better solution is examined. In this study for an improvement of a performance of a solution for JSP, processes to a optimization by LCO is examined, and a scheduling solution-structure is extended to a new solution-structure based on machine-division. A solving method introduced into effective local clustering for the solution-structure is proposed as an extended LCO. An extended LCO has an algorithm which improves scheduling evaluation efficiently by clustering of parallel search which extends over plural machines. A result verified by an application of extended LCO on various scale of problems proved to conduce to minimizing make-span and improving on the stable performance.

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The Hiroshima City Children Tracking System has been developed. This model system consists of mobile ad-hoc network, high performance location detection system and children tracking server system. The children tracking server system provides the information for safety of the children to their parents. Specially the system indicate group number of their children.

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The author proposes a measuring method of complex bioimpedance in real time by using a potentiostat circuit and LabVIEW programming tools. In the system, 4-electrode method is applied on the dc-coupled potentiostat. Complex impedance is calculated from the frequency response function in LabVIEW. The resolutions of the resistive and reactive components are about 0.005% respectively. As a preliminary experiment, the complex impedance on a middle finger at 1kHz is measured with sampling time of 10ms. Pulse waves of resistive components which are synchronizing with ECG are clearly observed and very small reactive components.

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This paper proposes proximate optimality principle (POP) based multi-point tabu search, for solving combinatorial optimization problems. While the similarity measure of POP is defined using the concept of metric space on combinatorial optimization problems, some numerical simulations using several types of combinatorial optimization benchmark problems investigate POP. The proposed algorithm is applied to some typical combinatorial optimization problems in order to verify the performance of the proposed algorithm.

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In this paper, we propose a new digital watermarking method using fractal image coding and discrete wavelet transformation (DWT). We decompose an original image into low frequency band LL1 and LL2 using DWT. In fractal image coding, LL1 and LL2 are used for the range block area and domain block area, respectively. The watermark information is embedded into the address information of the range block. As the watermark is embedded into LL1, the robustness against JPEG compression is improved. Moreover, the quality of the watermarked image is improved, in spite of embedding the watermark into LL1, because the fractal image coding is applied only to the candidate range blocks for embedding. Experimental results prove the effectiveness of the proposed method.

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DEA (Data Envelopment Analysis) is well known as method to measure efficiency of DMUs (Decision Making Units) with multiple inputs and outputs where DEA has an efficiency model and an inefficiency model. The former model finds out the frontier which indicates the best practice line and evaluates the relative efficiency score by the aspect only of the strong points. On the other hand, the latter model finds out the frontier which indicates the worst practice line and evaluates the relative inefficiency score by the aspect only of the weak points. However, the evaluation values between efficiency and inefficiency scores are not consistent. Some DMUs may be in the both state of efficiency and inefficiency. Then, it is not easy for an analyst to understand the difference. To unify the evaluation values between two models, this paper proposes Multi-Viewpoint DEA model. In order to clarify how the proposed model works, this paper presents numerical study for an artificial sample data set. Numerical study shows that the proposed model has a feature which introducing viewpoint parameter allows analyst to evaluate DMUs based on their integrated models as well as the original two models.

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This paper explains a method of four-wheel steering control by using Sliding Mode Control (SMC). The purpose is to improve the control safety of four-wheeled vehicle by using invariable SMC for disturbance that satisfies the matching condition. Parameter variation and influence of the side wind are thought as disturbance related to driving the car. Steer control that corresponds to the influence of the side wind and parameter variation by using SMC is done, and it is examined to be able to do the achievement of the high stability and the decrease of the error margin with the target path. Then, we propose the adjustment method of the value of a nonlinear gain of the control input that considers the phase-lag to the steer of a lateral acceleration. The improvement of the control of disturbance is achieved by the proposal method.

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Inspection of microdrill bits is very important for quality control in Printed Circuit Board (PCB) production. Traditional methods mainly focus on geometric defects inspection. This paper proposes a new automatic optical inspection scheme which can not only be used for geometric defects inspection but also identify the phase of microdrill bits. The paper also investigates the effect of a level set method as a reliable technique for exact segmentation of the cutting plane from the acquired microdrill bit image. Following cutting plane segmentation, we employ an image registration approach to align it, and then three features of the cutting plane, length, width, and normalized end area, are extracted for microdrill bits phase identification. Experimental results show that the proposed method is effective for automatic inspection of microdrill bits in Printed Circuit Board (PCB) manufacturing.

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The chance to use the Internet as a communications tool has been increasing, and the consultation businesses for customers at remote places are diversifying in their communication media and forms. In the remote consultation, the lack of non-verbal information is reported as one of the reasons for inefficiency and customer's dissatisfaction compared with the face-to-face consultation. The technique for supplementing non-verbal information with a TV telephone is proposed, and helps to confirm understanding degree or the utterance timing by watching the movement of the face. But the displayed face of the partner causes strong feeling of strain between strangers and the participants also care about background scene displayed on the monitor producing risks in the consultation tasks. In this paper, we propose a remote consultation method that uses avatar technology in the virtual space in order to provide non-verbal information and also avoiding the problem of TV telephone at the same time. The effectiveness of the proposed remote consultation method is confirmed by experiments.

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In this paper, a new impulse-noise removing filter, which is composed of a improved PSM noise detector and a spline filter, is proposed. Using this filter, undetection of the noises can be avoided by controling the judgement value which is around maximum or minimum pixel value. In addition, it can also be possible to reduce mis-detection of the noises because the noise detecting threshold value can be set to a larger value. As a result of reducing both of undetection and mis-detection, proposed filtering method provides a good performance to remove impulsive noises. And the validity of the proposed method has been confirmed by computer simulations.

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In this paper, we propose a new framework of removing salt and pepper impulse noise. In our proposed framework, the most important point is that the number of noise-free white and black pixels in a noisy image can be determined by using the noise rates estimated by Fuzzy Impulse Noise Detection and Reduction Method (FINDRM) and Efficient Detail-Preserving Approach (EDPA). For the noisy image includes many noise-free white and black pixels, the detected noisy pixel from the FINDRM is re-checked by using the alpha-trimmed mean. Finally, the impulse noise filtering phase of the FINDRM is used to restore the image. Simulation results show that for the noisy image including many noise-free white and black pixels, the proposed framework can decrease the False Hit Rate (FHR) efficiently compared with the FINDRM. Therefore, the proposed framework can be used more widely than the FINDRM.

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We intend to construct an image diagnosis support system for Alzheimer-type Dementia (ATD) that extracts temporal lobe regions and an intracranial region as interest regions from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the regions of interest. In this paper, we specifically discuss extraction of regions of interest. The proposed method consists of three steps. First, we emphasis features of an obscure T2-weighted brain image. Second, we set a first contour that approximates a shape of the temporal lobe region to a triangle and apply Balloon models with the added presser force that push the initial contour outside in order to extract a temporal lobe region. Finally, we extract an intracranial region using extracted temporal lobe regions. Our proposed method can extract regions of interest along individual brain features by only a few interactions with three points. We demonstrate the potential of our method using actual diagnosis images. Moreover, we show a possibility to use of atrophy rate at the regions of interest for diagnosis support of ATD.

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This paper considers the synchronization in coupled oscillator with interrupted characteristics. First, we briefly explain the circuit model of the oscillator and its behavior. Next, the exact solution is derived from the differential equations. The hybrid return map can be realized by using symmetrical property. Also, we define the switching rate γ using the output pulses. From investigation of the bifurcation diagrams and the lyapunov exponent, we show the bifurcation structure, the existence regions of the periodic solutions and the quasi-periodic solutions within a parameter space. Moreover, we confirm the simulation results in the circuit experiments.

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